Dynamic Information Research Articles

A rule-driven method for disassembly sequence real-time planning of intelligent mixed-flow disassembly line

Intelligent mixed-flow disassembly serves an important role in enhancing the flexible operation capability of the disassembly line for multi-variety and small-batch used electromechanical products, and in forming the scale benefit of recycling. However, in mixed-flow disassembly lines, pre-planning the disassembly sequence for products is typically unfeasible and can lead to “over-disassembly” or “under-disassembly”, resulting in economic losses. This is attributed to significant variations in disassembly features, such as internal structural damage and subassembly recycling income, even within the same type of used electromechanical product. These differences contribute to considerable uncertainties in disassembly sequence planning. To this end, a method is proposed to leverage dynamic disassembly feature information for mining disassembly rules, efficiently driving disassembly sequences real-time planning, and realizing intelligent optimization of product disassembly profit on mixed-flow disassembly line. First, a multi-matrix collaboration based dynamic representing method of product disassembly feature information is proposed. It aims to eliminates the influence of uncertain information on the disassembly sequence planning by recording real-time disassembly feature information, such as disassembly operation time and subassembly recycling income. On this basis, a rule-driven disassembly sequences real-time planning model is built. This model realizes disassembly sequence real-time planning based on offline mined rules using the updated disassembly feature information in matrices. Finally, an improved gene expression programming-based disassembly rule offline mining algorithm is proposed, which incorporates a mutation probability mapping function to improve the efficiency and effectiveness of disassembly rule mining. A case study containing two products with differences in structure and function are used to confirm the effectiveness of the proposed method.

A nomogram for cancer-specific survival of lung adenocarcinoma patients: A SEER based analysis

BackgroundNon-small cell lung cancer (NSCLC) accounts for 85 % of lung cancer cases. Among NSCLC subtypes, lung adenocarcinoma (LUAD) stands as the most prevalent. Regrettably, LUAD continues to exhibit a notably unfavorable overall prognosis. This study's primary aim was to develop and validate prognostic tools capable of predicting the likelihood of cancer-specific survival (CSS) in patients with LUAD. MethodsWe retrospectively collected 21,099 patients diagnosed with LUAD between 2010 and 2015, and 8290 patients diagnosed between 2004 and 2009 from SEER database. The cohort of 21,099 patients served as the prognostic group for the exploration of LUAD-related prognostic risk factors. The cohort of 8290 patients was designated for external validation. We created a training set and an internal validation set in the prognostic group for the development and internal validation of CSS nomograms. CSS predictors were identified through the least absolute shrinkage and selection operator (Lasso) regression analysis. Prognostic model was constructed via Cox hazard regression analysis, presented in the form of both static and dynamic network-based nomograms. ResultsSeveral independent prognostic factors were incorporated into the construction of nomogram. The nomogram accurately predicted CSS at 1, 3, and 5 years, with respective AUC values of 0.769, 0.761, and 0.748 for the training group, and 0.741, 0.752, and 0.740 for the testing group. The study demonstrated a strong agreement between anticipated and actual CSS values, supported by decision curve analysis (DCA) and time-dependent calibrated curves. High-risk patients based on the nomogram exhibiting significantly lower survival rates compared to their low-risk counterparts according to Kaplan-Meier (K-M) curves. The nomogram demonstrates excellent predictive power in the external validation cohort. ConclusionsA dependable and user-friendly nomogram has been developed, available in both static and online dynamic calculator formats, to facilitate healthcare professionals in accurately estimating the likelihood of CSS for patients diagnosed LUAD.

Triple S-scheme BiOBr@LaNiO3/CuBi2O4/Bi2WO6 heterojunction with plasmonic Bi-induced stability: deviation from quadruple S-scheme and mechanistic investigation

The investigation and understanding of heterointerfaces formation and charge transfer dynamics in two or more semiconductor heterojunctions increased ensuing establishment of S-scheme and dual S-scheme heterojunctions. However, investigations of possible charge transfer at interfaces and their type in four component systems are limited. Herein, a four-component heterojunction was investigated to postulate and demonstrate deviation between quadruple and triple S-scheme heterojunctions possibilities using LaNiO3, BiOBr, CuBi2O4, and Bi2WO6. DFT and XPS were used to construct the band structure and support the charge transfer at the interfaces to follow S-S strategy during OTC and SMX degradation under visible light. IEF, bend bending systematically modulated charge transfer, and the core-shell strategy restricted possible junctions’ formation to three to accord triple S-scheme heterojunction. This work demonstrated the construction of Triple S-scheme heterostructures as a promising strategy for efficient charge separation making it a suitable candidate for elimination of pollutants.

Investigating reactive transport and precipitation patterns of calcium carbonate in fractured porous media

HypothesisUnderstanding calcium carbonate (CaCO3) precipitation in various polymorphs from nanoparticle size (amorphous calcium carbonate) to microparticle size (vaterite, aragonite, dendrite, calcite) is important for practical applications, including carbon geo-storage (e.g., basalt formations), hydrogen storage, groundwater management, and soil stabilization. Our hypothesis suggests that the interplay of Péclet numbers (Pe), Damköhler numbers (Da), and Supersaturation Index (SI) significantly impacts the evolution of CaCO3 precipitation in fractured porous media in terms of mixing patterns, spatiotemporal evolution, crystal morphology, crystal size, and clogging behavior. ExperimentsThis study takes a novel approach to explore the colloidal formation and precipitation dynamics of CaCO3 within a fractured microfluidic system. Here, calcium chloride (CaCl2) and sodium bicarbonate (NaHCO3) solutions were injected and reacted under varied Pe (0–11), Da (0–1), and SI (2–5). FindingsOur analysis revealed distinct precipitation patterns and mixing types, such as transverse, longitudinal, and incomplete mixing, providing insights into the behavior in fractured porous media. We systematically analyzed the temporal and spatial evolution of precipitation, demonstrating how Pe, Da, and SI dictate precipitation rates and spatial distribution. Additionally, the study uncovered a range of CaCO3 polymorphic forms, illustrating their evolution and coexistence. Morphological changes and crystal sizes were examined to decode nucleation and growth processes. Significantly, our findings highlight the relationship between precipitation and clogging in the fractured medium, offering a deeper understanding of reactive transport in complex porous environments. These insights are crucial for enhancing carbon containment security and storage efficiency in underground formations, improving groundwater remediation techniques, and developing novel construction materials through controlled precipitation processes.

In silico study on helicenes in hydrophobic natural deep eutectic solvent

This research explores the behavior of helicenes in Deep Eutectic Solvents (DES) formed by thymol and dodecanoic acid using a combined theoretical approach. COSMOtherm, Density Functional Theory (DFT), and molecular dynamics (MD) simulations were employed to elucidate the interactions between helicenes and the DES components at the molecular level. The behavior of helicenes in water was also studied as a reference system. COSMOtherm calculations provided insights into the thermodynamic properties of the system. DFT simulations allowed for the investigation of the electronic structure and bonding interactions between helicenes and DES molecules. Additionally, MD simulations offered dynamic information on the solvation behavior and conformational preferences of helicenes within the DES media. The combined approach provides a comprehensive understanding of the interactions between helicenes and thymol-dodecanoic acid DES. The research findings will contribute to the development of a theoretical framework for predicting the behavior of other functional molecules in DES environments. This knowledge has potential applications in various fields, including material science, catalysis, and drug delivery.

Gravitational scattering and beyond from extreme mass ratio effective field theory

We explore a recently proposed effective field theory describing electromagnetically or gravitationally interacting massive particles in an expansion about their mass ratio, also known as the self-force (SF) expansion. By integrating out the deviation of the heavy particle about its inertial trajectory, we obtain an effective action whose only degrees of freedom are the lighter particle together with the photon or graviton, all propagating in a Coulomb or Schwarzschild background. The 0SF dynamics are described by the usual background field method, which at 1SF is supplemented by a “recoil operator” that encodes the wobble of the heavy particle, and similarly computable corrections appearing at 2SF and higher. Our formalism exploits the fact that the analytic expressions for classical backgrounds and particle trajectories encode dynamical information to all orders in the couplings, and from them we extract multiloop integrands for perturbative scattering. As a check, we study the two-loop classical scattering of scalar particles in electromagnetism and gravity, verifying known results. We then present new calculations for the two-loop classical scattering of dyons, and of particles interacting with an additional scalar or vector field coupling directly to the lighter particle but only gravitationally to the heavier particle.

"Visual verbs": Dynamic event types are extracted spontaneously during visual perception.

During visual processing, input that is continuous in space and time is segmented, resulting in the representation of discrete tokens-objects or events. And there has been a great deal of research about how object representations are generalized into types-as when we see an object as an instance of a broader category (e.g., an animal or plant). There has been much less attention, however, to the possibility that vision represents dynamic information in terms of a small number of primitive event types (such as twisting or bouncing). (In models that posit a "language of vision," these would be the foundational visual verbs.) Here we ask whether such event types are extracted spontaneously during visual perception, even when entirely task irrelevant during passive viewing. We exploited the phenomenon of categorical perception-wherein differences are more readily noticed when they are represented in terms of different underlying categories. Observers were better at detecting changes to images or short videos when the changes involved switches in the underlying event type-even when the changes that maintained the same event type were objectively larger (in terms of both brute image metrics and higher level feature change). We observed this categorical "cross-event-type" advantage for visual working memory for twisting versus rotating, scooping versus pouring, and rolling versus bouncing. Moreover, additional control experiments confirmed that such effects could not be explained by appeal to lower-level non-categorical stimulus differences. This spontaneous perception of "visual verbs" might promote both generalization and prediction about how events are likely to unfold. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Surface quality prediction in-situ monitoring system: A deep transfer learning-based regression approach with audible signal

Surface roughness plays an indispensable and fundamental role as a leading indicator of the surface quality of machined parts in the manufacturing process. The precise and effective monitoring and prediction of surface roughness is crucial for surface quality control. In this regard, the development of an in-process surface quality monitoring system is necessary, which has the promising potential to achieve this goal. Such a system typically comprises data-driven models for decision-making and sensing techniques for detecting associated process information. However, some challenges still exist in building such systems. Firstly, the architecture design and deployment of data-driven models, specifically deep learning (DL)-based models, demand adequate domain knowledge. Secondly, most models trained on specific tasks with limited datasets are prone to suppressing their versatility and generalization across different machining conditions. Additionally, in most cases, reliance on handcrafted features to represent dynamic information on various signals during model training necessitates extensive expertise in selecting appropriate feature types. Furthermore, due to the nature of their low dimensionality, handcrafted features have difficulty in capturing of overall process-related underlying patterns from dynamics signatures, which is time-varying and often occurs in transient events. To address these challenges, this paper proposes the regression-based pre-trained convolutional neural network (pre-trained CNN) combined with Mel-spectrogram images based on the transfer learning method for surface roughness prediction. Within the context, the architecture of the transfer model is slightly adapted from already well-trained CNNs. Initial weights in each layer of the CNN model are directly inherited and then fine-tuned through the Bayesian optimization tuning method. Besides, the audible sound signals are captured and subsequently converted into 2D Mel-spectrogram images with variant time lengths, which are separately engaged to retrain and validate four existing pre-trained CNN models (VGG16, VGG19, ResNet50V2 and InceptionResNetV2). Eventually, the effectiveness of proposed models and comparison of their predictive capabilities are further validated through a case study in the turning process. The results demonstrate that each applied pre-trained CNN model is capable of effectively predicting surface quality with satisfactory prediction results. Therefore, the proposed method can facilitate the establishment of a machining monitoring system concerning its accuracy, reliability, and robustness.

Multi-view brain functional connectivity and hierarchical fusion for EEG-based emotion recognition

Emotional states evolve gradually from their onset to full manifestation, reflected in changes in brain functional connectivity. Existing research often focuses only on the final emotional state, missing the rich, dynamic information of emotional evolution. This paper proposes a novel network framework leveraging global and local brain functional connectivity through a multi-view approach. We introduce a global and local connectivity modeling method using electroencephalogram signals to simulate brain connectivity changes across time and specific moments. A residual-based local detail feature extractor combined with a coordinate attention module captures long-range dependencies and maintains positional accuracy. A global feature extractor is also designed for the effective extraction of emotional information. Finally, a hierarchical multi-scale cross-attention feature fusion module integrates features across different levels to enhance emotion recognition performance and robustness. Extensive experiments on three publicly available datasets demonstrate the framework’s superiority and generalization capability.

Structural and Dynamical Quality Assessment of Gap‐Filled Sea Surface Temperature Products

AbstractPrevious studies that intercompared global Level‐4 (L4) sea surface temperature (SST) analyses were centered on the assessment of the accuracy and bias of SST by comparing them with independent near‐surface Argo profile temperature data. This type of assessment is centered on the absolute value of SST rather than on SST spatial properties (gradients), which is more relevant to the study of oceanographic features (e.g., fronts, eddies, etc.) and ocean dynamics. Here, we use, for the first time, the spectrum of singularity exponents to assess the structural and dynamical quality of different L4 gap‐filled products based on the multifractal theory of turbulence. Singularity exponents represent the geometrical projection of the turbulence cascade, and its singular spectrum can be related to the probability density function of the singularity exponents normalized by the scale. Our results reveal that the different schemes used to produce the L4 SST products generate different singularity spectra, which are then used to identify a potential loss of dynamical information or structural coherence. This new diagnostic constitutes a valuable tool to assess the structural quality of SST products and can support data satellite SST producers efforts to improve the interpolation schemes used to generate gap‐filled SST products.

Pediatric Urinary Tract Infection: Evaluation Antibiotic Susceptibility and Biofilm Formation Dynamics

Pediatric Urinary Tract Infection: Evaluation Antibiotic Susceptibility and Biofilm Formation Dynamics

Biofilm formation dynamics in long-distance water conveyance pipelines: Impacts of nutrient levels and metal stress

Biofilm formation in long-distance water conveyance pipelines poses significant risks to water quality, particularly under varying nutrient levels and heavy metal stress. However, the impacts of pipeline material on biofilm formation dynamics under different raw water conditions remain elusive. This study investigated the effects of nutrient availability and Fe-Mn stress on biofilm development, structural stability, bacterial community composition, and the occurrence of viable but non-culturable (VBNC) bacteria. Using reactors with different nutrient conditions, we observed that increased nutrient levels promote biofilm growth but lead to greater instability, heightening the risk of secondary contamination. Notably, nutrient escalation beyond a critical threshold had a diminishing impact on biofilm community composition. Additionally, Fe-Mn stress, while initially enhancing microbial adhesion and metabolic activity, ultimately inhibited biofilm formation over time and increases the prevalence of VBNC bacteria, particularly on stainless steel (SS) surfaces. Our findings also highlighted the importance of material selection for pipelines, with polyvinyl chloride (PVC) showing reduced biofilm formation compared to SS, making it a more suitable option for transporting raw water in environments with high metal content. Dispersal limitation determined the bacterial community assembly during the biofilm formation, accounting for 64.53–90.67 % of the variability in different scenarios. These insights offer valuable guidance for managing biofilm-related issues in water distribution systems, emphasizing the need for careful control of nutrient levels and material choice to ensure water safety over long distances.

Reinforcement Learning-Based Tracking Control under Stochastic Noise and Unmeasurable State for Tip–Tilt Mirror Systems

The tip–tilt mirror (TTM) is an important component of adaptive optics (AO) to achieve beam stabilization and pointing tracking. In many practical applications, the information of accurate TTM dynamics, complete system state, and noise characteristics is difficult to achieve due to the lack of sufficient sensors, which then restricts the implementation of high precision tracking control for TTM. To this end, this paper proposes a new method based on noisy-output feedback Q-learning. Without relying on neural networks or additional sensors, it infers the dynamics of the controlled system and reference jitter using only noisy measurements, thereby achieving optimal tracking control for the TTM system. We have established a modified Bellman equation based on estimation theory, directly linking noisy measurements to system performance. On this basis, a fast iterative learning of the control law is implemented through the adaptive transversal predictor and experience replay technique, making the algorithm more efficient. The proposed algorithm has been validated with an application to a TTM tracking control system, which is capable of quickly learning near-optimal control law under the interference of random noise. In terms of tracking performance, the method reduces the tracking error by up to 98.7% compared with the traditional integral control while maintaining a stable control process. Therefore, this approach may provide an intelligent solution for control issues in AO systems.

Конкурентоспособност на българския зеленчуков сектор на световния пазар

Depending on the scope of the concept of competitiveness, it is used in a variety of contexts in economic research, although there is a lack of a precise and generally accepted definition of what exactly it entails. In this study, competitiveness is considered from the point of view of market performance, using the developed theoretical framework for measuring the performance of an individual country or sector of the national economy, based on the definition of the Canadian Competitiveness Group: “The ability to sustainably win and maintain market share”. Two concepts are laid down in it – about the dynamics of market share and profit formation in order to achieve sustainable development over time. The aim of the present study is to evaluate the competitiveness of the Bulgarian vegetable production sector on the world market, to clearly note its change in a dynamic aspect. The research specifically adopted the methodology developed by B. Ivanov for calculating a composite index of competitiveness, containing two main components - production and value. The analysis shows the place of Bulgarian vegetable production in the national agriculture and in the world vegetable market, the dynamics and changes that reflect in the competitiveness of the sector during the years of EU membership – 2007 – 2021. The comprehensive assessment of the competitiveness of the vegetable crop sector in Bulgaria on world markets defines it as relatively low. The results obtained from the research confirm and complement the assessment of the competitiveness of the “Vegetable production” sector in Bulgaria, made by other Bulgarian researchers. Despite the favorable climate and natural features in our country, as well as our traditions in the production of vegetables, the sector is deteriorating its market position and the dynamics of competitiveness are rather negative. The research clearly shows that Bulgaria is losing positions in the production of vegetables, while at the same time its dependence on imports and foreign prices is increasing. The sector of vegetable production is among the vulnerable sectors of the Bulgarian agriculture, as the permanent negative trends of reduction of areas and production are projected in the inability to meet consumer needs in our country from high-quality domestic production. The future development of the sector and the increase of its competitiveness will depend on many factors, some of the most important of which are the extent to which vegetable producers will be able to be flexible and adapt to achieve the new environmental policy objectives; to what extent they will be able to respond to modern trends in agricultural production – digitalization, ecological/biological agriculture, new technological solutions.

Dynamic Gel Formation in Solutions of Associating Macromolecules with Mesogenic Groups

Dynamic Gel Formation in Solutions of Associating Macromolecules with Mesogenic Groups

Mass scaling relations for dark halos from an analytic universal outer density profile

Context. The average matter density within the turnaround scale, which demarcates where galaxies shift from clustering around a structure to joining the expansion of the Universe, is an important cosmological probe. However, a measurement of the mass enclosed by the turnaround radius is difficult. Analyses of the turnaround scale in simulated galaxy clusters place the turnaround radius at about three times the virial radius in a ΛCDM universe and at a (present-day) density contrast with the background matter density of the Universe of δ ~ 11. Assessing the mass at such extended distances from a cluster’s center is a challenge for current mass measurement techniques. Consequently, there is a need to develop and validate new mass-scaling relations, to connect observable masses at cluster interiors with masses at greater distances. Aims. Our research aims to establish an analytical framework for the most probable mass profile of galaxy clusters, leading to novel mass scaling relations, allowing us to estimate masses at larger scales. We derive such analytical mass profiles and compare them with those from cosmological simulations. Methods. We used excursion set theory, which provides a statistical framework for the density and local environment of dark matter halos, and complement it with the spherical collapse model to follow the non-linear growth of these halos. Results. The profile we developed analytically showed good agreement (better than 30%, and dependent on halo mass) with the mass profiles of simulated galaxy clusters. Mass scaling relations were obtained from the analytical profile with offset better than 15% from the simulated ones. This level of precision highlights the potential of our model for probing structure formation dynamics at the outskirts of galaxy clusters.

Constraining the Excessive Aggregation of Non-Fullerene Acceptor Molecules Enables Organic Solar Modules with the Efficiency >16.

Translating high-performance organic solar cell (OSC) materials from spin-coating to scalable processing is imperative for advancing organic photovoltaics. For bridging the gap between laboratory research and industrialization, it is essential to understand the structural formation dynamics within the photoactive layer during printing processes. In this study, two typical printing-compatible solvents in the doctor-blading process are employed to explore the intricate mechanisms governing the thin-film formation in the state-of-the-art photovoltaic system PM6:L8-BO. Our findings highlight the synergistic influence of both the donor polymer PM6 and the solvent with a high boiling point on the structural dynamics of L8-BO within the photoactive layer, significantly influencing its morphological properties. The optimized processing strategy effectively suppresses the excessive aggregation of L8-BO during the slow drying process in doctor-blading, enhancing thin-film crystallization with preferential molecular orientation. These improvements facilitate more efficient charge transport, suppress thin-film defects and charge recombination, and finally enhance the upscaling potential. Consequently, the optimized PM6:L8-BO OSCs demonstrate power conversion efficiencies of 18.42% in small-area devices (0.064 cm2) and 16.02% in modules (11.70 cm2), respectively. Overall, this research provides valuable insights into the interplay among thin-film formation kinetics, structure dynamics, and device performance in scalable processing.

Kinetics of methane hydrate formation in a 1200 ml unstirred reactor for natural gas storage

Solidified natural gas (SNG) technology via clathrate hydrates, enhanced by promoters such as tetrahydrofuran (THF), promises large-scale natural gas storage due to its high volumetric capacity and mild storage conditions. Despite its potential for industrial scale-up, limited research exists on methane hydrate formation behaviors in large reactors (>1000 cm3), which is crucial for evaluating practical storage performance. In this study, methane hydrate formation kinetics were investigated in a scaled-up unstirred reactor (1200 cm3) at 288.15 K and 283.15 K, and 5.0 MPa. Methane uptake, system pressure, gas and liquid phase temperatures, and hydrate morphologies were analyzed to describe the dynamic hydrate formation process. Results reveal three effects of increased reactor size in contrast to smaller reactors: (i) heterogeneous distribution of THF-rich and methane-rich hydrates; (ii) hydrate bulk show a hollow inner structure with a cavity due to the wall-climbing behavior of hydrate growth; (iii) optimal THF concentration no longer always agrees with stoichiometric 5.56 mol%, which is case-dependent. These effects are attributed to higher diffusion resistances for methane molecules and more intensive heat accumulation. These findings provide some insight into the kinetics of methane hydrate formation in industrial reactors, which is critical for the commercialization of SNG technology.

OptoAssay-Light-controlled dynamic bioassay using optogenetic switches.

Circumventing the limitations of current bioassays, we introduce a light-controlled assay, OptoAssay, toward wash- and pump-free point-of-care diagnostics. Extending the capabilities of standard bioassays with light-dependent and reversible interaction of optogenetic switches, OptoAssays enable a bidirectional movement of assay components, only by changing the wavelength of light. Demonstrating exceptional versatility, the OptoAssay showcases its efficacy on various substrates, delivering a dynamic bioassay format. The applicability of the OptoAssay is successfully demonstrated by the calibration of a competitive model assay, resulting in a superior limit of detection of 8 pg ml-1, which is beyond those of conventional ELISA tests. In the future, combined with smartphones, OptoAssays could obviate the need for external flow control systems such as pumps or valves and signal readout devices, enabling on-site analysis in resource-limited settings.

Object Motion Detection Enabled by Reconfigurable Neuromorphic Vision Sensor under Ferroelectric Modulation.

Increasing the demand for object motion detection (OMD) requires shifts of reducing redundancy, heightened power efficiency, and precise programming capabilities to ensure consistency and accuracy. Drawing inspiration from object motion-sensitive ganglion cells, we propose an OMD vision sensor with a simple device structure of a WSe2 homojunction modulated by a ferroelectric copolymer. Under optical mode and intermediate ferroelectric modulation, the vision sensor can generate progressive and bidirectional photocurrents with discrete multistates under zero power consumption. This design enables reconfigurable devices to emulate long-term potentiation and depression for synaptic weights updating, which exhibit 82 states (more than 6 bits) with a uniform step of 6 pA. Such OMD devices also demonstrate nonvolatility, reversibility, symmetry, and ultrahigh linearity, achieving a fitted R2 of 0.999 and nonlinearity values of 0.01/-0.01. Thus, a vision sensor could implement motion detection by sensing only dynamic information based on the brightness difference between frames, while eliminating redundant data from static scenes. Additionally, the neural network utilizing a linear result can recognize the essential moving information with a high recognition accuracy of 96.8%. We also present the scalable potential via a uniform 3 × 3 neuromorphic vision sensor array. Our work offers a platform to achieve motion detection based on controllable and energy-efficient ferroelectric programmability.