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Colloidal building blocks with re-configurable shapes and dynamic interactions can exhibit unusual self-assembly behaviors and pathways. In this work, we consider the phase behavior of colloids coated with surface-mobile polymer brushes that behave as "dynamic surfactants." Unlike traditional polymer-grafted colloids, we show that colloids coated with dynamic surfactants can acquire anisotropic macroscopic assemblies, even for spherical colloids with isotropic attractive interactions. We use Brownian Dynamics simulations and dynamic density functional theory to demonstrate that time-dependent reorganization of the dynamic surfactants leads to phase diagrams with anisotropic assemblies. We observed that the microscopic polymer distributions impose unique geometric constraints between colloids that control their packing into lamellar, string, and vesicle phases. Our work may help discover versatile building blocks and provide extensive design freedom for assembly out of thermodynamic equilibrium. [ABSTRACT FROM AUTHOR]

We combine on-the-fly trajectory surface hopping simulations and the doorway–window representation of nonlinear optical response functions to create an efficient protocol for the evaluation of time- and frequency-resolved fluorescence (TFRF) spectra and anisotropies of the realistic polyatomic systems. This approach gives the effective description of the proper (e.g., experimental) pulse envelopes, laser field polarizations, and the proper orientational averaging of TFRF signals directly from the well-established on-the-fly nonadiabatic dynamic simulations without extra computational cost. To discuss the implementation details of the developed protocol, we chose cis-azobenzene as a prototype to simulate the time evolution of the TFRF spectra governed by its nonadiabatic dynamics. The results show that the TFRF is determined by the interplay of several key factors, i.e., decays of excited-state populations, evolution of the transition dipole moments along with the dynamic propagation, and scaling factor of the TFRF signals associated with the cube of emission frequency. This work not only provides an efficient and effective approach to simulate the TFRF and anisotropies of realistic polyatomic systems but also discusses the important relationship between the TFRF signals and the underlining nonadiabatic dynamics. [ABSTRACT FROM AUTHOR]

This article presents methodological advances in the state-of-the-art for making time-dependent, thermochemical measurements within kilogram-scale explosive post-detonation fireballs utilizing tunable laser absorption spectroscopy. This measurement capability is critical for validating multi-scale, multi-physics models of post-detonation dynamics. The technique is based on hardened gauges built around rapidly-tunable lasers and custom post-processing algorithms that provide quantitative thermochemical data interior to large and opaque explosive fireballs. The authors present a holistic overview of the technique including gauge design, the laser absorption diagnostic, and the custom data processing algorithms. Additionally, fielding high-bandwidth laser absorption probes at stand-off ranges presents new challenges in data processing that must compensate for long distance signal transmission effects. We highlight representative data from a hardened gauge measurement at 0.81 m stand-off from a 2.78 kg LX-14 explosive charge detonated in an outdoor test arena. We discuss progress in all-optical measurement of temperature, pressure, and water vapor number density at a 100 kHz repetition rate during the first 10 ms of the fireball evolution. We conclude the article with a brief discussion on our current approach for comparing hardened gauge measurements with computational fluid dynamic simulations. [ABSTRACT FROM AUTHOR]

Modelling of many real-world processes, such as drug delivery, wastewater treatment, and pharmaceutical production, requires accurate descriptions of the dynamics of hard particles confined in complicated domains. In particular, when modelling sedimentation processes or systems with driven flows, it is important to accurately capture volume exclusion effects. This work applies Dynamic Density Functional Theory to the evolution of a particle density under diffusion, external forces, particle–particle interaction, and volume exclusion. Using a spectral element framework, for the first time it is possible to include all of these effects in dynamic simulations on complex domains. Moreover, this allows one to apply complicated no-flux, and other non-local, non-linear, boundary conditions. The methodology is also extended to control problems, addressing questions of how to enhance production set-up in industrially-motivated processes. In this work the relevant models are introduced, numerical methods are discussed, and several example problems are solved to demonstrate the methods' versatility. It is shown that incorporating volume exclusion is crucial for simulation accuracy and we illustrate that the choice of boundary conditions significantly impacts the dynamics. [ABSTRACT FROM AUTHOR]

Parkinson's disease is accompanied by the presence of amyloids in the brain that are formed of α-synuclein chains. The correlation between COVID-19 and the onset of Parkinson's disease led to the idea that amyloidogenic segments in SARS-COV-2 proteins can induce aggregation of α-synuclein. Using molecular dynamic simulations, we show that the fragment FKNIDGYFKI of the spike protein, which is unique for SARS-COV-2, preferentially shifts the ensemble of α-synuclein monomer toward rod-like fibril seeding conformations and, at the same time, differentially stabilizes this polymorph over the competing twister-like structure. Our results are compared with earlier work relying on a different protein fragment that is not specific for SARS-COV-2. [ABSTRACT FROM AUTHOR]

Enhancing the understanding of hydroxide transport mechanisms in anion exchange membranes (AEMs) is beneficial for the rational design of high‐performance AEMs in the renewable energy system. However, the high complexity and lack of adequate analytic tools make it challenging to clarify different mechanisms unambiguously. Herein, we developed an in‐house toolkit, the Reactive Diffusion Analyzer (RDAnalyzer), to conduct an effective analysis of hydroxide diffusion mechanisms from ReaxFF molecular dynamic simulations. Using the experimentally well‐synthesized T20NC6NC5N as a model system, we successfully decoupled the hydroxide diffusion mechanisms into free Vehicular and free Grotthuss, as well as associated Vehicular and associated Grotthuss, which was not yet achieved previously. Meanwhile, RDAnalyzer managed to specifically identify the drift length of hydroxide species for each mechanism under the electric field, which worked as a useful variable for calculating the conductivity of AEMs. Our theoretically predicted conductivity for T20NC6NC5N agrees reasonably with experimental results, indicating the reliability of RDAnalyzer. This work not only provides a rational understanding of the complex hydroxide diffusion mechanisms in AEMs but also holds the potential to guide the rational design of high‐performance AEMs with computations. [ABSTRACT FROM AUTHOR]

The efficient production and sustainable operation of water electrolysis hydrogen production equipment within renewable energy systems, characterized by intermittency and volatility, necessitates dynamic simulation for the development of optimized control systems. This paper proposes the application and modification of a data-driven deep-learning model to simultaneously simulate the thermal and electrochemical responses of an alkaline water electrolyzer (AWE), yielding exceptional performance and versatility. By utilizing 4840 h of comprehensive experimental data, the causal relationships among electrolyzer operating parameters are resolved, classifying them into set values and response values for the proposed dynamic model. The model architecture is based on an attention mechanism and designed with hybrid input. Notable enhancements include embedding flow-leveraged polarization function, creating set-value cross-attention, and adopting probability-sparse attention, all of which are validated through both the validation and simulation results. Extensive exploration of various structural and training parameters leads to the selection of the best-performing model. When applied to simulation across all experimental data, the median mean percentage absolute error of the optimal model in cell voltage is a mere 1.3%, while the error in temperature is only 3.5%. Moreover, under typical operating conditions, the simulation error for both voltage and temperature remain below 2%, highlighting the outstanding dynamic modeling and simulation capabilities. These results demonstrate the potential of the proposed model for future hydrogen energy applications in dynamic control and operational optimization within renewable energy system for higher performance and better conversion efficiency. • Deep-learning model for dynamic thermoelectric simulation of water electrolyzers. • Flow-leveraged polarization function and set-value cross-attention mechanism. • Excellent accuracy, thermoelectric error of 3.5% and 1.3%, respectively. • Insights for optimizing control strategies of electrolyzer for higher efficiency. [ABSTRACT FROM AUTHOR]

Introduction: Muscles play a critical role in supporting joints during activities of daily living, owing, in part, to the phenomenon of short-range stiffness. Briefly, when an active muscle is lengthened, bound cross-bridges are stretched, yielding forces greater than what is predicted from the force length relationship. For this reason, short-range stiffness has been proposed as an attractive mechanism for providing joint stability. However, there has yet to be a forward dynamic simulation employing a cross-bridge model, that demonstrates this stabilizing role. Therefore, the purpose of this investigation was to test whether Huxley-type muscle elements, which exhibit short-range stiffness, can stabilize a joint while at constant activation. Methods: We analyzed the stability of an inverted pendulum (moment of inertia: 2.7 kg m2) supported by Huxley-type muscle models that reproduce the short-range stiffness phenomenon. We calculated the muscle forces that would provide sufficient short-range stiffness to stabilize the system based in minimizing the potential energy. Simulations consisted of a 50 ms long, 5 Nm square-wave perturbation, with numerical simulations carried out in ArtiSynth. Results: Despite the initial analysis predicting shared activity of antagonist and agonist muscles to maintain stable equilibrium, the inverted pendulum model was not stable, and did not maintain an upright posture even with fully activated muscles. Discussion & conclusion: Our simulations suggested that short-range stiffness cannot be solely responsible for joint stability, even for modest perturbations. We argue that short-range stiffness cannot achieve stability because its dynamics do not behave like a typical spring. Instead, an alternative conceptual model for short-range stiffness is that of a Maxwell element (spring and damper in series), which can be obtained as a first-order approximation to the Huxley model. We postulate that the damping that results from short-range stiffness slows down the mechanical response and allows the central nervous system time to react and stabilize the joint. We speculate that other mechanisms, like reflexes or residual force enhancement/depression, may also play a role in joint stability. Joint stability is due to a combination of factors, and further research is needed to fully understand this complex system. [ABSTRACT FROM AUTHOR]

Point clouds captured using laser scanners mounted on mobile platforms contain errors at the centimetre to decimetre level due to motion distortion. In applications such as lidar odometry or SLAM, this motion distortion is often ignored. However, in applications such as HD mapping or precise vehicle localisation, it is necessary to correct the effect of motion distortion or ‘deskew’ the point clouds before using them. Existing methods for deskewing point clouds mostly rely on high frequency IMU, which may not always be available. In this paper, we propose a straightforward approach that uses the registration of consecutive point clouds to estimate the motion of the scanner and deskew the point clouds. We introduce a novel surface‐based evaluation method to evaluate the performance of the proposed deskewing method. Furthermore, we develop a lidar simulator using the reverse of the proposed deskewing method which can produce synthetic point clouds with realistic motion distortion. [ABSTRACT FROM AUTHOR]

Cells store triacylglycerol (TAG) within lipid droplets (LDs). A dynamic model describing complete LD formation at the endoplasmic reticulum (ER) membrane does not yet exist. A biochemical‐biophysical model of LD synthesis is proposed. It describes the time‐dependent accumulation of TAG in the ER membrane as the formation of a potential LD (pLD) bounded by spherical caps of the inner and outer monolayers of the membrane. The expansion rate of the pLD depends on the TAG supply, the elastic properties of the ER membrane, and the recruitment of phospholipids (PLs) to the cap‐covering monolayers. Model simulations provided the following insights: (a) Marginal differences in the surface tension of the cap monolayers are sufficient to fully drive the expansion of the pLD towards the cytosol or lumen. (b) Selective reduction of PL supply to the luminal monolayer ensures stable formation of cytosolic LDs, irrespective of variations in the elasto‐mechanical properties of the ER membrane. (c) The rate of TAG supply to the cytosolic monolayer has a major effect on the size and maturation time of LDs but has no significant effect on the TAG export per individual LD. The recruitment of additional PLs to the cap monolayers of pLDs critically controls the budding direction, size, and maturation time of LDs. The ability of cells to acquire additional LD initiation sites appears to be key to coping with acutely high levels of potentially toxic free fatty acids. [ABSTRACT FROM AUTHOR]

The practical application of traditional data‐driven techniques for process monitoring encounters significant challenges due to the inherent nonlinear and dynamic nature of most industrial processes. Aiming at the problem of nonlinear dynamic process monitoring, a novel fault detection method based on dynamic kernel principal component analysis combined with weighted structural difference (DKPCA‐WSD) is proposed in this paper. Initially, the proposed method leverages a sophisticated nonlinear transformation to project the augmented matrix of the original input data into a high‐dimensional feature space, thereby facilitating the establishment of a DKPCA model. Subsequently, the WSD statistic is computed, utilizing a widely known sliding window technique, to quantify the mean and standard deviation differences across data structures. Ultimately, the WSD statistic is utilized for fault detection, completing the process monitoring task. By integrating the capability of DKPCA to capture nonlinear dynamic characteristics with the effectiveness of the WSD statistic in mitigating the impact of non‐Gaussian data distributions, DKPCA‐WSD significantly enhances the monitoring performance of traditional DKPCA in nonlinear dynamic processes. The proposed method is evaluated through a numerical case exhibiting nonlinear dynamic behaviors and a simulation model of a continuous stirred tank reactor. A comparative analysis with conventional methods, including principal component analysis (PCA), dynamic principal component analysis, KPCA, PCA similarity factor (SPCA), DKPCA, and moving window KPCA (MWKPCA), demonstrates that DKPCA‐WSD outperforms traditional fault detection techniques in nonlinear dynamic processes, offering a substantial improvement in monitoring performance. [ABSTRACT FROM AUTHOR]

The seismic hazard of a fault system is controlled by the maximum possible earthquake magnitude it can host. However, existing methods to estimate maximum magnitudes can result in large uncertainties or ignore their temporal evolution. Here, we show how the maximum possible earthquake magnitude of a fault system can be assessed by combining high-resolution fault coupling maps with a physics-based model from three-dimensional dynamic fracture mechanics confirmed by dynamic rupture simulations. We demonstrate the method on the Anninghe-Zemuhe fault system in southwestern China, where dense near-fault geodetic data has been acquired. Our results show that this fault system currently has the potential to generate Mw7.0 earthquakes with maximum magnitudes increasing to Mw7.3 by 2200. These results are supported by the observed rupture extents and recurrence times of historical earthquakes and the b values of current seismicity. Our work provides a practical way to assess the earthquake potential of natural faults. In this study, the authors show how the maximum possible earthquake magnitude of a fault system can be assessed by combining high-resolution fault coupling maps with a physics-based theoretical model. They demonstrate the method on the Anninghe-Zemuhe fault system in southwestern China. [ABSTRACT FROM AUTHOR]

This paper presents a novel adaptation of the conventional approximate Bayesian computation sequential Monte Carlo (ABC-SMC) sampling algorithm for parameter estimation in the presence of uncertainties, coined combinatorial ABC-SMC. Inference of this type is used in situations where there does not exist a closed form of the associated likelihood function, which is replaced by a simulating model capable of producing artificial data. In the literature, conventional ABC-SMC is utilised to perform inference on continuous parameters. The novel scheme presented here has been developed to perform inference on parameters that are high-dimensional binary, rather than continuous. By altering the form of the proposal distribution from which to sample candidates in subsequent iterations (referred to as waves), high-dimensional binary variables may be targeted and inferred by the scheme. The efficacy of the proposed scheme is demonstrated through application to vibration data obtained in a structural dynamics experiment on a fibre-optic sensor simulated as a finite plate with uncertain boundary conditions at its edges. Results indicate that the method provides sound inference on the plate boundary conditions, which is validated through subsequent application of the method to multiple vibration datasets. Comparisons between appropriate forms of the metric function used in the scheme are also developed to highlight the effect of this element in the schemes convergence. [ABSTRACT FROM AUTHOR]

A methodology is presented for downscaling the Euro‐CORDEX climatic projections in order to derive spatially and temporally correlated weather fields that can be used for risk and resilience assessment of large‐scale asset portfolios or interconnected infrastructure. The temporal resolution of the Euro‐CORDEX data is downscaled to a 10 min basis by employing a modified analogue‐type approach that utilizes the k‐NN algorithm along with measurements from weather stations. The aim is to generate composite "Frankenstein" days comprising 144 jigsaw pieces of observed 10 min timeseries that are scaled and/or shifted, and matched together to form a continuous daily record. These point‐estimates, valid only at the locations of the weather stations, are expanded spatially by employing high‐fidelity weather intensity measure fields that provide variable yet synchronous patterns of weather parameters at all locations of interest. As a case study, the Euro‐CORDEX projections for wind, temperature, and precipitation are downscaled for the Metsovo‐Panagia segment of Egnatia Odos highway in Greece, by employing high‐fidelity Computational Fluid Dynamic simulations that account for the topography of the site to simulate turbulent wind flows. These are combined with measurements of two local weather stations to generate the Frankenstein timeseries and corresponding weather fields that can be used for estimating operability, recovery and direct/indirect loss statistics on an event‐by‐event basis for an ensemble of interconnected highway assets. [ABSTRACT FROM AUTHOR]

This manuscript presents a new report on the in vitro antimicrobial photo-inactivation of foodborne microorganisms (Salmonella spp. and Listeria monocytogenes) using tetra-cationic porphyrins. Isomeric tetra-cationic porphyrins (3MeTPyP, 4MeTPyP, 3PtTPyP, and 4PtTPyP) were tested, and antimicrobial activity assays were performed at specific photosensitizer concentrations under dark and white-light LED irradiation conditions. Among the tested bacterial strains, 4MeTPyP exhibited the highest efficiency, inhibiting bacterial growth within just 60 min at low concentrations (17.5 μM). The minimal inhibitory concentration of 4MeTPyP increased when reactive oxygen species scavengers were present, indicating the significant involvement of singlet oxygen species in the photooxidation mechanism. Furthermore, the checkerboard assay testing the association of 4MeTPyP showed an indifferent effect. Atomic force microscopy analyses and dynamic simulations were conducted to enhance our understanding of the interaction between this porphyrin and the strain's membrane. [ABSTRACT FROM AUTHOR]

Mathematical modeling of bearing faults, worn tool holder taper contact interface, and unbalance are presented and integrated into a digital dynamic model of spindles in Part I of this paper. These faults lead to changes in preload and dynamic stiffness over time, consequently resulting in observable vibrations. This paper predicts the vibrations of a spindle at a particular measurement location by simulating the presence of a specific fault or multiple faults during spindle rotation. The vibration spectra generated by the digital spindle model at the spindle speed and its harmonics, the changes in the natural frequencies, and dynamic stiffnesses are correlated to faults with experimental validations. The simulated vibration spectrums are later used in training an artificial neural network for fault condition monitoring presented in Part III of the paper. [ABSTRACT FROM AUTHOR]

For conformal contact situations, some of the assumptions employed in classical contact theories, such as the flatness of the contact area, lose their validity. Existing conformal contact models are challenged by either accuracy or computational efficiency. With the aim of achieving a balanced trade-off between these contrasting requirements, this paper proposes a new simplified method to solve conformal rolling contact problems, which is based on fast non-Hertzian contact model INFCON and FaStrip. Numerical experiments are performed for two typical wheel/rail conformal contact scenarios to assess the performance of the proposed method, taking as the reference the CONTACT method for conformal contacts. The comparative analyses show that the proposed method is in good agreement with the reference method in terms of the determination of the contact stresses, total forces, and the prediction of wear, whereas the traditional half-space approach fails to capture the main features of the conformal contacts. In addition, the high computational efficiency of the proposed method shows the potential for application in vehicle dynamic simulation and damage analysis. [ABSTRACT FROM AUTHOR]

The objective of the paper is to investigate the internal flow characteristics inside the inducer under the gas-water condition using a combination of experiment and numerical simulations. Gas-water mixing is achieved by an annular gas mixing device and visualized by high-speed imaging technology. Meanwhile, the shear stress transport (SST) k - ω model coupled with the VOF model is applied to investigate the gas phase distribution. Numerically simulated inducer energy characteristics and internal gas-phase distribution are in good agreement with experimental data. The results show that the mixed gas-liquid flow inside the inducer exhibits three flow patterns: wavy flow, bubbly flow, and plug flow. For the wavy flow, a series of wavy structures on the blade surface and gas-pocket bubbles near the blade rim are observed. From wavy flow to bubbly flow, uniform bubble streams on the blade surface and spiraling strip bubbles near the edge of the blade are observed. For plug flow, bubble accumulation results in a bullet-shaped plug, and the strip-shaped bubbles near the blade rim are more obvious. In the same axial position of the inducer, the proportion of gas phase is increasing as the flow coefficient increases, thus affecting the inducer performance. The main frequency amplitude of the pressure pulsations gradually rise when the flow pattern changes from wavy to plug flow. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Beijing University of Technology is the property of Journal of Beijing University of Technology, Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In Ecuador, the growth of the transportation sector has significantly increased greenhouse gas emissions. According to experts, this sector currently contributes to 49.8% of total greenhouse gas emissions in this country. This poses significant challenges for environmental sustainability, emphasizing the urgent need for effective strategies to mitigate these emissions and promote environmentally friendly practices. Therefore, this study focuses on developing a dynamic simulation of energy scenarios for the year 2035 in the transportation sector, with the goal of transitioning to sustainable mobility, as fossil fuels are the main pollutants in the country. This study proposes system dynamics models using VENSIM 6.0b software to estimate the total energy demand and pollutant emissions in the transportation sector. The results suggest that if Ecuador aligns with global mobility trends and implements public policies promoting the use of electric vehicles, total CO2 emissions could potentially decrease from 50,161,432 kilobarrels of oil equivalent (kBOE) to 20,589,720 kBOE by the year 2035. [ABSTRACT FROM AUTHOR]

Pulsed water jet technology has broad application prospects in the field of rock breaking. The pressurized pulsed water jet (PPWJ) is a new type of pulsed jet that offers high-amplitude pressurization, variable pulse pressure and frequency, and a high energy usage rate. To achieve a more destructive and powerful pulsed water jet, a dynamic simulation model of the device was established by using the AMESim software (v1400) based on the operational principle of PPWJs, and the simulation model was validated against the experimental results. The relationships between the key structural parameters of the PPWJ device and the pulse parameters were quantitatively investigated. The pulse pressure and frequency can be increased by appropriately increasing the nozzle diameter or boost ratio, and the pulse pressure will drop if the nozzle diameter or boost ratio exceeds a threshold value. Increasing the maximum displacement or action area of the piston will increase pulse length while decreasing pulse frequency; a proper match of the maximum displacement or action area of the piston will assure pulse peak pressure. The maximum outer diameter of the piston only affects the pulse frequency. The key structural parameters of the device were optimized on that foundation. Compared to the original device, the optimized device resulted in an increase in pulse frequency and jet output energy, leading to larger diameter and volume of erosion pits at the same stand-off distance and erosion time. The findings of this study offer valuable scientific insights for achieving efficient rock breaking with PPWJ. [ABSTRACT FROM AUTHOR]

For the purpose of environment protecting and energy saving, renewable energy has been distributed into the power grid in a considerable scale. However, the consuming capacity of the power grid for renewable energy is relatively limited. As an effective way to absorb the excessive renewable energy, the power to gas (P2G) technology is able to convert excessive renewable energy into hydrogen. Hydrogen-blending natural gas pipeline is an efficient approach for hydrogen transportation. However, hydrogen-blending natural gas complicates the whole integrated energy system (IES), making it more problematic to cope with the equipment failure, demand response and dynamic optimization. Nevertheless, dynamic simulation of distribution parameters of gas–electricity–hydrogen (GEH) energy system, especially for hydrogen concentration, still remains a challenge. The dynamics of hydrogen-blending IES is undiscovered. To tackle the issue, an iterative solving framework of the GEH-IES and a cell segment-based method for hydrogen mixing ratio distribution are proposed in this paper. Two typical numerical cases studying the conditions under which renewables fluctuate and generators fail are conducted on a real-word system. The results show that hydrogen blending timely and spatially influences the flow parameters, of which the hydrogen mixing ratio and gas pressure loss along the gas pipeline are negatively correlated and the response to hydrogen mixing ratio is time-delayed. Moreover, the hydrogen-blending amount and position also have a significant impact on the performance of the compressor. [ABSTRACT FROM AUTHOR]

The potential of clean energy is constrained by various social, environmental, and technological challenges. The current work is likely the first to observe the influence of economic complexity on production-based carbon emissions to develop clean and sustainable energy solutions that address climate change apprehensions. The study examines the impacts of clean energy, economic complexity, public service transportation, and improving technology on production-based carbon emissions. This study employ the novel dynamic autoregressive distributive lag simulation method and a novel machine learning technique, Kernel-based regularized least squares, to ascertain the causal effect among variables. The results reveal that economic complexity is escalating environmental contamination. Similarly, public service transportation increases environmental costs, but clean energy and improving technology decrease the production-based emissions in China. These findings offer valuable guidance for China's public transportation system to integrate and consolidate the road, railway, airline transportation, and aviation sectors. [ABSTRACT FROM AUTHOR]

The complex topography and inherent nonlinearity affiliated with influential hydrological processes of urban catchments, coupled with limited availability of measured data, limits the prediction accuracy of conventional models. Artificial Neural Network models (ANNs) have displayed commendable progress in recognising and simulating highly complex, non-linear associations allied with input-output variables, with limited comprehension of the underlying physical processes. Therefore, this paper investigates the effectiveness and accuracy of ANN models, in estimating the urban catchment runoff, employing minimal and commonly available hydrological data variables – rainfall and upstream catchment flow data, employing two powerful supervised-learning-algorithms, Bayesian-Regularization (BR) and Levenberg-Marquardt (LM). Gardiners Creek catchment, encompassed in Melbourne, Australia, with more than thirty years of quality-checked rainfall and streamflow data was chosen as the study location. Two significant storm events that transpired within the last fifteen years - the 4th of February 2011 and the 6th of November 2018, were nominated for calibration and validation of the ANN model. The study results advocate that the use of the LM-ANN model stipulates accurate estimates of the historical storm events, with a stronger correlation and lower generalisation error, in contrast to the BR-ANN model, while the integration of upstream catchment flow alongside rainfall, vindicate for their collective impact upon the dynamics of the flow being spawned at the downstream catchment locations, significantly enhancing the model performance and providing a more cost-effective and near-realistic modelling approach that can be considered for application in studies of urban catchment responses, with limited data availability. [ABSTRACT FROM AUTHOR]

This research paper presents a comprehensive investigation aimed at enhancing the 2G bioethanol production process through the implementation of a dynamic process simulator. The simulator, developed using the Julia programming language, enables the prediction of acid hydrolysis behaviour by manipulating critical variables, including liquid–solid ratio (LSR), acid concentration (CA), and processing time (t). Through meticulous simulations and subsequent experimental validation, optimal operating conditions were revealed, with an H2SO4 concentration of 2% v/v, a LSR of 4% v/w, and 60‐min processing time at 121.1°C. This configuration led to remarkable outcomes, including a xylose concentration of 47.45 g L−1 and an 87.4% hemicellulose removal percentage. Moreover, the simulator unveiled the adverse influence of low LSR values on xylose production and the generation of degradation products. The recalibration of kinetic parameters, guided by experimental data, further fine‐tuned the simulator's predictive accuracy. Overall, this study underscores the potential of the simulator in optimizing various raw materials and presents a promising avenue for advancing 2G bioethanol industry practices. [ABSTRACT FROM AUTHOR]

Several computational methods have been developed to identify neutralizing antibodies (NAbs) covering four dengue virus serotypes (DENV-1 to DENV-4); however, limitations of the dataset and the resulting performance remain. Here, we developed a new computational framework to predict potent and stable NAbs against DENV-1 to DENV-4 using only antibody (CDR-H3) and epitope sequences as input. Specifically, our proposed computational framework employed sequence-based ML and molecular dynamic simulation (MD) methods to achieve more accurate identification. First, we built a novel dataset (n = 1108) by compiling the interactions of CDR-H3 and epitope sequences with the half maximum inhibitory concentration (IC50) values, which represent neutralizing activities. Second, we achieved an accurately predictive ML model that showed high AUC values of 0.879 and 0.885 by tenfold cross-validation and independent tests, respectively. Finally, our computational framework could be applied to filter approximately 2.5 million unseen antibodies into two final candidates that showed strong and stable binding to all four serotypes. In addition, the most potent and stable candidate (1B3B9_V21) was evaluated for its development potential as a therapeutic agent by molecular docking and MD simulations. This study provides an antibody computational approach to facilitate the high-throughput identification of NAbs and accelerate the development of therapeutic antibodies. [ABSTRACT FROM AUTHOR]

The dynamic control of an active magnetic bearing (AMB) rotor after the rotor falls to its touch-down bearings has always been a difficult problem for applications such as flywheel energy storage. The rotor drop process has obvious nonlinear dynamic characteristics. This paper first discusses the structure of AMBs and the basic principles of their control. Starting from the electromagnetic forces that electromagnets can provide, the problem is simplified to the influence of an electromagnetic force with constant damping characteristics on the dynamic characteristics of a dropped rotor. A dynamic model of an AMB rotor with touch-down bearings was built and the contact force model between the rotor and the touch-down bearings was determined. A constant damping electromagnetic force was applied in two ways to verify the dynamic control feasibility of a dropped rotor through magnetic bearings. The simulation results show that the dropped rotor recovery control is feasible by applying a reasonable electromagnetic force. [ABSTRACT FROM AUTHOR]

Crushing is a critical operation in mineral processing, and its efficient performance is vital for minimizing energy consumption, maximizing productivity, and maintaining product quality. However, due to variations in feed material characteristics and safety constraints, achieving the intended circuit performance can be challenging. In this study, a centralized control strategy based on a finite state machine (FSM) is developed to improve the operations of an iron ore crushing circuit. The aim is to increase productivity by manipulating the closed-side-setting (CSS) of cone crushers and the speed of an apron feeder while considering intermediate storage silo levels and cone crusher power limits, as well as product quality. A dynamic simulation was conducted to compare the proposed control strategy with the usual practice of setting CSS to a constant value. Four scenarios were analyzed based on variations in bond work index (BWI) and particle size distribution. The simulation results demonstrate that the proposed control strategy increased average productivity by 6.88% and 48.77% when compared to the operation with a constant CSS of 38 mm and 41 mm, respectively. The proposed strategy resulted in smoother oscillation without interlocking, and it maintained constant flow rates. This ultimately improved circuit reliability and predictability, leading to reduced maintenance costs. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Histone deacetylase 3 (HDAC3) is a member of the histone deacetylase family that has emerged as a crucial target in the quest for novel therapeutic interventions against various complex diseases, including cancer. The repositioning of FDAapproved drugs presents a promising avenue for the rapid discovery of potential HDAC3 inhibitors. In this study, we performed a structure-based virtual screening of FDA-approved drugs obtained from DrugBank. Candidate hits were selected based on their binding affinities and interactions with HDAC3. These promising hits were then subjected to a comprehensive assessment of their biological properties and drug profiles. Our investigation identified two FDA-approved drugs, Imatinib and Carpipramine, characterized by their exceptional affinity and specificity for the binding pocket of HDAC3. These molecules demonstrated a strong preference for HDAC3 binding site and formed interactions with functionally significant residues within the active site pocket. To gain deeper insights into the binding dynamics, structural stability, and interaction mechanisms, we performed molecular dynamics (MD) simulations spanning 300 nanoseconds (ns). The results of MD simulations indicated that Imatinib and Carpipramine stabilized the structure of HDAC3 and induced fewer conformational changes. Taken together, the findings from this study suggest that Imatinib and Carpipramine may offer significant therapeutic potential for treating complex diseases, especially cancer. [ABSTRACT FROM AUTHOR]

The aim of the study is to design and synthesize thiazolidinone derivatives having therapeutic potential using PABA as a core component. The characterization of thiazolidinones MS1–MS6was carried out using different spectroscopic techniques before being subjected to DFT studies to calculate various parameters such as HOMO/LUMO energy gaps and global chemical properties. Thiazolidinones MS1–MS6were screened to evaluate their in vitro enzyme inhibition potential. In addition to experimental work, theoretical approaches were used as supporting components to design the enzyme inhibitors. In-silico ADMET and docking studies were performed to check the therapeutic properties of thiazolidinone derivatives. The enzyme inhibition potential predicted that MS1–MS6have potential to inhibit AChE and BChE. The highest activity was depicted by MS4 with a percentage inhibition of 81.7 ±1.1% against AChE. The molecular dynamic simulation was run for MS4 showing a similar activity on enzyme compared to the standard inhibitor. Both experimental and theoretical assessments suggested the therapeutic importance of the thiazolidinone derivatives MS1–MS6. In-depth studies are ongoing to complete therapeutic probing in terms of toxicity and chemical uses. [ABSTRACT FROM AUTHOR]

BACKGROUND RESULTS CONCLUSION Mycotoxin contamination of food has been gaining increasing attention. Hidden mycotoxins that interact with biological macromolecules in food could make the detection of mycotoxins less accurate, potentially leading to the underestimation of the total exposure risk. Interactions of the mycotoxins alternariol (AOH) and alternariol monomethyl ether (AME) with high‐molecular glutenin were explored in this study.The recovery rates of AOH and AME (1, 2, and 10 μg kg−1) in three types of grains (rice, corn, and wheat) were relatively low. Molecular dynamics (MD) simulations indicated that AOH and AME bound to glutenin spontaneously. Hydrogen bonds and π‐π stacking were the primary interaction forces at the binding sites. Alternariol with one additional hydroxyl group exhibited stronger binding affinity to glutenin than AME when analyzing average local ionization energy. The average interaction energy between AOH and glutenin was −80.68 KJ mol−1, whereas that of AME was −67.11 KJ mol−1.This study revealed the mechanisms of the interactions between AOH (or AME) and high‐molecular glutenin using MD and molecular docking. This could be useful in the development of effective methods to detect pollution levels. These results could also play an important role in the evaluation of the toxicological properties of bound altertoxins. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

The maximum utilization of hydrocracking tail oil becomes increasingly important for petrochemical industry. The aim of this work is to develop optimized distillation processes to achieve various high‐valued qualified oil products from hydrocracking tail oil. Six different oil products is produced and the steady‐state distillation process, which aims to fractionate six qualified narrow distillates is established. The algorithm method incorporating divided‐wall column (DWC) configuration was introduced into the steady‐state design. Compared with traditional separation sequences, the DWC configuration leads to an energy‐saving potential up to 11.17%. Furthermore, effective dynamic control strategies were proposed, demonstrating precise and efficient control performance. In the presence of a 15% feed disturbance, the dynamic control structure is capable of maintaining the product distillation range near the set value. This comprehensive study provides a thorough investigation into the efficient utilization of hydrocracking tail oil, establishing a robust theoretical foundation for its industrial application. [ABSTRACT FROM AUTHOR]

Numerical dissipation is an important characteristic of numerical integration methods, which directly affects the accuracy of numerical simulation results. Numerical dissipation can improve numerical simulation results for dynamic systems with spurious high-frequency vibrations, but it can also cause distorted calculation results for dynamic systems with real high-frequency vibrations. A 2-sub-step implicit numerical integration method was proposed with controllable numerical damping dissipation to solve structural dynamic systems. Through theoretical derivations, the numerical properties of the new integration method, including the spectral radii, stability, amplitude decay, and period elongation, were introduced in detail. The new implicit integration method can utilize algorithm parameter α to control the numerical dissipation of spurious high-frequency vibration, with a corresponding dissipation ratio of 1-|α|,where -1≤α≤1.The advantages of the new method in terms of the computational accuracy, the high-frequency numerical dissipation, and the nonlinear solving ability were demonstrated through 3 typical examples of a 1-DOF dynamic system, a high-frequency spurious vibration system, and a multi-DOF nonlinear spring-mass system. [ABSTRACT FROM AUTHOR]

Flemingia macrophylla has traditionally been applied to relieve inflammation, diabetes, and circulatory complications. The leaf extract of F. macrophylla and its fractions were investigated for their in-vitro antioxidant and anti-diabetic properties. The phytochemical screening showed valuable phytochemicals, including glycosides, flavonoids, saponins, etc. GC-MS analysis of the phytochemicals in the methanol extract detected 19 bioactive compounds. Among the diverse fractions, the ethyl acetate fraction (EFM) exhibited the highest phenol and flavonoid contents of 557 mg GAE/g and 326 mg QCE/g, respectively. The total antioxidant content of EFM was found to be 292.41±19.16 mg AAE/g, while its antidiabetic study showed the greatest level of α -glucosidase (IC50: 11.27±1.25 µg/mL) and α -amylase (IC50: 10.04±0.63 µg/mL) inhibitory effects. The docking results showed that C6 had the highest binding scores of -9.0, -7.4, and -7.6 kcal/mol against antioxidant (6NGJ), α-glucosidase (5NN5), and α-amylase (4GQR) proteins, respectively. The dynamics simulation disclosed that C6-receptor protein complexes remained stable at the binding pocket under human body conditions and retained their stiff morphology for 100 nanoseconds (ns). ADMET results demonstrated their noncarcinogenic and well-absorbed properties, where PASS prediction data confirmed their efficacy as an antioxidant, antiulcerative, thrombolytic, and antidiabetic. Therefore, F. macrophylla has potential health benefits. [ABSTRACT FROM AUTHOR]

The dynamics of milling circuits, particularly those involving Semi-Autogenous Grinding (SAG) mills, are not adequately studied, despite their critical importance in mineral processing. This paper aims to investigate the dynamic behavior of an SAG mill pebble recycling circuit under varying feed ore conditions, focusing on both uncontrollable parameters (such as ore hardness) and controllable parameters (including circuit layout and pebble crusher configurations). The study is carried out with Simulink dynamic simulations. Our findings reveal several key insights. Firstly, plant designs based solely on static simulations may not be adequate for large or complex circuits, as they fail to account for the dynamic nature of milling processes. Second, incorporating stockpiles after pebble crushing can effectively mitigate the impact of dynamic fluctuations, leading to more stable circuit performance. Third, different circuit layouts can facilitate easier maintenance and operational flexibility. Notably, finer pebble crushing can enhance circuit throughput by 5% to 10%. [ABSTRACT FROM AUTHOR]

One of the most challenging issues scientists face is finding a suitable non-invasive treatment for cancer, as it is widespread around the world. The efficacy of phytochemicals that target oncogenic pathways appears to be quite promising and has gained attention over the past few years. We investigated the effect of docking phytochemicals isolated from the rhizomes of the Cimicifuga foetida plant on different domains of the IκB kinase alpha (IKK1/alpha) protein. The Cimicifugoside H-2 phytochemical registered a high docking score on the activation loop of IKK1/alpha amongst the other phytochemicals compared to the positive control. The interaction of the protein with Cimicifugoside H-2 was mostly stabilized by hydrogen bonds and hydrophobic interactions. A dynamic simulation was then performed with the Cimicifugoside H-2 phytochemical on the activation loop of IKK1/alpha, revealing that Cimicifugoside H-2 is a possible inhibitor of this protein. The pharmacokinetic properties of the drug were also examined to assess the safety of administering the drug. Therefore, in this in silico study, we discovered that the Cimicifugoside H-2 phytochemical inhibits the actively mutated conformation of IKK1/alpha, potentially suppressing the nuclear factor kappa light chain enhancer of activated B cells (NF-κB) pathway. [ABSTRACT FROM AUTHOR]

Pyrometallurgical slags are a valuable secondary resource for several technologically important critical materials/elements. A promising cost-efficient recovery currently under research depends on the incorporation of the element of interest into a specific compound. This new approach under research to recover these elements from the slag is called the "Engineered Artificial Minerals" (EnAM) concept. Optimization of slag system and cooling conditions potentially allow enrichment of a desired element in only one phase. Studying EnAM-formation requires multi model characterization of the slag at different states of formation. This includes bulk as well as microscopic elemental, structural, and species analytical tools and modeling approaches. Here, a characterization strategy mainly studying the formation of potential EnAMs for the recovery of lithium from battery recycling slags is reviewed. LiAlO2 as well as lithium manganates were identified as promising EnAMs. The manganese oxidation state was identified a decisive parameter. For a knowledge-based design of slags, it is necessary to understand the processes leading to oxidation state variability. Hence, besides structural, compositional characterization, speciation of redox active elements is discussed here using molecular dynamics simulations, powder X-ray diffraction, electron probe microanalysis, X-ray absorption near edge structure, micro-X-ray fluorescence, micro-X-ray diffraction, and micro-X-ray absorption near edge structure. [ABSTRACT FROM AUTHOR]

Passive damping treatments using viscoelastic materials (VEMs) are widely applied as addon solutions to control the dynamic response of light structures. These surface treatments are simple, reliable, and can be optimized by using proper design rules and simulation tools. In this work, a new treatment configuration is proposed, simulated, and assessed, being compared with the most effective configuration currently used. The novelty resides in the VEM layer's geometry, which in the proposed configuration assumes a waved shape. This waved shape is imposed to the viscoelastic layer due to indentations created in the inner faces of the host structure and the constraining layer. The results demonstrate that the wavy configuration, designated as corrugated viscoelastic layer (CVL), outperforms the typical damping treatments for relatively thick host structures and when using thick VEM layers. In addition, this new configuration reduces the stiffness decoupling effect due to the soft VEM layer, restoring the flexural stiffness of the original undamped structure. [ABSTRACT FROM AUTHOR]

This research introduces the NeuRaiSya (Neural Railway System Application), an innovative railway signaling system integrating deep learning for passenger analysis. The objectives of this research are to simulate the NeuRaiSya and evaluate its effectiveness using the GreatSPN tool (graphical editor for Petri nets). GreatSPN facilitates evaluations of system behavior, ensuring safety and efficiency. Five models were designed and simulated using the Petri nets model, including the Dynamics of Train Departure model, Train Operations with Passenger Counting model, Timestamp Data Collection model, Train Speed and Location model, and Train Related-Issues model. Through simulations and modeling using Petri nets, the study demonstrates the feasibility of the proposed NeuRaiSya system. The results highlight its potential in enhancing railway operations, ensuring passenger safety, and maintaining service quality amidst the evolving railway landscape in the Philippines. [ABSTRACT FROM AUTHOR]

Aiming at the difficulty of dynamic modeling of a hybrid robotic arm, a dynamic model system of industrial robotic arm based on Simscape Multibody was established with the MG400 robotic arm as the research object, which combines the motion control and data acquisition modules. The model is dynamically visualized and provides a convenient platform for studying the control algorithm of the robot arm. In response to the issues of sluggish speed and substantial position error in robot trajectory tracking control of a traditional controller, a variable gain iterative learning control methodology was designed. The robot arm control system model was employed to corroborate the trajectory tracking control under the stipulated target trajectory. The empirical outcomes indicate that in comparison to the traditional controller and the fixed-gain iterative learning controller, the variable-gain iterative learning controller can regulate the robot end trajectory more precisely, with swift tracking speed and accurate tracking posture, demonstrating commendable feasibility and portability. It offers an open-source research and development platform for the dynamic modeling of robot arm and a potent solution for the control strategy of robot arm. [ABSTRACT FROM AUTHOR]

To assess prevalence and incidence of severe visual impairment (VI) and blindness due to uveitis in Northrhine, Germany. A random sample of 5088 out of 20 390 cases of the blindness registry in Northrhine was evaluated. The number of cases of severe VI and blindness were projected up to the year 2030. The prevalence of registered severe VI and blindness due to uveitis was 1123.9 (95% CI: 1059.5 to 1191.2) per 100 000 in our study cohort and 550.31 (95% CI: 365.98–794.38) per 100 000 in Northrhine, respectively. The incidence of registered severe VI and blindness due to uveitis was 311.8 per 100 000 PYs (95% CI: 149.6–572.7) in our study cohort and 11.1 per 100 000 (95% CI: 5.6–19.8) in Northrhine, respectively. We report prevalence and incidence of severe VI and blindness due to uveitis in a large German cohort. [ABSTRACT FROM AUTHOR]

One colorimetric and one fluorescent‐indicator displacement assay (IDA) were studied to assess their interaction with methyl viologen (MV), an herbicide commercially known as paraquat. The host–guest complexes formed by cucurbit[7]uril (CB[7]) and the 4‐nitroaniline (NA) or Berberine (Be) dyes function as colorimetric‐ and fluorescent‐IDA for MV, respectively. The CB[7]@NA and CB[7]@Be complexes were characterized by UV–vis, fluorescence spectroscopy, and dynamic simulations. Isothermal titration calorimetry (ITC) experiments and dynamic simulations were employed to obtain thermodynamic data for the formation of the 1:1 inclusion complexes. Finally, NA is proposed as a reference colorimetric dye for determining CB[7]‐binding affinities using competitive host–guest titrations, and the fluorescent‐IDA as the best assay proposed, as it exhibits a high binding constant, a good detection limit of 2.7 × 10−6 mol L‐1, good linearity in the range from 4 × 10−6 mol L‐1 to 3.5 × 10−5 mol L‐1, in aqueous solution. [ABSTRACT FROM AUTHOR]

Iatrogenic aortic dissection is a rare but life-threatening complication of coronary artery bypass surgery. We report a case with incidentally detected iatrogenic aortic dissection related to aorta cross-clamping that was successfully managed with watchful follow-up. The decision making was based on 3-dimensional holographic and fluid dynamic analysis guidance. [ABSTRACT FROM AUTHOR]

High-frequency non-physical oscillations may occur due to shock waves in seismic wavefield and dynamic rupture simulation. In this study, we introduced the alternative flux finite-difference weighted essentially non-oscillatory scheme to address potential shock wave issues in computational seismology effectively. The wavefield of the body-fitted curvilinear domain was accurately computed through conservative grid mapping, ensuring accurate implementation of free surface boundary conditions on irregular surfaces using characteristic boundary conditions and minimizing artificial boundary reflections with exponential decay absorbing layers. Finally, we compared our scheme with the GRTM for flat surfaces and the CGFDM3D-EQR for irregular surfaces to demonstrate its correctness and accuracy, and validated its non-oscillatory characteristics. The aforementioned scheme is anticipated to assume a significant function in simulating more intricate seismic wavefields or dynamic ruptures. [ABSTRACT FROM AUTHOR]

We provide improved uniform error estimates for the time‐splitting Fourier pseudo‐spectral (TSFP) methods applied to the Klein–Gordon–Dirac system (KGDS) with the small parameter ε∈(0,1]$$ \varepsilon \in \left(0,1\right] $$. We first reformulate the KGDS into a coupled Schrödinger–Dirac system (CSDS) and then apply the second‐order Strang splitting method to CSDS with the spatial discretization provided by Fourier pseudo‐spectral method. Based on rigorous analysis, we establish improved uniform error bounds for the second‐order Strang splitting method at O(hm−1+ετ2)$$ O\left({h}^{m-1}+\varepsilon {\tau}^2\right) $$ up to the long time at O(1/ε)$$ O\left(1/\varepsilon \right) $$. In addition to the conventional analysis methods, we mainly apply the regularity compensation oscillation technique for the analysis of long time dynamic simulation. The numerical results show that our method and conclusion are not only suitable for one‐dimensional problem, but also can be directly extended to higher dimensional problem and highly oscillatory problem. As far as we know there has not been any relevant long time analysis and any improved uniform error bounds for the TSFP method solving the KGDS. Our methods are novel and provides a reference for analyzing the improved error bounds of other coupled systems similar to the KGDS. [ABSTRACT FROM AUTHOR]

With the progress of control and communication technology, coal mining is gradually transformed and upgraded from traditional mechanism to automation, the concept of intelligent mining in fully mechanized caving is also put forward in the mining technology research of thick and extra-thick coal seams. As a key technology and necessary condition of intelligent mining in fully mechanized caving, intelligent recognition of coal and gangue is a technical bottleneck to be broken through, which has seriously affected the coal mining efficiency and recovery rate. Based on this, this paper carries out a systematic study on coal gangue recognition in fully mechanized caving from the perspective of tail beam vibration. Firstly, the impact-contact dynamic theoretical model between coal gangue and the hydraulic support tail beam and its rigid-flexible coupling dynamic simulation model was constructed respectively. The theoretical and numerical simulation was combined to study the vibration characteristics induced by the contact between coal gangue and the tail beam, and the vibration characteristics caused by the difference of coal and gangue properties were compared and analyzed. Meanwhile, coal gangue recognition capability is summarized, which provides theoretical support for the vibration characteristics-based coal gangue recognition. Secondly, through analysis of fully mechanized caving environment, a single coal drawing experimental platform and vibration information acquisition system are designed and built in the laboratory environment. Then, a research on signal eigenvalue recognition sensitivity based on "coal gangue binary classification" recognition was carried out through the combination of signal acquisition and features extraction as well as machine learning, the optimal recognition eigenvector based on recognition effect analysis was obtained. On that premise, recognition research of "coal gangue multi-category recognition" based on the single point and multi-point vibration signals were proposed and conducted, which successfully realized coal gangue mixture ratio recognition under the condition of multi-category. The research provided the theoretical basis and technical support for accurate and real-time coal gangue mixture ratio recognition in top coal caving. [ABSTRACT FROM AUTHOR]