Search Results (476)

Based on Agenda-Setting Theory, Media Agenda Synergy (MAS) can enhance the communication effectiveness of public issues (e.g., climate change, social justice, and public health) through the information resonance and agenda complementarity among cross-media platforms, thus reconstructing the public perception. In this paper, we focus on the dynamic impact of cross-media agenda synergy on public agenda intensity and innovatively propose a “HMM-Granger” hybrid modeling framework for Media Agenda Synergy: Firstly, we quantify the causal weights of agenda shifting based on the deconstruction of the nonlinear time-series dependence of multisource media data by using LSTM neural networks. Secondly, the state transfer probability matrix of the Hidden Markov Model reveals the dual paths of “explicit collaboration” (e.g., issue resonance) and “implicit competition” (e.g., agenda masking) in media agenda coordination. The results of this study show that the Agenda Synergy between mainstream media and social media during major events can generate an Agenda Multiplier Effect, resulting in a significant increase in the intensity of the public agenda. This study provides a computable theoretical paradigm for Inter-Media Agenda Network modeling and data-driven decision support for optimizing opinion guidance strategies. Full article

Automatic speech recognition (ASR) has advanced rapidly, evolving from early template-matching systems to modern deep learning frameworks. This review systematically traces ASR’s technological evolution across four phases: the template-based era, statistical modeling approaches, the deep learning revolution, and the emergence of large-scale models under diverse learning paradigms. We analyze core technologies such as hidden Markov models (HMMs), Gaussian mixture models (GMMs), recurrent neural networks (RNNs), and recent architectures including Transformer-based models and Wav2Vec 2.0. Beyond algorithmic development, we examine how ASR integrates into intelligent information systems, analyzing real-world applications in healthcare, education, smart homes, enterprise systems, and automotive domains with attention to deployment considerations and system design. We also address persistent challenges—noise robustness, low-resource adaptation, and deployment efficiency—while exploring emerging solutions such as multimodal fusion, privacy-preserving modeling, and lightweight architectures. Finally, we outline future research directions to guide the development of robust, scalable, and intelligent ASR systems for complex, evolving environments. Full article

The rapid evolution of autonomous vehicle technologies has amplified the need for crash detection that operates robustly under complex traffic conditions with minimal latency. We propose a hybrid temporal hierarchy that augments a Region-based Convolutional Neural Network (R-CNN) with an adaptive time-variant Kalman filter (with total-variation prior), a Hidden Markov Model (HMM) for state stabilization, and a lightweight Artificial Neural Network (ANN) for learned temporal refinement, enabling real-time crash detection from monocular video. Evaluated on simulated traffic in CARLA and real-world driving in Istanbul, the full temporal stack achieves the best precision–recall balance, yielding 83.47% F1 offline and 82.57% in real time (corresponding to 94.5% and 91.2% detection accuracy, respectively). Ablations are consistent and interpretable: removing the HMM reduces F1 by 1.85–2.16 percentage points (pp), whereas removing the ANN has a larger impact of 2.94–4.58 pp, indicating that the ANN provides the largest marginal gains—especially under real-time constraints. The transition from offline to real time incurs a modest overall loss (−0.90 pp F1), driven more by recall than precision. Compared to strong single-frame baselines, YOLOv10 attains 82.16% F1 and a real-time Transformer detector reaches 82.41% F1, while our full temporal stack remains slightly ahead in real time and offers a more favorable precision–recall trade-off. Notably, integrating the ANN into the HMM-based pipeline improves accuracy by 2.2%, while the time-variant Kalman configuration reduces detection lag by approximately 0.5 s—an improvement that directly addresses the human reaction time gap. Under identical conditions, the best RCNN-based configuration yields AP@0.50 ≈ 0.79 with an end-to-end latency of 119 ± 21 ms per frame (~8–9 FPS). Overall, coupling deep learning with probabilistic reasoning yields additive temporal benefits and advances deployable, camera-only crash detection that is cost-efficient and scalable for intelligent transportation systems. Full article

Learning from demonstration with multiple executions must contend with time warping, sensor noise, and alternating quasi-stationary and transition phases. We propose a label-free pipeline that couples unsupervised segmentation, duration-explicit alignment, and probabilistic encoding. A dimensionless multi-feature saliency (velocity, acceleration, curvature, direction-change rate) yields scale-robust keyframes via persistent peak–valley pairs and non-maximum suppression. A hidden semi-Markov model (HSMM) with explicit duration distributions is jointly trained across demonstrations to align trajectories on a shared semantic time base. Segment-level probabilistic motion models (GMM/GMR or ProMP, optionally combined with DMP) produce mean trajectories with calibrated covariances, directly interfacing with constrained planners. Feature weights are tuned without labels by minimizing cross-demonstration structural dispersion on the simplex via CMA-ES. Across UAV flight, autonomous driving, and robotic manipulation, the method reduces phase-boundary dispersion by 31% on UAV-Sim and by 30–36% under monotone time warps, noise, and missing data (vs. HMM); improves the sparsity–fidelity trade-off (higher time compression at comparable reconstruction error) with lower jerk; and attains nominal 2σ coverage (94–96%), indicating well-calibrated uncertainty. Ablations attribute the gains to persistence plus NMS, weight self-calibration, and duration-explicit alignment. The framework is scale-aware and computationally practical, and its uncertainty outputs feed directly into MPC/OMPL for risk-aware execution. Full article

Banana (Musa cavendish) peel, usually discarded as waste, is a polyphenol-rich source with antioxidant and chelating properties. This study evaluated its ability to mitigate ovarian toxicity induced by a heavy metal mixture (HMM) consisting of Hg, Mn, Pb, and Al in female rats. Animals received the HMM with or without banana peel extract at 200, 400, and 800 mg/kg dosages for 60 days. Co-treatment dose-dependently reduced ovarian metal accumulation, attenuated oxidative and nitrosative stress (MDA, NO), restored antioxidant enzyme activities (SOD, CAT, GSH, GPx), and modulated pro-inflammatory (IL-6, TNF-α), apoptotic (Caspase-3), and transcriptional factors (NF-κB, Nrf2). The gonadal endocrine profile also improved gonadotropins (FSH, LH), prolactin (PRL), and progesterone (P), which were normalized at the medium dose (400 mg/kg), and demonstrated a clear dose-related effect. Histological examination further revealed that this dose most effectively improved ovarian tissue. GC–MS analysis identified bioactive compounds including resveratrol, proanthocyanidins, and anthocyanidins, supporting both antioxidant and chelating actions. These findings demonstrate that banana peel extract exerts a dual, dose-dependent protective role in the gonad, limiting metal burden while enhancing redox defenses, and highlight its translational potential as a sustainable agro-food waste product in reproductive toxicology. Full article

Map-matching involves aligning raw positioning data with actual road networks. It is a complex process due to measurement inaccuracies, ambiguous street layouts, and sensor noise. The paper explores the challenges in map-matching and vehicle route prediction and presents an overview of existing methods and algorithms. The solutions employing hidden Markov models (HMMs), where emission and transition probabilities are crucial in correctly matching positions to roads, are examined and evaluated. Machine Learning (ML) offers robust algorithms capable of managing complex urban environments and varied data sources. While HMMs have demonstrated their efficacy in capturing sequential dependencies, more advanced ML techniques, including deep learning, provide enhanced capabilities for learning spatial and temporal relationships. They improve prediction accuracy and adapt to evolving traffic conditions and diverse vehicle behaviours. Special attention is paid to a holistic solution, assuming a combination of map-matching and route prediction within a unified framework. It fosters more efficient route planning, real-time traffic management, and overall decision-making in intelligent transportation systems. Full article

Lithium-ion batteries serve as critical energy storage devices and are extensively utilized across diverse applications. The accurate estimation of State of Charge (SOC) is critically important for Battery Management Systems. Traditional SOC estimation methods have achieved progress, such as the Extended Kalman Filter (EKF) and particle filter. However, when there exist uncertainties in battery model parameters and the parameters change dynamically with operating conditions, the EKF tends to produce accumulated errors, which leads to a decline in estimation accuracy. This paper proposes a hybrid approach integrating the EKF with a Hidden Markov Factor Graph (HMM-FG). First, this method uses the EKF to achieve a real-time estimation of the SOC. Then, it treats the EKF-estimated value as an observation through the HMM-FG and combines current and voltage measurement data. It also introduces a factor function to describe the temporal correlation of the SOC and the uncertainty of EKF modeling errors, thereby performing Maximum A Posteriori (MAP) estimation correction on the SOC. Different from the traditional EKF, this method can use future observation information to suppress the error accumulation of the EKF under dynamic parameter changes. Experiments were conducted under different temperatures (0 °C, 25 °C, 45 °C), and a variety of different dynamic operating conditions (FUDS, DST), and comparisons were made with the EKF, Extended Kalman Smoother (EKS), and data-driven method based on LSTM. Full article

Eukaryotes use diverse nutrient acquisition strategies, including autotrophy, heterotrophy, mixotrophy, and symbiosis, which shape the evolution of cell regulatory networks. The Target of Rapamycin (TOR) kinase is a conserved growth regulator that in most species functions within two complexes, TORC1 and TORC2. TORC1 is broadly conserved and uniquely sensitive to rapamycin, whereas the evolutionary distribution of TORC2 is less well-defined. We built a sensitive hidden Markov model (HMM)-based pipeline to survey core TORC1 and TORC2 components across more than 800 sequenced eukaryotic genomes spanning multiple major supergroups. Both complexes are present in early-branching lineages, consistent with their presence in the last eukaryotic common ancestor, followed by multiple lineage-specific losses of TORC2 and, more rarely, TORC1. A striking pattern emerges in which TORC2 is uniformly absent from photosynthetic autotrophs derived from primary endosymbiosis and frequently lost in those derived from secondary or tertiary events. In contrast, TORC2 is consistently retained in mixotrophs, which obtain carbon from both photosynthesis and environmental uptake, and in free-living obligate heterotrophs. These findings suggest that TORC2 supports heterotrophic metabolism and is often dispensable under strict autotrophy. Our results provide a framework for the evolutionary divergence of TOR signaling and highlight metabolic and ecological pressures that shape TOR complex retention across eukaryotes. Full article

Unsafe events such as slips and trips occur regularly on construction sites. Efficient identification of these events can help protect workers from accidents and improve site safety. However, current detection methods rely on subjective reporting, which has several limitations. To address these limitations, this study presents a sound-based slip and trip classification method using wearable sound sensors and machine learning. Audio signals were recorded using a smartwatch during simulated slip and trip events. Various 1D and 2D features were extracted from the processed audio signals and used to train several classifiers. Three key findings are as follows: (1) The hybrid CNN-LSTM network achieved the highest classification accuracy of 0.966 with 2D MFCC features, while GMM-HMM achieved the highest accuracy of 0.918 with 1D sound features. (2) 1D MFCC features achieved an accuracy of 0.867, outperforming time- and frequency-domain 1D features. (3) MFCC images were the best 2D features for slip and trip classification. This study presents an objective method for detecting slip and trip events, thereby providing a complementary approach to manual assessments. Practically, the findings serve as a foundation for developing automated near-miss detection systems, identification of workers constantly vulnerable to unsafe events, and detection of unsafe and hazardous areas on construction sites. Full article

Nanopore-based DNA sequencing has emerged as a transformative biosensing technology, enabling real-time molecular diagnostics in compact and mobile form factors. However, the computational complexity of the basecalling process—the step that translates raw nanopore signals into nucleotide sequences—poses a critical energy challenge for mobile deployment. While deep learning (DL) models currently dominate this task due to their high accuracy, they demand substantial power budgets and computing resources, making them unsuitable for portable or field-scale biosensor platforms. In this work, we propose an embedded hardware–software framework for DNA sequence detection that leverages a Viterbi-based Hidden Markov Model (HMM) implemented on a custom 64-bit RISC-V core. The proposed HMM detector is realized on an off-the-shelf Virtex-7 FPGA and evaluated against state-of-the-art DL-based basecallers in terms of energy efficiency and inference accuracy. From one side, the experimental results show that our system achieves an energy efficiency improvement of 6.5×, 5.5×, and 4.6×, respectively, compared to similar HMM-based detectors implemented on a commodity x86 processor, Cortex-A9 ARM embedded system, and a previously published Rocket-based system. From another side, the proposed detector demonstrates 15× and 2.4× energy efficiency superiority over state-of-the-art DL-based detectors, with competitive accuracy and sufficient throughput for field-based genomic surveillance applications and point-of-care diagnostics. This study highlights the practical advantages of classical probabilistic algorithms when tightly integrated with lightweight embedded processors for biosensing applications constrained by energy, size, and latency. Full article

South African cattle comprise diverse breeds with distinct evolutionary histories, potentially reflecting differences in recombination landscapes. This study assessed genome-wide recombination rates and hotspots in Bonsmara (n = 190) and Nguni (n = 119) cattle using three-generation half-sib pedigrees genotyped with the Illumina Bovine SNP50 BeadChip. Phasing across 29 autosomes was conducted using SHAPEIT v2, and crossover events were inferred using the DuoHMM algorithm. The total number of crossover events detected was higher in Nguni (n = 8982) than in Bonsmara (n = 7462); however, the average recombination rate per 1 Mb window was significantly higher in Bonsmara (0.31) compared to Nguni (0.18) (p < 0.01). This apparent discrepancy reflects differences in genomic distribution and crossover clustering across breeds, rather than overall recombination frequency. A critical limitation of the study is the reliance on half-sib families with small family sizes, which may underestimate recombination rates due to limited meiotic sampling and increased variance in crossover detection. We identified 407 recombination hotspots in Bonsmara and 179 in Nguni, defined as intervals exceeding 2.5 standard deviations above the mean recombination rate. Genes such as PDE1B and FP which are associated with productions traits were located within hotspot-enriched regions. However, functional causality between these genes and local recombination activity remains unverified. Our results provide statistically supported evidence for breed-specific recombination patterns and hotspot distributions, underscoring the importance of incorporating recombination architecture into genetic improvement strategies for South African cattle. Full article

Rice blast, caused by Magnaporthe oryzae, significantly threatens global rice production. Disease control is complicated by the pathogen’s high genetic diversity, which is driven by heterothallic recombination between opposite mating types that underlies variation. However, mechanisms governing sexual reproduction in this fungus remain poorly characterized, largely due to the absence of reliable methods for scalable ascospore progeny production. In this study, we established two novel mating methods, namely Conidial Mixing Mating (CMM) and Hyphal Segments Mixed Mating (HMM). Both methods employed optimized suspensions (5 × 10⁴ conidia/mL or equivalent hyphal density) mixed at 1:1 ratios, incubated under standardized conditions: 20 °C with a 12 h/12 h photoperiod. We characterized perithecia, asci, and ascospore morphology using fluorescence microscopy, paraffin sectioning, cryo-scanning electron microscopy, and transmission electron microscopy. Furthermore, both methods enabled phenotypic characterization of sexual reproduction-deficient mutants, including ΔMopmk1 and ΔMoopy2. In conclusion, we established two efficient methods for investigating M. oryzae sexual reproduction, providing foundational tools to advance studies of sexual mechanisms, pathogenicity evolution, and genetic variation. Full article

RNA-directed DNA methylation (RdDM) is an epigenetic mechanism involved in several biological processes in plants, requiring complex machinery including the chromatin remodeling protein CLASSY (CLSY). The CLSY family regulates global and locus-specific DNA methylation and was initially identified in Arabidopsis thaliana. Despite reports in other plants, detailed knowledge about CLSY proteins in soybean is scarce. In this work, we used profile hidden Markov models (profile HMMs) specifically constructed for CLSY detection to identify new members in soybean and to analyze their phylogenetic relationships across bryophyte, basal angiosperm, basal eudicot, monocots, and eudicots. We identified two new candidates for CLSY1-2 and one for DRD1 in soybean and, for the first time, detected CLSY and DRD1 genes in Aquilegia coerulea. Phylogenetic analysis indicated two main CLSY groups: one similar to Arabidopsis CLSY1-2 and another to CLSY3-4. Gene duplication analysis demonstrated that whole-genome duplication/segmental duplication events contributed to CLSY family expansion in soybean. RT-qPCR analysis showed that CLSY and five other epigenetic regulator genes had stress-modulated expression during soybean germination under salt and osmotic stress, with variation among cultivars. Our findings enhance comprehension of the evolutionary dynamics of the CLSY family and furnish insights into their response to abiotic stress in soybean. Full article

We previously developed SA-conf, a method designed to quantify backbone structural variability in protein targets. This approach is based on the HMM-SA structural alphabet, which enables efficient and rapid comparison of local backbone conformations across multiple structures of a given target. In this study, SA-conf (version for python2.7) was applied to a dataset of 130 crystallographic chains of Bcl-xL, a protein involved in promoting cell survival. SA-conf quantified and mapped backbone structural variability, revealing the protein’s capacity for conformational rearrangement. Our results showed that while most mutations had minimal impact on backbone conformation, some were associated with long-range structural effects. By jointly analyzing residue flexibility and backbone rearrangements across apo and holo structures, SA-conf identified key regions where the backbone undergoes structural adjustments upon ligand binding. Notably, the $\alpha 2$–$\alpha 3$ region was shown to be a hotspot of structural plasticity, exhibiting ligand-specific conformational signatures. Furthermore, SA-conf enabled the construction of a structural map of the binding site, distinguishing a conserved anchoring core from flexible peripheral regions that contribute to ligand specificity. Overall, this study highlights SA-conf’s capacity to detect conformational changes in protein backbones upon ligand binding and to uncover structural determinants of selective ligand recognition. Full article

Quantum finance, as a key application scenario of quantum computing, showcases multiple significant advantages of quantum machine learning over traditional machine learning methods. This paper first aims to overcome the limitations of the hidden quantum Markov model (HQMM) in handling continuous data and proposes an innovative method to convert continuous data into discrete-time sequence data. Second, a hybrid quantum computing model is developed to forecast stock market trends. The model was used to predict 15 stock indices from the Shanghai and Shenzhen Stock Exchanges between June 2018 and June 2021. Experimental results demonstrate that the proposed quantum model outperforms classical algorithmic models in handling higher complexity, achieving improved efficiency, reduced computation time, and superior predictive performance. This validation of quantum advantage in financial forecasting enables the practical deployment of quantum-inspired prediction models by investors and institutions in trading environments. This quantum-enhanced model empowers investors to predict market regimes (bullish/bearish/range-bound) using real-time data, enabling dynamic portfolio adjustments, optimized risk controls, and data-driven allocation shifts. Full article