Comprehensive Model Research Articles

A Blockchain-Based Model for Traceability and Quality Management of Raw Materials in Clothing Products

In this study, we employ blockchain-based traceability technology to construct a comprehensive model for raw material traceability and quality management in clothing products. Additionally, we investigate engineered water nanostructures (EWNS) for their antibacterial, hydrophilic, and anti-pilling effects on textiles. The EWNS can serve as an effective tool for monitoring quality, enabling precise tracking and verification of nanomaterials during clothing production. The research validated the traceability performance of the blockchain-based model for raw material traceability and quality management in clothing products, achieving a remarkable traceability accuracy of 99.92% and an exceptionally low traceability response time of merely 1.83 seconds. Moreover, after being treated with EWNS, antibacterial rates against E. coli and S. aureus in clothing antibacterial quality management reached 99.9% and 99.0%, respectively. In the hydrophilicity test, EWNS was applied to clothing for a duration of 1 to 7 hours, resulting in a significant reduction in the wetting time of clothing from 10.8 seconds to 1.2 seconds. In the anti-pilling tests, there was a notable enhancement in the effectiveness of anti-pilling properties after EWNS treatment. In summary, our model has achieved remarkable outcomes in material traceability and quality management, offering consumers and the clothing industry a reliable method to ensure product quality and traceability, while simultaneously improving performance aspects such as antibacterial efficacy, hydrophilicity, and resistance against pilling.

Comprehensive Model of Laminar Jamming Variable Stiffness Driven by Electrostatic Adhesion

Comprehensive Model of Laminar Jamming Variable Stiffness Driven by Electrostatic Adhesion

A Comprehensive Model of Variable Pitch Electric Propulsion Unit for Optimal-Power Thrust Control

A Comprehensive Model of Variable Pitch Electric Propulsion Unit for Optimal-Power Thrust Control

Sensitive Business Process Modeling Aspects

In order to improve their performance, modern organizations are increasingly aware of the need to identify, preserve and manage the crucial knowledge (individual and collective) that are mobilized by their sensitive business processes (SBPs). Thus, it will be necessary to characterize, identify, specify, model and analyze these processes, in order to optimize the activities of identification, localization and management of the knowledge on which it is necessary to capitalize. This paper introduces the problematic of the SBP modeling. Our objective is to provide a conceptual analysis related to the concept of SBP. First of all, we propose a rigorous characterization of SBP (which distinguishes it from classic, structured and conventional BPs). Secondly, we propose a multidimensional classification of SBP modeling aspects and requirements to develop expressive, com-prehensive and rigorous models. Besides, we present an in-depth study of the different modeling approaches and languages, in order to analyze their expressiveness and their ability to perfectly and explicitly represent the new specific requirements of SBP modeling. In this study, we choose the better one positioned nowadays, BPMN 2.0, as the best suited standard for SBP representation. Finally, we propose a semantically rich conceptualization of a SBP organized in a core ontology.

A Series Impedance Reshaping Control Method Considering PLL Dynamics for Grid-Connected Inverters Under Weak Grid Conditions

Grid-connected inverters are the key part in renewable energy power generation systems. Usually, phase-locked loop (PLL) is adopted in grid-connected inverters to achieve synchronization. However, prior-art works have revealed that the negative resistance behavior in the low-frequency band caused by PLLs may lead to system instability under weak grids. In this paper, first, a comprehensive small-signal output impedance modeling of the LCL-filtered single-phase grid-connected inverter is derived for stability analysis, where the digital control delays and PLL dynamics have been taken into account. Then, a simple but effective virtual series based impedance reshaping method is proposed. With that, the inverter system can operate stably even under a very weak grid (e.g., short-circuit ratio (SCR) is as low as 1.2). Compared with the traditional grid impedance compensation by virtually increasing grid resistance or reducing the grid inductance, the proposed method does not require grid impedance estimation and is robust against system parameter variations. Thus, its feasibility is improved. Finally, experimental tests are carried out on a single-phase 5-kW prototype, validating the effectiveness of the proposed scheme.

Investigating the dark matter halo of NGC 5128 using a discrete dynamical model

Context. As the nearest accessible massive early-type galaxy, NGC 5128 presents an exceptional opportunity to measure dark matter halo parameters for a representative elliptical galaxy. Aims. Here we take advantage of rich new observational datasets of large-radius tracers to perform dynamical modeling of NGC 5128 Methods. We used a discrete axisymmetric anisotropic Jeans approach with a total tracer population of nearly 1800 planetary nebulae, globular clusters, and dwarf satellite galaxies extending to a projected distance of ∼250 kpc from the galaxy center to model the dynamics of NGC 5128. Results. We find that a standard Navarro-Frenk-White (NFW) halo provides an excellent fit to nearly all the data, except for a subset of the planetary nebulae that appear to be out of virial equilibrium. The best-fit dark matter halo has a virial mass of Mvir = 4.4−1.4+2.4 × 1012 M⊙, and NGC 5128 appears to sit below the mean stellar mass–halo mass and globular cluster mass–halo mass relations, which both predict a halo virial mass closer to Mvir ∼ 1013 M⊙. The inferred NFW virial concentration is cvir = 5.6−1.6+2.4, which is nominally lower than cvir ∼ 9 predicted from published cvir–Mvir relations, but within the ∼30% scatter found in simulations. The best-fit dark matter halo constitutes only ∼10% of the total mass at one effective radius but ∼50% at five effective radii. The derived halo parameters are consistent within the uncertainties for models with differing tracer populations, anisotropies, and inclinations. Conclusions. Our analysis highlights the value of comprehensive dynamical modeling of nearby galaxies and the importance of using multiple tracers to allow cross-checks for model robustness.

Combining permuted language model and adversarial training for Chinese machine reading comprehension

Due to the polysemy and complexity of the Chinese language, Chinese machine reading comprehension has always been a challenging task. To improve the semantic understanding and robustness of Chinese machine reading comprehension models, we propose a model that utilizes adversarial training algorithms and Permuted Language Model (PERT). Firstly, we employ the PERT pre-training model to embed paragraphs and questions into vector space to obtain corresponding sequential representations. Secondly, we use a multi-head self-attention mechanism to extract key textual information from the sequence and employ a Bi-GRU network to semantically fuse the output feature vectors, aiming to learn deep semantic representations in the text. Finally, we introduce perturbations into the model training process. We achieve this by utilizing adversarial training algorithms such as Fast Gradient Method (FGM) and Projected Gradient Descent (PGD). These algorithms generate adversarial samples to enhance the model’s robustness and stability when facing diverse inputs. We conducted comparative experiments on the publicly available Chinese reading comprehension datasets CMRC2018 and DRCD. The experimental results show that our proposed model has achieved significant improvements in both EM and F1-Score compared to the baseline model. To validate the model’s generalization and robustness, we utilized ChatGPT to construct a scientific dataset that includes a large number of domain-specific terms, sentences with mixed Chinese and English, and complex comprehension tasks. Our model also performed remarkably well on the self-built dataset. In conclusion, the proposed model not only effectively enhances the understanding of semantic information in Chinese text but also demonstrates a certain level of generalization capability.

Characterizing Drop-Wall Interactions of Engine Fuels at Engine-Relevant Conditions Using Smoothed Particle Hydrodynamics

Abstract In an internal combustion engine, interactions of fuel droplets and heated walls can significantly affect the combustion process and engine performance. The formation and characteristics of secondary droplets from drop-wall interactions are functions of various factors such as fuel properties, impact velocity, ambient conditions, and wall temperature. Understanding the impact behavior is important to optimize the distribution of the fuel-air mixture for efficient and clean combustion and to develop a comprehensive spray-wall interaction model. In this study, three-dimensional smoothed particle hydrodynamics (SPH) simulations are performed to investigate the interactions of fuel droplets with a heated wall at atmospheric and elevated pressures over a range of Weber numbers (We). The SPH model is validated using available experimental data. Secondary atomization is characterized by using size distributions for different fuels. The resulting droplets vary in size, where secondary droplets are mostly below 7 μm in diameter. Following these cases, this paper qualitatively describes the impact process and proposes empirical correlation relating the mean secondary droplet size to ambient pressure in the film-boiling regime. Postimpingement vaporization characteristics are also analyzed and compared for fuels with drastically different vapor pressures.

Development of a Comprehensive Dynamic Model for Giant Magnetostrictive Transducers Considering Bias Magnetic Field

Development of a Comprehensive Dynamic Model for Giant Magnetostrictive Transducers Considering Bias Magnetic Field

AI-Based Holistic Model for Decision-Support System in Inventory Optimization using deep Learning

Abstract: This study introduces an innovative artificial intelligence-driven comprehensive model designed for a decision-support system in inventory optimization. This model was developed using advanced deep learning techniques. The model aims to surpass the constraints of typical inventory management systems by effectively capturing temporal correlations and nuanced patterns in the data. This is achieved by integrating Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures. To navigate modern supply chains effectively, the primary focus should be on offering real-time decision support. The approach aims to enhance the decision-making processes in inventory management. This brief introduction offers insight into a distinctive method aimed at addressing the challenges of modern supply networks. The strategy focuses on providing realtime decision support and optimization strategies to enhance inventory management efficiency. The model's effectiveness is demonstrated by thorough performance evaluations, showing higher accuracy, precision, and recall compared to existing logistic regression models. The suggested LSTM model achieves an accuracy of 0.99, precision of 0.96, recall of 0.94, and an F1 Score of 0.97, surpassing existing models with higher scores in all metrics. The GRU model, albeit somewhat less effective than the LSTM, shows great overall performance, especially in recall with a score of 0.97. This investigation compares the effectiveness of different models, focusing on the potential advancements made possible by LSTM and GRU architectures in improving decision-support systems for inventory optimization. The AI-Based Holistic Model provided is at the forefront of technology advancements, offering a comprehensive solution for making educated decisions and optimizing inventories sustainably in today's highly competitive corporate climate.

Chemical engineering and the circular water economy: Simulations for sustainable water management in environmental systems

In the face of escalating water scarcity and environmental degradation, the imperative for sustainable water management has never been more urgent. Chemical engineering emerges as a pivotal discipline in the pursuit of solutions to these pressing challenges. This review explores the role of chemical engineering in advancing the circular water economy paradigm through simulations aimed at fostering sustainable water management within environmental systems. The circular water economy concept advocates for the efficient utilization, recycling, and reclamation of water resources to minimize waste and maximize resource efficiency. Chemical engineering techniques play a fundamental role in realizing this vision through the design and optimization of water treatment processes, resource recovery systems, and advanced simulation methodologies. This review delves into the application of computational simulations within the realm of chemical engineering to model and analyze various aspects of the water cycle. Such simulations enable the assessment of complex environmental systems, aiding in the identification of optimal strategies for water resource allocation, pollution control, and ecosystem preservation. Key areas of focus include the simulation of wastewater treatment processes, such as biological, physical, and chemical treatment methods, to enhance pollutant removal efficiency and promote water reuse. Furthermore, advanced modeling techniques facilitate the evaluation of innovative technologies like membrane filtration, adsorption, and electrochemical processes for the purification and desalination of water resources. Moreover, chemical engineering simulations enable the assessment of integrated water management strategies, encompassing aspects of urban water systems, industrial processes, and agricultural practices. By considering the interconnectedness of various sectors, holistic approaches to water resource management can be formulated, promoting resilience and sustainability in the face of changing environmental conditions. The integration of chemical engineering simulations into the framework of the circular water economy offers a promising avenue for advancing sustainable water management practices. Through comprehensive modeling and analysis, informed decision-making can pave the way towards a more resilient and equitable water future, ensuring the long-term viability of our environmental systems.

Numerical simulation analysis of high-temperature bent sodium heat pipes

Abstract High-temperature bent heat pipes are safety-level heat transfer components used in heat pipe cooled reactor, and their applications are becoming increasingly widespread. In this study, a comprehensive three-dimensional numerical model was established to evaluate the effects of different bending angles and radii on the performance of high-temperature heat pipes. The model solved for the temperature, pressure, and velocity fields in the wall, wick, and evaporator, considers the compressibility effect of the vapor. The study investigated parameters such as vapor pressure, vapor velocity, radial secondary flow field, wall temperature, wick temperature, and equivalent thermal resistance of powder sintered core sodium heat pipes with bending angles of 45°, 90°, 135°, 180°, and a bending radius under 90°. The results showed that when sodium vapor enters the bent pipe, the vapor flow deviates towards the outer side, resulting in higher vapor pressure at the outer side than the vapor pressure at the inner side, and the generation of radial secondary vortex. This leads to higher temperature in the evaporator and adiabatic sections of the bent heat pipe, while lower temperature in the condenser. Fractal patterns are observed in the outer and inner wall temperature curve in the adiabatic section, with lower temperatures on the inner side and higher temperatures on the outer side. The bent heat pipe exhibits higher equivalent thermal resistance than that of a straight heat pipe, and the equivalent thermal resistance increases with increasing bending angle, and the equivalent thermal resistance decreases with increasing bending radius.

Abstract 395: An alternating magnetic field suppresses tumor growth

Abstract Application of physical forces, ranging from ultrasound to electric field, is recommended in various clinical practice guidelines, including those for bone fracture or cancer treatment. However, mechanistic information for such treatments has been poorly provided, due to the lack of comprehensive study model. Here, we show an alternating magnetic field (AMF) per se exhibits direct anti-cancer effect through enhancing oxidative phosphorylation (OXPOHS) and thus metabolic reprograming. Proliferation of human glioblastoma cells (U87 and LN229), but not normal human astrocytes (NHA), was inhibited upon exposure to AMF within the narrow frequency range. Extracellular acidification rate was decreased while oxygen consumption was increased. These data indicated that AMF induced metabolic reprograming. This was accompanied by increased reactive oxygen species (ROS) production. Mouse models grafted with human glioblastoma cells showed that AMF exposure, 30 minutes per day for two weeks, suppressed the tumor growth and prolonged the overall survival. Our results suggest that the AMF exposure may be a simple strategy to inhibit cancer cell growth by utilizing oxidative stress via metabolic reprograming. Citation Format: Masanari Umemura, Taisuke Akimoto, Rafikul Islam, Kazuhito Kishi, Akane Nagasako, Rina Nakakaji, Takashi Yamaguchi, Yuto Mizuno, Soichiro Ishikawa, Tetsuya Yamamoto, Yoshihiro Ishikawa. An alternating magnetic field suppresses tumor growth [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 395.

An improved iterative approach with a comprehensive friction model for identifying dynamic parameters of collaborative robots

AbstractCollaborative robots are becoming intelligent assistants of human in industrial settings and daily lives. Dynamic model identification is an active topic for collaborative robots because it can provide effective ways to achieve precise control, fast collision detection and smooth lead-through programming. In this research, an improved iterative approach with a comprehensive friction model for dynamic model identification is proposed for collaborative robots when the joint velocity, temperature and load torque effects are considered. Experiments are conducted on the AUBO I5 collaborative robots. Two other existing identification algorithms are adopted to make comparison with the proposed approach. It is verified that the average error of the proposed I-IRLS algorithm is reduced by over 14% than that of the classical IRLS algorithm. The proposed I-IRLS method can be widely used in various application scenarios of collaborative robots.

Electrocatalytic Mechanism and Sabatier Principle in C2N-Supported Atomically Dispersed Catalysts for the Sulfur Reduction Reaction in Lithium-Sulfur Batteries.

The sluggish kinetics of the sulfur reduction reaction (SRR) impedes the practical application of lithium-sulfur batteries (LSBs). Electrocatalysts are necessary to expedite the conversion of polysulfides. Here, we systematically investigate the chemical mechanisms and size dependence of catalytic activities toward the SRR from Li2S4 to Li2S on single-, double-, and triple-atom catalysts supported on C2N (Mn@C2N, where M is a 3d transitional metal and n = 1-3) as model systems by using first-principles calculations and a comprehensive electrocatalytic model. Our results reveal that the adsorption strength of the LiS• intermediate is identified as an optimal descriptor for catalytic activity. M1@C2N exhibits superior stability and exceptional activity compared to those of the other two catalyst types. Cu1@C2N exhibits the lowest overpotential of 0.426 V. Li embedding or a prelithiation strategy verifies the therein Sabatier principle. This work emphasizes the precise control of the active site structure and microenvironment in catalytic SRR and offers guidance for the design of electrocatalysts for metal-sulfur batteries.

Simulation of Vortex-Induced Vibration for a Cylinder with Different Rounded Corners under Re = 150

A comprehensive 2D numerical model was conscientiously developed to investigate the vortex-induced vibration phenomena in a cylindrical structure with rounded corners. The Navier-Stokes equation was adeptly solved under the specific condition of a Reynolds number (Re) of 150. The investigation reveals intricate details of the phenomena. The study aimed to systematically analyze the interaction between drag and lift force coefficients, cylinder vibration amplitude, and the patterns of vortex shedding modes under various conditions. This study systematically altered the radius of the cylinder’s rounded corners to evaluate their effects on both structural and hydrodynamic responses. This variation was crucial in comprehending how slight alterations in the cylinder’s geometry impact significant changes in the flow dynamics and correlated vibration behavior. The model’s numerical results revealed the significant impact of the curved edge ratio on both the hydrodynamic forces acting on the cylinder and its vibration response. The variation in edge curvature resulted in changes in drag and lift coefficients, leading to a significant impact on the amplitude of vibration. This elucidates the crucial role of geometric design in controlling and optimizing the structural behavior of cylindrical structures under fluid flow conditions.

The Impact of Schoolpreneur Model on Effectiveness of Entrepreneurship in Malaysian Secondary School

Purpose: The implementation of entrepreneurship development among youths can commence early in their education, particularly targeting secondary school students. Thus, forming a comprehensive entrepreneurship model in secondary schools would facilitate the systematic and organised conduct of entrepreneurial activities. The primary objective of this study is to determine the relationship between the schoolpreneur model and the effectiveness of entrepreneurship in secondary schools. Methods: The research encompassed a sample of 391 student participants chosen from 14 MRSM and 50 PROTUNe schools in the Perak, Selangor, and Negeri Sembilan regions in Malaysia. Result and Conclusion: Research has proven that the schoolpreneur model has six distinct components: individual personality, incubators, role models, mentors, talent skills, and family background. This model has also demonstrated a significant relationship towards the effectiveness of entrepreneurship in secondary schools. Research Implications: This finding indicates that the school entrepreneurship model has the potential to act as a guideline to drive strategies aimed at increasing the practicality of executing entrepreneurial activities in secondary schools. Originality/ Value: Schoolpreneur model improves the design of entrepreneurship program at secondary school.

Advancing Legged Wall Climbing Robot Performance Through Dynamic Contact-Integrated Climbing Model

Abstract Climbing robots have gained significance in hazardous and steep terrains, yet adapting to complex environments remains a challenge. Inspired by nature's climbers, this paper introduces a climbing dynamics model that integrates foot-end contact forces, crucial for safe and efficient wall climbing. Drawing insights from animal locomotion and biomechanics, we present a comprehensive dynamic model for quadruped robots. Our model, built upon multibody dynamics and a dynamic contact model based on spiny claw mechanisms, accurately simulates robot forces and motion during climbing, even predicting failure scenarios. Experimental validation further establishes model accuracy. This study advances climbing robot research by addressing attachment interaction dynamics and provides valuable insights for optimizing robot structural design and gait strategies.

Accounting for Measurement Error and Untruthfulness in Binary RRT Models

This study examines the effect of measurement error on binary Randomized Response Technique models. We discuss a method for estimating and accounting for measurement error and untruthfulness in two basic models and one comprehensive model. Both theoretical and empirical results show that not accounting for measurement error leads to inaccurate estimates. We introduce estimators that account for the effect of measurement error. Furthermore, we introduce a new measure of model privacy using an odds ratio statistic, which offers better interpretability than traditional methods.

Integrated energy system evaluation and optimization based on integrated evaluation model and time-series optimization

Against the backdrop of global attention to climate change and environmental sustainability, the development timing and comprehensive cost of regional renewable energy power generation projects have become a focus of attention. By constructing effective models to evaluate them, it can help promote the healthy development of renewable energy projects. The research aims to quantitatively evaluate the development status of local renewable energy projects by constructing a comprehensive evaluation model, minimize information loss, and improve the accuracy of evaluation results. This study adopted a comprehensive evaluation model that combines Analytic Hierarchy Process (AHP) based on accelerated genetic algorithm, entropy weight method, and ideal point method. Firstly, the subjective weights of the development evaluation indicators for regional renewable energy power generation projects are calculated. Secondly, the entropy weight method is used to analyze the trend of each indicator and obtain objective weights. Finally, combined with the objective weights and the evaluation results calculated using the TOPSIS method, a comprehensive evaluation of renewable energy power generation projects in various regions is conducted. Through analysis, the core indicators of the development level of renewable energy power generation projects in various regions show specific performance, such as Hebei’s evaluation value of 0.4945 in the proportion of comprehensive energy development, and Inner Mongolia’s evaluation value of 0.4045 in the proportion of comprehensive energy installed capacity. Meanwhile, genetic optimization methods exhibit significant advantages in the calculation of optimization schemes compared to dynamic programming methods, possessing strong global search capabilities and high-precision solutions. This study provides a new research method and approach for the evaluation of regional renewable energy power generation projects, demonstrating the practical value and certain advantages of the research method.