Search Results (693)

Human–machine shared control has been widely adopted to enhance driving performance and facilitate smooth transitions between manual and fully autonomous driving. However, existing authority allocation strategies often neglect real-time assessment of scenario complexity and driver workload. To address this gap, we leverage non-invasive eye-tracking devices and the 3D virtual driving simulator Car Learning to Act (CARLA) to collect multimodal data—including physiological measures and vehicle dynamics—for the real-time classification of scenario complexity and cognitive workload. Feature importance is quantified using the SHAP (SHapley Additive exPlanations) values derived from Random Forest classifiers, enabling robust feature selection. Building upon a Hidden Markov Model (HMM) for workload inference and a Model Predictive Control (MPC) framework, we propose a novel human–machine shared control architecture with adaptive authority allocation. Human-in-the-loop validation experiments under both high- and low-workload conditions demonstrate that the proposed strategy significantly improves driving safety, stability, and overall performance. Notably, under high-workload scenarios, it achieves substantially greater reductions in Time to Collision (TTC) and Time to Lane Crossing (TLC) compared to low-workload conditions. Moreover, the adaptive approach yields lower controller load than alternative authority allocation methods, thereby minimizing human–machine conflict. Full article

Stylosanthes scabra, a legume native to the Brazilian semiarid region, exhibits remarkable drought tolerance and represents a valuable model for studying molecular adaptation in legumes. Transcription factors of the AP2/ERF superfamily play central roles in plant development and stress response. This study aimed to identify and characterize AP2/ERF genes in Stylosanthes scabra and to analyze their transcriptional response to root dehydration. Candidate genes were identified through a Hidden Markov Model (HMM) search using the AP2 domain profile (PF00847), followed by validation of conserved domains, physicochemical characterization, prediction of subcellular localization, phylogenetic and structural analyses, and functional annotation. A total of 295 AP2/ERF proteins were identified and designated as SscAP2/ERF, most of which were predicted to be localized in the nucleus. These proteins exhibited a wide range of molecular weights and isoelectric points, reflecting structural diversity, and were classified into four subfamilies: AP2, ERF, DREB, and RAV. Functional annotation revealed predominant roles in DNA binding and transcriptional regulation, while promoter analysis identified numerous stress-related cis-elements. A total of 32 transcripts were differentially expressed under 24 h of water deficit, and four selected genes had their expression patterns validated by qPCR. These findings provide new insights into the AP2/ERF gene subfamily in Stylosanthes scabra and lay the groundwork for future biotechnological approaches to enhance stress tolerance in legumes. Full article

Background: Caryocar brasiliense Camb. Caryocaraceae is a typical tree from the Brazilian Cerrado with commercial importance due to its edible fruit, known as pequi. This native plant holds significant economic value and is a key candidate for cropping systems. Rich in phytochemicals, such as phenolics, flavonoids, and terpenoids, it has shown notable health benefits. Methods: Considering the importance of terpenes and their biological properties, and based on the first draft genome of C. brasiliense, this study aimed to identify putative terpene synthase genes and classify them into the phylogenetic subfamilies previously identified across all plant lineages. The presence of terpenes was also verified in samples of the outer portion of the fruit by solid-phase microextraction gas chromatography mass-spectrometry. Results: Analysis of genome completeness showed that over 90% of genes were identified despite a highly fragmented assembly, with 71% containing complete gene sequences. Twenty-two genes were retained as putative terpene synthase genes considering their homology with the terpene synthase Hidden Markov Model (HMM) profiles in the Pfam-A database. Ten sequences with a minimum length of 298 amino acids were used for phylogenetic inference. In the resulting phylogenetic tree, C. brasiliense terpene synthase genes clustered within the different previously identified Angiosperm clades and allowed us to classify each gene into different phylogenetic subfamilies: six genes belonged to the h/d/a/b/g, three to the c, and one to the e/f. The headspace solid-phase microextraction technique, in conjunction with gas chromatography mass-spectrometry, has allowed for the identification of eleven chemical compounds, including a terpene. Conclusions: This initial identification of putative terpene synthase genes in pequi, together with the chemical analysis of the outer fruits, lays the groundwork for future studies aimed at optimizing terpene biosynthesis for both biological and commercial applications. Full article

This paper presents a case study framework for the operational reliability monitoring of a grid-connected photovoltaic (PV) inverter using SCADA measurements collected during February–April 2025. The workflow combines correlation-based drift analysis, probabilistic outputs from established machine learning models (XGBoost and LSTM), and temporal consistency modeled through a hidden Markov model (HMM). The resulting evidence is summarized into two interpretable composite indicators: a Health Index (HI), intended to capture short-term deviations, and a Reliability Score (RS), intended to provide a smoother reliability-oriented summary over time. A time-aware evaluation protocol is employed to reduce temporal leakage and to assess predictive utility under rare-event conditions, complemented by baseline comparisons and sensitivity checks for key thresholds and modeling settings. Within the analyzed dataset, the results suggest that HI is responsive to transient disturbances, while RS supports trend monitoring and maintenance prioritization by consolidating multiple weak signals into a consistent operational view. The proposed indicators are positioned as data-driven risk summaries for decision support rather than direct physical measures of deviation patterns. Generalization to other inverters and sites requires further validation on longer horizons and with additional operational/maintenance records. Full article

The cation/H⁺ exchanger (CHX) gene family plays a vital role in maintaining K⁺/Na⁺ homeostasis in plants, yet its functional characterization in pepper (Capsicum annuum L.) remains largely unexplored. To elucidate the potential roles of CHX genes in stress adaptation and development in pepper, a genome-wide identification and systematic analysis of this gene family was performed. Using a combination of Hidden Markov Model (HMM) searches, phylogenetic reconstruction, conserved motif and promoter analysis, and expression profiling across tissues and under multiple stress conditions, a total of 23 CaCHX genes were identified, which are unevenly distributed across 10 chromosomes and classified into 6 phylogenetic subfamilies. Expression profiling revealed that most CaCHX genes were highly expressed in flowers, suggesting their potential involvement in reproductive development, while only CaCHX12 and CaCHX17 were detected in leaves. Under treatments such as abscisic acid (ABA), gibberellic acid (GA), NaCl, and jasmonic acid (JA), CaCHX1, CaCHX20, and CaCHX23 exhibited distinct temporal expression patterns, suggesting their involvement in hormone-mediated stress responses. This study provides the first comprehensive genomic and transcriptomic overview of the CHX family in pepper, offering novel insights into its regulatory roles in flower development and stress tolerance and, thus supplying valuable genetic resources for molecular breeding aimed at enhancing pepper resilience. Full article

Financial markets evolve through recurring phases of stability, turbulence, and structural transformation. Standard Hidden Markov Models (HMMs) assume fixed transition probabilities, which limits their ability to capture such higher-order changes in market behavior. This study introduces an Adaptive Hierarchical Hidden Markov Model (AH-HMM), where regime transitions depend on an unobserved meta-regime that reflects the broader macro-financial environment. Each meta-regime defines its own transition matrix across market states such as bull, bear, and turbulent phases. In this way, the model adapts dynamically to structural changes arising from crises, policy shifts, or variations in investor sentiment. Using weekly data for major equity indices, aggregated from daily prices, together with macro-uncertainty indicators, we show that the AH-HMM identifies key turning points including the Global Financial Crisis, the COVID-19 shock, and the post-2022 tightening cycle. In our empirical application, where we approximate the latent structural layer by low- and high-uncertainty environments defined from the VIX, the adaptive model attains a higher in-sample likelihood and delivers competitive out-of-sample forecasts and Value-at-Risk coverage relative to conventional HMMs and time-varying transition alternatives. Overall, the results highlight a mechanism of structural learning within market regimes and offer tools for risk management and policy analysis under uncertainty. Full article

Flexible pressure sensors have garnered significant attention over the past few decades owing to their indispensable role in electronic skin and health monitoring, and there is an urgent demand for high sensitivity to meet the requirements of large-scale applications. In this work, we demonstrate a resistive pressure sensor with self-assembled MXene/MWCNTs complex conductive networks, whose hollow substrate is achieved via designed molds and thermally expandable microspheres. Herein, the pressure sensor exhibits the desired performances, including a high sensitivity of 2.63 kPa⁻¹, an ultra-low detection limit of ~0.25% relative resistance change, and rapid response times of 340 ms. The high performance enables promising prospects for detecting diverse human body movements. More importantly, it has been applied in numerical classification based on machine learning with the Hidden Markov Model, achieving an impressive accuracy of ~99.2%. Our research offers novel insights for enhancing the performance of pressure sensors, which hold great potential for practical applications. Full article

Phospholipase D (PLD) genes play key roles in plant abiotic stress responses, but the systematic identification of the maize (Zea mays) PLD family and its cold tolerance mechanism remain unclear. Using 26 maize genomes (pangenome), we identified 21 ZmPLD members via Hidden Markov Model (HMM) search (Pfam domain PF00614), including five private genes—avoiding gene omission from single reference genomes. Phylogenetic analysis showed ZmPLD conservation with Arabidopsis and rice PLDs; Ka/Ks analysis revealed most ZmPLDs under purifying selection, while three genes (including ZmPLD15) had positive selection signals, suggesting roles in maize adaptive domestication. For ZmPLD15, five shared structural variations (SVs) were found in its promoter; some contained ERF/bHLH binding sites, and SVs in Region1/5 significantly regulated ZmPLD15 expression. Protein structure prediction and molecular docking showed conserved ZmPLD15 structure and substrate (1,2-diacyl-sn-glycero-3-phosphocholine) binding energy across germplasms. Transgenic maize (B73 background) overexpressing ZmPLD15 was generated. Cold stress (8–10 °C, 6 h) and recovery (24 h) on three-leaf seedlings showed transgenic plants had better leaf cell integrity than wild type (WT). Transgenic plants retained 45.8% net photosynthetic rate (Pn), 47.9% stomatal conductance (Gs), and 55.8% transpiration rate (Tr) versus 7.6%, 21.3%, 13.8% in WT; intercellular CO₂ concentration (Ci) was maintained properly. This confirms ZmPLD15 enhances maize cold tolerance by protecting photosynthetic systems, providing a framework for ZmPLD research and a key gene for cold-tolerant maize breeding. Full article

Ultra-dense heterogeneous cellular networks in 6G and beyond face an escalating vulnerability to cell outages stemming from complex issues like parameter misconfigurations, hidden conflicts among Autonomous Network Functions (ANFs), multivendor incompatibility, and software/hardware failures. While ANF-based automated fault detection is a core capability for next-generation networks, existing solutions are predominantly reactive, identifying faults only after reliability is compromised. To overcome this critical limitation and maintain high service quality, a proactive fault prediction capability is essential. We introduce a novel Discrete-Time Markov Chain (DTMC)-based stochastic framework designed to model network reliability dynamics. This framework forecasts the transition of a cell from normal operation to suboptimal or degraded states, offering a crucial shift from reactive to proactive fault management. Our model rigorously quantifies the effects of fault arrivals, estimates the fraction of time the network remains degraded, and, uniquely, identifies sensitive parameters whose misconfigurations pose the most significant threat to performance. Numerical evaluations demonstrate the model’s high applicability in accurately predicting both the timing and probable causes of faults. By enabling true anticipation and mitigation, this framework is a key enabler for significantly reducing the cell outage time and enhancing the reliability and resilience of next-generation wireless networks. Full article

Background: Sucrose metabolism plays a crucial role in plant responses to abiotic stresses such as drought and high temperatures, significantly influencing plant growth and yield formation. In higher plants, the second step in sucrose bioconversion involves sucrose phosphate phosphatase (SPP) hydrolyzing sucrose-6-phosphate to form sucrose. This study determined the number of SPP gene family members in upland cotton (Gossypium hirsutum), systematically analyzed their fundamental characteristics, physicochemical properties, phylogenetic relationships, chromosomal localization, and expression patterns across different tissues and under various abiotic stresses. Methods: The SPP gene family in hirsutum was identified using Hidden Markov Models (HMMER) and the NCBI Conserved Domain Database (NCBI CDD), and its physico-chemical properties were analyzed via the SOPMA online analysis website. Phylogenetic relationships were determined using MEGA 12.0 software. Promoter regions were analyzed with PlantCARE, sequence patterns were identified via MEME, and transcriptome data were downloaded from the CottonMD database. Results: This study identified four members of the hirsutum SPP gene family, with amino acid lengths ranging from 335 to 1015, molecular weights between 38.38 and 113.28 kDa, and theoretical isoelectric points (pI) between 5.39 and 6.33. These genes are localized across four chromosomes. The SPP gene family in hirsutum exhibits closer phylo-genetic relationships with SPP genes in Arabidopsis thaliana and Chenopodium quinoa. Their promoter regions are rich in cis-elements associated with multiple abiotic stress resistance functions, and their expression patterns vary across different tissues and under different abiotic stress conditions. Conclusions: The GhSPP gene may play an important role in the growth and development of upland cotton and its responses to salt stress and drought. Therefore, it could be considered as a candidate gene for future functional analysis of cotton resistance to salt and drought stress. Full article

Early adolescence is a sensitive period for digital media overdependence; however, persistent high-risk patterns remain poorly understood. Using data from the 2008 birth panel of the Panel Study on Korean Children (n = 1354), we examined predictors measured from early childhood to Grades 1–2 (2014–2016) and modeled digital media overdependence from Grades 3 to 6 (2017–2020). Hidden Markov Models (HMMs) were used to identify developmental trajectories, and machine learning models characterized risk signals using SHAP-informed feature importance. Five trajectories emerged, including one subgroup that maintained persistently high risk. The predictive model showed good discriminative performance (strong predictive performance [Receiver Operating Characteristic Area Under the Curve (ROC AUC) = 0.84]). Executive function difficulties in Grade 1 and their worsening through Grade 2 predicted an elevated risk, whereas longer or increasing sleep duration, stronger family interactions, and appropriate parental control were protective. In contrast, higher maternal parenting stress, greater overall media use time, and a larger proportion of game-centered media use functioned as risk factors. These findings identify modifiable early childhood and early elementary predictors of high-risk maintenance trajectories of media overdependence and may inform early screening and preventive interventions in families, schools, and communities. Full article

Understanding degradation is crucial for ensuring the longevity and performance of materials, systems, and organisms. To illustrate the similarities across applications, this article provides a review of data-based methods in materials science, engineering, and medicine. The methods analyzed in this paper include regression analysis, factor analysis, cluster analysis, Markov Chain Monte Carlo, Bayesian statistics, hidden Markov models, nonparametric Bayesian modeling of time series, supervised learning, and deep learning. The review provides an overview of degradation models, referencing books and methods, and includes detailed tables highlighting the applications and insights offered in medicine, power engineering, and material science. It also discusses the classification of methods, emphasizing statistical inference, dynamic prediction, machine learning, and hybrid modeling techniques. Overall, this review enhances understanding of degradation modeling across diverse domains. Full article

Background: As key members of the plant receptor-like kinase family, rice CrRLK1Ls play diverse roles in plant growth, development, and stress responses. Although rice CrRLK1Ls have been initially characterized, our understanding of their functions remains limited. Methods: We identified OsCrRLK1L genes via Hidden Markov Model (HMM) searches against the rice genome. Subsequent analyses encompassed their physicochemical properties, chromosomal distribution, gene structure, phylogenetic relationships, conserved domains, and cis-acting elements.Salt-responsive candidates were screened using a GEO dataset, and their expression profiles were validated under salt stress using quantitative real-time PCR. Result: A total of 36 OsCrRLK1L genes, all containing both Malectin and tyrosine kinase domains, were identified in the rice genome and showed an uneven chromosomal distribution. Phylogenetic analysis classified them into three subclades, with Group II and Group III being specific to rice and Arabidopsis thaliana, respectively. Promoter analysis revealed that the promoter regions of these genes contained an abundance of cis-acting elements related to hormone and stress responses. RNA-Seq and enrichment analysis indicated that OsCrRLK1L genes exhibit tissue specificity and participate in salt stress responses. Furthermore, CrRLK1L2 and CrRLK1L10 showed tissue-specific differential expression under salt stress. Conclusions: In summary, our study lays the groundwork for future research into the biological roles of OsCrRLK1L genes during salt stress. Full article

Cognitive radio networks (CRNs) rely heavily on spectral sensing to detect primary user (PU) activity, yet detection at low signal-to-noise ratios (SNRs) remains a major challenge. Hence, a novel “Deep Wavelet Cyclostationary Independent Gaussian Markov Fourier Transform Dempster–Shafer Network” is proposed. When the signal waveform is submerged within the noise envelope and residual correlation emerges in the noise, it violates white Gaussian assumptions, leading to misidentification of signal presence. To resolve this, the Adaptive Continuous Wavelet Cyclostationary Denoising Autoencoder (ACWC-DAE) is introduced, in which the Adaptive Continuous Wavelet Transform (ACWT), Cyclostationary Independent Component Analysis Detection (CICAD), and Denoising Autoencoder (DAE) are introduced into the first hidden layer of a Deep Q-Network (DQN). It restores the bursty signal structure, separates the structured noise, and reconstructs clean signals, leading to accurate signal detection. Additionally, bursty and fading-affected primary user signals become fragmented and dip below the noise floor, causing conventional fixed-window sensing to fail in accumulating reliable evidence for detection under intermittent and low-duty-cycle conditions. Therefore, the Adaptive Gaussian Short-Time Fourier Transform Dempster–Shafer Model (AGSTFT-DSM) is incorporated into the second DQN layer, Adaptive Gaussian Mixture Hidden Markov Modeling (AGMHMM) tracks the hidden activity states, Adaptive Short-Time Fourier Transform (ASFT) resolves brief signal bursts, and Dempster–Shafer Theory (DST) fuses uncertain evidence to infer occupancy, thereby detecting an accurate user signal. The results obtained by the proposed model have a low error and detection time of 0.12 and 30.10 ms and a high accuracy of 97.8%, revealing the novel insight that adaptive wavelet denoising, along with uncertainty-aware evidence fusion, supports reliable spectrum detection under low-SNR conditions where existing models fail. Full article

The increasing availability of IoT-enabled mobility data and intelligent transportation systems in Smart Cities demands efficient and interpretable models for destination prediction. This study presents a comparative analysis between Markov Chains and Hidden Markov Models applied to urban mobility trajectories, evaluated through mean precision values. To ensure methodological rigor, the Smart Sampling with Data Filtering (SSDF) method was developed, integrating trajectory segmentation, spatial tessellation, frequency aggregation, and 10-fold cross-validation. Using data from 23 vehicles in the Vehicle Energy Dataset (VED) and a filtering threshold based on trajectory recurrence, the results show that the HMM achieved 61% precision versus 59% for Markov Chains (p = 0.0248). Incorporating day-of-week contextual information led to statistically significant precision improvements in 78.3% of cases for precision (95.7% for recall, 87.0% for F1-score). The remaining 21.7% indicate that model selection should balance model complexity and precision-efficiency trade-off. The proposed SSDF method establishes a replicable foundation for evaluating probabilistic models in IoT-based mobility systems, contributing to scalable, explainable, and sustainable Smart City transportation analytics. Full article