Search Results (3,093)

Driving a vehicle, whether motorized or not, is a risky activity that can lead to a traffic accident and directly or indirectly affect all road users. In particular, road crashes involving pedestrians have caused the highest number of deaths and serious injuries in recent years. In order to prevent and reduce the occurrence of these types of traffic accidents and to optimize the use of the available resources of the administrations in charge of road safety, an updatable predictive model using Markov chains is proposed in this work. Markov chains are used in fields as diverse as hospital management or electronic engineering, but their application in the field of road safety is considered innovative. They are prediction and decision techniques that allow the estimation of the state of a given system by simulating its stochastic risk level. To carry out this study, the available information on traffic accidents involving pedestrians in the database of the Local Police of Badajoz (a medium-sized city in the southwest of Spain) in the period 2016 to 2023 were analyzed. These data were used to train a predictive model that was subsequently used to estimate the probability of occurrence of a traffic crash involving pedestrians in different areas of this city, information that could be used by the authorities to focus their efforts in those areas with the highest probability of a road crash occurring. This model can improve the identification of high-risk locations, and urban planners can optimize decision making in designing appropriate preventive measures and increase efficiency to reduce pedestrian crashes. Full article

Epidemiological approaches for examining human health responses to environmental exposures in observational studies frequently address confounding by employing advanced matching techniques and statistical methods grounded in conditional likelihood. This study incorporates a recently developed Bayesian hierarchical spatiotemporal model within a conditional logistic regression framework to capture the heterogeneous effects of environmental exposures in a case-crossover (CCO) design. Spatial and temporal dependencies are modeled through random effects incorporating multivariate conditional autoregressive priors. Flexible frailty structures are introduced to explore strategies for managing temporal variables. Parameter estimation and inference are conducted using a Monte Carlo Markov chain method within a Bayesian framework. Model fit and optimal model selection are evaluated using the deviance information criterion. Simulations assess and compare model performance across various scenarios. Finally, the approach is illustrated with workers’ compensation claims data from New York and Florida to examine spatiotemporal heterogeneity in hospitalization rates related to heat prostration. Full article

In this research, we present a new distribution, which is the bivariate alpha power Burr-XII distribution, based on the alpha power Burr-XII distribution. We thoroughly examine the key features of our newly developed bivariate model. We introduce a new class of bivariate models, which are built with the copula function. The statistical properties of the proposed distribution, such as conditional distributions, conditional expectations, marginal distributions, moment-generating functions, and product moments were studied. This was accomplished with two datasets of real data that came from two distinct devices. We employed Bayesian, maximum likelihood estimation, and least squares estimation strategies to obtain estimated points and intervals. Additionally, we generated bootstrap confidence intervals and conducted numerical analyses using the Markov chain Monte Carlo method. Lastly, we compared this novel bivariate distribution’s performance to earlier bivariate models, to determine how well it fit the real data. Full article

The sea surface skin temperature (SST_skin) is of critical importance with regard to air–sea interactions and marine carbon circulation. At present, no single remote sensor is capable of providing a gap-free SST_skin. The use of data fusion techniques is therefore essential for the purpose of filling these gaps. The extant fusion methodologies frequently fail to account for the influence of depth disparities and the diurnal variability of sea surface temperatures (SSTs) retrieved from multi-sensors. We have developed a novel approach that integrates depth and diurnal corrections and employs advanced data fusion techniques to generate hourly gap-free SST datasets. The General Ocean Turbulence Model (GOTM) is employed to model the diurnal variability of the SST profile, incorporating depth and diurnal corrections. Subsequently, the corrected SSTs at the same observed time and depth are blended using the Markov method and the remaining data gaps are filled with optimal interpolation. The overall precision of the hourly gap-free SST_skin generated demonstrates a mean bias of −0.14 °C and a root mean square error of 0.57 °C, which is comparable to the precision of satellite observations. The hourly gap-free SST_skin is vital for improving our comprehension of air–sea interactions and monitoring critical oceanographic processes with high-frequency variability. Full article

Particle Swarm Optimization (PSO) is a widespread evolutionary technique that has successfully solved diverse optimization problems across various application fields. However, when dealing with more complex optimization problems, PSO can suffer from premature convergence and may become stuck in local optima. The primary goal is accelerating convergence and preventing solutions from falling into these local optima. This paper introduces a new approach to address these shortcomings and improve overall performance: utilizing a reinforcement deep learning method to carry out online adjustments of parameters in a homogeneous Particle Swarm Optimization, where all particles exhibit identical search behaviors inspired by models of social influence among uniform individuals. The present method utilizes an online parameter control to analyze and adjust each primary PSO parameter, particularly the acceleration factors and the inertia weight. Initially, a partially observed Markov decision process model at the PSO level is used to model the online parameter adaptation. Subsequently, a Hidden Markov Model classification, combined with a Deep Q-Network, is implemented to create a novel Particle Swarm Optimization named DPQ-PSO, and its parameters are adjusted according to deep reinforcement learning. Experiments on different benchmark unimodal and multimodal functions demonstrate superior results over most state-of-the-art methods regarding solution accuracy and convergence speed. Full article

This study proposes an enhanced ship-type classification model that employs a sequential processing methodology integrating hidden Markov model (HMM), deep neural network (DNN), and convolutional neural network (CNN) techniques. Four different ship types—fishing boat, passenger, container, and other ship—were classified using multiple ship trajectory features extracted from the automatic identification system (AIS) and small fishing vessel tracking system. For model optimization, both ship datasets were transformed into various formats corresponding to multiple models, incorporating data enhancement and augmentation approaches. Speed over ground, course over ground, rate of turn, rate of turn in speed, berth distance, latitude/longitude, and heading were used as input parameters. The HMM–DNN–CNN combination was obtained as the optimal model (average F-1 score: 97.54%), achieving individual classification performances of 99.03%, 97.46%, and 95.83% for fishing boats, passenger ships, and container ships, respectively. The proposed approach outperformed previous approaches in prediction accuracy, with further improvements anticipated when implemented on a large-scale real-time data collection system. Full article

Arabic raw audio datasets were initially gathered to produce a corresponding signal spectrum, which was further used to extract the Mel-Frequency Cepstral Coefficients (MFCCs). The pronunciation dictionary, language model, and acoustic model were further derived from the MFCCs’ features. These output data were processed into Baidu’s Deep Speech model (ASR system) to attain the text corpus. Baidu’s Deep Speech model was implemented to precisely identify the global optimal value rapidly while preserving a low word and character discrepancy rate by attaining an excellent performance in isolated and end-to-end speech recognition. The desired outcome in this work is to forecast the next word and character in a sequential and systematic order that applies under natural language processing (NLP). This work combines the trained Arabic language model ARABERT with the potential of Long Short-Term Memory (LSTM) networks to predict the next word and character in an Arabic text. We used the pre-trained ARABERT embedding to improve the model’s capacity and, to capture semantic relationships within the language, we educated LSTM + CNN and Markov models on Arabic text data to assess the efficacy of this model. Python libraries such as TensorFlow, Pickle, Keras, and NumPy were used to effectively design our development model. We extensively assessed the model’s performance using new Arabic text, focusing on evaluation metrics like accuracy, word error rate, character error rate, BLEU score, and perplexity. The results show how well the combined LSTM + ARABERT and Markov models have outperformed the baseline models in envisaging the next word or character in the Arabic text. The accuracy rates of 64.9% for LSTM, 74.6% for ARABERT + LSTM, and 78% for Markov chain models were achieved in predicting the next word, and the accuracy rates of 72% for LSTM, 72.22% for LSTM + CNN, and 73% for ARABERET + LSTM models were achieved for the next-character prediction. This work unveils a novelty in Arabic natural language processing tasks, estimating a potential future expansion in deriving a precise next-word and next-character forecasting, which can be an efficient utility for text generation and machine translation applications. Full article

The imbalance in the “production–living–ecology” function (PLEF) has become a major issue for global cities due to the rapid advancement of urbanization and industrialization worldwide. The realization of PLEF coupling and coordination is crucial for a region’s sustainable development. Existing research has defined the concept of PLEF from the perspective of land function and measured its coupling coordination level using relevant models. However, there is still room for improvement in the indicator system, research methods, and other aspects. This work builds a PLEF coupling coordination evaluation-index system based on the perspective of human habitat using multi-source data in order to examine the spatial differences in PLEF coupling coordination level and the influencing factors in the Yellow River Basin (YRB). Using the modified coupling coordination model, the Moran index, spatial Markov chain model, and geographically weighted random forest model were introduced to analyze its spatial and temporal differentiation and influencing factors. The results found that (a) the level of PLEF coupling coordination in the YRB from 2010 to 2022 has been improving, and the number of severely imbalanced cities has been reduced from 23 to 15, but the level of downstream cities’ coupling coordination is significantly higher than that of upstream cities. The probability of cities maintaining their own level is greater than 50%, and there is basically no cross-level transfer. (b) The Moran index of the PLEF coupling coordination level has risen from 0.137 to 0.229, which shows a significant positive clustering phenomenon and is continually strengthening. The intercity polarization effect is being continually enhanced as seen in the LISA clustering diagram. (c) There is significant heterogeneity between the influencing factors in time and space. In terms of importance level, the series is per capita disposable income (0.416) > nighttime lighting index (0.370) > local general public budget expenditure (0.332) > number of beds per 1000 people (0.191) > NO₂ content in the air (0.110). This study systematically investigates the dynamic evolution of the coupled coordination level of PLEF in the YRB and its influencing mechanism, which is of great practical use. Full article

With societal progress, urban brownfields have become restrictive, and redevelopment studies have become an important part of urban renewal. In this work, we developed a two-step model for urban brownfield redevelopment, while considering the Wangping brownfield as the study area. Site suitability evaluation models for brownfield parks, agricultural picking gardens, and creative industrial centers were developed based on the elevation, slope, and surface runoff, and the evaluation results were categorized into five levels. The redevelopment plan was formulated based on these evaluation results. To study the effect of the plan, a transition matrix of land use was assessed using satellite images and the cellular automata (CA)–Markov model; based on the analysis, we predicted the land use situation of the Wangping brownfield, with respect to natural development, for 2030. A comparison of the redevelopment planning with the forecasted results revealed that the proportions of grassland, construction, and unused land decreased by 25.68, 3.12, and 2.38% and those of plowland and forest land increased by 6.61 and 24.57%. This confirms the advantages of redevelopment planning for restoring plowland and increasing biological carbon sinks. Notably, our two-step urban brownfield redevelopment model can enrich the current research on urban brownfields and guide similar urban renewal projects. Full article

The state of a machine is modeled as a controlled continuous-time Markov chain. Moreover, the machine is being serviced at random times. The aim is to maximize the time until the machine must be repaired, while taking the maintenance costs into account. The dynamic programming equation satisfied by the value function is derived, enabling optimal decision-making regarding inspection rates, and special problems are solved explicitly. This approach minimizes direct maintenance costs along with potential failure expenses, establishing a robust methodology for determining inspection frequencies in reliability-centered maintenance. The results contribute to the advancement of maintenance strategies and provide explicit solutions for particular cases, offering ideas for application in reliability engineering. Full article

In the context of integrating renewable energy sources such as wind and solar energy sources into distribution networks, this paper proposes a proactive low-carbon dispatch model for active distribution networks based on carbon flow calculation theory. This model aims to achieve accurate carbon measurement across all operational aspects of distribution networks, reduce their carbon emissions through controlling unit operations, and ensure stable and safe operation. First, we propose a method for measuring carbon emission intensity on the source and network sides of active distribution networks with network losses, allowing for the calculation of total carbon emissions throughout the operation of networks and their equipment. Next, based on the carbon flow distribution of distribution networks, we construct a low-carbon dispatch model and formulate its optimization problem within a Markov Decision Process framework. We improve the Soft Actor–Critic (SAC) algorithm by adopting a Gaussian-distribution-based reward function to train and deploy agents for optimal low-carbon dispatch. Finally, the effectiveness of the proposed model and the superiority of the improved algorithm are demonstrated using a modified IEEE 33-bus distribution network test case. Full article

There is a widely spread belief in the Bayesian network (BN) community that the overall accuracy of results of BN inference is not too sensitive to the precision of their parameters. We present the results of several experiments in which we put this belief to a test in the context of medical diagnostic models. We study the deterioration of accuracy under random symmetric noise but also biased noise that represents overconfidence and underconfidence of human experts.Our results demonstrate consistently, across all models studied, that while noise leads to deterioration of accuracy, small amounts of noise have minimal effect on the diagnostic accuracy of BN models. Overconfidence, common among human experts, appears to be safer than symmetric noise and much safer than underconfidence in terms of the resulting accuracy. Noise in medical laboratory results and disease nodes as well as in nodes forming the Markov blanket of the disease nodes has the largest effect on accuracy. In light of these results, knowledge engineers should moderately worry about the overall quality of the numerical parameters of BNs and direct their effort where it is most needed, as indicated by sensitivity analysis. Full article

As an important part of the urban ecosystem, urban green space provides a variety of ecosystem services, including climate regulation, soil conservation, carbon sink and oxygen release, and biodiversity protection. However, existing remote sensing evaluation methods for ecological service value lack the evaluation indicators of ecosystem service value for Guangzhou, China, and the evaluation method depends on the land cover type. Based on remote sensing technology and random forest algorithm, this study addresses these gaps by integrating remote sensing technology with a random forest algorithm to enhance the accuracy and rationality of ESV assessments. Focusing on Guangzhou, China, we improved the ecological service value evaluation system and conducted dynamic predictions based on land-use change scenarios. Our results indicate that the total ESV of Guangzhou’s green space was USD 7.323 billion in 2020, with a projected decline to USD 6.496 billion by 2030, representing a 12.37% reduction due to urbanization-driven land-use changes. This research highlights the noticeable role of green spaces in urban sustainability and provides robust, data-driven insights for policymakers to design more effective green space protection and management strategies. The improved assessment framework offers a novel approach for accurately quantifying urban ecosystem services and predicting future trends. Full article

The game of pursuit–evasion has always been a popular research subject in the field of Unmanned Aerial Vehicles (UAVs). Current evasion decision making based on reinforcement learning is generally trained only for specific pursuers, and it has limited performance for evading unknown pursuers and exhibits poor generalizability. To enhance the ability of an evasion policy learned by reinforcement learning (RL) to evade unknown pursuers, this paper proposes a pursuit UAV attitude estimation and pursuit strategy identification method and a Model Reference Policy Adaptation (MRPA) algorithm. Firstly, this paper constructs a Markov decision model for the pursuit–evasion game of UAVs that includes the pursuer’s attitude and trains an evasion policy for a specific pursuit strategy using the Soft Actor–Critic (SAC) algorithm. Secondly, this paper establishes a novel relative motion model of UAVs in pursuit–evasion games under the assumption that proportional guidance is used as the pursuit strategy, based on which the pursuit UAV attitude estimation and pursuit strategy identification algorithm is proposed to provide adequate information for decision making and policy adaptation. Furthermore, a Model Reference Policy Adaptation (MRPA) algorithm is presented to improve the generalizability of the evasion policy trained by RL in certain environments. Finally, various numerical simulations imply the precision of pursuit UAV attitude estimation and the accuracy of pursuit strategy identification. Also, the ablation experiment verifies that the MRPA algorithm can effectively enhance the performance of the evasion policy to deal with unknown pursuers. Full article

Due to the real applications, optimal consensus reinforcement learning with switching topology is still challenging due to the complexity of topological changes. This paper investigates the optimal consensus control problem for discrete multi-agent systems under Markov switching topologies. The goal is to design an appropriate algorithm to find the optimal control policies that minimize the performance index while achieving consensus among the agents. The concept of mean-square consensus is introduced, and the relationship between consensus error and tracking error to achieve mean-square consensus is studied. A performance function for each agent under switching topologies is established and a policy iteration algorithm using system data is proposed based on the Bellman optimality principle. The theoretical analysis shows that the consensus error realizes mean-square consensus and the performance function is optimized. The efficacy of the suggested approach is confirmed by numerical simulation using an actor–critic neural network. As a result, the value function is the optimum and the mean-square consensus can be reached using this technique. Full article