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Detection and classification of degradation types is a fundamental activity in road pavement management. Monitoring activities through manual / visual inspections are time consuming, costly and cause safety issues. The introduction of new high-efficiency equipment for the detection and classification of degradation opens a new perspective in the analysis and management of roads. Thus, the automatic monitoring of the road asset introduces new performance indicators and more complex criteria for classifying problems. In this paper, an analysis is presented by referring to examples of measurements in Romania, detection, and classification of the main types of degradation, study, and capabilities of ACD (Automatic Crack Detection). A numerical and experimental study was also conducted to improve the operational optimization of ACD equipment to increase its usability and performance.

Cooperative spatial exploration in initially unknown surroundings is a common embodied task in various applications and requires satisfactory coordination among the agents. Unlike many other research questions, there is a lack of simulation platforms for the cooperative exploration problem to perform and statistically evaluate different methods before they are deployed in practical scenarios. To this end, this paper designs a simulation framework to run different models, which features efficient event scheduling and data sharing. On top of such a framework, we propose and implement two different cooperative exploration strategies, i.e., the synchronous and asynchronous ones. While the coordination in the former approach is conducted after gathering the perceptive information from all agents in each round, the latter enables an ad-hoc coordination. Accordingly, they exploit different principles for assigning target points for the agents. Extensive experiments on different types of environments and settings not only validate the scheduling efficiency of our simulation engine, but also demonstrate the respective advantages of the two strategies on different metrics.

Enhanced oil recovery by CO2 injection technology (CO2-EOR) plays a crucial role in enhancing oil production and the permanent sequestration of anthropogenic CO2 in depleted oil reservoirs. However, the availability of CO2 in oil field locations and its mobility in contrast with reservoir fluids are prime challenges in CO2-EOR. The cost of CO2 and its availability at the oil fields has prompted investigations on efficient injection of CO2 at the fields to achieve the best sweep efficiency possible. Injection strategies such as water-alternating-gas (WAG), simultaneous vertical and horizontal WAG, simultaneous water injection into the aquifer and vertical WAG, water and gas injection simultaneously but separately (SS-WAG), and water and gas injection simultaneously but not separately (SNS-WAG) play a significant role, as well as the purity of CO2. In this work, the significance of the above criteria was investigated individually and in combination. The coupled sequence of injection rate, soaking time, WAG ratio, and purity of injected CO2 for enhancement of oil production were delineated. A realistic reservoir simulation model conceptualizing the CO2-EOR system with five spot injection patterns was developed by the company CMG. The history-matched model that was developed was used to investigate the sensitivity of the coupled effects to the criteria listed above on oil recovery. Numerical investigations quantitatively emphasized that purity and soaking time of CO2 have an inverse effect in the oil production rate and that SNS-WAG resulted in a better oil production rate than SS-WAG.

We present a mesoscale model and the simulation results of a system composed of polyacrylonitrile (PAN), carbon nanotubes (CNTs), and a mixed solvent of dimethylsulfoxide (DMSO) and water. The model describes a fragment of a nascent PAN/CNT composite fiber during coagulation. This process represents one of the stages in the production of PAN composite fibers, which are considered as precursors for carbon fibers with improved properties. All calculations are based on dynamic density functional theory. The results obtained show that the greatest structural heterogeneity of the system is observed when water dominates in the composition of the mixed solvent, which is identified with the conditions of a non-solvent coagulation bath. The model also predicts that the introduction of CNTs can lead to an increase in structural heterogeneity in the polymer matrix with increasing water content in the system. In addition, it is shown that the presence of a surface modifier on the CNT surface, which increases the affinity of the filler to the polymer, can sufficiently reduce the inhomogeneity of the nascent fiber structure.

Cyber-Physical Systems (CPSs) offer significant potential to address the evolving demands of industrial development. In the Industry 4.0 era, a framework integrating sensing, data exchange, numerical analysis, and real-time feedback is essential for meeting modern industrial needs. However, implementing this integration presents challenges across multiple domains, particularly in digital analysis, information sensing, and data exchange during corporate transformation. Companies yet to undergo transformation face distinct challenges, including the risks and trial-and-error costs of adopting new technologies. This study focuses on a heavy-duty workpiece processing factory, with a specific emphasis on the painting process. The complexity of this process frequently results in congestion, which is approached as a multi-objective, multi-constraint optimization problem. This paper proposes the Improved Non-dominated Sorting Genetic Algorithm II (INSGA-II) to address the requirements of multi-objective optimization. The proposed approach uses multi-chromosome structures, listeners, and recursive backtracking initialization to optimize the search for solutions under constraints. This enables the factory to automatically streamline production lines based on workpiece processing sequences, leading to increased efficiency. Additionally, a CPS framework focused on simulation modeling has been designed. First, the INSGA-II algorithm processes order data to generate production schedules. Next, the data transmission formats and input-output interfaces are designed. Then, a simulation model is built using the algorithm’s results. These components collectively form the CPS framework for this study. The proposed method offers an automated digital solution through the algorithm, enabling verification of its feasibility via the simulation model. As a result, it significantly enhances decision-making speed, reliability, and equipment utilization.

Oncology drug discovery and development has always been an area facing many challenges. Phase 1 oncology studies are typically small, open-label, sequential studies enrolling a small sample of adult patients (i.e., 3-6 patients/cohort) in dose escalation. Pediatric evaluations typically lag behind the adult development program. The pediatric starting dose is traditionally referenced on the recommended phase 2 dose in adults with the incorporation of body size scaling. The size of the study is also small and dependent upon the prevalence of the disease in the pediatric population. Similar to adult development, the dose is escalated or de-escalated until reaching the maximum tolerated dose (MTD) that also provides desired biological activities or efficacy. The escalation steps and identification of MTD are often rule-based and do not incorporate all the available information, such as pharmacokinetic (PK), pharmacodynamic (PD), tolerability and efficacy data. Therefore, it is doubtful if the MTD approach is optimal to determine the dosage. Hence, it is important to evaluate whether there is an optimal dosage below the MTD, especially considering the emerging complexity of combination therapies and the long-term tolerability and safety of the treatments. Identification of an optimal dosage is also vital not only for adult patients but for pediatric populations as well. Dosage-finding is much more challenging for pediatric populations due to the limited patient population and differences among the pediatric age range in terms of maturation and ontogeny that could impact PK. Many sponsors defer the pediatric strategy as they are often perplexed by the challenges presented by pediatric oncology drug development (model of action relevancy to pediatric population, budget, timeline and regulatory requirements). This leads to a limited number of approved drugs for pediatric oncology patients. This review article provides the current regulatory landscape, incentives and how they impact pediatric drug discovery and development. We also consider different pediatric cancers and potential clinical trial challenges/opportunities when designing pediatric clinical trials. An outline of how quantitative methods such as pharmacometrics/modelling & simulation can support the dosage-finding and justification is also included. Finally, we provide some reflections that we consider helpful to accelerate pediatric drug discovery and development. [ABSTRACT FROM AUTHOR]

The performance improvement of CuO/TiO2 heterojunctions was investigated to contribute to the understanding of factors influencing the performance of the heterojunctions and their potential applications in sunlight-driven photodetectors and photocatalysts. By using optimal parameters (TiO2 (CuO)'s thickness of 5 µm (60 nm), acceptor (donor) doping concentration of 1017 cm−3 (1018 cm−3), required interface defect of 1013 cm−3, and under AM 1.5G illumination at 27 °C and 0 V bias voltage, the device achieved a photocurrent density of 13 mA/cm2, a photoresponsivity of 3.25 A/W, a detectivity of 1.8 × 1014 Jones, and effectiveness across UVA and visible light. At temperatures approaching 300 °C, the CuO/TiO2 device maintained its high photoresponsivity and efficiency, indicating its suitability for applications in high-temperature environments, such as hydrogen production through photocatalysis. The results suggest that the devices have the potential to be utilized in sunlight-driven optoelectronic and photocatalyst industries, offering cost-effectiveness and high efficiency. [ABSTRACT FROM AUTHOR]

As a popular applied artificial intelligence tool, Bayesian networks are increasingly being used to model multistage manufacturing processes for fault diagnosis purposes. However, the major issue limiting the practical adoption of Bayesian networks is the difficulty of learning the network structure for large multistage processes. Traditionally, Bayesian network structures are learned either with the help of domain experts or by utilizing data-driven structure learning algorithms through trial and error. Both approaches have their limitations. On the one hand, the expert-driven approach is costly, time-consuming, cumbersome for large networks, and susceptible to errors in assessing probabilities; on the other hand, data-driven approaches suffer from noise, biases, and inadequacy of training data and often fail to capture the physical causal structure of the data. Therefore, in this article, we propose a Bayesian network structure learning approach where popular manufacturing knowledge sources like the failure mode and effect analysis (FMEA) and hierarchical variable ordering are used as structural priors to guide the data-driven structure learning process. In addition, to introduce modularity and flexibility into the learning process, we present a sequential modeling approach for structure learning so that large multistage networks can be learned stage by stage progressively. Furthermore, through simulation studies, we compare and analyze the performance of the knowledge source-based structurally biased networks in the context of multistage process fault diagnosis. [ABSTRACT FROM AUTHOR]

The current demand for pharmacometricians outmatches the supply provided by academic institutions and considerable investments are made to develop the competencies of these scientists on-the-job. Even with the observed increase in academic programs related to pharmacometrics, this need is unlikely to change in the foreseeable future, as the demand and scope of pharmacometrics applications keep expanding. Further, the field of pharmacometrics is changing. The field largely started when Lewis Sheiner and Stuart Beal published their seminal papers on population pharmacokinetics in the late 1970's and early 1980's and has continued to grow in impact and use since its inception. Physiological-based pharmacokinetics and systems pharmacology have grown rapidly in scope and impact in the last decade and machine learning is just on the horizon. While all these methodologies are categorized as pharmacometrics, no one person can be an expert in everything. So how do you train future pharmacometricians? Leading experts in academia, industry, contract research organizations, clinical medicine, and regulatory gave their opinions on how to best train future pharmacometricians. Their opinions were collected and synthesized to create some general recommendations. [ABSTRACT FROM AUTHOR]

Aims: This study aimed to develop a population pharmacokinetic (PopPK) model of ilaprazole in healthy subjects and patients with duodenal ulcer in Chinese and investigate the effect of potential covariates on pharmacokinetic (PK) parameters. Methods: Pharmacokinetic data from 4 phase I clinical trials and 1 phase IIa clinical trial of ilaprazole were included in PopPK analysis. Phoenix NLME 8.3 was used to establish a PopPK model and quantify the effects of covariate, such as demographic data, biochemical indicators and disease state on the PK parameters of ilaprazole. The final model was evaluated by goodness-of-fit plots, bootstrap analysis, and visual predictive check. Results: A two-compartment model with first-order elimination successfully described the pharmacokinetic properties of ilaprazole. In the final PopPK model, body weight and sex were identified as statistically significant covariates for volume of peripheral compartment (Vp) and clearance of central compartment (CL), respectively, and disease status was also screened as a significant covariate affecting both CL and Vp. The validation results demonstrated the good predictability of the model, which was accurate and reliable. Conclusion: This is the first population pharmacokinetics study of ilaprazole in the Chinese, and the PopPK model developed in this study is expected to be helpful in providing relevant PK parameters and covariates information for further studies of ilaprazole [ABSTRACT FROM AUTHOR]

The effect of non-metallic inclusions on the fracture behavior of LZ50 railway axle steel at high temperature was studied by combining experiment and finite element method. The evolution process of the micro void initiation, growth and coalescence was observed, and the strain characteristics around the void were analyzed. It is found that the non-metallic inclusions are broken or debonded from the matrix to form micro voids during deformation. The strain around the void is enlarged and concentrated, leading to void growth and coalescence; then, the continuity of the matrix is destroyed to make the fracture occur in advance. The effects of initial void morphology on the matrix fracture behavior were further studied. It is found that the larger the initial void diameter, the closer the axial distance is to the center of the specimen, the easier the matrix is to fracture. However, changing the radial position of the void has little effect on the matrix fracture behavior. When multiple voids exist, void coalescence is likely to occur, which will intensify the discontinuity of the matrix and accelerate the matrix fracture. [ABSTRACT FROM AUTHOR]

This paper presents the high-fidelity physical modeling of a Thrust Vector Control (TVC) system operated by Electro-Mechanical Actuators (EMAs) for Reusable Launch Vehicles (RLVs). In contrast to simplified, often linear, models, high-fidelity physical models enable a better assessment of the actual system performance and a deeper verification of the on-board software robustness against disturbances, unmodeled dynamics or degradation. This aspect is essential to enable the reusability concept as driven by long-term reliability requirements. Moreover, critical systems like Fault Detection, Isolation and Recovery (FDIR) logic, whose robustness and efficacy are often difficult to prove, can be thoroughly tested without requiring hardware experiments with eventual invasive modifications for fault injections. This work captures the whole dynamics of the EMA and TVC components, including the power drive electronics, the electrical motor, and the mechanical transmission for the EMA, as well as the mechanical properties of the engine nozzle acting as a load. The resulting differential algebraic equation system is modeled in the Modelica acausal object-oriented modeling language. An ad-hoc framework is implemented to guarantee flexibility and modularity, which allows models with different fidelity levels to be easily exchanged to achieve the simulation objectives and needed accuracy level. The impact of the different models on the closed-loop TVC dynamics is analyzed in both the frequency and time domain, and then benchmarked with a realistic RLV mission. The results show that the high-fidelity physical models provide a better understanding of the more complex effects governing the TVC dynamics and can, in turn, effectively improve its requirement definition process. • The physical modeling and simulation of an EMA-based TVC system is presented. • High-fidelity physical models help assessing TVC system control software performance. • EMA component models with incremental fidelity levels are proposed. • EMA and TVC system models are integrated in a Modelica-based framework. • Different closed-loop responses are analyzed in the frequency and time domains. [ABSTRACT FROM AUTHOR]

The goal of this work was to develop a physiologically-based pharmacokinetic (PBPK) modeling framework for cisplatin. The model was constructed based on 11 published data sets from rodents; and rabbit, dog, and human data were used to evaluate its utility in predicting plasma and tissue distribution of platinum in larger species, including humans. The model included biotransformation of cisplatin into mobile (k 1) and fixed (k 2) metabolites in all tissues, and subsequent conversion of fixed metabolites to mobile metabolites (k 3) due to protein degradation and turnover. The model successfully captured complex pharmacokinetics of platinum in rodents, and all parameters were estimated with sufficient precision. A separate k 2 parameter was estimated for each included tissue, and the relationship between the rates of formation of mobile and fixed metabolites was established through a scaling factor (k 1 = k 2 · S F , SF=0.74). For interspecies predictions, k 1 and k 2 were shared across all species, and k 3 was scaled allometrically based on protein turnover rate (with an exponent of -0.28). Scaled PBPK model provided a good prediction of total platinum profiles in humans and reasonably captured platinum measurements in human tissues (as obtained from autopsy). [Display omitted] [ABSTRACT FROM AUTHOR]

First-principles calculations have been performed on (HfO 2) p and Hf q Zr r O 2 (q + r) clusters using DFT and TDDFT. The effect of Zr-substitution on the structural, electronic, thermodynamic, and optical properties of (HfO 2) p clusters has been investigated using B3LYP and B3PW91 functionals with LANL2DZ basis set. The calculated properties depend upon geometry, type, and number of atoms present in the clusters. Zr-substitution in (HfO 2) p clusters leads to the reduced molecular mass of Hf q Zr r O 2 (q + r) clusters so that they can be used in making light weight semiconductor devices. Hf 3 Zr 2 O 10 is found to be the most reactive after Zr-substitution in (HfO 2) 5 because of the minimum value of HOMO-LUMO gap. Zr-substitution in (HfO 2) 5 increases the refractive index of Hf 3 Zr 2 O 10 , making it suitable for multi-layer antireflecting coatings. The dielectric constant of Hf 3 Zr 2 O 10 has increased after Zr-substitution finding applications in the MOSFET industry. The absorption spectra can be tuned over the ultraviolet and visible regions depending upon the Zr-substitution. [ABSTRACT FROM AUTHOR]

Quadrupeds are capable of dynamic movements such as fast running and long-distance jumping due to their flexible and elastic torso structures. In this paper, a compliant parallel mechanism is proposed as a bionic torso to simulate the diversified behaviors and agile locomotion of the tetrapod torso. The spring module is incorporated into the limb of the parallel mechanism to absorb external shocks, cushion, and dampen vibrations, thus improving the compliance performance of the bionic torso. For the compliant parallel mechanism, its kinematics and kinetics are analyzed, and the overall electromechanical system and control framework are devised. The multidimensional damping dynamic characteristics of the proposed mechanism are qualitatively analyzed by simplifying the limb into a spring–mass damping system. The parallel mechanism with compliant spring modules absorbs external forces to different degrees with different stiffness coefficients to avoid damage to the structure by external impacts. The parallel mechanism with different initial positions exhibits the inherent variable stiffness characteristics of the mechanism. The parallel mechanism simulates the diverse behavior of the animal torso, with independent and synthesized locomotor behavior of the six underlying motion patterns. Simulations and experiments demonstrated that the compliant parallel mechanism is effective in vibration damping and cushioning, with a rapid response and small steady-state error. The motion of the compliant parallel mechanism in one direction and the motion of the integrated multi-degree of freedom (DOF) are confirmed and exhibited in the behavioral experiment. [ABSTRACT FROM AUTHOR]

As an accurate 2D/3D fabrication tool, inkjet printing technology has great potential in preparation of micro electronic devices. The morphology of droplets produced by the inkjet printer has a great impact on the accuracy of deposition. In this study, the drop-on-demand (DoD) inkjet simulation model was established, and the accuracy of the simulation model was verified by corresponding experiments. The simulation result shows that the velocity of the droplet front and tail, as well as the time to disconnect from the nozzle is mainly affected by density (1), viscosity (µ) and surface tension (s) of droplets. When the liquid filament is about to disconnect from the nozzle, the filament length and filament front velocity are found to have a linear correlation with s/µ and ln(/(µs 2 1)). [ABSTRACT FROM AUTHOR]

Free route airspace allows airspace users to freely plan a route in en-route airspaces within certain restrictions. It is anticipated to offer the benefit of fuel saving and operational flexibility. Regarding its efficient implementation into the ASEAN airspace, the key challenge would be reducing hotspots with clusters of potential conflicts. This paper designed a time-varying queuing network model, which contributed to untangle trajectory complexity in the most congested hotspot area. A series of fast-time simulation experiments were conducted to identify hotspots in en-route airspace in Singapore FIR. The application of departure time control using time-varying queuing networks successfully reduced up to 45% of potential conflicts. This was achieved within an average delay of 30 min by controlling time of less than 60% of candidate flights. The original contribution of this paper is to develop a novel modeling and simulation framework for composing ideal air traffic patterns. Lastly, we discussed the extension of this study toward a generalized application of the proposed approach in future air traffic management. [ABSTRACT FROM AUTHOR]

Compared to the use of positive temperature coefficient (PTC) materials that consume electrical energy for low-temperature heating, heat pump air conditioners can provide more energy-efficient heating performance by absorbing and utilizing heat from the outdoor air to heat the cab in order to improve the range of electric vehicles. In addition, in order to make the battery work under safe working conditions, this paper proposes battery thermal management coupled with vapor injection heat pump air conditioning. The system is modeled and analyzed through simulation, and the impact of the compressor speed and ambient temperature changes in the battery cooling performance of the system. The results show that under different compressor RPM (Revolution Per Minute) with an ambient temperature of 5 °C, the average temperature of the battery pack remains below 30 °C, and the majority of individual cell temperatures are maintained within the range of 20 to 35 °C. At a constant compressor RPM of 4000/min under varying ambient temperatures, the average temperature of the battery pack remains below 30 °C, with the majority of individual cell temperatures staying within the range of 20 to 35 °C. And the battery cooling performance still performs well. In the low temperature of −10 °C and −20 °C, the system can still maintain a relatively stable heating capacity compared with the 2009.1W, provided by the environment temperature of 5 °C at the same RPM. [ABSTRACT FROM AUTHOR]

This study presents a coupled thermo-mechanical simulation of the laser powder bed fusion (LPBF) process with the use of the representative volume (RV) approach. The aim of the study is to evaluate the effects of laser speed, laser power, and pre-heating temperature on the resultant residual stress, which significantly influences component fatigue life and shape distortion. The developed finite element method (FEM) reduces computational time without sacrificing accuracy. The numerical modeling indicates that the top layers are influenced by the normal stress that acts on the material in the transverse directions (S11 and S22), while the middle section is mostly under the influence of the normal stress in the build direction (S33). Moreover, the analysis shows that pre-heating the build platform results in a significant decrease in residual stress. The FEM results indicate that preheating has a greater impact than laser speed, which in turn has a more significant effect than laser power, in reducing the residual stress. These findings demonstrate the importance of considering various processing parameters for minimizing residual stress in additive manufacturing processes. [ABSTRACT FROM AUTHOR]

When two compatible polymer melts are brought into contact, a diffuse interphase is formed that governs the adhesion between them. The degree of interdiffusion is defined mainly by the processing conditions and the physico‐chemical properties of the materials. Despite the great industrial relevance of co‐extrusion in manufacturing multilayer structures, little research has dealt with interdiffusion under these processing conditions, where polymer‐polymer interfaces are subject to shear load. In this work, procedures for investigating interdiffusion both under static and real co‐extrusion flow conditions were developed. Using various grades of poly(methyl methacrylate) and styrene‐co‐acrylonitrile copolymer, we studied both theoretically and experimentally the effects of interfacial contact time, temperature, material compatibility, and interfacial shear stress on interdiffusion. Spectroscopic analysis of the interfaces using confocal Raman microscopy showed that the general relationships between interdiffusion, time (Fick's law) and temperature (Arrhenius relationship) are identical under both static and co‐extrusion conditions. However, the interdiffusion rate is significantly higher under shear influence. Since the prediction of the interdiffusion process supports material selection and process design in multilayer manufacturing, a simple analytical model was developed to quantify the effects of aforementioned material properties and processing conditions on the apparent mutual interdiffusion coefficient. [ABSTRACT FROM AUTHOR]

Rational drug use in special populations is a clinical problem that doctors and pharma-cists must consider seriously. Neonates are the most physiologically immature and vulnerable to drug dosing. There is a pronounced difference in the anatomical and physiological profiles be-tween neonates and older people, affecting the absorption, distribution, metabolism, and excretion of drugs in vivo, ultimately leading to changes in drug concentration. Thus, dose adjustments in neonates are necessary to achieve adequate therapeutic concentrations and avoid drug toxicity. Over the past few decades, modeling and simulation techniques, especially physiologically based pharmacokinetic (PBPK) modeling, have been increasingly used in pediatric drug development and clinical therapy. This rigorously designed and verified model can effectively compensate for the deficiencies of clinical trials in neonates, provide a valuable reference for clinical research design, and even replace some clinical trials to predict drug plasma concentrations in newborns. This review introduces previous findings regarding age-dependent physiological changes and pathological factors affecting neonatal pharmacokinetics, along with their research means. The application of PBPK modeling in neonatal pharmacokinetic studies of various medications is also reviewed. Based on this, we propose future perspectives on neonatal PBPK modeling and hope for its broader application. [ABSTRACT FROM AUTHOR]

The influence of correlations on the thermal performance modeling of parabolic trough collectors was analyzed in this work. A versatile model for a parabolic trough collector was developed that allows one- and two-dimensional analysis and enables the use of correlations to calculate thermophysical properties and convection heat transfer coefficients. The model also allows the use of constant values for properties and/or coefficients obtained from the evaluation correlations at a specific temperature. The effect of each correlation was evaluated independently, and the results were compared with a reference case that considered a two-dimensional approach and used all the correlations. For the analyzed cases, the correlation for the absorber emittance has the strongest impact on the collector efficiency, leading to a lower error when used. Based on the results, a one-dimensional model approach considering a correlation for the absorber emittance leads to efficiency errors below 3% for collector lengths of up to 243.6 m. Compared with the reference case, a one-dimensional approach using all correlations for a collector with a length of 500 m, and operating with an inlet temperature of 773 K, can result in errors around 9%. However, using constant values for properties and heat transfer coefficients could lead to errors of up to 50%. Multiple thermal models for parabolic trough collectors proposed in the literature rely on a one-dimensional approach, estimated values for the heat transfer coefficients, and constant thermophysical properties. The errors associated with those approaches are analyzed and quantified in this work as a function of the collector length and operation temperature. [ABSTRACT FROM AUTHOR]

Silicon carbide nanowires (SiC NWs) are widely used as reinforcing materials in composite materials based on ceramics, metals, and polymers, and their high-temperature mechanical properties have become a focus of attention. In this paper, the effect of temperature and crystal orientation on the tensile mechanical behavior of SiC NWs was explored through molecular dynamics simulation. It is observed that the fracture mode and mechanical properties of SiC NWs express a significant temperature dependence. Both critical stress and Young's modulus of nanowires with different orientations decrease with increasing temperature and the [111]- and [112]-oriented nanowires exhibit brittle fracture characteristics at low temperatures and become ductile fractures at high temperatures. The transition temperature for ductile–brittle fracture is between 1300-1800 K. The fracture surfaces of SiC NWs with different orientations are all {111} planes at low temperatures. This study provides theoretical support for SiC NWs' laboratory growth and toughening mechanism research. [ABSTRACT FROM AUTHOR]

This work studied the adsorption of nitrogen monoxide gas on Cu-modified B12N12 nanocage as a chemical sensor application. We performed quantum chemical calculations using density functional theory (DFT) at the B3LYP-D3/6-31G(d,p) level. Seven adsorption configurations of NO molecule on pristine B12N12 were tested to find the best interaction mode. The modification of B12N12 with copper resulted in five optimized structures: CuB11N12 and B12N11Cu (doped), Cu@b66 and Cu@b64 (decorated), and Cu@B12N12 (encapsulated). The quantum molecular dynamic analysis revealed that almost all isolated systems are stable. The results indicate that the NO gas weakly physisorbed on pure B12N12 nanocage, but the Cu-modifications improved adsorption performance. We also found that, over B12N12, the NO molecule prefers to adsorb on the O site rather than the N site. In addition, our findings showed that Cu@B12N12 has high electronic sensitivity (ΔEgap = 87.4%) and great conductometric potential (∆Φ = 48.9%) towards NO gas, when compared to the other modified systems. Regarding selective detection of NO gas among other gas molecules (CH4, N2O, SO2, CO2, H2, and NH3), the Cu@B12N12 system showed high selectivity for NO detection. Finally, the results for energy gap and work function demonstrated the capability of Cu@B12N12 as a conductometric and work function sensor material type for applications in the selective detection of NO gas. Schematic representation of the detection of toxic gases onto Cu@B12N12 surface. [ABSTRACT FROM AUTHOR]

This study reports the application of KBiO3 as both an excellent photocatalyst and an effective adsorbent for the removal of crystal violet dye (CV) from wastewater. CV was rapidly adsorbed from water at a removal rate higher than 90% within 60 min by following the pseudo-second-order kinetics and Langmuir isotherm models. The captured CV was then completely photodecomposed in the adsorption and subsequent photocatalytic regeneration phases, thus resulting in an effective adsorption capacity regeneration of KBiO3. This proposed photodegradation-assisted adsorption reveals the efficiency of combining adsorption and photodegradation processes in the treatment of wastewater as it does not require secondary treatment of the organic pollutants, has a low cost, and is environmentally friendly. [ABSTRACT FROM AUTHOR]

The interaction of a PEGylated and non-PEGylated Fe3O4 nanoparticle drug-delivery system, with 5-fluorouracil (5-FU) as the chemotherapy drug, is investigated via atomistic molecular dynamics (MD). The induced pore formation in a dipalmitoylphosphatidylcholine (DPPC) bilayer phospholipid (BLPL) is studied, and the resulting hourglass-shaped pores with hydrophilic lipid headgroups lining the pores are observed. Furthermore, we optimize the required number of ligands that are required to allow for the formed pores to spontaneously reseal. Additionally, the number of water molecules that transverse through the water bridge is investigated. These results may be useful to design nanocarrier systems that will maintain the cellular osmotic pressure and stability, while the 5-FU is converted to the required metabolites inside the cell to serve its purpose as a chemotherapeutic drug. [ABSTRACT FROM AUTHOR]

The integration of mobile robots in material handling in flexible manufacturing systems (FMS) is made possible by the recent advancements in Industry 4.0 and industrial artificial intelligence. However, effectively scheduling these robots in real-time remains a challenge due to the constantly changing, complex, and uncertain nature of the shop floor environment. Therefore, this paper studies the robot scheduling problem for a multiproduct FMS using a mobile robot for loading/unloading parts among machines and buffers. The problem is formulated as a Markov Decision Process, and the Q-learning algorithm is used to find an optimal policy for the robot's movements in handling different product types. The performance of the system is evaluated using a reward function based on permanent production loss and the cost of demand dissatisfaction. The proposed approach is validated through a numerical case study that compares the proposed policy to a similar policy with different reward function and the first-come-first-served policy, showing a significant improvement in production throughput of approximately 23%. [ABSTRACT FROM AUTHOR]

The selection of an appropriate development methodology is a critical strategic decision when managing a New Product Development (NPD) project. However, accurately estimating project duration based on the chosen methodology remains a challenge. This paper addresses the limitations of existing models and proposes a novel NPD project model that allows for testing and evaluation of different product development strategies. The model considers Waterfall, Spiral, Agile, and Hybrid methodologies and provides system engineers and project managers with decision-making tools to determine the optimal strategy and understand associated tradeoffs. The model is validated using real projects from various organizations and methodologies. It incorporates stochastic variables, risk management, and dynamic resource allocation, while addressing both Waterfall and Agile methodologies. The study contributes to the body of knowledge by offering practical tools for system engineers and project managers for choosing development methodology, improving project duration estimation, and identifying critical processes and risks in NPD projects. The research results also provide a basis for further studies and can benefit researchers interested in systems engineering methodologies. The proposed model fills a gap in the literature by providing a validated NPD model to evaluate the impact of different product development methodologies on project duration. [ABSTRACT FROM AUTHOR]

One of the critical issues faced by the desalination plants is an accurate analysis of their real-time performance. Soft computing techniques are efficient in overcoming these issues and predicting accurate outcomes. In this paper, models based on the third version of the modified particle swarm optimization algorithm called autonomous groups particles swarm optimization—based artificial neural network (AGPSO3-ANN) have been proposed for accurate prediction of permeate flux of reverse osmosis (RO) desalination plant. It employs remarkable optimization strategy that demonstrates superiority than the conventional PSO-ANN techniques, to update acceleration factors (c1 and c2) and to screen the global best solution. Here, four input parameters: evaporator inlet temperature, feedwater salt concentration, condenser inlet temperature, and feed flow rate have been considered for the modeling, and models' performance evaluated in terms of the regression coefficient (R2) and mean square errors (MSE). The results show an impressive agreement between simulated and experimental datasets indicating that the proposed approach is strongly capable of finding optimal solutions to predict accurate results with minimum errors (R2 = 99.2%, MSE = 0.005) compared to the existing ANN approaches. This demonstrates that the proposed models based on such soft computing tools like AGPSO3-ANN are perfect for analyzing and predicting real-time desalination plant performance that would present an effective way for improved process control and efficiency for plant engineers. [ABSTRACT FROM AUTHOR]

The generation of clean energy from wind has recently received huge attention. Thanks to the current advances of adaptive algorithms due to their benefits and flexibility. The paper introduces a new smart radial basis function (RBF) neural network to extract the optimal energy from wind for wind energy conversion systems. This scheme uses the electrical energy of the doubly fed induction-generator (DFIG) as an input in wind turbines drives a DFIG to acquire maximum energy from the available wind under uncertainties and fast-changing wind conditions. Thus, to prove the quality of our proposed intelligent scheme, a comparative study with conventional optimum power is applied to a wind turbine driving a class of 1.5 MW DFIG during the transient operation. Furthermore, the analysis and the interpretation of raw and processed real measured data using the process of linear interpolation through Matlab/Simulink illustrate the relevance and the performance of the sensorless controller for the overall wind turbine system. Briefly, the numerical simulation studies show that a good efficiency and improved tracking of the smart RBF-neural network controller when implemented online below the real wind speed despite the unknown parameters. [ABSTRACT FROM AUTHOR]

Feedwater leakages due to excessive loads and cracking caused by corrosion or fatigue failure can affect the reliability of the production facilities. In the present work, a numerical study of a small leakage accident type SB-LOCA on the feed water pipeline was investigated using Computational Fluid Dynamics (CFD) and Relap5 computer codes. The aim is to understand the behavior of the incompressible water flow and its effect on the relevant parameters at the leakage location vicinity, including the mass flow rate, velocity, pressure, and temperature. For this, a mathematical model was developed and validated to evaluate the release of water through the pipe, which is mainly based on the variables that may affect the leakage. The results of CFD show that the leakage has important effects on the distribution of main parameters of the water flow through the pipe, which has an identical outcome from the Relap5 code simulation. The change of fluid velocity only has a little impact on the flow behavior at the leakage region. [ABSTRACT FROM AUTHOR]

This article describes a coaxial plasma–MIG hybrid welding (PMHW) method to improve the welding quality of titanium alloy plates. Aiming at the temperature field analysis of a Ti-6Al-4V alloy plate in the PMHW process, an improved hybrid heat source model capable of accurately characterising the heat input of a hybrid arc on workpieces and the arc deflection phenomenon, is proposed. Experiments were conducted on 4-mm Ti-6Al-4V plates in PMHW surfacing, and a corresponding simulation analysis was performed. To calculate the weld geometry, a weld pool shape was used in accordance with the experimental results. The results showed that, as plasma current increased from 60 to 80 A, the weld reinforcement and offset Δ d decreased, and the size of the weld pool increased. Plasma arc power had a greater effect on the length than on the width of the molten pool. Hybrid arcs had a stronger penetrating ability; hence, achieving deep penetration welding and high welding efficiency in the PMHW of the Ti-6Al-4V alloy was easier. Microstructure analysis showed that the Ti-6Al-4V alloy base metal zone was organised by α- and β-phase transformations; β grains in the weld zone were internally formed by a mixture of Weischer tissue and net basket tissue; and the size of β grains in the heat-affected zone decreased with the increase in the distance from the weld. [ABSTRACT FROM AUTHOR]

Modeling energy systems is important to generate a range of insights and analyses to improve energy efficiency. However, some details are missing in most end‐use models for industrial systems, which has a profound effect on energy modeling for petrochemical plants. In an enterprise-wide optimization conducted in a petrochemical plant, the unit's utility consumptions were modeled and some energy efficiency improvements were observed but with seasonality behaviors that were not appropriately represented and explained. The objective of this study was to obtain a relationship between energy efficiency gains with energy market cost (PLD), electricity demand, room temperature and plant load using the following methods: LRM, ARIMA, and Data Mining, comparing their performance in terms of accuracy and easy-of-use. LRM, despite being more used in the literature, was not applicable, ARIMA had a 67.9% goodness-of-fit, and Data Mining had the best results, with 82.8% and 98.8% goodness-of-fits using the M5P and RandomTree algorithms, respectively. In terms of data visualization, Data Mining is easy with the M5P algorithm, but the RandomTree algorithm has a very extensive regression tree, with 975 rows. The approach can support the organizations to empowerment these employees seeking to handle, store, and analyze all the data available on the company. At the end, the best approach to modelling and better understanding the energy efficiency improvements in a utilities supply on a petrochemical plant was stated as a M5P and its framework can be used to support the decision makers. [ABSTRACT FROM AUTHOR]

This study reports the application of KBiO3 as both an excellent photocatalyst and an effective adsorbent for the removal of crystal violet dye (CV) from wastewater. CV was rapidly adsorbed from water at a removal rate higher than 90% within 60 min by following the pseudo-second-order kinetics and Langmuir isotherm models. The captured CV was then completely photodecomposed in the adsorption and subsequent photocatalytic regeneration phases, thus resulting in an effective adsorption capacity regeneration of KBiO3. This proposed photodegradation-assisted adsorption reveals the efficiency of combining adsorption and photodegradation processes in the treatment of wastewater as it does not require secondary treatment of the organic pollutants, has a low cost, and is environmentally friendly. [ABSTRACT FROM AUTHOR]

Abstract Mitiperstat is a myeloperoxidase inhibitor in clinical development for treatment of patients with heart failure and preserved or mildly reduced ejection fraction, non‐alcoholic steatohepatits and chronic obstructive pulmonary disease. We aimed to assess the risk of QT‐interval prolongation with mitiperstat using concentration–QT (C‐QT) modeling. Healthy male volunteers were randomized to receive single oral doses of mitiperstat 5, 15, 45, 135, or 405 mg (n = 6 per dose) or matching placebo (n = 10) in a phase 1 study (NCT02712372). Time‐matched pharmacokinetic and digital electrocardiogram data were collected at the baseline (pre‐dose) and at 11 time‐points up to 48 h post‐dose. C‐QT analysis was prespecified as an exploratory objective. The prespecified linear mixed effects model used baseline‐adjusted QT interval corrected for the heart rate by Fridericia's formula (ΔQTcF) as a dependent variable and plasma mitiperstat concentration as an independent variable. Initial exploratory analyses indicated that all model assumptions were met (no effect on heart rate; appropriate use of QTcF; no hysteresis; linear concentration–response relationship). Model‐predicted mean baseline‐corrected and placebo‐adjusted ΔΔQTcF was +0.73 ms (90% confidence interval [CI]: −1.73, +3.19) at the highest anticipated clinical exposure (0.093 μmol/L) during treatment with mitiperstat 5 mg once daily. The upper 90% CI was below the established threshold of regulatory concern. The 16‐fold margin to the highest observed exposure was high enough to mean that a positive control was not needed. Mitiperstat is not associated with risk of QT‐interval prolongation at expected therapeutic concentrations.

BackgroundThe concepts of “individualization” and “preventive treatment” should be incorporated into the precise diagnosis and treatment of coronary heart disease (CHD). Both hemodynamics and Chinese medicine constitution studies align with these two concepts.MethodsThis study utilized data from 81 patients with CHD, including 12 patients with balanced constitution (BC), 20 patients with blood stasis constitution (BSC), 17 patients with phlegm-dampness constitution (PDC), 15 patients with qi-deficiency constitution (QDC), and 17 patients with other constitutions. Clinical data provided information on the patients' blood property, heart function, degree of coronary stenosis, coronary hemodynamics, and so on. These parameters were compared between patients with balanced constitution vs. biased constitutions as well as between those with blood stasis constitution, phlegm-dampness constitution, and qi-deficiency constitution.ResultsCompared to biased constitution (BC), patients with balanced constitution exhibited lower total cholesterol (TC) levels and low-density lipoprotein (LDL) levels. Additionally, they had lighter stenosis degrees in the Left anterior descending branch (LAD) and Left circumflex branch (LCX) branches. The hemodynamic condition of the LAD and LCX was better for those with balanced constitution; however there was no difference in heart function. Among the groups categorized by blood stasis, phlegm dampness or qi deficiency constituions, patients classified under phlegm dampness had higher levels of LDL compared to those classified under blood stasis or qi deficiency, while patients classified under qi deficiency had higher levels of blood glucose compared to those classified under blood stasis or phlegm dampness. Hemodynamic environments also differed among the LAD and LCX for each group but there were no significant differences observed in heart function or degree of coronary stenosis among these three groups.ConclusionThe balanced constitution demonstrates superior blood property, degree of coronary artery stenosis, and coronary hemodynamics compared to the biased constitution. Furthermore, among the three constitutions with CHD, variations in blood property and certain hemodynamic parameters are observed. These findings emphasize the significant clinical value of incorporating physical factors into the diagnosis and treatment of patients with CHD.

While equation-based object-oriented modeling language Modelica can evaluate practical energy improvements for district cooling systems, few have adopted Modelica for this type of large-scale thermo-fluid system. Further, to our best knowledge, district cooling modeling studies have yet to include hydraulics in piping networks alongside plant models featuring realistic mechanical systems and controls. These are critical details to include when looking to make energy and control improvements in many physical system installations. To fill these gaps, this study released new open-source district cooling models at the Modelica Buildings Library and leveraged these models for a real-world case study at the University of Colorado Boulder. The site includes six buildings connected to a central chiller plant featuring a waterside economizer. Several energy saving strategies are pursued based on the validated model, including control setpoint optimization, equipment modification, and pump setpoint adjustments. Results indicate that a combination of the studied measures can save the campus annually 84.6 MWh of energy, 8.9% of electricity costs, 58.0 metric tons of carbon dioxide emissions, while the waterside economizer cuts down chillers’ run times by 201 days/year, reducing maintenance costs and extending chiller life.

The paper deals with alliances and coalitions that can be formed by agents or entities. It is assumed that alliance agents cooperate and form coalitions for performing the tasks or missions. It is considered that alliance agents are unselfish. That is, they are more interested in achieving the common goal(s) than in getting personal benefits. In the paper, the concept of fuzzy alliance was introduced. A fuzzy alliance is considered as generalization of traditional alliance allowing agents to decide on the capabilities that their agents can and wanted deliver to coalition. Coalitions that can be formed by fuzzy alliance agents were considered. The definition of the “best” coalition was explained. The method of how to find the “best” coalition among all possible coalitions was suggested and verified by computer simulation.

Under the background of dual-carbon national strategy and the widespread application of new-generation information technologies, systematization, precision, and intellectualization are the important development directions for the governance of mine ecological environment. Informatization of the governance of mine ecological environment in China has been carried out in a relatively rich and long-term way, but there are still problems such as lack of comprehensive monitoring system, inaccurate modeling of ecological evolution, ecological governance relying on expert experience, and no decision-making tools on informatization. Accordingly, in this study, the digital twin of mine ecological environment (DTME) is proposed, and its general framework, functional features and key technologies for system construction are introduced. The DTME is formally defined as a twin system of the physical mine ecosystem in virtual space by mapping three aspects: ecological elements, evolutionary processes, and impact mechanisms. The DTME utilizes spaceborne, airborne, and ground multi-source monitoring devices to dynamically detect the changes of ecological elements in the mine area, and constructs the complex driving models of the ecological evolution based on the artificial intelligence. The DTME supports the whole process of the scientific governance of mine ecological environment through program optimization or feedback control. Furthermore, the general framework of the DTME is introduced, including physical mine ecological environment (physical entity), monitoring terminal, twin data, information link, cloud-edge-terminal service, virtual mine ecological environment (virtual entity), and control terminal. Guided by real-life application requirements, the DTME can achieve mine ecological quality dynamic monitoring and health examination, mine ecological evolution simulation and impact factor analysis, prediction of mine ecological changes, mine ecological risk warning, intelligent grading and zoning of ecological restoration, mine ecological restoration effect evaluation, and mine ecological environment supervision. Finally, it is illustrated that the construction of the digital twin system of mine ecological environment involves four key technologies: digital base of mine ecosystem, digital scene of mine ecosystem, simulation and prediction of mine ecological evolution, and intelligent decision of mine ecological governance. The application of DTME can support the sensing, representation, monitoring, simulation, emulation, prediction, and control of the whole governance process of mine ecological environment, which is promising to significantly improve the quality and efficiency of governance.

Analysis using simulation is a natural and logical extension of the analytical and mathematical models inherent in operations research. Simulation has become a modern tool that helps in studying many systems that we could not study or predict the results that we could obtain during the operation of these systems over time before the existence of Simulation, since the main interest in statistical analysis is to obtain a series of random variables that follow the probability distribution in which the system under study operates, through a series of random numbers that follow a uniform distribution over the domain [0, 1], using scientific methods provided by the efforts of researchers. In the field of modeling and simulation, such as the reverse transformation method, the rejection and acceptance method, and other methods that we have reformulated using the concepts of neutrosophic science in previous research. In summary of what we have done previously, we present in this research a study whose purpose is to generate random variables that follow the beta distribution, which is used in many applications. In administrative processes, especially in analyzing network diagrams using the neutrosophic rejection and acceptance method.

Feedwater leakages due to excessive loads and cracking caused by corrosion or fatigue failure can affect the reliability of the production facilities. In the present work, a numerical study of a small leakage accident type SB-LOCA on the feed water pipeline was investigated using Computational Fluid Dynamics (CFD) and Relap5 computer codes. The aim is to understand the behavior of the incompressible water flow and its effect on the relevant parameters at the leakage location vicinity, including the mass flow rate, velocity, pressure, and temperature. For this, a mathematical model was developed and validated to evaluate the release of water through the pipe, which is mainly based on the variables that may affect the leakage. The results of CFD show that the leakage has important effects on the distribution of main parameters of the water flow through the pipe, which has an identical outcome from the Relap5 code simulation. The change of fluid velocity only has a little impact on the flow behavior at the leakage region.

Licensing the next-generation of nuclear reactor designs requires extensive use of Modeling and Simulation (M&S) to investigate system response to many operational conditions, identify possible accidental scenarios and predict their evolution to undesirable consequences that are to be prevented or mitigated via the deployment of adequate safety barriers. Deep Learning (DL) and Artificial Intelligence (AI) can support M&S computationally by providing surrogates of the complex multi-physics high-fidelity models used for design. However, DL and AI are, generally, low-fidelity ‘black-box’ models that do not assure any structure based on physical laws and constraints, and may, thus, lack interpretability and accuracy of the results. This poses limitations on their credibility and doubts about their adoption for the safety assessment and licensing of novel reactor designs.In this regard, Physics Informed Neural Networks (PINNs) are receiving growing attention for their ability to integrate fundamental physics laws and domain knowledge in the neural networks, thus assuring credible generalization capabilities and credible predictions. This paper presents the use of PINNs as surrogate models for accidental scenarios simulation in Nuclear Power Plants (NPPs). A case study of a Loss of Heat Sink (LOHS) accidental scenario in a Nuclear Battery (NB), a unique class of transportable, plug-and-play microreactors, is considered. A PINN is developed and compared with a Deep Neural Network (DNN). The results show the advantages of PINNs in providing accurate solutions, avoiding overfitting, underfitting and intrinsically ensuring physics-consistent results.

This document is an editorial published in the journal Frontiers in Energy Research. It discusses the importance of multi-physics and multi-scale modeling and simulation methods for nuclear reactor applications. The editorial highlights the need for comprehensive analysis of various factors such as neutronics, fuel properties, thermal and hydraulic engineering, and chemical kinetics in order to achieve safe and cost-effective operation of nuclear reactors. The editorial also mentions several research papers that have explored the coupling of different physics and scales in nuclear reactor simulations, as well as advancements in modeling and simulation tools. The document concludes by emphasizing the potential benefits of incorporating modeling and simulation in the design, operation, and maintenance of nuclear reactors. [Extracted from the article]

In order to address the issues of significant energy and resource waste, low-energy management efficiency, and high building-maintenance costs in hot-summer and cold-winter regions of China, a research project was conducted on an office building located in Nantong. In this study, a data-driven golden jackal optimization (GJO)-based Long Short-Term Memory (LSTM) short-term energy-consumption prediction and optimization system is proposed. The system creates an equivalent model of the office building and employs the genetic algorithm tool Wallacei to automatically optimize and control the building’s air conditioning system, thereby achieving the objective of reducing energy consumption. To validate the authenticity of the optimization scheme, unoptimized building energy consumption was predicted using a data-driven short-term energy consumption-prediction model. The actual comparison data confirmed that the reduction in energy consumption resulted from implementing the air conditioning-optimization scheme rather than external factors. The optimized building can achieve an hourly energy saving rate of 6% to 9%, with an average daily energy-saving rate reaching 8%. The entire system, therefore, enables decision-makers to swiftly assess and validate the efficacy of energy consumption-optimization programs, thereby furnishing a scientific foundation for energy management and optimization in real-world buildings.

With the development of lithium-ion batteries and fuel cells, the application of hybrid power systems is becoming more and more widespread. To better optimize the energy management problem of fuel cell hybrid systems, the accuracy of system modeling and simulation is very important. The hybrid system is formed by connecting the battery to the fuel cell through an active topology. Digital twin technology is applicable to the mapping of physical entities to each other with high interactivity and fast optimization iterations. In this paper, a relevant model based on mathematical logic is established by collecting actual operational data; subsequently, the accuracy of the model is verified by combining relevant operating conditions and simulating the model. Subsequently, a three-dimensional visualization model of a hybrid power system-based sightseeing vehicle and its operating environment was established using digital twin technology to improve the model simulation of the fuel cell hybrid power system. At low speeds, the simulation results of the hybrid power system-based sightseeing vehicle have a small error compared with the actual running state, and the accuracy of the data related to each internal subcomponent is high. In the simple interaction between the model display vehicle and the environment, the communication state can meet the basic requirements of the digital twin model because the amount of data to be transferred is small. This study makes a preliminary attempt at digital parallelism by combining mathematical logic with visualization models and can be used as a basis for the subsequent development of more mature digital twin models.

The automotive industry is entering a digital revolution, driven by the need to develop new products in less time that are high-quality and environmentally friendly. A proper manufacturing process influences the performance of the door grommet during its lifetime. In this work, uniaxial tensile tests based on molecular dynamics simulations have been performed on an ethylene–propylene–diene monomer (EPDM) material to investigate the effect of the crosslink density and its variation with temperature. The Mooney–Rivlin (MR) model is used to fit the results of molecular dynamics (MD) simulations in this paper and an exponential-type model is proposed to calculate the parameters C1(T) and C2T. The experimental results, confirmed by hardness tests of the cured part according to ASTM 1415-88, show that the free volume fraction and the crosslink density have a significant effect on the stiffness of the EPDM material in a deformed state. The results of molecular dynamics superposition on the MR model agree reasonably well with the macroscopically observed mechanical behavior and tensile stress of the EPDM at the molecular level. This work allows the accurate characterization of the stress–strain behavior of rubber-like materials subjected to deformation and can provide valuable information for their widespread application in the injection molding industry.