Search Results (90)

Sign language recognition (SLR) plays a crucial role in bridging the communication gap between deaf individuals and the hearing population. However, achieving subject-independent SLR remains a significant challenge due to variations in signing styles, hand shapes, and movement patterns among users. Traditional Markov Chain-based models struggle with generalizing across different signers, often leading to reduced recognition accuracy and increased uncertainty. These limitations arise from the inability of conventional models to effectively capture diverse gesture dynamics while maintaining robustness to inter-user variability. To address these challenges, this study proposes an adaptive SLR framework that integrates Markov Chains with a Niching Genetic Algorithm (NGA). The NGA optimizes the transition probabilities and structural parameters of the Markov Chain model, enabling it to learn diverse signing patterns while avoiding premature convergence to suboptimal solutions. In the proposed SLR framework, GA is employed to determine the optimal transition probabilities for the Markov Chain components operating across multiple signing contexts. To enhance the diversity of the initial population and improve the model’s adaptability to signer variations, a niche model is integrated using a Context-Based Clearing (CBC) technique. This approach mitigates premature convergence by promoting genetic diversity, ensuring that the population maintains a wide range of potential solutions. By minimizing gene association within chromosomes, the CBC technique enhances the model’s ability to learn diverse gesture transitions and movement dynamics across different users. This optimization process enables the Markov Chain to better generalize subject-independent sign language recognition, leading to improved classification accuracy, robustness against signer variability, and reduced misclassification rates. Experimental evaluations demonstrate a significant improvement in recognition performance, reduced error rates, and enhanced generalization across unseen signers, validating the effectiveness of the proposed approach. Full article

The four-way shuttle storage and retrieval system (FWSS/RS) is an advanced automated warehousing solution for achieving green and intelligent logistics, and task allocation is crucial to its logistics efficiency. However, current research on task allocation in three-dimensional storage environments is mostly conducted in the single-operation mode that handles inbound or outbound tasks individually, with limited attention paid to the more prevalent composite operation mode where inbound and outbound tasks coexist. To bridge this gap, this study investigates the task allocation problem in an FWSS/RS under the composite operation mode, and deep reinforcement learning (DRL) is introduced to solve it. Initially, the FWSS/RS operational workflows and equipment motion characteristics are analyzed, and a task allocation model with the total task completion time as the optimization objective is established. Furthermore, the task allocation problem is transformed into a partially observable Markov decision process corresponding to reinforcement learning. Each shuttle is regarded as an independent agent that receives localized observations, including shuttle position information and task completion status, as inputs, and a deep neural network is employed to fit value functions to output action selections. Correspondingly, all agents are trained within an independent deep Q-network (IDQN) framework that facilitates collaborative learning through experience sharing while maintaining decentralized decision-making based on individual observations. Moreover, to validate the efficiency and effectiveness of the proposed model and method, experiments were conducted across various problem scales and transport resource configurations. The experimental results demonstrate that the DRL-based approach outperforms conventional task allocation methods, including the auction algorithm and the genetic algorithm. Specifically, the proposed IDQN-based method reduces the task completion time by up to 12.88% compared to the auction algorithm, and up to 8.64% compared to the genetic algorithm across multiple scenarios. Moreover, task-related factors are found to have a more significant impact on the optimization objectives of task allocation than transport resource-related factors. Full article

Background: Oral squamous cell carcinoma (OSCC) is a multifactorial and multistage disease influenced by both genetic susceptibility and environmental risk factors. However, conventional oral cancer preventions are often based on environmental exposures but do not allow for genetic susceptibility and both factors contributing to multistage progressions. This study developed a comprehensive multistate risk model combining both types of factors. Methods: Using data from literature, the researchers built a multistate progression model and calculated transition risks to simulate outcomes in a high-risk population, similar to those eligible for oral cancer screening in Taiwan. Results: The findings showed that OSCC risk varied dramatically across the population, ranging from 362 to over 24,000 cases per 100,000, depending on risk level. The integration of genetic and environmental risk factors into a multistate disease model allows for more accurate risk stratifications of precancerous and invasive OSCC. Frequent screening is more effective, notably in high-risk individuals. Incorporating a health education program provided an additional 2 to 6% reduction in incidence, particularly benefiting higher-risk groups. Simulation findings indicate that tailored screening strategies, particularly when combined with health education interventions, can significantly improve the effectiveness of oral cancer prevention. Conclusions: Quantifying the effects of genetic susceptibility and environmental factors on multistate natural history of precancerous lesions and oral cancer provides a valuable framework for developing the risk-guided policies for oral cancer prevention. Full article

Recent advancements in agricultural mobile robots (agribots) have enabled the execution of critical tasks such as crop inspection, precision spraying, and selective harvesting. While agribots show significant potential, conventional path-planning algorithms suffer from three limitations: (1) inadequate dynamic obstacle avoidance, which may compromise operational safety, (2) premature convergence to local optima, and (3) excessive energy consumption due to suboptimal trajectories. To overcome these challenges, this study proposes an enhanced Dynamic Genetic Algorithm—Ant Colony Optimization (DGA-ACO) framework. It integrates a 2D risk-penalty mapping model with dynamic obstacle avoidance mechanisms, improves max–min ant system pheromone allocation through adaptive crossover-mutation operators, and incorporates a hidden Markov model for accurately forecasting obstacle trajectories. A multi-objective fitness function simultaneously optimizes path length, energy efficiency, and safety metrics, while genetic operators prevent algorithmic stagnation. Simulations in different scenarios show that DGA-ACO outperforms Dijkstra, A*, genetic algorithm, ant colony optimization, and other state-of-the-art methods. It achieves shortened path lengths and improved motion smoothness while achieving a certain degree of dynamic obstacle avoidance in the global path-planning process. Full article

Causal discovery involves searching intractably large spaces. Decomposing the search space into classes of observationally equivalent causal models is a well-studied avenue to making discovery tractable. This paper studies the topological structure underlying causal equivalence to develop a categorical formulation of Chickering’s transformational characterization of Bayesian networks. A homotopic generalization of the Meek–Chickering theorem on the connectivity structure within causal equivalence classes and a topological representation of Greedy Equivalence Search (GES) that moves from one equivalence class of models to the next are described. Specifically, this work defines causal models as propable symmetric monoidal categories (cPROPs), which define a functor category ${\mathcal{C}}^P$ from a coalgebraic PROP P to a symmetric monoidal category $\mathcal{C}$. Such functor categories were first studied by Fox, who showed that they define the right adjoint of the inclusion of Cartesian categories in the larger category of all symmetric monoidal categories. cPROPs are an algebraic theory in the sense of Lawvere. cPROPs are related to previous categorical causal models, such as Markov categories and affine CDU categories, which can be viewed as defined by cPROP maps specifying the semantics of comonoidal structures corresponding to the “copy-delete” mechanisms. This work characterizes Pearl’s structural causal models (SCMs) in terms of Cartesian cPROPs, where the morphisms that define the endogenous variables are purely deterministic. A higher algebraic K-theory of causality is developed by studying the classifying spaces of observationally equivalent causal cPROP models by constructing their simplicial realization through the nerve functor. It is shown that Meek–Chickering causal DAG equivalence generalizes to induce a homotopic equivalence across observationally equivalent cPROP functors. A homotopic generalization of the Meek–Chickering theorem is presented, where covered edge reversals connecting equivalent DAGs induce natural transformations between homotopically equivalent cPROP functors and correspond to an equivalence structure on the corresponding string diagrams. The Grothendieck group completion of cPROP causal models is defined using the Grayson–Quillen construction and relate the classifying space of cPROP causal equivalence classes to classifying spaces of an induced groupoid. A real-world domain modeling genetic mutations in cancer is used to illustrate the framework in this paper. Full article

Lung cancer remains the leading cause of cancer-related deaths worldwide, driven by its complexity and the heterogeneity of its subtypes, which influence pathogenesis, tumor microenvironment, and genetic alterations. We developed a novel weighted gene regulatory network reconstruction method based on maximum entropy and Markov chain entropy principles, which integrates gene expression and DNA methylation data to generate biologically informed networks. Applied to LUAD and LUSC datasets, we define a network methylation index to determine whether gene methylation acts as oncogenic or tumor-suppressive. By revealing a stable core set of pathogenic genes, we identify not only genes with significant expression changes, such as CD74 and HGF, but also pathogenic genes with stable expression, such as BRAF and KDM6A. Additionally, we uncover potential driver genes, such as CORO2B and C20orf194, associated with disease stage, gender, and smoking status. This method offers a more comprehensive understanding of NSCLC mechanisms, paving the way for improved therapeutic strategies. Full article

In robotic surface UV printing, the irregular shape of the workpiece and frequent curvature changes require the printing robot to maintain the nozzle’s perpendicular orientation to the surface during path planning, which imposes high demands on trajectory accuracy and path smoothness. To address this challenge, this paper proposes a reinforcement-learning-based path planning method. First, an ideal main path is defined based on the nozzle characteristics, and then a robot motion accuracy model is established and transformed into a Markov Decision Process (MDP) to improve path accuracy and smoothness. Next, a framework combining Generative Adversarial Imitation Learning (GAIL) and Soft Actor–Critic (SAC) methods is proposed to solve the MDP problem and accelerate the convergence of SAC training. Experimental results show that the proposed method outperforms traditional path planning methods, as well as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Specifically, the maximum Cartesian space error in path accuracy is reduced from 1.89 mm with PSO and 2.29 mm with GA to 0.63 mm. In terms of joint space smoothness, the reinforcement learning method achieves the smallest standard deviation, especially with a standard deviation of 0.00795 for joint 2, significantly lower than 0.58 with PSO and 0.729 with GA. Moreover, the proposed method also demonstrates superior training speed compared to the baseline SAC algorithm. The experimental results validate the application potential of this method in intelligent manufacturing, particularly in industries such as automotive manufacturing, aerospace, and medical devices, with significant practical value. Full article

Forests are crucial for human well-being and the health of our planet, particularly due to their role in carbon storage and climate mitigation. Mediterranean forests, in particular, are a vital natural resource for the region. They help absorb anthropogenic carbon emissions, reduce erosion, and provide essential habitats for various species, which in turn increases genetic diversity and species richness. This study combines Random Forest and Markov chain models to propose a highly accurate method for predicting land use. This approach offers substantial scientific support for sustainable land management policies. The methods used demonstrated excellent classification performance over time, allowing for an examination of the evolution of Mediterranean forests in the Aspromonte region. This study also provides a foundation for estimating carbon stored above and below ground using remote sensing images. The model achieved an impressive accuracy of 98.88%, making it a reliable tool for predicting the dynamics of Mediterranean forests. The results of this study have significant implications for urban planning and climate change mitigation efforts. Full article

Gaussian process (GP)-based robust optimization is an effective tool in product quality improvement. However, most existing variable selection methods are designed for parametric models and are unsuitable for nonparametric GP models. Additionally, improving the prediction accuracy of GP models with limited design points remains a significant challenge in robust optimization. To address these issues, this article proposes a GP-based multi-stage robust parameter optimization method that integrates symmetry modeling, sensitivity analysis (SA), and Markov Chain Monte Carlo (MCMC) techniques. First, a modified expected improvement (EI) criterion is introduced to enhance the utilization efficiency of design points. Second, a nonparametric variable selection technique based on SA is developed for GP models to identify significant variables. This method considers both independent variables and their interactions, improving the interpretability of GP models. Finally, the selected variables are used to construct the robust optimization model, and the genetic algorithm (GA) is employed to search for the optimal solution within the feasible domain. Numerical simulations and real-world experiments demonstrate the effectiveness of the proposed method. Comparative results indicate that the proposed method obtains more robust optimal input parameter settings compared to existing approaches. Full article

Cameras, with their low cost and efficiency, are widely used in construction management and structural health monitoring. However, existing reviews on camera sensor placement (CSP) are outdated due to rapid technological advancements. Furthermore, the construction industry poses unique challenges for CSP implementation due to its scale, complexity, and dynamic nature. Previous reviews have not specifically addressed these industry-specific demands. This study aims to fill this gap by analyzing articles from the Web of Science and ASCE databases that focus exclusively on CSP in construction. A rigorous selection process ensures the relevance and quality of the included studies. This comprehensive review navigates through the complexities of camera and environment models, advocating for advanced optimization techniques like genetic algorithms, greedy algorithms, Swarm Intelligence, and Markov Chain Monte Carlo to refine CSP strategies. Simultaneously, Building Information Modeling is employed to consider the progress of construction and visualize optimized layouts, improving the effect of CSP. This paper delves into perspective distortion, the field of view considerations, and the occlusion impacts, proposing a unified framework that bridges practical execution with the theory of optimal CSP. Furthermore, the roadmap for future exploration in the CSP of construction is proposed. This work enriches the study of construction CSP, charting a course for future inquiry, and emphasizes the need for adaptable and technologically congruent CSP approaches amid evolving application landscapes. Full article

Understanding energy demands and costs is important for policy makers and the energy sector, especially in the context of residential heating and cooling systems. To estimate the thermal demand of a residential house, a grey-box modelling method with a resistance–capacitance (RC) analogy was implemented. The architectural properties used to parameterize the grey-box model were derived from a house used for research purposes in Vaughan, Ontario, Canada (TRCA-House A). The house model accounts for solar irradiance on exterior building surfaces, thermal conductivity through all surfaces, solar heat gains through windows, and thermal gains from ventilation. Two parallel short- and long-term calibrations were performed such that model outputs reflected the real-world operation of the house as best as possible. To define the unknown model parameters (such as the conductivity of building materials and some constant parameters), a hybrid optimization scheme including a genetic algorithm (GA) and the Quasi-Newton algorithm was introduced and implemented using Bayesian approximation and Markov Chain Monte Carlo (MCMC) methods. The temperature outputs from the model were compared to the data retrieved from TRCA-House A. The final iteration of the model had an RMSE for interior zone temperature estimation of 0.22 °C when compared to the retrieved interior zone temperature data from TRCA-House A. Furthermore, the annual heating and cooling energy consumption values are within 1.50% and 0.08% of target values, respectively. According to these preliminary results, the introduced model and optimization techniques could be adjusted for different types of housing, as well as for smart control applications on both a short- and long-term basis. Full article

Energy efficiency constitutes a pivotal performance indicator for 5G New Radio (NR) networks and beyond, and achieving optimal efficiency necessitates the meticulous consideration of trade-offs against other performance parameters, including latency, throughput, connection densities, and reliability. Energy efficiency assumes it is of paramount importance for both User Equipment (UE) to achieve battery prologue and base stations to achieve savings in power and operation cost. This paper presents an exhaustive review of power-saving research conducted for 5G and beyond 5G networks in recent years, elucidating the advantages, disadvantages, and key characteristics of each technique. Reinforcement learning, heuristic algorithms, genetic algorithms, Markov Decision Processes, and the hybridization of various standard algorithms inherent to 5G and 5G NR represent a subset of the available solutions that shall undergo scrutiny. In the final chapters, this work identifies key limitations, namely, computational expense, deployment complexity, and scalability constraints, and proposes a future research direction by theoretically exploring online learning, the clustering of the network base station, and hard HO to lower the consumption of networks like 2G or 4G. In lowering carbon emissions and lowering OPEX, these three additional features could help mobile network operators achieve their targets. Full article

Due to the limitations of low coverage, high repetition rate, and slow convergence speed of the basic genetic algorithm (GA) in robot complete coverage path planning, the state transition matrix of the Markov chain is introduced to guide individual mutation based on the genetic mutation path planning algorithm, which can improve the quality of population individuals, enhancing the search ability and convergence speed of the genetic algorithm. The proposed improved genetic algorithm is used for complete coverage path planning simulation analysis in different work areas. The analysis results show that compared to traditional genetic algorithms, the improved genetic algorithm proposed in this paper reduces the average path length by 21.8%, the average number of turns by 6 times, the repetition rate by 83.8%, and the coverage rate by 7.76% in 6 different work areas. The results prove that the proposed improved genetic algorithm is applicable in complete coverage path planning. To verify whether the Markov chain genetic algorithm (MCGA) proposed is suitable for agricultural robot path tracking and operation, it was used to plan the path of an actual land parcel. An automatic navigation robot can track the planned path, which can verify the feasibility of the MCGA proposed. Full article

Chimeric antigen receptor (CAR)-T cell therapy represents a breakthrough in treating resistant hematologic cancers. It is based on genetically modifying T cells transferred from the patient or a donor. Although its implementation has increased over the last few years, CAR-T has many challenges to be addressed, for instance, the associated severe toxicities, such as cytokine release syndrome. To model CAR-T cell dynamics, focusing on their proliferation and cytotoxic activity, we developed a mathematical framework using ordinary differential equations (ODEs) with Bayesian parameter estimation. Bayesian statistics were used to estimate model parameters through Monte Carlo integration, Bayesian inference, and Markov chain Monte Carlo (MCMC) methods. This paper explores MCMC methods, including the Metropolis–Hastings algorithm and DEMetropolis and DEMetropolisZ algorithms, which integrate differential evolution to enhance convergence rates. The theoretical findings and algorithms were validated using Python and Jupyter Notebooks. A real medical dataset of CAR-T cell therapy was analyzed, employing optimization algorithms to fit the mathematical model to the data, with the PyMC library facilitating Bayesian analysis. The results demonstrated that our model accurately captured the key dynamics of CAR-T cell therapy. This conclusion underscores the potential of parameter estimation to improve the understanding and effectiveness of CAR-T cell therapy in clinical settings. Full article

The human parainfluenza virus type 4 (HPIV4) can be classified into two distinct subtypes, 4a and 4b. The full lengths of the fusion gene (F gene) of 48 HPIV4 strains collected during the period of 1966–2022 were analyzed. Based on these gene sequences, the time-scaled evolutionary tree was constructed using Bayesian Markov chain Monte Carlo methods. A phylogenetic tree showed that the first division of the two subtypes occurred around 1823, and the most recent common ancestors of each type, 4a and 4b, existed until about 1940 and 1939, respectively. Although the mean genetic distances of all strains were relatively wide, the distances in each subtype were not wide, indicating that this gene was conserved in each subtype. The evolutionary rates of the genes were relatively low (4.41 × 10⁻⁴ substitutions/site/year). Moreover, conformational B-cell epitopes were predicted in the apex of the trimer fusion protein. These results suggest that HPIV4 subtypes diverged 200 years ago and the progenies further diverged and evolved. Full article