Search Results (652)

Selective catalytic reduction (SCR) flue gas denitrification systems are inherently complex, typically embodying characteristics of non-linearity, significant time delays, and susceptibility to multiple disturbances. In the context of coal power units engaging in deep load cycling and rapid frequency adjustment, conventional proportional-integral-derivative (PID) control struggles to meet the demands of effective control. This study introduces a control strategy that incorporates a “state observer + Linear Quadratic Regulator (LQR) state feedback + Improved Quantum Genetic Algorithm (IQGA) optimized PID”. Initially, local linear mathematical models of an SCR denitrification system at 340 MW, 450 MW, and 540 MW loads were used to design state observer and LQR state feedback control parameters for each operational condition. At a single load point, the IQGA was employed to optimize the outer loop PID parameters, followed by simulation experiments of load increases and decreases between 340 MW and 540 MW. The results demonstrated that, compared to two other strategies, the proposed approach reduced the overshoot by a minimum of 1.5% and shortened the adjustment time by 31.7% under conditions of step disturbances and internal perturbations. Throughout variable operational conditions, the strategy consistently exhibited minimal output fluctuations, rapid adjustment capabilities, strong disturbance rejection, and robust stability. This algorithm proves to be an effective method for controlling NOx concentrations, offering insights for precise ammonia injection control in future applications. Full article

The brain is a biological system comprising nerve cells and orchestrates its embodied agent’s perception, behavior, and learning in dynamic environments. The free-energy principle (FEP) advocated by Karl Friston explicates the local, recurrent, and self-supervised cognitive dynamics of the brain’s higher-order functions. In this study, we continue to refine the FEP through a physics-guided formulation; specifically, we apply our theory to synaptic learning by considering it an inference problem under the FEP and derive the governing equations, called Bayesian mechanics. Our study uncovers how the brain infers weight changes and postsynaptic activity, conditioned on the presynaptic input, by deploying generative models of the likelihood and prior belief. Consequently, we exemplify the synaptic efficacy in the brain with a simple model; in particular, we illustrate that the brain organizes an optimal trajectory in neural phase space during synaptic learning in continuous time, which variationally minimizes synaptic surprisal. Full article

This paper presents a taxonomy of agents’ embodiment in physical and virtual environments. It categorizes embodiment based on five entities: the agent being embodied, the possible mediator of the embodiment, the environment in which sensing and acting take place, the degree of body, and the intertwining of body, mind, and environment. The taxonomy is applied to a wide range of embodiment of humans, artifacts, and programs, including recent technological and scientific innovations related to virtual reality, augmented reality, telepresence, the metaverse, digital twins, and large language models. The presented taxonomy is a powerful tool to analyze, clarify, and compare complex cases of embodiment. For example, it makes the choice between a dualistic and non-dualistic perspective of an agent’s embodiment explicit and clear. The taxonomy also aided us to formulate the term “embodiment by proxy” to denote how seemingly non-embodied agents may affect the world by using humans as “extended arms”. We also introduce the concept “off-line embodiment” to describe large language models’ ability to create an illusion of human perception. Full article

Port coasts are affected by waves and tidal currents, and sediment continues to silt up, leading to a reduction in the depth of water in the channel, blocking the channel and seriously affecting the safe operation of ports. The main cause of sediment deposition in ports is suspended sediment transport, and the characteristics of the vertical distribution of suspended sediment concentrations are the embodiment of the suspended sediment transport law. This paper is divided into three parts to study the vertical distribution characteristics of suspended sediment concentrations. Firstly, the shortcomings of the traditional diffusion model were analysed by using the finite mixing theory (FMT); secondly, the sediment mixing length coefficient κ_s model was introduced and combined with the sediment group settling velocity model to establish the vertical distribution model of suspended sediment concentrations under wave–current; finally, the effects of various factors on the vertical distribution of the suspended sediment concentration were investigated. The results show that the model in this paper has the characteristics of “low variance and low bias”, which solves the problem that κ_s is difficult to determine. When the model κ_s < κ_s′ (κ_s′ = 0.4), the concentration of suspended sediment predicted by κ_s′ is overestimated, and vice versa. As the sediment concentration increases, the interaction between particles increases and the vertical distribution of the suspended sediment concentration shows the pattern of “small top and large bottom”. The larger the particle size, the greater the sedimentation rate of the suspended sediment, and a large amount of sediment will be suspended near the bottom without mixing. The higher the wave height, the stronger the boundary layer turbulence and the movement of the water particles’ trajectory, and the smaller the difference in sediment concentration between the bottom and the sea surface. Full article

The emerging paradigm of personalised bone repair embodies a transformative triad comprising bio-inspired design, digital fabrication, and the exploration of innovative materials. The increasing average age of the population, alongside the rising incidence of fractures associated with age-related conditions such as osteoporosis, necessitates the development of customised, efficient, and minimally invasive treatment modalities as alternatives to conventional methods (e.g., autografts, allografts, Ilizarov distraction, and bone fixators) typically employed to promote bone regeneration. A promising innovative technique involves the use of cellularised scaffolds incorporating mesenchymal stem cells (MSCs). The selection of materials—ranging from metals and ceramics to synthetic or natural bio-derived polymers—combined with a design inspired by natural sources (including bone, corals, algae, shells, silk, and plants) facilitates the replication of geometries, architectures, porosities, biodegradation capabilities, and mechanical properties conducive to physiological bone regeneration. To mimic internal structures and geometries for construct customisation, scaffolds can be designed using Computer-aided Design (CAD) and fabricated via 3D-printing techniques. This approach not only enables precise control over external shapes and internal architectures but also accommodates the use of diverse materials that improve biological performance and provide economic advantages. Finally, advanced numerical models are employed to simulate, analyse, and optimise the complex processes involved in personalised bone regeneration, with computational predictions validated against experimental data and in vivo studies to ascertain the model’s ability to predict the recovery of bone shape and function. Full article

To achieve carbon reduction in architecture, this study establishes a carbon emission calculation model for wooden structures based on life cycle assessment (LCA) theory, using the emission factor method. Carbon emission factors involved in the entire life cycle of wooden buildings are identified and calculated for two modern wooden structures at Beijing Forestry University. The results are quantified and compared to analyze the causes of high carbon emissions, and lightweight design strategies for wooden structures are proposed through case studies. The two case buildings consumed 0.36 m³ and 0.29 m³ of wood material per square meter of building area, with carbon emissions of 311.23 kgCO₂e/m² and 292.03 kgCO₂e/m², respectively. During the building life cycle, waste disposal, material production, and material transportation accounted for the highest carbon emissions, accounting for 40%, 25%, and 20%, respectively. This study shows that factors such as the building shape coefficient, structural design, component design, material type, and decoration influence material usage in wooden structures, thereby affecting carbon emissions. Key strategies for reducing embodied carbon include optimizing building shape and structural design, using lightweight materials, and minimizing decoration. Full article

Gerty MacDowell’s initial, albeit brief, appearance in James Joyce’s Ulysses has sparked debates regarding her identity and agency. In the critical literature, there are interpretations that characterize Gerty as a woman and disabled person whose actions conform to patriarchal beauty standards that objectify her. This paper argues for revising such readings by applying an intersectional feminist disability perspective attuned to the interconnections between her womanhood and disability. Rather than positing Gerty’s identities as inherently conflicting, I illustrate how her disability and feminine social position co-constitute and transform one another. Her self-care practices aimed at securing a husband, though partly conforming to norms, also foster confidence and counter pervasive cultural assumptions of disabled women as ugly, useless, and asexual. Gerty’s exhibition of sexual desire and pursuit of pleasure likewise contest views of disabled women as unsuitable for romance or unable to be agentic sexual subjects. Furthermore, conceptualizing agency beyond neoliberal notions of rational autonomy acting against all constraints upholds Gerty’s agentic power. She makes strategic use of available discourses and resources to expand her precarious options given material and ideological limitations. Overall, analyzing Gerty’s intersectional experience denaturalizes the reductive models of identity and agency that have dominated Ulysses criticism. Applying fresh perspectives opens new symbolic interpretations of embodied identity and sexuality. Full article

Anomaly detection in hyperspectral imaging is crucial for remote sensing, driving the development of numerous algorithms. However, systematic studies reveal a dichotomy where algorithms generally excel at either detecting anomalies in specific datasets or generalizing across heterogeneous datasets (i.e., lack adaptability). A key source of this dichotomy may center on the singular and like biases frequently employed by existing algorithms. Current research lacks experimentation into how integrating insights from diverse biases might counteract problems in singularly biased approaches. Addressing this gap, we propose stacking-based ensemble learning for hyperspectral anomaly detection (SELHAD). SELHAD introduces the integration of hyperspectral anomaly detection algorithms with diverse biases (e.g., Gaussian, density, partition) into a singular ensemble learning model and learns the factor to which each bias should contribute so anomaly detection performance is optimized. Additionally, it introduces bootstrapping strategies into hyperspectral anomaly detection algorithms to further increase robustness. We focused on five representative algorithms embodying common biases in hyperspectral anomaly detection and demonstrated how they result in the previously highlighted dichotomy. Subsequently, we demonstrated how SELHAD learns the interplay between these biases, enabling their collaborative utilization. In doing so, SELHAD transcends the limitations inherent in individual biases, thereby alleviating the dichotomy and advancing toward more adaptable solutions. Full article

Fishery plays an important role in world trade; however, the embodied energy associated with fishery remains incompletely quantified. In this study, we applied the multi-regional input-output (MRIO) model and logarithmic mean Divisia index (LMDI) approach to understand the evolution and drivers of embodied energy in the intermediate and final fishery trade between China and countries along the 21st century Maritime Silk Road (MSR) from 2006 to 2021. The findings are as follows: (1) Embodied energy in the intermediate fishery trade averaged 92.2% of embodied energy from the total fishery trade. China has gradually shifted from being a net exporter to a net importer of embodied energy in intermediate, final, and total fishery trade with countries along the MSR. (2) From a regional perspective, the embodied energy in China’s fishery trade with Japan, South Korea, and Southeast Asia comprises the majority of the embodied energy from China’s total fishery trade (82.0% on average annually). From a sectoral perspective, petroleum, chemical and non-metallic mineral products, and transport equipment were prominent in the embodied energy of China’s intermediate fishery trade (64.0% on average annually). (3) Economic output increases were the main contributors to the increasing embodied energy in all types of fishery trade in China. The improvement in energy efficiency effectively reduced the embodied energy in all types of fishery trade in China, but its negative driving force weakened in recent years owing to minor energy efficiency improvements. Understanding the embodied energy transactions behind the intermediate and final fishery trade with countries along the MSR can provide a theoretical reference for China to optimize its fishery trade strategy and save energy. Full article

Urbanization-led economic growth drives infrastructure investments and population accumulation in cities, hence exploiting natural resources at an extreme rate. In this context, coastal cities have become vulnerable to climate change-induced extreme weather events and human-made disasters in recent history, where effective measures to improve the resilience of cities are pivotal for developing sustainable living environments. This study proposes a framework for assessing urban resilience to natural disasters (floods) using bottom-up spatial interactions among natural, physical, and social systems within cities and regions. It is noted that seminal studies focus on either the mitigation or adaptation strategies within urban environments to assess disaster resilience, where limited multidisciplinary and operational models hinder evaluations at the city scale. Therefore, urban system interactions and quantifiable parameters proposed in this framework are essential for policymakers and disaster management agencies in the timely allocation of resources to optimize the recovery process. Moreover, spatial planning agencies can adopt resilience mapping to identify the potential risk zones and orient sustainable land use management. Urban resilience can be embodied in spatial strategies with the operational framework proposed here, and future urban growth scenarios can be tested in multiple disaster conditions. Full article

Background/Objectives: Sport persistence is the embodiment of sports performance and mental toughness. It refers to our attempts concerning the performance plateau, failures, injuries, or even the resolution and processing of stressful situations associated with success and positive events. In our research, we used qualitative methods based on Bronfenbrenner’s socio-ecological model to investigate the factors influencing sport persistence among high school and university athletes. Methods: The research was based on semi-structured interviews with 133 athletes. ATLAS.ti software and the grounded theory methodology were applied for data analysis. Our analysis grouped the responses according to Bronfenbrenner’s categorisation system, highlighting motivational factors at the microsystem level. Our research question was as follows: What kind of factors dominate the development of sport persistence among adolescent (high school) and young adult (university) athletes along Bronfenbrenner’s dimension of the microsystem? Results: Regarding the microsystem, family, peers, and coaches were mentioned as influential factors. Concerning the family, general, person-specific, family value-related, future-oriented, introjected, and disadvantage-compensating motivational components were identified. General, individual, community and relational factors were identified concerning peers. Concerning the coach, general, individual, community, and coach personality-driven motivational segments were detected. Conclusions: By recognising the complex interplay of systemic factors, we can design interventions targeting these factors at various socio-ecological levels, promoting youth sports and increasing physical activity among young people. These findings instil hope and motivation for the future of sports and physical activity. Full article

Microbially induced calcite precipitation is a soil improvement technique in which bacteria are used to produce calcium carbonate (biocement), precipitated after the hydrolysis of urea by the urease enzyme present in the microorganisms. This technique is becoming popular, and there have been several real cases of its use; however, the dosages and reaction times used to attain a required percentage of biocement mainly stem from previous experimental tests, and calculations are not performed. Thus, it is fundamental to have more robust tools and the existence of numerical models able to compute the amount precipitated, such as the one proposed in this paper, can be an important contribution. A two-phase porous medium model is created to analyse the precipitation process. The solid phase contains soil particles, bacteria and biocement, while the fluid phase contains water, urea and other dissolved species. A coupled bio-chemo-hydro-mechanical finite element formulation is defined, embodying the biochemical reaction, water seepage, the diffusion of species and soil deformation. The main novelties of this study are as follows: (i) porosity changes are computed considering the generation of solid mass due to biocement precipitation, and, therefore, soil permeability is updated during the calculation, with these highly coupled equations being integrated in time simultaneously and not sequentially; and (ii) the model is calibrated with experimental tests conceived especially for this purpose. The model is then used to compute the biocement precipitated in a sand column simulating a real experimental test. The results of the simulations present a distribution of biocement along the column closer to that observed in the experimental tests, validating the model. Full article

A four-dimensional (4D) trajectory is a multi-dimensional time series that embodies rich spatiotemporal features. However, its high complexity and inherent uncertainty pose significant challenges for accurate prediction. In this paper, we present a novel 4D trajectory prediction model that integrates convolutional neural networks (CNNs), bidirectional long short-term memory networks (BiLSTMs), and multi-head attention mechanisms. This model effectively addresses the characteristics of aircraft flight trajectories and the difficulties associated with simultaneously extracting spatiotemporal features using existing prediction methods. Specifically, we leverage the local feature extraction capabilities of CNNs to extract key spatial and temporal features from the original trajectory data, such as geometric shape information and dynamic change patterns. The BiLSTM network is employed to consider both forward and backward temporal orders in the trajectory data, allowing for a more comprehensive capture of long-term dependencies. Furthermore, we introduce a multi-head attention mechanism that enhances the model’s ability to accurately identify key information in the trajectory data while minimizing the interference of redundant information. We validated our approach through experiments conducted on a real ADS-B trajectory dataset. The experimental results demonstrate that the proposed method significantly outperforms comparative approaches in terms of trajectory estimation accuracy. Full article

A thriving electric vehicles (EVs) market serves as a pivotal embodiment of the global push towards sustainable mobility. As one of the leading global EV sellers, China owns a huge used EV market, which should be spotlighted. While most studies focus on the mechanism of new EV purchases, few put their insight into the trade of used EVs. To fill this gap, this paper aims to clarify the mechanism of consumption behaviour towards used EVs. First, we identified 11 variables that have a direct or indirect impact on consumers’ purchase intention and constructed a conceptual framework. Then, we checked the structural relationships of the model through an empirical study (n = 431). The results showed that purchase intention was determined by two variables: perceived risk and attitude. We also observed an association between income and purchase intention. Functional risk had a direct and significant impact on perceived risk. Economic value, brand trust, and after-sales service were crucial predictors of attitude. Education could moderate the relationship between attitudes and purchase intention. Based on theoretical findings, we present the design strategies to enhance consumers’ purchase willingness from car companies’ and policymakers’ viewpoints. In practical situations, this article offers valuable insights for stakeholders related to the used EV industry, providing a critical reference for advancing sustainable mobility. Full article

Since the emergence of ChatGPT, research on large language models (LLMs) has actively progressed across various fields. LLMs, pre-trained on vast text datasets, have exhibited exceptional abilities in understanding natural language and planning tasks. These abilities of LLMs are promising in robotics. In general, traditional supervised learning-based robot intelligence systems have a significant lack of adaptability to dynamically changing environments. However, LLMs help a robot intelligence system to improve its generalization ability in dynamic and complex real-world environments. Indeed, findings from ongoing robotics studies indicate that LLMs can significantly improve robots’ behavior planning and execution capabilities. Additionally, vision-language models (VLMs), trained on extensive visual and linguistic data for the vision question answering (VQA) problem, excel at integrating computer vision with natural language processing. VLMs can comprehend visual contexts and execute actions through natural language. They also provide descriptions of scenes in natural language. Several studies have explored the enhancement of robot intelligence using multimodal data, including object recognition and description by VLMs, along with the execution of language-driven commands integrated with visual information. This review paper thoroughly investigates how foundation models such as LLMs and VLMs have been employed to boost robot intelligence. For clarity, the research areas are categorized into five topics: reward design in reinforcement learning, low-level control, high-level planning, manipulation, and scene understanding. This review also summarizes studies that show how foundation models, such as the Eureka model for automating reward function design in reinforcement learning, RT-2 for integrating visual data, language, and robot actions in vision-language-action models, and AutoRT for generating feasible tasks and executing robot behavior policies via LLMs, have improved robot intelligence. Full article