3D bioprinting, like 3D printing, is a process that builds structures by depositing material layer by layer. In the specific case of bioprinting, these layers are composed of a biocompatible material together with living cells, allowing the creation of three-dimensional structures that recreate functional tissues and organs. Bioprinting cell-laden structures is complicated by the high rate of cell damage and stress that occurs during the process, caused by the high pressures and stresses to which they are subjected. To minimise damage during the process, it is important to study and optimise certain bioprinting conditions beforehand and to analyse how they affect the cells by means of computational simulations. In this review, both quantitative and qualitative data are collected to improve the nozzle geometry through computational simulation studies. Optimal ranges for nozzle diameter (0.2-1mm) and length (8-10mm; 300-900μm) have been defined, and recommendations for improving nozzle performance during bioprinting, such as the provision of an inner angle of 20-30°, an inner EDTA coating and a shear stress of less than 10kPa, have been gathered. Finally, based on the data collected, a design of experiments is proposed to obtain an optimal bioprinting configuration for a particular bioink.

Shut-in after fracturing is generally adopted for wells in shale oil reservoirs, and imbibition occurring in matrix nanopores has been proven as an effective way to improve recovery. In this research, the molecular dynamics (MD) simulation was used to investigate the effects of wettability and pressure on nanopores imbibition during shut-in for a typical shale reservoir Jimsar. The results indicate that the microscopic advancement mechanism of the imbibition front is the competitive adsorption between “interfacial water molecules” at the imbibition front and “adsorbed oil molecules” on the pore wall. The essence of spontaneous imbibition involves the adsorption and aggregation of water molecules onto the hydroxyl groups on the pore wall. The flow characteristics of shale oil suggest that the overall push of the injected water to the oil phase is the main reason for the displacement of adsorbed oil molecules. Thus, shale oil especially the heavy hydrocarbon component in the adsorbed layer tends to slip on the walls. However, the weak slip ability of heavy components on the wall surface is an important reason that restricts the displacement efficiency of shale oil during spontaneous imbibition. The effectiveness of spontaneous imbibition is strongly dependent on the hydrophilicity of the matrix pore's wall. The better hydrophilicity of the matrix pore's wall facilitates higher levels of adsorption and accumulation of water molecules on the pore wall and requires less time for “interfacial water molecules” to compete with adsorbed oil molecules. During the forced imbibition process, the pressure difference acts on both the bulk oil and the boundary adsorption oil, but mainly on the bulk oil, which leads to the occurrence of wetting hysteresis. Meanwhile, shale oil still existing in the pore always maintains a good stratified adsorption structure. Because of the wetting hysteresis phenomenon, as the pressure difference increases, the imbibition effect gradually increases, but the actual capillary pressure gradually decreases and there is a loss in the imbibition velocity relative to the theoretical value. Simultaneously, the decline in hydrophilicity further weakens the synergistic effect of pressure difference because of the more pronounced wetting hysteresis. Thus, selecting an appropriate well pressure enables cost savings and maximizes the utilization of the formation's natural power for EOR.

Among other fossil fuels, natural gas is a major energy source. It is generally created saturated with water vapor. Dehydration of natural gas is vital in the gas business to remove water vapor from the gas supply, which may cause hydrate development in pipes. Gas dehydration employs Tri ethylene glycol (TEG) to remove water vapor from natural gas flow. The wet gas is dehydrated with lean glycol in an absorber, and the rich glycol is recovered and reused. This work investigates the use of dry natural gas instead of nitrogen in the glycol dehydration re-generator, and compares the results using HYSYS modeling and simulation. The two techniques were compared for capital and utility expenses, while maintaining the identical glycol purity criteria. The wet gas from the stripping mechanisms may also be utilized to run steam pumps and compressors or recycled. The model is based on the real mechanism flow diagram. Finally, the findings of this model might be used to design a new heat and material balance for the plant.

A problem of efficacy of crystal growth methods for crystallization from solution or melt has been investigated. Axial vibrational control (AVC) technique was considered as a perspective method to manage both heat-mass transfer and chemical component composition of the melts in the case of crystallization of complex chemical compounds. Numerical modeling and the search for generalized dependencies made it possible to predict the AVC parameters that provide optimal heat and mass transfer modes for creating flat liquid-solid interfaces, as well as the component composition of dissociated melts of various chemical compounds – Ge, NaNO3, CdTe.

Computational rhinology is a specialized branch of biomechanics leveraging engineering techniques for mathematical modelling and simulation to complement the medical field of rhinology. Computational rhinology has already contributed significantly to advancing our understanding of the nasal function, including airflow patterns, mucosal cooling, particle deposition, and drug delivery, and is foreseen as a crucial element in e.g. the development of virtual surgery as a clinical, patient-specific decision support tool. The current paper delves into the field of computational rhinology from a nasal airflow perspective, highlighting the use of computational fluid dynamics to enhance diagnostics and treatment of breathing disorders. The paper consists of three distinct parts – an introduction to and review of the field of computational rhinology, a review of published literature on in vitro and in silico studies of nasal airflow, and the presentation and analysis of previously unpublished high fidelity CFD simulation data of in silico rhinomanometry. While the two first parts of the paper summarize the current status and challenges in the application of computational tools in rhinology, the last part addresses the gross disagreement commonly observed when comparing in silico and in vivo rhinomanometry results. It is concluded that this discrepancy cannot readily be explained by CFD model deficiencies caused by poor choice of turbulence model, insufficient spatial or temporal resolution, or neglecting transient effects. Hence, alternative explanations such as nasal cavity compliance or drag effects due to nasal hair should be investigated.

In the literature, estimation of weighted extropy are least considered. In this paper some non parametric estimators of weighted extropy are given. The validation and comparison of the estimators are implemented with the help of simulation study and data illustration. The usefulness of the estimators are demonstrated using real data sets.

Generalized distributions have been studied a lot recently because of their flexibility and relia-bility in modeling lifetime data. The two-parameter exponentially-modified logistic distribution is a new flexible modified distribution that was introduced recently. It is regarded as a strong competitor for widely used classical symmetrical and non-symmetrical distributions such as normal, logistic, lognormal, and log-logistic. In this study, the unknown parameters of the dis-tribution are estimated using the maximum likelihood method. The Grey Wolf Optimization al-gorithm, which is a new meta-heuristic algorithm, is applied in order to solve the nonlinear like-lihood equations of the study model. The performance of the Grey Wolf Optimization method is compared to that of the other meta-heuristic algorithms used in this study, which include the Whale Optimization Algorithm, the Sine Cosine Algorithm, and the Particle Swarm Optimiza-tion Algorithm. The efficiencies of maximum likelihood estimates for all algorithms are com-pared via an extensive Monte-Carlo simulation study. The likelihood estimates for the location α and scale β parameters of the exponentially-modified logistic distribution developed with the Grey Wolf Optimization algorithm are the most efficient among others, according to simulation findings. Four real datasets are analyzed to show the flexibility of this distribution.

Matrix asphalt material has poor road performance, which is improved by adding polymer modified agents. The original asphalt material experiment research has a certain limitation in that it cannot truly expose the mechanism and influence of modifiers on asphalt materials. To explore the gain effect of the modified agent on the asphalt material's road performance, the molecular dynamic simulation technology is used to explain its mechanism in the micro dimension. The three parameters of the free volume score, radial distribution function and average directional shift diffusion coefficient are analysed to improve the performance of the modifier on asphalt material, an exploration of the microdimension shows that the amount of SBS modifiers is 6% and the amount of rubber modified asphalt material is 22%. The improvement of materials is better than that of rubber modifiers.

To accurately predict the matching relationships between the various components and the engine performance in the whole aero-engine environment, this study introduces a two-dimensional throughflow simulation method for the whole aero-engine. This method is based on individual throughflow solvers for the turbo-machinery and the combustor. It establishes a throughflow simulation model for the whole engine by integrating with the compressor-turbine co-operating equations and boundary conditions. The turbo-machinery throughflow solver employs a circumferentially averaged form of the time-dependent Navier-Stokes equations (N-S) as the governing equation. The combustor solver uses the Reynolds Average Navier-Stokes (RANS) method to solve flow and chemical reaction processes by constructing turbulence, combustion, and radiation models. The accuracy of the component solver is validated using Pratt & Whitney’s three-stage axial compressor (P&W3S1) and General Electric’s high-pressure turbine (GE-EEE HPT), and the predicted results are consistent with the experimental data. Finally, the developed throughflow method is applied to simulate the throttling characteristics of the WZ-X turboshaft engine. The results predicted by the throughflow program are consistent with the GasTurb calculations, including the trends of shaft power delivered, specific fuel consumption (SFC), inlet airflow, and total pressure ratio of the compressor. The developed method to perform throughflow simulation of the whole aero-engine eliminates the dependence on a general component map. It can quickly obtain the meridian flow field parameters and overall engine characteristics, which is expected to guide the design and modification of the engine in the future.

The results of numerical experiments with the chemistry-climate model of the Institute of Numerical Mathematics of the Russian Academy of Sciences - Russian State Hydro-Meteorological University (INM-RSHU CCM) in comparison with the MERRA2 reanalysis data are presented. To assess the impact of sea surface temperature (SST) on stratospheric processes and the ozone layer, numerical experiments were carried out for scenarios corresponding to the El Niño and La Niña Southern Oscillation phases. It is shown that the El Niño phase contributes to the enhancement of heat and mass transfer to the polar stratosphere, which strengthen the Brewer-Dobson circulation and the flux of wave activity that affect the zonal wind. As a result, during El Niño, the probability of sudden stratospheric warming increases and the stratospheric polar vortex (SPV) weakens, re-sulting in an increase in total polar stratospheric ozone. The La Niña phase, on the contrary, is accompanied by a weakening of heat and mass transfer from the troposphere to the stratosphere, as a result of which the stratospheric polar vortex strengthens and the total column ozone decreases. It is shown that the effect associated with the Southern Oscillation is more pronounced in the Northern Hemisphere than in the Southern Hemisphere. Numerical experiments were also carried out for scenarios in which the sea surface temperature was set corresponding to the early 1980s, and the results were compared with calculations in which the SST was set corresponding to the late 2010s. This allowed us to assess the influence of the multiyear trend in the SST increase on stra-tospheric processes and the ozone layer. It was shown that the SST increase is most pronounced in the Arctic, which corresponds to the phenomenon of "Arctic amplification", and therefore, the heat and mass transfer to the stratosphere increases in the Northern Hemisphere, which contributes to some weakening of the SPV and increase of the total column ozone. But, compared to the Southern Oscillation, the influence of the SST trend on the SPV and total column ozone is much weaker and is observed mainly in the Northern Hemisphere.

The global energy landscape is undergoing a transformative shift, and the potential of LNG as a major energy source is a topic of intense debate. In order to meet the growing demand for sustainable energy supply, it is crucial to maximize the efficiency and environmental friendliness of the LNG supply chain. This research aims to leverage computational modeling and optimization techniques to enhance the production units involved in LNG production. By harnessing the power of advanced algorithms and simulations, we can identify and implement innovative strategies that minimize energy consumption, reduce greenhouse gas emissions, and enhance the overall sustainability of LNG. Through this study, we seek to uncover novel approaches to address the challenges faced by the LNG industry, including improving operational efficiency, optimizing liquefaction processes, and enhancing the utilization of natural resources. By integrating cutting-edge computational tools and considering environmental factors, we aspire to pave the way for a more sustainable and environmentally friendly future powered by LNG. By exploring the immense potential of computational modeling and optimization, we strive to contribute to the ongoing efforts in advancing energy sustainability and shaping the future of LNG as a crucial global energy source."In this paper, our focus is on simulating and optimizing the process of converting natural gas to LNG. Our goal is to achieve the minimum energy consumption per ton of LNG produced. Through our research, we have identified that utilizing a three-stage exchanger is the most effective approach for minimizing energy consumption in an LNG industrial production unit. Moreover, we have discovered that the outlet pressure from the compressor and the type of refrigerant in the cooling system play significant roles in determining the rate of energy conservation. By carefully considering these factors and optimizing their settings, we can further enhance the overall energy efficiency of the LNG production process. Our research aims to provide valuable insights and guidance to industry professionals and decision-makers in the LNG sector. By implementing the findings of this study, we can contribute to the sustainable development and utilization of LNG as a cleaner and more environmentally energy source. Friendly It's great to see the optimized parameters for the refrigerants and pressure settings in the liquefaction and subcooling cycles. With the mass fraction of 0.89 for methane and 0.14 for ethane in the liquefaction cycle, and 0.59 for methane and 0.3 for nitrogen in the composition for achieving energy efficiency in the LNG production subcooling cycle, The optimized outlet pressure of 650 kPa for the compressors in the liquefaction cycle and 1800 kPa for the subcooling cycle furtherr ccontribute to minimizing energy consumption. Based on your findings, the amount of consumed energy at 14.81 kW per ton of produced LNG highlights the success of the optimization efforts. Reducing the energy consumption per ton of LNG produced is a significant accomplishment towards achieving energy sustainability and environmental friendliness in the LNG industry. These results demonstrate the importance of computational modeling and optimization in identifying the best parameters for eenhancing energy efficiency in LNG production. By implementing these optimized settings, we can work towards a more sustainable future with reduced energy consumption and lower environmental impact

The Hollow fiber membrane contactor (HFMC) is an intensification process that combines membrane and absorption technologies, being a technology in expansion, so the dynamics of this process is crucial for an increase in industrial scale-up. In this manuscript a rigorous dynamic model of an HFMC is developed and simulated in Python software for the absorption of post-combustion CO2 using a monoethanolamine (MEA) solution, which was validated using available data in literature in steady state. Dynamic simulations were performed to analyze the model's capability to predict the effect of disturbances on the responses of CO2 capture ratio and loading. Four distinct experiments were conducted by step changes and pulse disturbances at the following variables: gas and liquid volumetric flow rates, CO2 composition in flue gases, and CO2 lean load. The gas phase showed a faster response compared to the liquid. The CO2 capture ratio and rich loading presented opposite responses when disturbing both liquid flows, and for the composition and lean loading variations, the capture ratio presented an inverse response characteristic, while the loading showed a delayed response. As the model proved to be capable of returning to the reference state, it can potentially be used for control studies purposes.

In response to growing industrial demands, necessitated by larger ships and increased cargo volumes, many port authorities worldwide are undertaking a significant expansion of their infrastructure to increase their capacity. Significant expansion raises challenges such as potential congestion and environmental concerns, especially for port located in urban area as the case study explored in this paper. To address these issues, the study employs a combination of scenario analysis developed in collaboration with the port authority and logistic system simulation. This approach assesses the capacity of existing access gates to handle the increased trucking traffic and optimizes the unloading process of construction trucks at a new terminal. Key findings suggest that while the current gate capacities are adequate, optimizing unloading times at the new terminal is critical to prevent road congestion in urban area before the gate and reduce negative impacts such as sound and atmospheric pollution. The study advocates for an appointment system during peak hours to manage traffic flow effectively and underscores the importance of collaboration between the city and port authorities in managing truck traffic to mitigate congestion on the city side. Nonetheless, the study recognizes certain constraints, including the incomplete evaluation of the influence of intermodal transport on truck traffic. Furthermore, the study emphasizes the need for future research to consider the correlation between vehicle numbers and service levels, aiming to effectively adjust the time in the system.

The energy crisis is currently a major topic in scientific and managerial discussions in the building and construction industry. While employing low-cost strategies for reducing energy usage is beneficial for principals and decision-makers, the primary purpose of this study is to recommend the use of phase change materials (PCM) to improve cooling and heating load management in buildings. In order to accomplish this, the study applies design-builder simulations to a developing country case study. The results indicate that integrating PCM in buildings can reduce energy usage by around 6 percent. Although this value may be lower than other techniques, it is nevertheless important for reaching green building goals in both developed and developing nations. The case study, which takes place in a four-season region, demonstrates the significant potential of the PCM system for both cooling and heating energy management. In addition, the reciprocal performance improvement of PCMs is a remarkable characteristic, especially when contrasted to some conventional systems that focus on a single energy management objective in buildings. Lastly, it should be underlined that the suggested platform is consistent with Sustainable Development Goal 7 (Affordable and Clean Energy Targets).

The aim of this study is to present a method for developing fragility curves for soil liquefaction that are consistent with the seismic hazard using Monte Carlo simulation. This method can account for all the uncertainties and variabilities of the input parameters. The seismic parameters of earthquake magnitude (M) and the associated peak ground acceleration (PGA) are considered together for the liquefaction assessment. The liquefaction potential and damages obtained by this method are more realistic. A case study is performed using data from a sand-boil site in Yuanlin, Changhua county, where liquefaction occurred during the Chi-Chi earthquake in Taiwan in 1999. The results show that the liquefaction potential index, IL, the post-liquefaction settlement, St, and the liquefaction probability index, PW, are all suitable parameters for evaluating the liquefaction damages. The fragility curves for soil liquefaction developed by this method can support the performance-based earthquake engineering (PBEE) approach, provide guidance for liquefaction evaluation to the ‘Taiwan Earthquake Loss Estimation System’-TELES, and serve as a basis for scenario simulation and an earthquake early warning system for liquefaction damages.

In recent years, wireless power transfer (WPT) has progressed rapidly in both theory and commercialization abroad. However, existing research into WPT coil design for low-power devices is limited. This paper investigates coil structure using printed circuit board (PCB) for magnetic resonant coupling WPT to address this gap. Specifically, the impacts of key geometric parameters on the coil quality factor and coupling coefficient are analysed through models and simulations. Equivalent PCB coils were formed for mutual inductance models, and four basic compensation circuits were analysed. The impacts of changes in coil thickness, line width, turn spacing, and number of turns on the quality factor of PCB coils were analysed with a fixed outer diameter of the coil. In this paper, to mitigate the coil offset while retaining PCB manufacturing simplicity for practical uses, a dual layer PCB coil structure is proposed. The structure optimizated PCB coil could offer remarkable improvements in the WPT system for low power electronic devices.

As most of the current dynamic positioning systems are based on model ships, they cannot accu-rately reflect the motion state, position changes, and mutual influence of each part of the dynamic positioning system of actual ships in complex environments. Other actual ships such as cargo ships cannot add various sensors and auxiliary equipment to verify and analyze the positioning system. This article takes the intelligent research and training dual-use ship of Dalian Maritime University, which integrates scientific research and training, as the object of study. This ship will not be affected by the voyage period and route and can choose a suitable sea area for research. Therefore, in order to improve the accuracy and reliability of the dynamic positioning system, research on the ship’s dynamic positioning system was carried out. Firstly, an accurate mathematical model of ship motion was established, and the hydrodynamic derivatives of ship model motion were obtained by nu-merical calculation, and applied to the three-degree-of-freedom model of the intelligent research and training dual-use ship. Then, the model was used as part of the closed-loop simulation model of the ship’s dynamic positioning system, and the terminal sliding mode controller was used for sim-ulation and emulation, thereby obtaining ideal simulation test results. This research helps to im-prove the accuracy and reliability of the dynamic positioning system and provides new ideas and methods for ship motion control.

Hydrogen storage in high pressure tanks can occur with different filling strategies. Many studies have been carried out on supplies with increasing pressure rates. However, hydrogen filling with constant mass inflow rate have not received the same attention. The present work aims to carry out numerical simulations to study hydrogen flow, in a type 3 tank of 70 MPa NWP and constant inflow rate condition. From the analysis of the results, it was possible to verify that there is a linear relationship between the increase in the total equilibrium temperature and the final hydrogen temperature. There is also a linear increasing relationship between Inlet and final temperatures when the equilibrium temperature is fixed. Considering fully adiabatic walls results in significant increases in temperature. The difference between total and static temperatures found in the Inlet tube decreases with the decrease in mass flow rate. The choice of a polymer for the tank lining led to higher temperatures when compared to an aluminum lining.

Salmonella causes various type of disease worldwide with a remarkable pace. Salmonella enteric serotype typhi (S. typhi), a gram-negative bacterium (only cause disease in man) is the mainly causative agent of Typhoid fever. Typhoid fever is most common in poor and unprivileged developing countries of Asia and Africa. One of the major components of virulence factors produced during salmonella infection is Lipid A, which acts as a potent human immuno-modulator bacterial endotoxin. Regulation of Lipid A biosynthetic pathway occurs at second step, catalyzed by LpxC, a Zn2+ dependent metalloamidase. Systematic Screening of a pool of drug datasets like natural products library from Zinc database, Asinex database, Thiophene analogues fruitfully provided us 3 potent lead molecules s1_dl_mseq2, s1_ll_mseq2, and s2_ll_mseq8 which actively binds with LpxC enzyme and could be developed into sound inhibitors of LpxC enzyme after the application of drug development and processing strategies. Wet lab experimentation is required to validate these results for further use.

For both conventional and renewable energy conversion processes, computational fluid dynamics (CFD) has been used to address more energy-related challenges in recent decades. Using CFD to investigate vertical-axis wind turbines has become more common in recent years. The main goals of this application have been to more accurately predict the turbine's performance and to comprehend the complicated nature of the complex turbulent flow. The Vertical Axis Wind Turbine (VAWT) simulation for energy-generating applications has several intricate components. One of them is being aware of the chaotic flow that occurs during the first stages of the starting process, which influences overall effectiveness. In this article, the performance of the wind turbine was increased by using a passive flow control approach. The numerical research was carried out using Large Eddy Simulation for four alternative tip speed ratios in both cases, the classic and the optimized, equipped with a vortex trap on the exterior of the blades. The power and torque coefficient variations, as well as the velocity magnitude contours, show that the starting process may begin with a significant improvement in efficiency when flow control is used.

Optimal operation of petroleum production is important in a transition from energy systems based on fossil fuel to sustainable systems. One sub-process in petroleum production deals with transport from the (subsea) well-bore to a topside separator. A simple model in [] of Electric Submersible Pump [ESP] lifted production was previously streamlined into a dynamic model suitable for illustration of the dynamics of oil transport, as well for control studies, with some comparison of two popular modeling languages: Modelica, and ModelingToolkit for Julia, []. Here, the discussion on dimensionless equipment models goes into more detail, and the comparison between Modelica and ModelingToolkit is significantly expanded upon with code comparison, numerical performance, and more experiments. Some added possibilities with ModelingToolkit and Julia wrt. sensitivity analysis and control design is included.

Industrial Control Systems (ICS) are integral to managing and optimizing processes in various industries, including manufacturing, power generation, and more. However, the scarcity of widely adopted ICS datasets hampers research efforts in areas like optimization and security. This scarcity arises due to the substantial cost and technical expertise required to create physical ICS environments. In response to these challenges, this paper presents a groundbreaking approach to generating synthetic ICS data through a data-driven ICS network simulation. We circumvent the need for expensive hardware by recreating the entire ICS environment in software. Moreover, rather than manually replicating the control logic of ICS components, we leverage existing data to autonomously generate control logic. The core of our method involves the stochastic setting of setpoints, which introduces randomness into the generated data. Setpoints serve as target values for controlling the operation of the ICS process. This approach enables us to augment existing ICS datasets and cater to the data requirements of machine learning-based ICS intrusion detection systems and other data-driven applications. Our simulated ICS environment employs virtualized containers to mimic the behavior of real-world PLCs and SCADA systems, while control logic is deduced from publicly available ICS datasets. Setpoints are generated probabilistically to ensure data diversity. Experimental results validate the fidelity of our synthetic data, emphasizing its ability to closely replicate temporal and statistical characteristics of real-world ICS networks. In conclusion, this innovative data-driven ICS network simulation offers a cost-effective and scalable solution for generating synthetic ICS data. It empowers researchers in the field of ICS optimization and security with diverse, realistic datasets, furthering advancements in this critical domain. Future work may involve refining the simulation model and exploring additional applications for synthetic ICS data.

A systematic investigation of the dynamic clustering behavior of active particles under confinement, including the effects of both particle density and active driving force is presented based on a hybrid coarse-grained molecular dynamics simulation. First, a series of scaling laws were derived with power relationships for the dynamic clustering time as a function of both particle density and active driving force. Notably, the average number of clusters N ̅ assembled from active particles in the simulation system exhibits a scaling relationship with clustering time t described by N ̅∝t^(-m). Simultaneously, the scaling behavior of the average cluster size S ̅ is characterized by S ̅∝t^m. Our findings reveals the presence of up to four distinct dynamic regions concerning clustering over time, with transitions contingent upon the particle density within the system. Furthermore, as the active driving force increases, the aggregation behavior also accelerates, while an increase in density of active particles induce alterations in the dynamic procession of the system.

This paper introduces an innovative framework for the integration of artificial empathy into robot swarms to enhance communication and cooperation. The proposed model relies on fuzzy state vectors to represent the knowledge and environment of individual agents, accommodating uncertainties in the real world. Utilizing similarity measures, the model compares states, enabling empathetic reasoning for synchronized swarm behavior. A practical application example demonstrates the model’s efficacy in a robot swarm striving for a common goal. The evaluation methodology involves the Open Source Physical-based Experimentation Platform (OPEP), emphasizing empirical validation in real-world scenarios. The paper proposes a transitional environment that allows automated and repeatable execution of experiments on a swarm of robots using physical devices.

Compressed gas storage of hydrogen has emerged as the preferred choice for fuel cell vehicle man- ufacturers, as well as for various applications like road transport, and aviation. However, designers face increasing challenges in designing safe and efficient composite overwrapped pressure vessels (COPVs) for hydrogen storage. One challenge lies in the development of precise software that con- sider a multitude of factors associated with the filament winding process. These factors include layer thickness, stacking sequence, and the development of particularly robust models for the dome region. Another challenge is the formulation of predictive behavior and failure models to ensure that COPVs have optimal structural integrity. The present study offers an exploration of numerical methods used in modeling COPVs, aiming to enhance our understanding of their performance characteristics. The methods examined include finite element analysis in Abaqus, involving conventional shell elements, continuum shell elements, three-dimensional solid elements, and specialized homogenization tech- niques for multilayered composite pressure vessels. Through rigorous comparisons with type-III pressure vessels from the literature, the research highlights the most suitable choice for simulating COPVs and their practicality. Finally, we propose a new design for type-IV hydrogen composite pressure vessels using one explored method, paving the way for future developments in this critical field.

Digital twins are gaining in popularity for simulating complex natural and urban environments. In this context, accurate segmentation of objects within 3D urban environments is of crucial importance. The aim of this project is to develop a methodology for extracting buildings from textured 3D meshes. To this end, PicassoNet-II, a semantic segmentation architecture is employed. The methodology also incorporates Markov field-based contextual analysis to assess post-segmentation features. In addition, building instantiation is performed using cluster analysis algorithms. Training this model to fit various datasets requires a large amount of annotated data, both from Quebec City, Canada, and from simulated data. Experimental results show that the use of simulated data improves segmentation accuracy, and the DBScan algorithm proves effective in extracting isolated buildings. This project paves the way for improved applications in 3D urban modeling, offering opportunities in fields ranging from urban planning to resource management. The positive results with simulated data reinforce the impact of this research on improving digital models of our ever-changing urban environments.

The real-time Railway Traffic Management Problem (rtRTMP) is discussed in the field of railway transportation. In this paper, rtRTMP is considered for planning shunting routes based on the use of routing methods on a spatial-temporal graph. An approach and algorithm of Spatial-Temporal Graph formation for rail yard transportation network is proposed. The idea is to construct an undirected loop-free Double Vertex Graph for determining optimal shunting routes consisting of several half-runs for several trains shunting under draw-out tracks capacity constraints. Experimental results on the rail yard simulation model are presented.

An algorithm is presented for a micro-model of a mining dump truck movement, which allows determining the optimal speed and acceleration of a vehicle, considering the change of the road grade. The structure of the simulation macro-model of an excavator-and-dump truck complex has been developed. The procedure for integrating a micro-model of a dump truck movement into a simulation model is shown. As a result of a series of experiments with a macro-model, the influence of open pit roads grade on the performance and efficiency of excavator-and-dump truck complexes was established. The necessity of considering in simulation excavator-and-dump truck complexes the speed regulation of dump trucks depending on the change in road grade is shown.

The problem of intensification of melt crystal growth process has been analyzed using CdTe as an actual material. Numerical simulation of 100 mm diameter CdTe crystal growth by VGF technique has been carried out. The heat-mass transfer was controlled by introducing of low-frequency oscillating baffle into the melt, so called axial vibrational control (AVC) technique. The baffle configuration has been optimized to destroy solid «tails», which were formed near the crucible walls at high cooling rates due to the low thermoconductivity and the corresponding latent heat. Analysis of CdTe homogeneity range showed that during fast crystal cooling Te micro precipitations were formed resulting from decay of oversaturated Cd-riched nonstoichiometric solid solution at Bridgman crystal growth technique. A VGF grown CdTe crystal after full crystallization stays inside the phase field of the high temperature wurtzite polymorph. This makes it possible to go through the polymorph transition without Te micro-precipitating using the advantages of VGF specific feature of very slow cooling.

Using metaheuristics, such as the Evolutionary Algorithm (EA), within control structures is a realistic approach for certain optimal control problems. Their role is often predicting the optimal control values over a prediction horizon using a process model (PM). The big computational effort sometimes causes problems. Our work addresses a new issue: whether a machine learning (ML) algorithm could “learn” the optimal behaviour of the couple (EA, PM). A positive answer is given by proposing datasets apprehending this couple's optimal behaviour and appropriate ML models. Following a design procedure, a number of closed-loop simulations will provide the sequences of optimal control and state values, which are collected and aggregated in a data structure. For each sampling period, datasets are extracted from the aggregated data. The ML algorithm experiencing these datasets will produce a set of regression functions. Replacing the EA predictor with the ML model, new simulations are carried out, proving that the state evolution is almost identical. The execution time decreases drastically because the PM’s numerical integrations are totally avoided. The performance index equals the best-known value. In different case studies, the ML models succeeded in capturing the optimal behaviour of the couple (EA, PM) and yielded efficient controllers.

The concrete sector is known for its significant contribution to the CO2 emissions. There are two main contributing factors for this situation: the large amount of concrete consumed per year on the planet and the high CO2 released from Portland cement manufacture, the key binding agent in concrete. To face the consequent sustainability issues, diverse strategies have been explored on the carbon capture and storage potential of cementitious materials. This paper addresses the potential of storing CO2 in concrete during the curing stage, applied to the precast Portuguese industry. To this purpose, it was assumed that CO2 will become a waste that will require an outlet in the future, considering that carbon capture will become mandatory in many industries. This work concluded that the net benefit in terms of carbon retention is positive for the process of storing carbon in concrete during the curing stage. More specifically, it was demonstrated that the additional emissions from the introduction of this new operation are only 10% of the stored amount, returning a storage potential of 76 000 tonnes of CO2 yearly. Moreover, the overall net reduction in the concrete life cycle averages 9.4% and 8.8% for precast elements and only non-structural elements, respectively. When a low cement dosage strategy is coupled with carbonation curing technology, the overall carbon net reduction is estimated to be 45%.

The continuous casting tundish is non-isothermal due to heat losses and temperature variation from the inlet stream generating relevant convection forces. This condition is commonly avoided through qualitative fluid dynamic analysis only. This work searches to establish the conditions for which non-isothermal simulations are mandatory or for which isothermal simulations are enough to accurately describe the fluid dynamics inside the tundish by quantifying the buoyant and inertial forces. The mathematical model, simulated by CFD software, considers the Navier-Stokes equations, the realizable k- model for solving the turbulence, and the Lagrangian discrete face to track the inclusion trajectories. The results show that temperature does not significantly impact the volume fraction percentages or the mean residence time results, nevertheless, bigger velocity magnitudes under non-isothermal conditions than in isothermal conditions and noticeable changes in the fluid dynamics between isothermal and non-isothermal cases in all the zones where buoyancy force dominates over inertial forces were observed. Because of the results, it is concluded that isothermal simulations can accurately describe the flow behavior in tundishes when the flow control devices control the fluid dynamic, but the simulation without control devices or with a weak fluid dynamic dependence on the control devices requires non-isothermal simulations.

This study investigated riverbank protection strategies along the dynamic Jamuna River in Bangladesh, a braided river prone to erosion and sedimentation. It reviews past countermeasures and emphasizes the effectiveness of groyne-type structures in redirecting flow and preventing erosion. Notably, the left bank exhibited greater stability than the right bank, emphasizing the need for effective groyne installations. A systematic methodology utilizing numerical modeling using International River Interface Cooperation (iRIC Nays2DH) ensured accuracy in assessing morphological impacts. This study presents novel countermeasures incorporating groyne installations in Zone 1 (from Singrabari to Meghai) and Zone 2 (from Pachthakuri to Subhagacga), accompanied by simulations for various flood conditions. Zone 2, identified as highly erosion-vulnerable, showed the greatest susceptibility. Simulations (Cases_0 1, 2, and 3) revealed groynes in Zone 1 alone ineffective, while Zone 2 installations successfully diverted oblique flow, reducing erosion risk. Although Case_3's combined approach was effective, it was uneconomical for Zone 1. Case_2, focusing on Zone 2 groynes, emerged as the optimal solution, redirecting flow and shaping sandbars to mitigate Jamuna River's riverbank erosion. It provides a practical and economical solution. Further research is recommended for groyne length and spacing variations. This study enhances Jamuna River protection, emphasizing groyne-type structures' importance and promoting a holistic understanding of morphological dynamics for future river management and effective countermeasures.

An approach based on the OpenFOAM library has been developed to solve a high-speed, multicomponent mixture of a reacting, compressible flow. This work presents comprehensive validation of the newly developed solver called compressibleCentralReactingFoam with different supersonic flows, including shocks, expansion waves, and turbulence-combustion interaction. The comparisons of the simulation results with experimental and computational data confirm the fidelity of this solver for problems involving multicomponent high-speed reactive flows. The gas dynamics of turbulence-chemistry interaction are modeled using a partially-stirred reactor formulation and provide promising results to better understand the complex physics involved in supersonic combustors. A time-scale analysis based on local Damköhler numbers reveals different regimes of turbulent combustion. In the core of the jet flow, the Damköhler number is relatively high indicating that the reaction time scale is smaller than the turbulent mixing time scale. This means that the combustion is controlled by turbulent mixing. In the shear layer, where the heat release rate and the scalar dissipation rate have the highest value, the flame is stabilized due to finite rate chemistry with small Damköhler numbers and a high fraction of fine structure. This solver allows three-dimensional gas dynamic simulation of high-speed multicomponent reactive flows relevant to practical combustion applications.

Thermostatically controlled loads have great potential to make a significant contribution to improving energy efficiency in the building sector, which is responsible for 40% of greenhouse gas emissions in the EU. This, in addition to the environmental damage, represents a huge expense in the electricity bill. Therefore, it is very important to train engineers on how to design energy management systems for TCLs. With this goal in mind, it would be very useful to have a simulation-based educational tool (SBET) to understand thermostatically controlled loads, their characteristics and possibilities in energy efficiency. In addition, it would be very useful if this tool could be introduced in engineering curricula to help students to be better trained and enter the labor market with more opportunities. Based on the shortcomings detected, this work develops a SBET specifically designed to teach about TCLs (SBET-TCLs), both about their intrinsic characteristics and their better management. To verify the developed SBET-TCL, it has been tested in a real scenario: A survey was carried out among the students of the subject 'Alternative Energy Sources' of the degrees of Industrial Engineering. The results show that the use of SBET-TCL has very positive effects on the learning process.

This work aims at improving surface integrity and LCF life of machined Inconel 718. The correlation between the LCF life of Inconel 718 and various states of machined surface integrity is explored. In this paper, the surface integrity of Inconel 718 specimens is enhanced by low plasticity burnishing (LPB). The LCF life with different surface integrity specimens is predicted by micro-macro finite element method (FEM). Firstly, the LCF specimens with different surface integrity are machined by turning (turned specimen), polishing (matrix specimen) and LPB process (LPBed specimen). Secondly, the LCF experiment is carried out to reveal the effect of surface integrity on LCF life. Finally, the LCF micro-macro FEM model is proposed to predict the LCF of machined Inconel 718 specimens. The representative volume element (RVE) model is established based on the measured surface integrity and microstructure of Inconel 718 specimens. The effect of surface integrity on LCF life is transformed to equivalent load. The micro-macro FEM model combined with Tanaka-Mura dislocation crack initiation theory and extended finite element method (XFEM) is applied to predict the LCF life the machined specimens. The study results show that the LCF life of LPBed specimens can be improved by 90.5% and 36.1% compared with that of turned specimens and polished matrix specimens, respectively. The errors between FEM prediction results and experimental results are 13.1%, 9.2% and 12.2%, respectively. The proposed micro-macro FEM model could be utilized to predict the LCF life of Inconel 718 with different surface integrity, and further to apply the LCF life prediction in industry.

This paper proposes a metaphysical framework for distinguishing between human and machine intelligence. By drawing an analogy from Kant’s incongruent counterparts, it posits two identical deterministic worlds -- one comprising a human agent and the other comprising a machine agent. These agents exhibit different types of information processing mechanisms despite their apparent sameness in a causal sense. By postulating the distinctiveness of human over machine intelligence, this paper resolves what it refers to as “the vantage point problem” – namely, how to legitimize a determinist’s assertion of determinism by placing the determinist within the universe.

The research on the impact of excavation of underpass tunnels has already had certain results, but there is a lack of research cases of double-line highway tunnels with oblique angles underpass the existing railway tunnels, especially the method of determining the area of the impact of the dynamic and static loads of the new tunnels and existing trains to be enriched by considering them at the same time. In this paper, based on the tunnel project of the new double-line Shiqian Highway Tunnel with oblique angles underpass the Hurong Railway in Wanshoushan. By constructing a three-dimensional finite element model and simulating the application of static tunnel excavation load, dynamic blasting load and dynamic train operation load of the existing tunnel, the overall displacement of the existing tunnel and the settlement value of the bottom plate of the track surface by the static tunnel excavation load were analyzed. Then, the stresses, vibration speeds and displacements of the tunnel due to the dynamic blasting loads and the dynamic train operation loads were obtained. The results show that: the area of influence of the static loads of the new tunnel excavation on the existing tunnel is divided into three types of perturbations presenting strong, weak and slight; the area affected by the blasting dynamic load of new tunnel excavation is a circular domain with the datum point in the tunnel section as the center; the area affected by the dynamic load of train operation in the existing tunnel is an ellipse with the center of the track surface of the existing tunnel as the datum; there is an anomalous shape in the area affected by the blasting dynamic load of new tunnel excavation and the area affected by the dynamic load of train operation in the existing tunnel.

The crosslinking behavior of polyethylene (PE) determines its exceptional performance and application. In this study, we investigated the crosslinking behaviors of different PE resins through model simulation and rheological methods. Specifically, the mathematical equation of "S" model was established for PE resin. According to this equation, the optimal maximum gel content for high-density polyethylene (HDPE) was found to be around 85%. Moreover, the maximum crosslinking degrees for different PE resins depended largely on their density and molecular weight. The melt viscosities before crosslinking in PE resins were highly influenced by their melt index. The higher melt indexes resulted in the lower storage moduli, improving melt processability during processing. In addition, the crosslinking rates of PE resins were strongly influenced by peroxide concentration, independent of PE resin structures. For high molecular weight and low-density PE resins, they exhibited decreased ti values, increased A0 values, and decreased k6 values. However, there were no noticeable variations in the values of k2 and phi among different PE resins. And all simulated modeling outcomes showed the remarkable consistency with the experimental rheological data. These findings are of utmost significance in the industrial manufacture of PE resin.

In this study, we focused on the eco-driving of electric vehicles (EVs). The target vehicle is an electric bus developed by our research team. Using the parameters of the bus and speed pattern optimization algorithm, we derived the EV eco-driving speed pattern. Compared to eco-driving of internal combustion engine vehicles (ICVs), we found several different characteristics. We verified these characteristics with actual vehicle driving test data of the target bus, and the results confirmed its rationality. The EV eco-driving method can improve electricity consumption by about 10% - 20% under the same average speed.

To investigate the variation patterns of spectral characteristics of main branches in fruit trees due to uneven distribution of growth, pruning, and fruit quality, as well as to determine the optimal fruit vi-bration harvesting, a vibration differential model of tree branches was constructed using the concen-trated mass method. A three-dimensional model of the fruit tree was built using finite element software. The spectral characteristics of each branch were obtained using the linear sweep frequency method. The method of changing the mass of each branch was employed to simulate the variation in branch mass. The frequency of occurrence, mean acceleration, and coefficient of variation of the inherent frequencies were used as evaluation indicators. The results revealed that when the vibration frequency of the fruit tree was at the peak frequency in the tree trunk spectrum, the frequency of occurrence and mean accel-eration were optimal. And the coefficient of variation of branch acceleration was less than 1, indicating a moderate dispersion. Additionally, reducing the branch mass resulted in an increase in the peak fre-quency of the tree trunk spectrum. Therefore, the growth, pruning, and uneven distribution of the branches in fruit trees can alter the spectral characteristics. When harvesting fruit by vibration, selecting the peak frequency in the trunk vibration spectrum as the excitation force frequency yields the best fruit detachment.

In this paper we investigate in different scenarios the feasibility of using massive multiple-input multiple-output (mMIMO) with Reconfigurable Intelligent Surfaces (RISs) to boost the throughput and coverage performances with high energy efficiency, considering sub-6 GHz, mmWave and THz bands. With that objective, a centralized radio access network (C-RAN) suitable for beyond fifth generation (B5G) systems is considered, where we integrate the base stations (BSs) with multiple RISs and devices or user equipment. RISs with a large number of quasi-passive reflecting elements constitute a low-cost approach capable of shaping the radio wave propagation and improve wireless connectivity. We consider a scenario where multiple RISs are combined with mMIMO in the uplink in order to provide connectivity to Smart City (thousands of active low power devices) wirelessly, in the 3.6GHz and 28GHz bands. We also address a scenario where RISs are adopted with mMIMO in the downlink so as to offer connectivity to a Stadium with Pitch, (thousands of active user equipment) in the 28GHz band. Finally, we have also studied the connectivity at 100GHz of a Factory where several RIS panels replacing most of the BSs equipped with mMIMO assure improved throughput and coverage. We concluded that RISs are capable of improving the performance in any of these analyzed scenarios at the different frequency bands, justifying that they are a key enabling technology for 6G.

In several emergency situations, during natural or human-caused disasters, frontline responders need to be able to communicate and collaborate to properly carry out the relief mission. Some countries are building their national Public Safety Mobile Broadband based on cellular LTE technology to provide fast, safe, and secure emergency services. Otherwise, in several emergency situations, the cellular antennas can be overloaded or partially damaged in manner to affect the group communication services. Since few last years, direct device-to-device (D2D) communications have been proposed by 3GPP as an underlay to Long Term Evolution (LTE) networks based on proximity, reuse, and hop gains. This paper focuses on the loosely coupled model based on D2D direct communication for Public Safety context. Many scenarios related to user membership and network management are detailed. Both “less cost” and “optimized tree” approaches are proposed, implemented, and their performance evaluated in terms of network updates number and resulting average Channel Quality Indicator (CQI). Other optimization approaches with different CQI threshold and optimization interval values have been implemented, simulated and their performance are compared with the “optimized tree”.

The effects of the coolant pulsation and the film cooling with the plasma aerodynamic actuation (PAA) could be herein explored via large eddy simulations. The electrohydrodynamic force derived from the PAA was solved through the phenomenological plasma model. The Strouhal number of the sinusoidal coolant pulsation and the averaged pulsation blowing ratio were 0.25 and 1.0, respectively. Comprehensive analyses were carried out on the time-averaged flow fields, and the results revealed that the pulsed cooling jet might cause a deeper penetration into the crossflow, and this phenomenon could be remarkably mitigated by the downward force of the PAA. Furthermore, because of the coolant pulsation, the counter rotating vortex pair (CRVP) expanded to lifted off the wall less well, and the PAA lessened the negative lift-off impact and entrapment from the CRVP. Then, the spatial-temporal development of the coherent structures was figured out by the alterations of the centerline temperature, reflecting formation of the intermittent coherent structures rather than hairpin vortices due to the coolant pulsation, and their size and upcast behaviors were reduced by the PAA. Consequently, there was a basic suppression of the turbulent mixing of the cooling fluid with the crossflow. Finally, the three-dimensional streamlines confirmed that the coherent structure dynamic behaviors were significantly regulated by the PAA for alleviating the adverse influences of the coolant pulsation. In summary, through controlling how the dominating coherent structures evolve throughout time and space, the PAA can effectively increase the pulsed film cooling efficiency.

Fatigue fractures in materials are the main cause of approximately 80% of all material failures, and it is believed that such failures can be predicted and mathematically calculated in a reliable manner. It is possible to establish prediction modalities in cases of fatigue fracture, according to three fundamental variables in fatigue, such as volume, number of fracture cycles, as well as applied stress, with the integration of Weibull constants (length characteristic). This investigation was carried out mechanical fatigue tests on specimens smaller than 4 mm2 in section of different industrial materials for their subsequent analysis through precision computed tomography in search of microfractures. The measurement of these microfractures, along with their metrics and classifications, was recorded. A convolutional neural network trained with deep learning was used to achieve the detection of microfractures in image processing. The detection of microfractures in images with 480x854 or 960x960 pixels is the primary objective of this network, and its accuracy is above 95%. Images that have microfractures and those that do not are classified by the network. Subsequently, by means of image processing, the microfracture is isolated. Finally, the images that do contain this feature are interpreted by image processing to obtain their area, perimeter, characteristic length, circularity, orientation, and type microfracture metrics. All values will be obtained in pixels and converted to metric units (μm) through a conversion factor based on image resolution.

The first paper of this two-part series paper discussed the development of a novel lightweight 3D wood dowel-reinforced glass epoxy hybrid composite material (3DdrFRP) and its manufacturing procedures. It also experimentally compared the performance of scaled utility poles made from conventional 2D E-glass epoxy and 3DdrFRP materials. In the second part, we developed robust finite element (FE) models in LS-DYNA, calibrated them based on experimental results, and used them to simulate the performance of equivalent 2D and 3D poles, proving the integrity of the numerical model. Additionally, we developed a simplified analytical calculation method for engi-neers to determine the stiffness of 3D-DrFRP poles accurately and quickly.

The CMT (Cold Metal Transfer) arc welding process is a convenient instrument for the assembly of thin sheets [1], [2]. More particularly, it permits the decrease of the amount of heat transferred to the parts to-be assembled, thus reducing their subsequent deformations. The present work aims at simulating the CMT welding of thin stainless-steel sheets in order to develop an approach that allows the prediction of temperature fields, as well as final deformations induced by the welding process. For that purpose, instrumented tests and numerical simulations were set aiming at comparing the experiments with simulations. Butt-welding of stainless-steel sheets was considered, with a thickness ranging from 1 to 1.2 mm. The weld seam samples were observed in order to set an equivalent heat source for each configuration. Moreover, the electric current and voltage were surveyed, and temperature measurements were performed using K-type thermocouples. In addition, displacement measurements were obtained using the DIC (Digital Image Correlation) technique. Thermomechanical simulations were then carried out using an equivalent heat source approach, considering the phase changes of elements, i.e. the transitions from solid to liquid and from liquid to solid. These simulations also consider the actual geometry of seams induced by addition of a filler material.

Eruca sativa is a commonly used edible plant in Italian cuisine. E. sativa 70% ethanol extract (ES) was fractionated with five organic solvents. Ethyl acetate fraction(EEA) had the highest antioxidant activity, which was correlated with the total polyphenol and flavonoid content. ES and EEA acted as PPAR-α ligands by PPAR-α competitive binding assay. EEA significantly increased cornified envelope formation as a keratinocyte terminal differentiation marker in HaCaT cells. Further, it significantly reduced nitric oxide and pro-inflammatory cytokines in lipopolysaccharide-stimulated RAW 264.7 cells. The main flavonol forms detected in high amounts from EEA are mono-, and di-glycoside of each aglycone. Flavonol mono-glycosides were shown to be a potent PPAR-α ligand using molecular docking simulation and showed the inhibition of nitric oxide. These results suggest that E. sativa is suitable for use in improving skin barrier function and inflammation in skin disorders, such as atopic dermatitis.

The main issue in this work is to study limit functions necessary for the reliability assessment of the structural steel with the use of the relative entropy apparatus. This would be done using a few different mathematical theories relevant to this relative entropy, namely those proposed by Bhattacharyya, Kullback-Leibler, Jeffreys, and Hellinger. Probabilistic analysis in the presence of uncertainty in material characteristics would be delivered using three different numerical strategies – Monte-Carlo simulation, stochastic perturbation method as well as the semi-analytical approach. All these methods are based on the Weighted Least Squares Method approximations of the structural response functions versus the given uncertainty source and they would allow efficient determination of the first two probabilistic moments of the structural responses including stresses, displacements, and strains. The entire computational implementation would be delivered using the Finite Element Method system ABAQUS and computer algebra program MAPLE, where relative entropies, as well as polynomial response functions, would be determined. This study would demonstrate that the relative entropies may be efficiently used in reliability assessment close to the widely engaged First Order Reliability Method (FORM).

Bifacial PV modules have become the pervasive choice for utility-scale solar photovoltaic (PV) installations. Understanding bifacial PV performance for commercial deployments is, therefore, critically important. Although there have been studies on the modeling of Bifacial PV performance, to the best of our knowledge, the comprehensive integration of racking and mounting geometry for Bifacial PV performance simulation is yet to be reported. In this work, we modeled the racking and mounting geometry for a field-deployed operational PV array. We then applied optical ray-trace simulation to study the impact of those structures on array performance. The study reveals that the support structures have a considerable impact on the rear surface irradiance and overall bifacial PV performance, amounting to as much as 30% shading in certain situations. The work analyzes the impact of installation parameters such as tilt, height, and row spacing on the bifacial PV array’s performance. Thus, a novel holistic approach to studying the sensitivity of various installation parameters’ impact on bifacial PV array performance, with the racking and mounting structure’s geometry integrated, has been demonstrated through the work. This knowledge and its practice enables site-specific design optimization for utility-scale bifacial solar PV arrays and synergistic PV applications such as agrivoltaics and carports, where maximizing power generation is not necessarily the sole objective.

Recently, the Third Generation Partnership Project (3GPP) introduced the New Radio (NR) technology for Vehicle-to-everything (V2X) communication to enable delay-sensitive and bandwidth-hungry applications in vehicular communication. The NR system is strategically crafted to complement the existing Long Term Evolution (LTE) Cellular-Vehicle-to-Everything (C-V2X) infrastructure, particularly to support advanced services such as the operation of automated vehicles. It is widely anticipated that the 5th generation (5G) NR system will surpass LTE C-V2X in terms of achieving superior performance in scenarios characterized by high throughput, low latency, and enhanced reliability, especially in the context of congested traffic conditions and a diverse range of vehicular applications. This article will provide a comprehensive literature review on vehicular communications from Dedicated Short Range Communication (DSRC) to NR V2X. Subsequently, it delves into a detailed examination of the challenges and opportunities inherent in NR V2X technology. Finally, we proceed to elucidate the process of creating and analyzing an open-source 5G NR V2X module in network simulation-3 (ns-3) and then demonstrate the NR V2X performance in terms of different key performance indicators implemented through diverse operational scenarios.

Vibration monitoring is a critical aspect of assessing the health and performance of machinery and industrial processes. This study explores the application of machine learning techniques, specifically the Random Forest (RF) classification model, to predict and classify chatter—a detrimental self-excited vibration phenomenon—during machining operations. While sophisticated methods have been employed to address chatter, this research investigates the efficacy of a simplified RF model. The study leverages simulated vibration data, bypassing resource-intensive real-world data collection, to develop a versatile chatter detection model applicable across diverse machining configurations. The feature extraction process combines time-series features and Fast Fourier Transform (FFT) data features, streamlining the model while addressing challenges posed by feature selection. By focusing on the RF model’s simplicity and efficiency, this research advances chatter detection techniques, offering a practical tool with improved generalizability, computational efficiency, and ease of interpretation. The study demonstrates that innovation can reside in simplicity, opening avenues for wider applicability and accelerated progress in the machining industry.

Intelligent ship technology is currently an international research hotspot, and model predictive control is widely used in the path-tracking control of intelligent vehicles. To construct an intelligent ship anti-disturbance path-tracking control method, firstly, an environmental disturbance model was constructed with the actual meteorological data of the target sea area. Secondly, the Fossen ship equation of motion is linearized and discretized as the ship motion model. Thirdly, the expression of the prediction equation is derived from the ship motion model. Fourthly, the cost function is constructed by using the polar diameter and polar angle values of the ship. Fifth, the power function in the cost function is replaced with an exponential function to obtain an improved cost function. Sixthly, according to the Lyapunov theory and the MPC terminal constraint theory, the stability of the improved cost function is verified. Seventh, different test paths are set up, the environmental disturbance model is taken as the external disturbance, the ship motion model, the prediction equation, and the improved cost function are used to design the anti-disturbance path-tracking control algorithm according to the model prediction control idea for simulation experiments. Finally, different MATLAB simulation results show that the improved cost function can resist disturbance of the external wave, current, and wind, and effectively track the target path. Therefore, this study provides a reference for improving the navigation safety of ship path-tracking.

The accuracy of star position simulation is the key factor affecting the accuracy of ground calibration test of spacecraft attitude measurement system. To solve the impact of nonimaging polarized stray light on the simulation accuracy of star position in the optical engine of the system, the generation mechanism of non-imaging polarized stray light has been investigated, and an optimization method for polarization balance of the optical engine is proposed. The mathematical model of the incident light aperture angle and the ideal polarization parameter of the optical engine thin film is established, and the optimization evaluation function of polarization balance of the optical engine is constructed. An optical engine polarization balance film with the corresponding full-band binomial attenuation is <0.001 and the phase delay is <1°. Experimental results show that the simulation accuracy of the weak starlight simulation system is improved by 1.64 times using polarization balanced optical engine.

It is a critical issue to allocate redundancy to critical smart grid infrastructure for disaster recovery planning. In this study, we present a framework to combine statistical prediction methods and optimization models for the optimal redundancy allocation problem. First, we develop statistical simulation methods to identify critical nodes of very large-scale smart grid infrastructure based on the topological features of embedding networks, and then present a linear integer programming model based on generalized assignment problem (GAP) for redundancy allocation of critical nodes in smart grid infrastructure. The model is specifically implemented in the context of smart grid infrastructure. The findings demonstrate that the combined approach of statistical simulation and optimization effectively addresses the size limitations inherent in a sole optimization approach. Notably, the optimal solutions for redundancy allocation in very large grid systems highlight that the cost of redundancy is only a fraction of the economic losses incurred due to weather-related outages.

This study investigates the effect of Re addition on the microstructure evolution and oxidation resistance of Co-Cr-Ta-Ti-C alloys. The alloys tested in this study consist of a Co-rich matrix with mixed HCP and FCC phases, together with TaTiC2 and Ti-based carbides. The addition of Re promotes the precipitation and growth of a CoCr-rich σ phase. Differential thermal analysis (DTA) showed that the melting temperatures did not change drastically up to 10 at% Re addition, whereas 15 at% Re addition significantly increased the melting temperature of the alloys. Oxidation tests in air at 1200°C showed that excessive Re content could lead to deterioration in oxidation resistance, mainly due to the accelerated formation of volatile Re oxides.

This research presents a comprehensive computational analysis of the two-phase flow dynamics within a three-dimensional porous transport layer (PTL) in a proton exchange membrane (PEM) electrolyzer. Employing advanced computational fluid dynamics (CFD) and a volume of fluid (VOF) approach, the study utilizes the finite-volume method to model a time-dependent, isothermal process within a laminar flow regime. Notably, the study treats oxygen as the dispersed phase and water as the continuous phase within the system. Investigations on the anode side uncovered the formation of gas bubbles on the electrode's surface subsequent to the electrochemical reaction. The simulation, spanning 5 seconds with 0.25-second intervals, highlights the critical role of the time interval between the initiation and 2 seconds in achieving pressure and velocity equilibrium within the PTL. The initial 0.75 seconds witnessed a peak in oxygen concentration, followed by its movement across the PTL, exhibiting a transition from regions of lower to higher concentration, consistent with known physical behaviour. Remarkably, the model recorded the highest pressure of 3.5 Pa at 0.25 s. Furthermore, the study observed an incremental pressure drop from 0.25 s to 0.5 s and 1 s, approximately amounting to 28.5% and 20%, respectively, ultimately leading to a uniform pressure distribution within the model. Over time, the pressure drop intensified across the entire model due to the evolving nature of the oxygen gas. These findings provide valuable insights into the complex dynamics of two-phase flow processes within PEM electrolyzers, contributing to the advancement of sustainable energy technologies.

In this paper, four lithological parameters, i.e. thermal conductivity, particle size, porosity and saturation, are investigated by combining experimental observations with numerical simulations to study the influence laws and mechanisms of action on the combustion process of methane hydrate sediments. The variation of combustion characteristics parameters such as flame height, effective combustion ratio and dimensionless discharge water mass with the lithological parameters are studied. In addition, the combustion mechanism of lithologic parameters on methane hydrate deposits is revealed. Combining the experimental results and simulation calculations, to optimize the combustion of methane hydrate sediments, it is recommended to use methane hydrate sediment samples with high saturation and low thermal conductivity, while the oxidant concentration and porosity of methane hydrate sediment samples should be increased.

The pneumatic muscle is an actuator well known for its inherited compliance. In the last years, there have been a lot of applications of these actuators, mainly oriented toward medical devices. However, there are few papers that use muscle as a model associated with mechanical structures. These actuators are difficult to control because their stiffness depends on the applied pressure, determining a non-linear behavior. This article presents a model, created in Matlab, considering the muscle as a kinematic structure. The model is simulated as an individual structure and a PID controller is applied to test the response. Then, these results are validated by experimental tests done in the laboratory on different types of muscles. Comparison results validate the accuracy of the model.

Folk residence in the cold region of Northeast China is an important part of China's traditional folk residence, with significant geographical representation. In this paper, we take the residential compounds of villages and towns in the cold region of Northeast China as the research object, summarize four typical layout forms of residential compounds of villages and towns in the cold region of Northeast China, simulate the wind environment of compounds of different layout forms in winter by using the computer numerical simulation software Fluent, and then analyze and evaluate the wind environment of compounds by combining with the evaluation standard of wind speed ratio and considering the area of wind shadows, number of vortices and other factors.

Lateral vehicle control is a high importance in automated vehicles as it directly influences the vehicle’s performance and safety during operation. The Linear Quadratic Regulator (LQR) controller stands out due to its high-performance characteristics and is used in the open source for self driving functions. However, a notable limitation of the current approach is the manual calibration of LQR controllers based on the experience and intuition of the designers, leading to empirical uncertainties. To address this issue and enhance the lateral control performance, this paper concentrates on refining the LQR by employing three optimization algorithms: Artificial Bee Colony Optimization (ABC), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). These algorithms aim to overcome the reliance on empirical methods and enable a data-driven approach to LQR calibration. By comparing the outcomes of these optimization algorithms to the manual LQR controller within an offline multibody simulation as testing platform, the study highlights the superiority of the best-performing optimization approach. Following this, the optimal algorithm is implemented on a real-time system for the full vehicle level, revealing the Model-in-the-Loop and the Hardware-in-the-Loop gap up to 78,89% with lateral velocity when we use Relative Error Criterion (REC) method to validate and 2.35m with lateral displacement when considering by maximum absolute value method.

In this study, the generality and prediction accuracy of a generalised series model for large eddy simulation of premixed and non-premixed turbulent combustion is explored. The model is based on Taylor series expansion of the chemical source term in scalar space and implemented into OpenFOAM. The mathematical model does not depend on the combustion regimes, and has the correct limiting behaviour. The numerical error sources are outlined and analysed. The model is first applied to a piloted methane/air non-premixed jet flame (Sandia Flame D). The statistical (time-averaged and RMS) results agree well with experimental measurements, particularly with regard to the mixture fraction, velocity, temperature, and concentrations of major species CH4, CO2, H2O, and O2. The concentrations of intermediates CO and H2 are over-predicted, due to the limitations of the reduced reaction mechanism employed. Then, a Bunsen piloted flame is simulated. Most of the statistical properties of both reactive species and progress variable are well reproduced. The only major discrepancy evident is in the temperature, which is attributed to the experimental uncertainties of temperature fields in the pilot stream. These findings have demonstrated the model`s generality for both premixed and non-premixed combustion simulation, as well as the accuracy of prediction of reactive species distribution.

Road collisions are among the world’s critical issues causing many casualties, deaths, and economic losses. To deal with this scenario, precise analysis is required. In this paper, we use Machine Learning (ML) techniques to assess and predict road traffic collisions. An ensemble machine learning model with two layers was developed to analyze simulation data from the driving simulator. The first (base) layer integrates supervised learning techniques namely, k- Nearest Neighbors (k-NN) AdaBoost, Naive Bayes (NB), and Decision Trees (DT). The second layer predicts road collisions by combining the base layer outputs by employing the stacking ensemble method using logistic regression as a meta-classifier. The data for this study were imbalanced; therefore, we performed a SMOTE resampling strategy to handle the dataset imbalance. To simplify the model, the PSO algorithm was used to select the most important features in our dataset. The two-layer ensemble model proposed had the best outcomes with an accuracy of 88%, F1 score of 83%, and an AUC of 86% as compared with k-NN, DT, NB, and AdaBoost.

In this paper, we propose a method for the generation of simulation test scenarios for autonomous driving. Based on the requirements of standard regulatory test scenarios, we can generate virtual simulation scenarios and functional scenario libraries for autonomous driving, which can be used for simulation verification of different ADAS functions. Firstly, the design operation domain (ODD) of the functional scenario is selected and the weight values of the ODD elements taken are calculated; then a combination test algorithm based on parameter weights is improved to generate autonomous driving virtual simulation test cases for the ODD elements, which can effectively reduce the number of generated test cases compared with the traditional combination test algorithm; then the traffic participant elements under each test case are Then, the values of the subelements under the traffic participant element in each test case are sampled and clustered to obtain hazard specific scenarios. Finally, the specific scenarios are applied to the automatic emergency braking (AEB) system on the model-in-the-loop (MIL) testbed to verify the effectiveness of this scenario generation method.

Abstract: The population issue has been increasingly urgent in China in recent years. Fertility desire is an essential component of fertility research. Studies on fertility desire mainly contain two aspects: socio-economic structure and individual family[1]. The psychological structure of individuals in individual families is the primary factor of fertility desire. Based on the parasocial relationship theory, this study analyzed the changes in real behavioral intention achieved by players through the identification and attachment to avatars while constructing parasocial relationships[2]. The results indicate that character attachment can significantly enhance the variables of game concentration and player self-identity, actively mediates parasocial emotions and parasocial cognition, and ultimately increases fertility desire. As a theoretical contribution, this study provides a comprehensive interpretation of the psychology of Chinese players in a Simulation Game and explores the theoretical connections among role attachment, role identity, and parasocial relationships in virtual game players.

Embracing innovative digital technologies within educational systems is essential for overcoming challenges in modern learning environments, especially in the post-COVID-19 era. Teaching sustainability management in higher education institutions with innovative digital tools plays a fundamental role in the transition toward sustainable societies. Suitable game design elements play a significant role in facilitating sustainable learning. This study explored the effectiveness of incorporating business simulation games with project-based learning (PBL) in a flipped classroom setting. This approach was adopted within the context of a university cross-border e-commerce course to prepare students for acquiring 21st -century skills as higher-order thinking skills in a rapidly changing educational landscape. A quasi-experimental method was employed, involving a total of 60 university students from China's Zhejiang Province. Participants completed an online questionnaire designed to assess their learning engagement across three dimensions (cognitive, emotional, and behavioral) as well as their higher-order thinking skills (problem-solving, critical thinking, and creativity). The results show that the business simulation games combined with flipped classroom learning had a significantly positive impact on students’ learning outcomes, enhancing their problem-solving, critical thinking, and creative capabilities. Importantly, this approach also improved student engagement and promoted sustainable practices by applying real-life scenarios in an interactive environment. We conclude that business simulation games integrated with project-based learning (PBL) in flipped classroom settings represent a valuable educational approach. This approach not only enhances learning engagement but also fosters the development of higher-order thinking skills, encouraging students to adopt sustainable learning practices.

For the polymer electrolyte fuel cell (PEFC), the separator thickness plays an important role to determine the weight, the volume and the costs of the PEFC. In addition, thermal management, i.e. temperature distribution is also important for the PEFC system to obtain higher performance. However, there were few reports investigating the relationship between the temperature distribution and the power generation performance such as current density distribution of PEFC operated at higher temperature (HT-PEFC). The aim of this study is to clarify the impact of separator thickness on the relationship between the temperature distribution and the current density distribution of HT-PEFC. This study has also investigated the impact of separator thickness on the relationship between the temperature distribution and the gases such as H2, O2 and the current density distribution of HT-PEFC numerically using CFD software COMSOL Multiphysics. The separator thicknesses of 2.0 mm, 1.5 mm and 1.0 mm were studied. In the study, the operation temperature and the relative humidity (RH) of supply gas were also changed. As a result, it was revealed that the optimum separator thickness was 2.0 mm to obtain higher power generation of HT-PEFC. Since the heat capacity in the case of the separator thickness of 2.0 mm was the largest among the separators investigated in this study, the dehydration of PEM and catalyst layer was smaller compared with the thinner separator thicknesses. It was also revealed that the impacts of separator thickness on distribution of gases, e.g. O2, H2O, and current density distribution became larger under higher temperature and lower RH conditions. This study also concluded that the tendency of temperature distribution matched those of O2, H2O and current density distributions.

(1) Background: OCT imaging has been used to assess enamel demineralization in dental research but is not yet developed enough to qualify as a diagnostic technique in clinics. The current capabilities of most commercial acquisition software allow for visual and qualitative assessment. There is a need for a fast and verified batch-processing algorithm for segmenting and analyzing the de-mineralized enamel. This study suggests a GUI MATLAB algorithm for processing and quantitative analysis of the demineralized enamel. (2) Methods: A group of artificially demineralized human enamels was in vitro scanned under the OCT, and ROI frames were extracted. By using a selected intensity threshold colormap, inter- (Ie) and intra- (Ia) prismatic demineralization can be segmented. A set of quantitative measurements for average demineralized depth, average line profile, and integrated reflectivity can be obtained for an accurate assessment. Real and simulated OCT frames were used for algorithm verification. (3) Results: A strong correlation between automated and known Excel measurements for the average demineralization depth was found (R² > 0.97). (4) Conclusions: OCT image segmentation and quantification of the enamel demoralization zones are possible. The algorithm can assess the future development of a real-time assessment of dental diagnostics using an oral probe OCT.

In this study, we introduce the dynamics of a Hepatitis B virus (HBV) model with the class of asymptomatic carriers and conduct a comprehensive analysis to explore its theoretical aspects and examine the crossover effect within the HBV model. To investigate the crossover behavior of the operators, we divide the study interval into two subintervals. In the first interval, the classical derivative is employed to study the qualitative properties of the proposed system, while in the second interval, we utilize the ABC fractional differential operator. Consequently, the study is initiated using the piecewise Atangana-Baleanu derivative framework for the systems. The HBV model is then analyzed to determine the existence, Hyers-Ulam (HU) stability, and disease-free equilibrium point of the model. Moreover, we showcase the application of an Adams-type predictor-corrector (PC) technique for Atangana-Baleanu derivatives and an extended Adams-Bashforth-Moulton (ABM) method for Caputo derivatives through numerical results. Subsequently, we employ computational methods to numerically solve the models and visually present the obtained outcomes using different fractional-order values. This network is designed to provide more precise information for disease modeling, considering that communities often interact with one another, and the rate of disease spread is influenced by this factor.

African indigenous herbs and medicinal plants are extensively reported for their protective and therapeutic potential against Diabetes. However, most of the constituent phytochemicals that may account for the therapeutic activity are largely uninvestigated. Exploring novel phytochemical–target interactions in silico could help to provide useful insights into the mechanism underpinning the biological activities of these plants and their constituent phytochemicals. The aim of this study was to explore the interactions of terpene struc-tures previously reported from selected African antidiabetic with two emerging drug targets in diabetes. Structure-based virtual screening was used to screen 107 terpene structures against DPP-4 and PTP1B enzymes. The MD simulation, MM-GBSA free energy calculation and DFT were used to clarify the inter-actions of the in silico hits with the target enzymes. Molecular docking, post-docking Prime MM-GBSA analysis revealed the top terpenes with high binding affinity with the active site regions of DPP-4 and PTP1B. Ensemble docking revealed three triterpenes viz: cucurbitacin B (T1), 6-Oxoisoiguesterin (T4) and 20-Epi-isoiguesterinol (T2) as in silico hits which exhibit strong interaction potential with critical residues that define the catalytic triad (Ser630 and His740); oxyanion cavity (Ser631); hydrophobic S1 pocket (Tyr662) and the charged S2 pocket (Arg125) in the active site region of DPP-4. The interactions of these compounds with DPP-4 exhibited structural stability and conformational flexibility during 100 ns full atomistic MD simulation as indicated by the structure dynamics parameters including RMSD, RoG, SASA and hydrogen bond number. The post-MD MM/GBSA calculations further revealed the stability of the triterpene-DPP-4 complexes. Furthermore, Frontier molecular orbital calculations showed that, the triterpenes possess high interaction potential with the enzyme. The triterpenes showed desirable ADMET properties and drug-likeness. Therefore, cucurbitacin B, 6-Oxoisoiguesterin and 20-Epi-isoiguesterinol are recommended for experimental validation.

Agricultural drainage canals that connect upstream fish spawning areas to downstream rivers and lakes serve as crucial habitats for migrating fish. However, disconnections, such as drops and chutes, have been constructed in these canals due to agricultural modernization and flood control measures, hindering the movement of fish with limited swimming abilities. Portable fishways offer a promising solution to reconnect water bodies in agricultural canals, as they can be easily removed during high water discharges to avoid impeding the canals' drainage function. In addition to experimental assessments of fishway functionality, employing a hydrodynamic model to explore effective placement strategies for portable fishways is essential to maximize their effectiveness. This study presents a method to determine the best horizontal location of a portable fishway in an agricultural drainage canal using two-dimensional hydrodynamic simulations within the specified cases. The applicability of this method is demonstrated by addressing the positioning challenge of a portable fishway on a chute in an agricultural drainage canal in Japan. Results indicate that the proposed method allows for the selection of a suitable location, considering preferable hydraulic conditions both within the portable fishway and around its entrance.

Chromium poisoning strongly influences the performance of solid oxide fuel cell (SOFC) stacks. A three-dimensional, time-dependent, computational fluid dynamics model of a single channel of a F10 SOFC stack in Forschungszentrum Jülich GmbH is developed to investigate chromium poisoning for different stack designs, temperatures and absolute air humidities. The model takes into account both chemical and electrochemical mechanisms of chromium poisoning and is able to predict the spatial distribution of SrCrO4 and Cr2O3. The voltage degradation over 100 kh can be simulated quantitatively. According to the simulation results, chromium poisoning is almost eliminated by the application of the protective coating fabricated by the atmospheric plasma-spraying (APS) technology. Besides, with the help of the APS protective coating, operating a SOFC stack with less dehumidified air at 650 oC is possible according to the simulation.

Biomass as a whole offers a more diverse potential for valorisation than any other renew-able energy source. As one of the stages in the separation of bio-oil involves the liquid mixture of acetol and acetic acid, and as both components are particularly well suited for valorisation, a hybrid method has been developed for their separation with a high purity level through an approach combining liquid-liquid extraction and rectification. In order to design and simulate the flowsheet, the ChemCAD 7.0 simulation software has been used. Sensitive analyses have been carried out to investigate the influence of the different pa-rameters in the rectification columns such as the reflux ratio, the feed stage location, and the vapour/bottom molar flow ratio. The effect of different extractants and of their excess on the separation process as well as the possibility of regenerating the extractant has also been studied. Tri-n-octylamine has accordingly been selected as a separating agent which has been fully recycled. The end result for separating an initial 48/52 wt% ace-tol/acetic-acid liquid mixture has been acetol with a purity of 99.4 wt% and acetic acid with a purity of 100 wt%.

As a new logistics system, Physical Internet (PI) provides a solution to cope with the continuous rise of urban logistics demand by exploring same-level and cross-level paths and flattening. This research suggests a PI-based urban logistics distribution cost minimization model based on the examination of the primary elements influencing PI distribution, intending to maintain the logistic transportation process' integrity. This proposed model enhances the flexibility of goods operation and reduces the global cost of logistics distribution using the interconnected and open PI logistics distribution mode. The experimental results show that the distribution cost of the PI logistics model is significantly better than that of a traditional logistics model. The elastic and cost advantages relatively grow as the infrastructure for distribution scales up and the interruptions from emergencies occur.

The transportation of feed pellets via pneumatic conveying plays an improtant role in feed industry. In recent years, many efforts are made both exprimentally and numerically to gain a better understanding of the underlying mechanism for pneumatic conveying. Though few of those research is directly related to feed pellets, it can be beneficial to draw a full picture of rencent development in pneumatic conveying, with emphasis on aspects extendable to feed transportation. This paper reviews first the major factors concerning feed pellets pneumatic conveying, such as particle breakage, pipe wear and electrostatic. Building on that, new advancement including emerging new technologies in experiments and simulations are discussed, with a focus on the experimental measurements and mathematical models, repectively. The needs for future research and the analogy to feed pellets tranportation is given lastly.

Disassembly is a pivotal technology to enable the circularity of electric vehicle batteries by applying circular economy strategies that extend the life cycle of their components, like remanufacturing and repurposing, or by using efficient recycling to reintegrate the gained materials in producing new battery systems. This paper aims to develop a multi-method self-configuring simulation model for investigating disassembly scenarios while considering the battery design as well as the configuration and layout of the disassembly station. We demonstrate the developed model in a case study using a battery of Mercedes-Benz and the automated disassembly station of the project DeMoBat at Fraunhofer IPA. Furthermore, we introduce two disassembly scenarios, the component-oriented and the accessibility-oriented disassembly, and compare them using the simulation model to determine several indicators, such as the frequency of tool change, the number and distribution of robot routes, the tool utilization, and the disassembly time.

Several factors impact the durability of concrete bridge decks, including traffic loads, fatigue, temperature changes, environmental stress, and maintenance activities. Detecting problems such as corrosion, delamination, or concrete degradation early on can lower maintenance costs. Nondestructive evaluation (NDE) techniques can detect these issues at early stages. Each NDE method, meanwhile, has limitations that reduce the accuracy of the assessment. In this study, multiple NDE technologies were combined with machine learning algorithms to improve the interpretation of half-cell potential (HCP) and electrical resistivity (ER) measurements. A parametric study was performed to analyze the influence of five parameters on HCP and ER measurements, such as degree of saturation, corrosion length, delamination depth, concrete cover, and moisture condition of delamination. The results were obtained through finite element simulations and used to build two machine learning algorithms, a classification algorithm and a regression algorithm, based on the Random Forest methodology. The algorithms were tested using data collected from a bridge deck in the BEAST® facility. Both machine learning algorithms were effective in improving the interpretation of ER and HCP measurements using data from multiple NDE technologies.

Understanding the dynamic interaction between piles and surrounding soil under vehicular impacts is essential for effectively designing and optimizing soil-embedded vehicle barrier systems. The complex behavior of pile-soil systems under impact loading, attributed to the soil’s nonlinear behavior and large deformation experienced by both components, presents significant simulation challenges. Popular computation techniques, such as the Updated Lagrangian Finite Element Method (UL-FEM), encounter difficulties in scenarios marked by large soil deformation, e.g., impacts involving rigid piles. While mesh-free particle and discrete element methods offer another option, their computational demands for field-scale pile-soil impact simulations are considerable. We introduce the erosion method to bridge this gap by integrating UL-FEM with an erosion algorithm designed for simulating large soil deformations during vehicular impacts. Validation against established physical impact tests confirmed the method’s effectiveness for flexible and rigid pile failure mechanisms. Additionally, this method was used to investigate the effects of soil mesh density, soil domain sizes, and boundary conditions on the dynamic impact response of pile-soil systems. Our findings provide guidelines for optimal soil domain size, mesh density, and boundary conditions. This investigation sets the stage for improved, computationally efficient techniques for the pile-soil impact problem, leading to better pile designs for vehicular impacts.

Knowledge of water availability in terms of spatial (i.e., depth) and temporal variability of soil water is essential to describe soil water infiltration and storage processes accurately. Therefore, performing simulations of soil water dynamics can be a valuable tool to evaluate different land uses and their impact on water availability. Consequently, this study aimed to model the volumetric soil water content (θ, cm3 cm-3) under agroforestry systems (AFS) with cocoa. For this purpose, the θ was monitored at different depths (0-20, 20-40, 40-60, 60-80, 80-100 cm) in the soil profile of four plots (20 × 50 m) of cocoa under agroforestry systems. Environmental conditions significantly influenced the water balance of the cocoa crop. There were seven moments when evapotranspiration (ET0) was higher than 5 mm d-1. During those moments, the environment exhibited higher PAR values of 1041 µmol m-2 s-1, very low atmospheric relative humidity (RHa) levels (around 45%), higher ambient temperatures of 32.2 ºC and a vapor pressure deficit of 1.6 kPa whose θ levels reached 0.32 cm3 cm-3 and a water balance of -5.7 mm, which presented negative values during most of the study period. The θ obtained from the HYDRUS-1D model presented a slight bias as a high level of coherence between the observed values, based on the model's goodness-of-fit estimators. The above provides accurate simulations of soil water content at various depths, much-needed information for management schemes for cocoa cultivation under agroforestry systems.

The mechanical behavior, microstructures as well as the crystallographic textures of the Ti57-Nb43 alloy were investigated on cylindrical specimens compressed at high temperatures, in the range of 700-1000 °C, and strain rates between 0.001 and 1.0 s-1. Hardening, followed by softening behaviours were observed as a function of strain, due to the occurrence of dynamic recrystallization/recovery in hot deformation. The modified five-parameter Voce type equation described well the stress-strain curves, but for the present alloy, it was also possible with only four parameters. A new two-variables polynomial function was employed on the four parameters that described well the flow curves as a direct function of temperature and strain rate. It permitted to reduce the number of parameters and had the predictive capacity for the flow stress at any temperature, strain, and strain rate in the investigated range. The crystallographic textures were similar at all temperatures with an increase of intensity from 900 °C. The textures could be characterized by a double &lt;100&gt; and &lt;111&gt; fiber and a unique component of (001)&lt;110&gt;, the latter inherited from the initial hot rolling texture. Viscoplastic polycrystal self-consistent deformation modeling reproduced the measured textures showing that dynamic recrystallization did not alter the development of the deformation texture, only increased it intensity.

Zinc, being a part of many proteins and enzymes, plays an important role in the vital processes of the body. Racemic α-lipoic acid (LA) has a wide range of biological activities: antioxidant, antidiabetic, anticancer, and it is also used in diseases such as Parkinson's and Alzheimer's. Nowadays, the synthesis and structural studies of new zinc complexes with biologically active compounds are of great importance for drug discovery. This work shows the studies carried out on the synthesis, structure, identification, also biological activity using in silico methods of a complex compound based on LA and zinc. A complex compound of zinc with LA (ZnLA) was synthesized in the presence of LA and zinc acetate in ethanol. The identification of ZnLA by (FT-IR, XRD and TGA-DTA), also DFT calculations at the B3LYP/def2-TZVPP level of theory was performed. The molecular docking investigation involved docking ZnLA with two different protein receptors such us Glycogen phosphorylase (2GPA) and Kv potassium channel beta subunit (1ZSX) using CB-Dock 2 server and Auto-Dock Vina program to determine the mechanism of action and biological activity. This study reveals potential ligand-protein interactions at the atomic level and suggests that the treatment of diabetes (non-insulin dependent) with ZnLA.

Cycle times at workstations in offsite construction factories fluctuate widely due to various influencing factors. Consequently, relying on average rates, such as length per unit of time, for estimating cycle times proves to be inaccurate, often leading to significant deviations between production schedules and actual operations. To address this issue, this study proposes an estimation system that leverages machine-learning-based prediction, statistical methods, 3D simulation, and computer vision to predict cycle times at the workstation level. Testing of the system on a semi-automated wood-wall framing workstation in a panelized construction factory shows that it reduces the mean absolute error and sum of errors by approximately 36% and 68%, respectively, compared to the fixed rate method. The results also highlight the efficacy of using computer vision data for training machine-learning models for cycle time estimation, the importance of identifying and understanding the factors influencing cycle times, and the impact of random delays on the accuracy of cycle time estimation systems.

The water resources carrying capacity (WRCC) system comprises multiple complex and nonlinear interactions related to society, economy, water resources and the water environment. A full understanding of its internal mechanism is conducive to the further improvement of the regional WRCC. This article focuses on the temporal and spatial variability of WRCC to explore a method of dynamic successive assessment. Firstly, the Pressure-State-Response (PSR)framework is used to develop a systematic and causal indicator system. Then, The Variable Fuzzy Pattern Recognition (VFPR) model and an analytic hierarchy process-entropy (AHP-E) model are combined to successively and dynamically assess WRCC. The proposed method is applied to the dynamic successive assessment of WRCC in Hebei Province, and it is obtained that the poor water resources carrying capacity in Hebei Province is mainly due to the basic attribute of the decision of the water resources shortage, but Hebei Province actively adopts a variety of measures to save water and pressurize mining, which makes the province's water resources carrying capacity have a tendency to become better gradually. At the same time, the system dynamics model (SD) of water resources carrying capacity based on the analysis of the model structure was established. Moreover, three scenarios were designed, including existing continuation, high-efficiency water-saving, cross-regional water transfer and coordinated development, each scenario divided into high and low-speed economic development and population growth schemes, then total 6 schemes between 2023 and 2030 were simulated and predicted respectively. The results indicate that if the current development model is adopted, the water resource carrying capacity will continue to maintain low levels. It was concluded that the high-speed development of economy and population, the efficient water conservation and inter-basin transfer scenario (scenario 2 with high-speed) is the best choice for sustainable development of water resources and social economy in Hebei.

After reviewing the history of the struggle between human beings and plant diseases, insects and weeds, especially reviewing the thoughts of plant protection in ancient Chinese agricultural books, the target recognition of plant pests and the six types of plant protection methods and 36 subdi-vision measures were summarized. Then the development overview of pesticide application technology was focused and reviewed systematically by combining the development timeline of pesticide application and the key technologies including the performance measurement and the simulation and modeling of pesticide spraying system. And three suggestions for further research were proposed from the perspectives of human-being and environmental health, the sustainable and eco-friendly application medium, and the efficiently application equipment system in plant protection.

In this paper, we study the effect of parasitic channel on electrical characteristics and power consumptions of vertically stacked gate-all-around (GAA) silicon nanosheet (NS) MOSFETs of the bulk and silicon-on-insulator (SOI) substrates. Through deliberate control of the bottom parasitic channel coverage, we systematically assess its influence on DC, AC, and RF characteristics including power consumption. The observed findings highlight a significant differentiation among the bottom parasitic channels: GAA (i.e., control sample with 100% coverage), omega-shaped gate NS (70% and 80% coverages), and FinFET (60% coverage). Compared with the device with a 60% coverage channel, the device with a 100% coverage channel possesses 3520% and 8030% normalized leakage current improvement for both NFET and PFET under the bulk substrate, respectively. Compared with bulk-substrate devices, the SOI-substrate devices further significantly improve the leakage current. Due to enhanced off-state current leakages in the 100% covered bulk-substrate device, the static power demonstrates a notable improvement (about 98.1%), compared to 60% covered devices. Hence, the utilization of a 60% covered channel will result in a degradation of both device and circuit performance. The observation of GAA NS MOSFET indicates that an increase in the bottom parasitic channel coverage ratio leads to a positive impact on power consumption and leakage currents. The examination presented here is useful in the fabrication of stacked GAA NS MOSFETs.

In recent years, the genesis of natural gas and the definition of major gas source rocks in the Baiyun Sag of the Pearl River Mouth Basin have attracted extensive attention from scholars. There is still considerable controversy over the genesis and the main gas source rocks of natural gas. In this study, the chemical kinetics method of natural gas generation and carbon isotope fractionation was used to study the generation history and isotope evolution history of Paleogene natural gas in the Y5 well area located on the north slope of Baiyun Sag. And other geochemical discriminant methods were applied simultaneously to determine the type and source rocks of natural gas. The results show that the natural gas in the Y5 well area on the northern slope of Baiyun Sag is mainly kerogen degradation gas, mixed with a small amount of oil cracking gas. The mixture of kerogen degradation gas and a small amount of crude oil cracking gas caused the inversion of the carbon isotope sequence of ethane and propane. The natural gas in the Y5 well area mainly comes from delta–shallow lacustrine facies source rock. The high–quality reservoirs in the shallower strata accumulate natural gas generated in the early stage, and the tight reservoirs in the deeper strata mainly accumulate natural gas generated in the late stage.

The steel grid structure with bolted ball joints is gaining popularity in offshore platforms and aquaculture cages, but the corrosion resistance of these joints is a critical safety concern. This article introduces an innovative method to enhance the sealing performance of bolted ball joints, preventing seawater ingress and corrosion. The approach involves creating a sealed surface within the contact gap between the sleeve and connecting components. Circular grooves and sealing washers are added to both ends of the sleeve. A two-dimensional finite element analysis model of the bolted ball joint with the sealing structure was created using SOLIDWORKS and ANSYS. The study analyzes the sealing gasket's contact pressure at various compression levels and evaluates its performance with bubble tests for air tightness. Research results show a linear relationship between contact pressure and compression rate, with a sealing pressure of 5.95MPa achieved at a 14% compression rate. This simple, cost-effective sealing method effectively prevents corrosion from seawater, making it ideal for marine environments.

Surgical navigation has evolved as a vital tool in maxillofacial surgery, offering precise and patient-specific data. This study explores the clinical applications and accuracy of intraoperative tool tracking in maxillofacial surgery. The research includes 37 patients with various pathologies who underwent surgery assisted by a surgical navigation system using tracked instruments. The study showcases two representative cases: one involving coronoid hyperplasia with mouth opening deficit and another addressing nose-orbital-ethmoidal-frontal ossifying fibroma resection. The results indicate that surgical navigation with tracked instruments provides high precision (<1.5 mm error), reduced surgical time, and less invasive approaches. The study highlights the potential for reproducible outcomes and increased safety, especially in complex cases. Despite some limitations, the synergy between surgical navigation and tracked instruments offers a promising approach in maxillofacial surgery, expanding its applications beyond current practices.

The resistance of Candida spp to fluconazole is a serious problem; Therefore, it must be investigated to expand the understanding of the phenomenon with a view to its clinical applicability. The aim was to evaluate the expression of ERG3, ERG11, CDR1, and SNQ2 genes involving computational analysis. Relative expression was quantified by RT-qPCR. Metrics were obtained by molecular docking and through Fisher's discriminant functions, a predictive classification against susceptibility to fluconazole was performed. The expression of ERG3, CDR1, and SNQ2 genes was higher in resistant strains than in dose-dependent sensitive strains. The gene with the highest expression in strains exposed to fluconazole was CDR1 and, in both resistant and dose-dependent sensitive strains exposed to fluconazole, the ERG11 gene had lower expression. The molecular docking model generated lower median fluconazole-ERG11 contacts than the median fluconazole-ERG3, -CDR1, and -SNQ2 contacts. The predicted classification through the multivariate model for fluconazole sensitivity obtained an accuracy of 73.5%. Resistant strains had a significant expression of CDR1, SNQ2 and ERG3 genes, compared to a comparatively low expression of ERG11. Molecular analysis identified a low affinity between fluconazole and its drug target, which may explain a lower intrinsic susceptibility of the fungus.

Bare slope instability is a prevalent concern. The root system of, herbaceous vegetation enhances the shear strength of shallow–slope soil. Indoor experiments were conducted on rootless undisturbed soil (RUS) and undisturbed soil with a root system (USRS) using a triaxial compression apparatus to analyze the slope stability of composite soil with a Tagetes erecta root system. Significance tests and correlation analysis of the factors affecting shear performance were conducted using R software. The slope reinforcement, effect by the plant root system was simulated under 24 working conditions using the MIDAS finite element method. The results revealed the influence of the–root content (RC), moisture content (MC), and stress on the shear strength of USRS, the contribution degree, and the variables’ influences on slope stability. Both RUS and USRS exhibited strain hardening during shearing. The internal friction angle (φ) and cohesion (c) of USRS were negatively and positively correlated with the RC and MC (root burial depth), respectively, and a good fit was obtained for the relationship. The maximum deviatoric stress during shear failure was 1.29 times higher for USRS than for RUS. The RC (root depth) was positively correlated with the slope safety coefficient and the slope of the line under different working conditions, whereas the slope gradient was negatively correlated with the slope safety coefficient. The reinforcement effect by the root system resulted in a 13.2% increase in the safety coefficient and improved stability of slopes with a gradient larger than 1.5%. This article investigated the mechanism of the root–soil system and, the effects of different influencing factors on the shear strength of the soil, and slope stability. The findings provide new insights into shallow slope stability in practical slope protection projects.

In this study, we present different methods of estimating fuzzy reliability of a two-parameter Rayleigh distribution via the maximum likelihood estimator, median first-order statistics estimator, quartile estimator, L-moment estimator, and mixed Thompson-type estimator. The mean-square error MSE as a measurement for comparing the considered methods using simulation through different values for the parameters and unalike sample sizes is used. The results of simulation show that the fuzziness values are better than the real values for all sample sizes, as well as the fuzzy reliability at the estimation of the Maximum

Exposure to quasi-electrostatic field induced currents is a hazard of live-line transmission work. These steady-state induced currents are typically less than 1 mA, and their sensory effects range from imperceptible to painful depending on the person and conditions such as contact area and duration. Permanent injury from these currents is unlikely but they can distract workers, increasing the risk of injury from falls or other dangers. Identifying contact current severity and training workers can help reduce risk of accidents. Measuring induced currents along a climbing route is time consuming and simulation is challenging because of geometric complexity of the worker, transmission structure, conductor bundles, and electric fields in the climbing space. This research explores calculation of worker contact current using a recently developed adaptation of the charge simulation method. The method uses Fourier principles to improve computational efficiency when explicitly modeling all bundle subconductors. The research also examines reasonable simplifications for modeling lattice structures and human geometry. Calculated results compare well to measured currents for a worker climbing a 400 kV lattice structure. This indicates the method is a practical option for calculating the severity of steady-state contact currents. A simple calculation is suggested for estimating these currents.

A clinical simulation web-based program, known as FIRST2ACT (Feedback Incorporating Review and Simulation Techniques to Act on Clinical Trends) is designed to increase the efficacy of cli-nicians’ actions in the recognition and immediate response to a patient’s deterioration. This study, which was nested in a larger mixed method project, used ten focus groups (n=65) of graduate, enrolled, registered nurses, Associate Nurse Unit Managers, and general manag-ers/educators/coordinators from four different institutions to investigate whether nurses felt their practice was influenced by participating in either a face-to-face or web-based simulation educa-tional programme about patient deterioration. Results indicate individuals who were less ‘tech-savvy’ appreciated the flexibility of web-based learning, which increased their confidence. Face-to-face students appreciated self-reflection through performance evaluation. While face-to-face simulations were unable to completely duplicate symptoms, they did show nurses' adaptability. Both interventions enhanced clinical practice by improving documentation and re-plies while also boosting confidence and competence. Web learners initially experienced tech-related anxiety, which gradually subsided, demonstrating healthcare professionals' resilience to new learning approaches. Overall, the study highlighted the advantages and challenges of web-based and face-to-face education in clinical practice, emphasising the importance of adapta-bility and reflective learning for healthcare professionals. Further exploration of specific topics is required to improve practice, encourage knowledge sharing among colleagues, and improve early detection of patient deterioration.

In the context of post-harvest pest management in agricultural products, the adoption of modified atmospheres presents an eco-friendly alternative to conventional pesticides. This study focuses on nitrogen gas as a potential agent for insect control in stored commodities, utilizing computational simulations to investigate its penetration and distribution within two common storage configurations: chamber-contained pallets and silos. By employing the convection-diffusion equation, the simulations reveal insights into nitrogen distribution dynamics and duration required for oxygen reduction. The results highlight the influence of boundary conditions, commodity porosity, and convection effects on nitrogen dispersion. In chamber scenarios, boundary condition type significantly affects oxygen reduction time, while in silos, convection and diffusion interact to establish a consistent diffusion layer thickness. This research enhances the understanding of modified atmosphere techniques and their potential for sustainable pest management. It provides practitioners with valuable quantitative insights, paving the way for optimized modified atmosphere strategies in real-world agricultural contexts.

(1) Background: The imaging energy range of a typical Compton camera is limited due to the fact that scattered gamma photons are seldom fully absorbed when the incident energies are above 3 MeV. Further improving the upper energy limit of gamma-ray imaging has important application significance in active interrogation of special nuclear materials and chemical warfare agents, as well as range verification of proton therapy; (2) Methods: To realize gamma-ray imag-ing in a wide energy range of 0.3~7 MeV, a principle prototype, named a portable three-layer Compton camera, was developed using the scintillation detector that consist of an silicon photo-multiplier array coupled with a Gd3Al2Ga3O12:Ce pixelated scintillator array. Implemented in a list-mode maximum likelihood expectation maximization algorithm, a far-field energy-domain imaging method based on two-interaction and three-interaction events was applied to estimate the initial energy and spatial distribution of gamma-ray sources. The simulation model of detec-tors was established based on Monte Carlo simulation toolkit Geant4. The reconstructed images of a 133Ba, a 137Cs and a 60Co point-like sources were successfully obtained with our prototype in laboratory tests and compared with simulation studies; (3) Results: The proportion of effective imaging events accounted for about 2%, which made our prototype realize reconstructing the distribution of a 0.05 μSv/h 137Cs source in 10 seconds. The angular resolution for resolving two 137Cs point-like sources was 15°. Additional simulated imaging of the 6.13 MeV gamma-rays from 14.1 MeV neutron scattering with water preliminarily demonstrated the imaging capability for high incident energy; (4) Conclusions: We concluded that the prototype had good imaging per-formance in a wide energy range (0.3~7 MeV), which showed potential in several MeV gam-ma-ray imaging applications.

In the process of construction and operation of the pumping station, a large number of axial flow pumps pose a threat to the survival of fish in the natural environment. Through experiments, the survival conditions of many kinds of fish in extreme positive and negative pressure environment are explored, and -40 kPa is established as the low pressure damage threshold, which is used as a reference to analyze the probability of fish damage caused by pumps. The immersion boundary-lattice Boltzmann numerical method (LES-IB-LBM) based on large eddy simulation is used to optimize the parameters of the prototype runner of axial flow pump. A new type of runner with leading edge thickness of 11.4mm and blade cutting angle of 18 ° is optimized. This kind of runner has both hydraulic performance and fish-passing ability. The runner of the fish-friendly axial flow pump effectively guides the fish into the low risk area and reduces the risk of friction, shear and impact damage to the fish. the total impact mortality ratio of the runner of the axial flow pump before and after optimization is close to 7:1. The pressure damage analysis shows that the lowest negative pressure of the fish is -59.2 kPa, which is higher than -64.5 kPa of the prototype pump, and the highest positive pressure of the body surface is 11.8 kPa, which is lower than the 43.8 kPa of the prototype pump. Shear damage analysis shows that the fish will not be damaged by shear in the prototype pump and fish-friendly pump.

Swirling coal flames are encountered in swirl burners of boiler furnaces and cyclone coal combustors, and were studied using large-eddy simulation (LES) in reported references. However, the structures of swirling coal flames remain to be further studied. In the present paper LES of swirling coal flames is conducted for two combustors using two gas combustion models. The statistical simulation results are accessed by experiments and the instantaneous simulation results are used to analyze the specific features of flame structures of four cases. It is seen that different inlet flow conditions and different secondary-air flow rate lead to different flame structures

Developmental hazard evaluation is an important part of assessing chemical risks during pregnancy. Toxicological outcomes from prenatal testing in pregnant animals result from complex chemical-biological interactions, and while New Approach Methods (NAMs) based on in vitro bioactivity profiles of human cells offer promising alternatives to animal testing, most of these assays lack cellular positional information, physical constraints, and regional organization of the intact embryo. Here, we engineered a fully computable model of the embryonic disc in the compucell3d.org modeling environment to simulate epithelial-mesenchymal transition of epiblast cells and self-organization of mesodermal domains (chordamesoderm, paraxial, lateral plate, posterior/extraembryonic). Cell fate in the model is determined by an autonomous homeobox (HOX) clock driven by morphogenetic signals (e.g., FGF, WNT, ATRA, CDX). Executing the model renders a quantitative cell-level computation of mesodermal subpopulations and consequences of perturbation based on known embryogeny. For example, synthetic perturbation of the control network rendered altered phenotypes (cybermorphs) mirroring experimental mouse embryology, with 50% reductions in FGF4, FGF8 and BMP4 signaling resulting in 86%, 98% and 59% reductions, respectively in the posterior mesodermal population, while ATRA exposure also resulted in a 78% decrease in this population. This model enables integration of in vitro chemical bioactivity data for specific molecular targets with known embryology to test mechanistic veracity and quantitative prediction of altered development.

Additive manufactured interlocking structures often face challenges in achieving suitable joinability due to thermal deformation. This issue becomes particularly interesting when fabricating structures that require high density but not fully connected layers, such as soft-magnetic materials. This study focuses on systematical investigation of the deformation as a function of finger thickness and building direction in model interlocking structure. In the perpendicular direction to the build plate, the distortions of joint part were significantly reduced due to lower thermal stress. In addition, the effect of laser power and scan speed on interlocking structure are systematically discussed. Additionally, through stress and thermal simulations of additive manufacturing process, it was confirmed that the compensated model can enhance the joinability of interlocking structures. These findings contribute to the development of optimal design for additive manufactured parts of soft magnetic materials such as stators in electric motor components, which needs insulations and density.

Thermal analysis of a hypereutectic Al-Si alloy used for an automotive part was carried out in this research work. Solidification characteristics are recognized from the Temperature-time curve and its corresponding derivatives. This analysis was successfully used in a simulation software program with the aim of improving the resultant simulation accuracy. In this study, different types of molds, with and without cooling systems, were designed and used. The melt present in the furnaces of the factory was used to simulate the casting process. The effect of different cooling rates (1.2ºC/s, 2ºC/s, 2.3ºC/s and 2.9ºC/s) on the solidification parameters have been investigated. Differences in the resultant microstructure between wall and center of the molds have been analyzed and have been related to the T-t curve. Finally, the experimental results clearly indicate that the water-cooling rate of the molds and the melt temperature directly affects the solidification process as well as the final microstructure.