Dynamic Process Research Articles

Influence of Nonstationary Processes in Drill Rigs on the Durability of Structural Elements

Assessing the effects that nonstationary dynamic processes have on the durability of structural elements belongs to an important trend in modern dynamics and technical diagnostics of machines. Normally, fatigue strength calculations are performed taking into account only periodically variable stresses, as steady operating modes of machines are much longer in comparison with transient modes. However, a significant role in fatigue failure in machines and engineering structures is also played by nonstationary loads. This is explained by emerging intensive oscillations in the mechanical system during accelerating, braking, or changing the operation mode of a machine unit, which often lead to the accumulation of fatigue damages in the materials of parts in heavy loaded assemblies. The combination of stationary and nonstationary dynamic loads manifests itself, particularly in drilling rigs, where technological cycles include steady motion modes, starts, and stops. This paper represents a generalized mathematical model describing nonstationary processes in the lift system of a drill rig, which considers the relationship between electromagnetic processes in asynchronous motors and mechanical oscillatory phenomena, with the purpose of determining dynamic loads and stresses in structural elements of the rigs. Nonlinear physical systems include mechanical members with both concentrated and clearly expressed distributed parameters. The durability of structural elements is evaluated by means of a computer algorithm for analysis of crack growth rates using the NASGRO equation obtained with the presence of plastic deformation zones. An example of the crown block axis illustrates the influence of nonstationary dynamic processes in drill rigs on the durability of structural elements.

Influence of Nonstationary Processes in Drill Rigs on the Durability of Structural Elements

Assessing the effects that nonstationary dynamic processes have on the durability of structural elements belongs to an important trend in modern dynamics and technical diagnostics of machines. Normally, fatigue strength calculations are performed taking into account only periodically variable stresses, as steady operating modes of machines are much longer in comparison with transient modes. However, a significant role in fatigue failure in machines and engineering structures is also played by nonstationary loads. This is explained by emerging intensive oscillations in the mechanical system during accelerating, braking, or changing the operation mode of a machine unit, which often lead to the accumulation of fatigue damages in the materials of parts in heavy loaded assemblies. The combination of stationary and nonstationary dynamic loads manifests itself, particularly in drilling rigs, where technological cycles include steady motion modes, starts, and stops. This paper represents a generalized mathematical model describing nonstationary processes in the lift system of a drill rig, which considers the relationship between electromagnetic processes in asynchronous motors and mechanical oscillatory phenomena, with the purpose of determining dynamic loads and stresses in structural elements of the rigs. Nonlinear physical systems include mechanical members with both concentrated and clearly expressed distributed parameters. The durability of structural elements is evaluated by means of a computer algorithm for analysis of crack growth rates using the NASGRO equation obtained with the presence of plastic deformation zones. An example of the crown block axis illustrates the influence of nonstationary dynamic processes in drill rigs on the durability of structural elements.

Woodland in the Curonian spit during the 3rd millennium BC based on anthracological evidence from Neolithic sites

In this paper we present the first attempt to apply anthracological analysis in paleoenvironmental research in Lithuania. We present the results of charcoal analysis of three Neolithic archaeological sites in the Lithuanian part of the Curonian Spit. The formation and development of the Curonian Spit was a complex and dynamic process. Constant aeolian processes caused periods of sand deflation, which led to the movement of the dunes and drastic changes in the landscape until the 19th century when a protective dune ridge was built and systemic tree planting began. Curonian woodlands currently consist of mostly planted pine forests. Knowledge of the spit’s vegetation and its dynamics in prehistoric and historic periods is mainly based on palynological data. This study aims to gain a more detailed understanding of the composition and development of the Curonian Spit forests in the 3rd millennium BC. Anthracological analysis of charcoal samples from three sites located in the middle and northern parts of the spit show that broad-leaved tree species such as oak, maple and lime were spreading during the Neolithic and suggest that woodland vegetation in different parts of the spit was not uniform, with broad-leaved species thriving around Nida site and pine forests around Alksnynė. We propose that one of the reasons for the observed difference between the sites is the different phases of the spit formation that these sites underwent during the settlement period, as the middle part of the spit was formed earlier than the northern part and is therefore represented by less developed soils.

Design Photocatalysts to Boost Carrier Dynamics in Plastics Photoconversion into Fuels.

Solar-driven plastics conversion into valuable fuels has attracted broad attention in recent years, which has enormous potential for plastics recycling in the future. However, it usually encounters low conversion efficiency, where one of the reasons is attributed to the poor carrier dynamics in the photocatalytic process. In this Perspective, we critically review the developed strategies, involving defect engineering, doping engineering, heterojunction engineering, and composite construction, for boosted carrier separation efficiency. In addition, we provide an outlook for more potential strategies to engineer catalysts for promoted carrier dynamics. Finally, we also propose prospects for the future research direction of plastics photoconversion into fuels.

Temporal metabolic dynamics and heterogeneity of viable but nonculturable Pseudomonas aeruginosa induced by chlorine disinfection at single-cell resolution

Characterizing the metabolic network and dynamics of Viable But Nonculturable (VBNC) bacteria is crucial for their identification and control. In this study, single-cell Raman spectroscopy was used to explore the metabolic intricacies of VBNC P. aeruginosa induced by chlorine disinfection over 24 h. VBNC P. aeruginosa exhibited significant reductions of 75.98 %, 84.26 % and 81.07 % in carbohydrate, protein and nucleic acid Raman peaks, signaling decreased metabolic activity in these crucial components. Intriguingly, lipid metabolism in VBNC P. aeruginosa remained robust, indicating a potential adaptation to environmental stress. Furthermore, this study analyzed Raman characteristic peaks to examine metabolic conversion in these bacteria. VBNC P. aeruginosa displayed distinct phase-specific metabolic conversion patterns compared to culturable ones. These findings shed light on the temporal dynamics of metabolic processes, such as lipid-carbohydrate conversion, which appeared to be ongoing throughout the incubation period in VBNC P. aeruginosa. Compared to the culturable state, VBNC P. aeruginosa exhibited reduced isotopic assimilation rates for carbon, nitrogen and hydrogen by 40.57 %, 44.62 % and 39.45 %, indicating decreased substrate uptake for biomolecular synthesis. Additionally, the metabolic heterogeneity index of the VBNC bacterial population increased by 37.28 %, with this heterogeneity displaying a progressive increase with the duration of the culture. This observation suggests VBNC P. aeruginosa differentiation into distinct subpopulations, believed crucial for survival in challenging environments. These findings reveal the metabolic dynamics of VBNC state bacteria, providing insights into their adaptive strategies and implications for environmental health risk assessment and precise control technologies.

Prediction of the Dangerous Range of Dumps Based on a Dynamic Process

A dump is a loose accumulation of solid waste from mining operations that easily leads to disasters such as landslides and debris flows. Taking a dump in the Panzhihua region of China as an example, based on the MPM and SPH meshless methods, the dynamic calculation of the instability process of the spoil slope was carried out according to the realistic three-dimensional calculation model of the generated terrain. Firstly, the strain and displacement of the dump under normal conditions and heavy rainfall conditions were simulated by the MPM material point method. The maximum spatial displacement of the dump under heavy rainfall conditions reached up to 386 m. Then, the motion and morphology of the accumulation of the dump under ordinary working conditions and heavy rainfall conditions were analyzed using the smooth particle hydrodynamics (SPH) method. Under heavy rainfall conditions, the maximum horizontal displacement of the dump was approximately 394 m. The research results are conducive to the risk assessment of the spoil slope and provide theoretical support for the calculation of the range of potential threats from the dump.

The effect of bending angle on a flexible electrode DBD plasma under sinusoidal excitation

There is a critical demand for sophisticated surface disinfection and sterilization devices accessible to the public by using cold atmospheric pressure air plasmas. A flexible printed circuit design of a dielectric barrier discharge reactor under non-bending and two bending configurations with an angle of 120° and 180° was studied. The characteristics of power consumption, the optical emission spectrum, dynamic process, electrode temperature and ozone concentration are evaluated. The non-bending configuration produces more O3, as compared to the bending configuration at the same applied voltage. The 180° configuration has a maximum concentration of excited species at the expense of higher electrode temperature. Both bending configurations demonstrated the propagation of filaments to bending axis where the continues luminescence is observed due to the high electrical field. The energy efficiency for plasma-generated reactive species reaches to 40% for non-bending configuration and decreases with the increase of bending angle. This research provides a new strategy for perspective into the plasma generated reactive species in biomedical and environmental applications.

Unraveling the mesoscale organization induced by network-driven processes

Complex systems are characterized by emergent patterns created by the nontrivial interplay between dynamical processes and the networks of interactions on which these processes unfold. Topological or dynamical descriptors alone are not enough to fully embrace this interplay in all its complexity, and many times one has to resort to dynamics-specific approaches that limit a comprehension of general principles. To address this challenge, we employ a metric-that we name Jacobian distance-which captures the spatiotemporal spreading of perturbations, enabling us to uncover the latent geometry inherent in network-driven processes. We compute the Jacobian distance for a broad set of nonlinear dynamical models on synthetic and real-world networks of high interest for applications from biological to ecological and social contexts. We show, analytically and computationally, that the process-driven latent geometry of a complex network is sensitive to both the specific features of the dynamics and the topological properties of the network. This translates into potential mismatches between the functional and the topological mesoscale organization, which we explain by means of the spectrum of the Jacobian matrix. Finally, we demonstrate that the Jacobian distance offers a clear advantage with respect to traditional methods when studying human brain networks. In particular, we show that it outperforms classical network communication models in explaining functional communities from structural data, therefore highlighting its potential in linking structure and function in the brain.

Microsecond time-resolved X-ray scattering by utilizing MHz repetition rate at second-generation XFELs.

Detecting microsecond structural perturbations in biomolecules has wide relevance in biology, chemistry and medicine. Here we show how MHz repetition rates at X-ray free-electron lasers can be used to produce microsecond time-series of protein scattering with exceptionally low noise levels of 0.001%. We demonstrate the approach by examining Jɑ helix unfolding of a light-oxygen-voltage photosensory domain. This time-resolved acquisition strategy is easy to implement and widely applicable for direct observation of structural dynamics of many biochemical processes.

How do life course events affect the accumulation of digital literacy?: Based on qualitative research of 16 medical university teachers of China.

The purpose of this article is to examine the level of the accumulation of digital literacy in medical university of China. In this study, we argue that positive life events facilitate the formation of digital literacy. We review the development of research of life course and digital literacy since the 2010. From the perspective of life course theory, this article examines the significant impact of social changes in information technology on the accumulation of digital literacy of 16 medical university teachers, and understands the life course of medical university teachers as a sequence composed of multiple life events. The results show that the accumulation of medical university teachers' digital Literacy includes 4 types: linear accumulation, multi drive accumulation, parallel accumulation, and leading accumulation, of which multi drive accumulation and leading accumulation are the most conducive to the formation of medical university teachers' digital literacy. In addition, our findings reveal that subjective initiative plays an important role in the accumulation of medical university teachers' digital literacy. The accumulation of digital literacy is a dynamic and systematic process of the accumulation of individual life events of medical university teachers. This paper also discusses the relationship between order of life events and career outcomes.

Design of a Model Using Machine Learning and Deep Dyna Q Learning Integration for Improved Disease Prediction in Remote Healthcare

In the domain of proactive healthcare management, the imperative for remote health monitoring has escalated, the remote health care in this scenario specially means, the patient is seating at the remote location that is not in the hospital setting, and doctor or healthcare worker is monitoring the health parameters gathered using biomedical sensors and passed through the network. Conventional methodologies, while partially effective, encounter challenges in predictive precision, responsiveness to evolving health dynamics, and managing the vast array of patient data. These limitations underscore the demand for a sophisticated, holistic solution catering to diverse use cases. This work introduces a pioneering framework amalgamating traditional machine learning (ML) models with the advanced capabilities of Deep Dyna Q Learning process to overcome existing constraints. This framework strategically utilizes ensemble of traditional algorithms which amalgamates the strengths of these diverse models. Central to this model is the integration of Deep Dyna Q Learning, empowering the system with real-time adaptability and dynamic decision-making process through reinforcement learning principles, thereby deriving insights from historical and simulated datasets to foster more nuanced, patient-centric decisions. The impact of this comprehensive approach is profound, evidenced by preliminary results showcasing significant enhancements in the efficiency of remote health monitoring systems. Notably, the model achieves increase in precision, accuracy and recall for disease prediction. These improvements signify a paradigm shift towards proactive and efficient healthcare interventions, especially in remote settings. The fusion of traditional ML techniques with Deep Dyna Q Learning emerges as a potent solution, heralding a revolution in remote health monitoring and establishing a new benchmark for proactive healthcare delivery scenarios.

Quantifying mechanical forces during vertebrate morphogenesis.

Morphogenesis requires embryonic cells to generate forces and perform mechanical work to shape their tissues. Incorrect functioning of these force fields can lead to congenital malformations. Understanding these dynamic processes requires the quantification and profiling of three-dimensional mechanics during evolving vertebrate morphogenesis. Here we describe elastic spring-like force sensors with micrometre-level resolution, fabricated by intravital three-dimensional bioprinting directly in the closing neural tubes of growing chicken embryos. Integration of calibrated sensor read-outs with computational mechanical modelling allows direct quantification of the forces and work performed by the embryonic tissues. As they displace towards the embryonic midline, the two halves of the closing neural tube reach a compression of over a hundred nano-newtons during neural fold apposition. Pharmacological inhibition of Rho-associated kinase to decrease the pro-closure force shows the existence of active anti-closure forces, which progressively widen the neural tube and must be overcome to achieve neural tube closure. Overall, our approach and findings highlight the intricate interplay between mechanical forces and tissue morphogenesis.

Determination of optimal air supply form on sludge convective drying process: A CFD-DEM study

Convective thermal drying is usually used in sludge dryers, and the air supply forms significantly affect the sludge drying efficiency. In this work, the drying process of multi-layered sludge is numerically simulated using a coupled computational fluid dynamics-discrete element method (CFD-DEM) approach. Better than our previous work, the current CFD-DEM model further considers the dynamic process of humidity within the flow field. Firstly, the CFD-DEM model is validated based on the mechanistic experimental data. Then, extensive numerical simulations are carried out to investigate the two-layered sludge drying process in the dryer, and the effects of two conventional air supply forms (Top-to-Bottom and Bottom-to-Top) as well as a novel air supply form (Side-assist) on the two-phase flow system are discussed. The simulation results provide in detail the dynamic processes of momentum, heat and mass (moisture) in the convective drying system under different working conditions. Following recommendations are presented to facilitate the operation of the dryer: Bottom-to-Top is better for sludge drying and tends to utilize less heat from the heat source compared to the Top-to-Bottom. The drying efficiency can be further improved by supplying the Side-assist with an optimal flow ratio of side-wind to bottom-wind. Numerical results show that the optimal flow ratio is related to the total air volume, the deflection angle and the side-wind temperature. The present study reveals in depth the impact mechanism of the air supply form on the convective thermal drying process of sludge, which contributes to the design and optimization of high-efficiency dryers.

Quantitative Visualization of Lipid Nanoparticle Fusion as a Function of Formulation and Process Parameters.

Lipid nanoparticles (LNPs) have proven to be promising delivery vehicles for RNA-based vaccines and therapeutics, particularly in LNP formulations containing ionizable cationic lipids that undergo protonation/deprotonation in response to buffer pH changes. These nanoparticles are typically formulated using a rapid mixing technique at low pH, followed by a return to physiological pH that triggers LNP-LNP fusion. A detailed understanding of these dynamic processes is crucial to optimize the overall performance and efficiency of LNPs. However, knowledge gaps persist regarding how particle formation mechanisms impact drug loading and delivery functions. In this work, we employ single-molecule Convex Lens-induced Confinement (CLiC) microscopy in combination with Förster resonance energy transfer (FRET) measurements to study LNP fusion dynamics in relation to various formulation parameters, including lipid concentration, buffer conditions, drug loading ratio, PEG-lipid concentrations, and ionizable lipid selection. Our results reveal a strong correlation between the measured fusion dynamics and the formulation parameters used; these findings are consistent with DLS and Cryo-TEM-based assays. These measurements offer a cost-effective method for characterizing and screening potential drug candidates and can provide additional insights into their design, with opportunities for optimization.

F wave analysis based on the compound muscle action potential scan.

Conventional F wave analysis involves a relatively uniform physiological environment induced by supramaximal stimulations. The F wave characteristics in a dynamic physiological condition, however, are rarely investigated. This study aimed to improve understanding of F wave properties in the more dynamic process by introducing a novel method to analyze F waves based on the compound muscle action potential (CMAP) scan technique. Twenty four healthy subjects participated in the study. The CMAP scan was applied to record muscle responses in the abductor pollicis brevis (APB) and abductor digiti minimi (ADM) muscles, respectively. F wave characteristics including mean F wave amplitude and latency (F-M latency), persistence and activating threshold were quantified. An average of 200 F waves per muscle were obtained from the CMAP scan recording. Weak to moderate correlations between F wave amplitude and stimulating intensity were observed in most of the APB (19 muscles; r = 0.33 ± 0.14, all p < .05) and ADM (23 muscles, r = 0.46 ± 0.16, all p < .05) muscles. Significantly longer mean F latency and lower activating F-threshold were found in the ADM muscles (F-M latency: APB: 25.43 ± 2.39 ms, ADM: 26.15 ± 2.32 ms, p < .05; F-threshold: APB: 7.68 ± 8.96% CMAP, ADM: 2.35 ± 2.42% CMAP, p < .05). This study introduces new features of F waves using the CMAP scan technique and identifies differences of F wave characteristics between the hand muscles. The CMAP scan based F waves analysis can be combined with the motor unit number estimation to assess functional alterations in motor neurons in neurological disorders.

Tracking phonological regularities: exploring the influence of learning mode and regularity locus in adult phonological learning

Abstract Research on phonological learning has shown that adult learners are capable of effectively tracking regularities in phonological patterns. In our study, we investigated the dynamics of the learning process for regularity tracking. Adult learners participated in a phonological learning experiment where they acquired vowel harmony rules for forming plurals. The experiment had four conditions, varying in learning mode (goal-oriented vs. exploratory) and the locus of phonological regularity (phonotactics vs. alternation). When learners had no explicit learning goal and when the language involved random alternation patterns, their learning process showed a strong preference for regularity. This suggests that the application of statistical learning metrics is influenced by two factors: greater uncertainty in the exploratory conditions compared to the goal-oriented conditions, and a stronger inclination to avoid irregularities in alternation compared to phonotactics.

Emerging Synthetic Bioluminescent Reactions for Non-Invasive Imaging of Freely Moving Animals.

Bioluminescence imaging (BLI) is an indispensable technique for visualizing the dynamics of diverse biological processes in mammalian animal models, including cancer, viral infections, and immune responses. However, a critical scientific challenge remains: non-invasively visualizing homeostatic and disease mechanisms in freely moving animals to understand the molecular basis of exercises, social behavior, and other phenomena. Classical BLI relies on prolonged camera exposure to accumulate the limited number of photons that traveled from deep tissues in anesthetized or constrained animals. Recent advancements in synthetic bioluminescence reactions, utilizing artificial luciferin-luciferase pairs, have considerably increased the number of detectable photons from deep tissues, facilitating high-speed BLI to capture moving objects. In this review, I provide an overview of emerging synthetic bioluminescence reactions that enable the non-invasive imaging of freely moving animals. This approach holds the potential to uncover unique physiological processes that are inaccessible with current methodologies.

Unraveling the energy storage mechanism in graphene-based nonaqueous electrochemical capacitors by gap-enhanced Raman spectroscopy

Graphene has been extensively utilized as an electrode material for nonaqueous electrochemical capacitors. However, a comprehensive understanding of the charging mechanism and ion arrangement at the graphene/electrolyte interface remain elusive. Herein, a gap-enhanced Raman spectroscopic strategy is designed to characterize the dynamic interfacial process of graphene with an adjustable number of layers, which is based on synergistic enhancement of localized surface plasmons from shell-isolated nanoparticles and a metal substrate. By employing such a strategy combined with complementary characterization techniques, we study the potential-dependent configuration of adsorbed ions and capacitance curves for graphene based on the number of layers. As the number of layers increases, the properties of graphene transform from a metalloid nature to graphite-like behavior. The charging mechanism shifts from co-ion desorption in single-layer graphene to ion exchange domination in few-layer graphene. The increase in area specific capacitance from 64 to 145 µF cm–2 is attributed to the influence on ion packing, thereby impacting the electrochemical performance. Furthermore, the potential-dependent coordination structure of lithium bis(fluorosulfonyl) imide in tetraglyme ([Li(G4)][FSI]) at graphene/electrolyte interface is revealed. This work adds to the understanding of graphene interfaces with distinct properties, offering insights for optimization of electrochemical capacitors.

Construction and Implementation of a Digital System for Logistics Majors from the Perspective of Smart Education

Background: Faced with the new demand for logistics engineering talents in the modern logistics industry's mathematical transformation and upgrading, building an applied logistics engineering talent training system that meets the requirements of the new era is a key task of current teaching reform. Contribution: The contribution of this experiment is focuses on the current situation of school enterprise integration in school logistics engineering construction, and proposes a construction. Method: This experiment using framework method to strengthen the logistics engineering profession, clarify integration goals, build targeted curriculum systems, establish assessment and evaluation frameworks, and improve the mechanism of industry education integration. Results: The collaborative educational role of logistics professional construction and innovation and entrepreneurship education and cultivate more comprehensive logistics professionals. Based on this, this article will conduct a digital system for logistics majors from the perspective of smart education in vocational colleges under the background of intelligent logistics. Conclusion: Curriculum construction is built on a dynamic and authentic practical process, which reflects the logical interaction between curriculum builders and courses, shifting understanding from project logic to subjective logic

Making waves: Knowledge and data fusion in urban water modelling

Mathematical modeling plays a crucial role in understanding and managing urban water systems (UWS), with mechanistic models often serving as the foundation for their design and operations. Despite the wide adoptions, mechanistic models are challenged by the complexity of dynamic processes and high computational demands. Data-driven models bring opportunities to capture system complexities and reduce computational cost, by leveraging the abundant data made available by recent advance in sensor technologies. However, the interpretability and data availability hinder their wider adoption. This paper advocates for a paradigm shift in the application of data-driven models within the context of UWS. Integrating existing mechanistic knowledge into data-driven modeling offers a unique solution that reduces data requirements and enhances model interpretability. The knowledge-informed approach balances model complexity with dataset size, enabling more efficient and interpretable modeling in UWS. Furthermore, the integration of mechanistic and data-driven models offers a more accurate representation of UWS dynamics, addressing lingering uncertainties and advancing modelling capabilities. This paper presents perspectives and conceptual framework on developing and implementing knowledge-informed data-driven modeling, highlighting their potential to improve UWS management in the digital era.