Search Results (13,134)

Ensuring firefighter safety during oil tank fires is paramount, given the substantial risks posed by thermal radiation. This study employs both the Fire Dynamics Simulator (FDS) and Areal Locations of Hazardous Atmospheres (ALOHA) software to simulate a severe oil tank fire scenario at the Zhushan Branch Power Plant, where two heavy oil tanks and multiple light oil tanks are located. The simulation framework divides the combustion scenario into 22.4 million grids with a grid size of 0.5 m, allowing a fine-resolution assessment of thermal radiation. Assuming a worst-case scenario involving n-Heptane combustion, the FDS simulation calculates essential parameters, including temperature, velocity, and soot distribution fields, and suggests a minimum safe firefighting distance of 22 m (equivalent to one tank diameter, 1D) for those equipped with personal protective equipment when exposed to a 5 kW/m² heat flux. Meanwhile, ALOHA modeling extends the safety assessment, recommending a downwind safety distance of 62 m (approximately 2D) to establish a preliminary exclusion zone, crucial in early emergency response when data may be incomplete. Additionally, a grid sensitivity analysis was conducted to validate the accuracy of the numerical results. This study underscores the importance of coupling FDS and ALOHA outputs to develop a balanced, adaptive approach to firefighter safety, optimizing response protocols for high-risk environments. The results provide essential guidance for establishing safety zones, advancing standards within fire protection and emergency response, and supporting strategy development for large-scale oil and petrochemical storage facilities. Full article

Background/Objectives: This paper presents a Residual Neural Network (ResNet) based framework tailored for structured traffic accident data, aiming to improve accident severity prediction. The proposed model leverages residual learning to effectively model intricate relationships between numerical and categorical variables, resulting in a notable increase in prediction accuracy. Methods: A comparative analysis was performed with other Deep Learning (DL) architectures, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Darknet, and Extreme Inception (Xception), showing superior performance of the proposed Resnet. Key factors influencing accident severity were identified, with Shapley Additive Explanations (SHAP) values helping to address the need for transparent and explainable Artificial Intelligence (AI) in critical decision-making areas. Results: The generalizability of the ResNet model was assessed by training it, initially, on a UK road accidents dataset and validating it on a distinct dataset from India. The model consistently demonstrated high predictive accuracy, underscoring its robustness across diverse contexts, despite regional differences. Conclusions: These results suggest that the adapted ResNet model could significantly enhance traffic safety evaluations and contribute to the formulation of more effective traffic management strategies. Full article

The total mileage of the road network in China has been growing rapidly during the last twenty years. With the development of deep learning, the automatic road distr ess detection method is more accurate and effective than manual detection. However, the road are classified into five grades according to the Chinese road standard and each grade has its own characteristics. A single model cannot effectively identify multi-grade roads with different materials and levels of road distress. This study proposes a YOLOv8-based road distress detection strategy adapted for multiple road grades. The improved URetinex-Net network is used to enhance the spatial features and scenario diversity of the road distress datasets. Compared to the base YOLOv8 model, the enhancements have led to a 12% increase in accuracy for cement roads, a 22.3% improvement in detection speed, a 5.5% increase in accuracy for ordinary asphalt roads, a 7.5% increase in recognition accuracy for highways, and a 9.3% improvement in detection speed, with significant effects. This study refines the classification of roads based on their grades and matches them with corresponding artificial intelligence training strategies, providing guidance for road inspection and maintenance. Full article

This study aims to develop a multifaceted conceptual basis for employee collaboration with regard to promoting organizational sustainability, which encompasses environmental, social, and economic dimensions. Employing a mixed-methods framework, the study integrates a thorough literature review with a qualitative content analysis. A distinctive feature of this investigation is its emphasis on incorporating collaborative methodologies into sustainability strategies across various organizational frameworks, illustrating how collaboration can be refined through adaptive leadership, interdisciplinary teams, and digital technologies. The results indicate that organizations characterized by a robust collaborative culture demonstrate greater success in fostering sustainable innovations, minimizing environmental repercussions, and enhancing employee engagement. Furthermore, the study introduces a novel model that correlates collaboration with operational sustainability, taking into account diverse levels of resource sharing, leadership engagement, and employee empowerment. By focusing on actionable strategies, this research provides novel insights into how adaptive leadership, digital tools, and shared responsibility can transform collaboration into a driver of sustainability. This research enriches the existing body of literature by presenting an evidence-based framework for cultivating sustainable organizational cultures and provides valuable insights for prospective research on harnessing collaboration to attain long-term sustainability goals. Full article

To tackle the challenges of poor stability during real-time random gait switching and precise trajectory control for hexapod robots under limited stride and steering conditions, a novel real-time replanning gait switching control strategy based on an omnidirectional gait and fuzzy inference is proposed, along with an attitude control method based on the single-neuron adaptive proportional–integral–derivative (PID). To start, a kinematic model of a hexapod robot was developed through the Denavit–Hartenberg (D-H) kinematics analysis, linking joint movement parameters to the end foot’s endpoint pose, which formed the foundation for designing various gaits, including omnidirectional and compound gaits. Incorporating an omnidirectional gait could effectively resolve the challenge of precise trajectory control for the hexapod robot under limited stride and steering conditions. Next, a real-time replanning gait switching strategy based on an omnidirectional gait and fuzzy inference was introduced to tackle the issue of significant impacts and low stability encountered during gait transitions. Finally, in view of further enhancing the stability of the hexapod robot, an attitude adjustment algorithm based on the single-neuron adaptive PID was presented. Extensive experiments confirmed the effectiveness of this approach. The results show that our approach enabled the robot to switch gaits seamlessly in real time, effectively addressing the challenge of precise trajectory control under limited stride and steering conditions; moreover, it significantly improved the hexapod robot’s dynamic stability during its motion, enabling it to adapt to complex and changing environments. Full article

Long regarded as illicit substances with no clinical value, N-dimethylated tryptamines—such as N,N-dimethyltryptamine, 5-methoxy-N,N-dimethyltryptamine, and bufotenine—have been found to produce naturally in a wide variety of species, including humans. Known for their psychoactive effects through serotonin receptors (5-HTRs), N-dimethylated tryptamines are currently being reinvestigated clinically for their long-term benefits in mental disorders. Endogenous tryptamine is methylated by indolethylamine-N-methyltransferase (INMT), which can then serve as an agonist to pro-survival pathways, such as sigma non-opioid intracellular receptor 1 (SIGMAR1) signaling. Fibrogenic diseases, like metabolic-associated fatty liver disease (MAFLD), steatohepatitis (MASH), and chronic kidney disease (CKD) have shown changes in INMT and SIGMAR1 activity in the progression of disease pathogenesis. At the cellular level, endothelial cells and fibroblasts have been found to express INMT in various tissues; however, little is known about tryptamines in endothelial injury and fibrosis. In this review, I will give an overview of the biochemistry, molecular biology, and current evidence of INMT’s role in hepatic fibrogenesis. I will also discuss current pre-clinical and clinical findings of N-methylated tryptamines and highlight new and upcoming therapeutic strategies that may be adapted for mitigating fibrogenic diseases. Finally, I will mention recent findings for mutualistic gut bacteria influencing endogenous tryptamine signaling and metabolism. Full article

Dealing with uncertainty is a pivotal skill for adaptive decision-making across various real-life contexts. Cognitive models suggest that individuals continuously update their knowledge based on past choices and outcomes. Traditionally, uncertainty has been linked to negative states such as fear and anxiety. Recent evidence, however, highlights that uncertainty can also evoke positive emotions, such as surprise, interest, excitement, and enthusiasm, depending on one’s task expectations. Despite this, the interplay between mood, confidence, and learning remains underexplored. Some studies indicate that self-reported mood does not always align with confidence, as these constructs evolve on different timescales. We propose that mood influences confidence, thereby enhancing decision flexibility—defined as the ability to switch effectively between exploration and exploitation. This increased flexibility is expected to improve task performance by increasing accuracy. Our findings support this hypothesis, revealing that confidence modulates exploration/exploitation strategies and learning rates, while mood affects reward perception and confidence levels. These findings indicate that metacognition entails a dynamic balance between exploration and exploitation, integrating mood states with high-level cognitive processes. Full article

The concept of the water footprint (WF) has not adequately explored the combined effects of climate change and salinity. For this aim, the effects of future climate conditions on tomato WF irrigated with moderately saline water (EC = 2.9 dS m⁻¹) were examined, considering an expected increase in salinity reaching 5.9 dS m⁻¹ by 2050. Reference evapotranspiration (ETo), effective rainfall (ER), tomato crop evapotranspiration (ETc), leaching requirement (LR), net irrigation requirement (NIR), and tomato yield were estimated using CropWat and AquaCrop models. The blue (WFBlue), green (WFGreen), gray (WFGray), and total WF (TWF) were calculated. Results showed that ETo, ETc, and ER are expected to increase, while tomato yields will show a slight decrease. NIR is expected to increase depending on climate change scenarios and the increasing salinity of water irrigation. Calculated WF components showed significant increases, which consequently led to an increase in WFT exceeding the Tunisian national and regional levels by 15% and 18% between 2023 and 2050 under two scenarios, RCP4.5 and RCP8.5. The results highlighted the importance of WF for developing adaptation strategies to manage limited water resources, while advanced research on a large scale based on smart assessment tools is required to find best practices for water use reduction. Full article

Background: Phage therapy, a treatment utilizing bacteriophages to combat bacterial infections, is gaining attention as a promising alternative to antibiotics, particularly for managing antibiotic-resistant bacteria. This study aims to provide a comprehensive review of phage therapy by examining its safety, efficacy, influencing factors, future prospects, and regulatory considerations. The study also seeks to identify strategies for optimizing its application and to propose a systematic framework for its clinical implementation. Methods: A comprehensive analysis of preclinical studies, clinical trials, and regulatory frameworks was undertaken to evaluate the therapeutic potential of phage therapy. This included an in-depth assessment of key factors influencing clinical outcomes, such as infection site, phage–host specificity, bacterial burden, and immune response. Additionally, innovative strategies—such as combination therapies, bioengineered phages, and phage cocktails—were explored to enhance efficacy. Critical considerations related to dosing, including inoculum size, multiplicity of infection, therapeutic windows, and personalized medicine approaches, were also examined to optimize treatment outcomes. Results: Phage therapy has demonstrated a favorable safety profile in both preclinical and clinical settings, with minimal adverse effects. Its ability to specifically target harmful bacteria while preserving beneficial microbiota underpins its efficacy in treating a range of infections. However, variable outcomes in some studies highlight the importance of addressing critical factors that influence therapeutic success. Innovative approaches, including combination therapies, bioengineered phages, expanded access to diverse phage banks, phage cocktails, and personalized medicine, hold significant promise for improving efficacy. Optimizing dosing strategies remains a key area for enhancement, with critical considerations including inoculum size, multiplicity of infection, phage kinetics, resistance potential, therapeutic windows, dosing frequency, and patient-specific factors. To support the clinical application of phage therapy, a streamlined four-step guideline has been developed, providing a systematic framework for effective treatment planning and implementation. Conclusion: Phage therapy offers a highly adaptable, targeted, and cost-effective approach to addressing antibiotic-resistant infections. While several critical factors must be thoroughly evaluated to optimize treatment efficacy, there remains significant potential for improvement through innovative strategies and refined methodologies. Although phage therapy has yet to achieve widespread approval in the U.S. and Europe, its accessibility through Expanded Access programs and FDA authorizations for food pathogen control underscores its promise. Established practices in countries such as Poland and Georgia further demonstrate its clinical feasibility. To enable broader adoption, regulatory harmonization and advancements in production, delivery, and quality control will be essential. Notably, the affordability and scalability of phage therapy position it as an especially valuable solution for developing regions grappling with escalating rates of antibiotic resistance. Full article

The development of vaccines against RNA viruses has undergone a rapid evolution in recent years, particularly driven by the COVID-19 pandemic. This review examines the key roles that RNA viruses, with their high mutation rates and zoonotic potential, play in fostering vaccine innovation. We also discuss both traditional and modern vaccine platforms and the impact of new technologies, such as artificial intelligence, on optimizing immunization strategies. This review evaluates various vaccine platforms, ranging from traditional approaches (inactivated and live-attenuated vaccines) to modern technologies (subunit vaccines, viral and bacterial vectors, nucleic acid vaccines such as mRNA and DNA, and phage-like particle vaccines). To illustrate these platforms’ practical applications, we present case studies of vaccines developed for RNA viruses such as SARS-CoV-2, influenza, Zika, and dengue. Additionally, we assess the role of artificial intelligence in predicting viral mutations and enhancing vaccine design. The case studies underscore the successful application of RNA-based vaccines, particularly in the fight against COVID-19, which has saved millions of lives. Current clinical trials for influenza, Zika, and dengue vaccines continue to show promise, highlighting the growing efficacy and adaptability of these platforms. Furthermore, artificial intelligence is driving improvements in vaccine candidate optimization and providing predictive models for viral evolution, enhancing our ability to respond to future outbreaks. Advances in vaccine technology, such as the success of mRNA vaccines against SARS-CoV-2, highlight the potential of nucleic acid platforms in combating RNA viruses. Ongoing trials for influenza, Zika, and dengue demonstrate platform adaptability, while artificial intelligence enhances vaccine design by predicting viral mutations. Integrating these innovations with the One Health approach, which unites human, animal, and environmental health, is essential for strengthening global preparedness against future RNA virus threats. Full article

Beluga whale optimization (BWO) is a swarm-based metaheuristic algorithm inspired by the group behavior of beluga whales. BWO suffers from drawbacks such as an insufficient exploration capability and the tendency to fall into local optima. To address these shortcomings, this paper proposes augmented multi-strategy beluga optimization (AMBWO). The adaptive population learning strategy is proposed to improve the global exploration capability of BWO. The introduction of the roulette equilibrium selection strategy allows BWO to have more reference points to choose among during the exploitation phase, which enhances the flexibility of the algorithm. In addition, the adaptive avoidance strategy improves the algorithm’s ability to escape from local optima and enriches the population quality. In order to validate the performance of the proposed AMBWO, extensive evaluation comparisons with other state-of-the-art improved algorithms were conducted on the CEC2017 and CEC2022 test sets. Statistical tests, convergence analysis, and stability analysis show that the AMBWO exhibits a superior overall performance. Finally, the applicability and superiority of the AMBWO was further verified by several engineering optimization problems. Full article

Aiming at the problem that the existing human skeleton behavior recognition methods are insensitive to human local movements and show inaccurate recognition in distinguishing similar behaviors, a multi-scale spatio-temporal graph convolution method incorporating multi-granularity features is proposed for human behavior recognition. Firstly, a skeleton fine-grained partitioning strategy is proposed, which initializes the skeleton data into data streams of different granularities. An adaptive cross-scale feature fusion layer is designed using a normalized Gaussian function to perform feature fusion among different granularities, guiding the model to focus on discriminative feature representations among similar behaviors through fine-grained features. Secondly, a sparse multi-scale adjacency matrix is introduced to solve the bias weighting problem that amplifies the multi-scale spatial domain modeling process under multi-granularity conditions. Finally, an end-to-end graph convolutional neural network is constructed to improve the feature expression ability of spatio-temporal receptive field information and enhance the robustness of recognition between similar behaviors. The feasibility of the proposed algorithm was verified on the public behavior recognition dataset MSR Action 3D, with a accuracy of 95.67%, which is superior to existing behavior recognition methods. Full article

In this paper, we address the modeling, simulation, and control of a rotary inverted pendulum (RIP). The RIP model assembled via the MATLAB (Matlab 2021a)^®/Simulink (Simulink 10.3) Simscape (Simscape 7.3)™ environment demonstrates a high degree of fidelity in its capacity to capture the dynamic characteristics of an actual system, including nonlinear friction. The mathematical model of the RIP is obtained via the Euler–Lagrange approach, and a parameter identification procedure is carried out over the Simscape model for the purpose of validating the mathematical model. The usefulness of the proposed Simscape model is demonstrated by the implementation of a variety of control strategies, including linear controllers as the linear quadratic regulator (LQR), proportional–integral–derivative (PID) and model predictive control (MPC), nonlinear controllers such as feedback linearization (FL) and sliding mode control (SMC), and artificial intelligence (AI)-based controllers such as FL with adaptive neural network compensation (FL-ANC) and reinforcement learning (RL). A design methodology that integrates RL with other control techniques is proposed. Following the proposed methodology, a FL-RL and a proportional–derivative control with RL (PD-RL) are implemented as strategies to achieve stabilization of the RIP. The swing-up control is incorporated into all controllers. The visual environment provided by Simscape facilitates a better comprehension and understanding of the RIP behavior. A comprehensive analysis of the performance of each control strategy is conducted, revealing that AI-based controllers demonstrate superior performance compared to linear and nonlinear controllers. In addition, the FL-RL and PD-RL controllers exhibit improved performance with respect to the FL-ANC and RL controllers when subjected to external disturbance. Full article

This paper suggests an adaptive car-following strategy for autonomous connected vehicles (ACVs) that integrates a robust controller with an extended disturbance estimator (EDE) and reinforcement learning (RL) to improve performance in dynamic traffic environments. Traditional car-following methods struggle to handle external disturbances and uncertainties in vehicle dynamics. The suggested method addresses this by dynamically adjusting the EDE gain using RL, enabling the system to optimize its control strategy in real time continuously. Simulations were conducted in two scenarios, a single following vehicle and two following vehicles, each tracking a leading vehicle. Results showed significant improvements in velocity tracking, with the RL-based control method reducing velocity error by over 50% compared to conventional approaches. The technique also led to smoother acceleration control, enhancing stability and driving comfort. Quantitative metrics, such as total reward, velocity error, and acceleration magnitude, indicate that the suggested EDE-RL-based strategy provides a robust and adaptable solution for autonomous vehicle control. These findings indicate that RL, combined with robust control, can improve the performance and safety of ACV systems, making it suitable for broader applications in autonomous vehicle platooning and complex traffic scenarios, including vehicle-to-vehicle (V2V) communication. Full article

This study introduces a quantitative risk assessment (QRA) model aimed at evaluating the risk of invasive listeriosis linked to the consumption of ready-to-eat (RTE) smoked and gravad fish. The QRA model, based on published data, simulates the production process from fish harvest through to consumer intake, specifically focusing on smoked brine-injected, smoked dry-salted, and gravad fish. In a reference scenario, model predictions reveal substantial probabilities of lot and pack contamination at the end of processing (38.7% and 8.14% for smoked brined fish, 34.4% and 6.49% for smoked dry-salted fish, and 52.2% and 11.1% for gravad fish), although the concentrations of L. monocytogenes are very low, with virtually no packs exceeding 10 CFU/g at the point of sale. The risk of listeriosis for an elderly consumer per serving is also quantified. The lot-level mean risk of listeriosis per serving in the elderly population was 9.751 × 10⁻⁸ for smoked brined fish, 9.634 × 10⁻⁸ for smoked dry-salted fish, and 2.086 × 10⁻⁷ for gravad fish. Risk reduction strategies were then analyzed, indicating that the application of protective cultures and maintaining lower cold storage temperatures significantly mitigate listeriosis risk compared to reducing incoming fish lot contamination. The model also addresses the effectiveness of control measures during processing, such as minimizing cross-contamination. The comprehensive QRA model has been made available as a fully documented qraLm R package. This facilitates its adaptation for risk assessment of other RTE seafood, making it a valuable tool for public health officials to evaluate and manage food safety risks more effectively. Full article