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The desert climate region attracts a multitude of tourists due to its distinctive landforms and climatic conditions, however, it also presents challenges for environmental protection. This article constructs a theoretical model that examines the influence of thermal stress on tourists’ environmental responsibility behavior intention (ERBI), with anticipated pride and anticipated guilt serving as mediating factors. An empirical study is conducted in Turpan, Xinjiang, which represents a typical inland arid area in China. The results indicate that: (1) thermal stress does not have a significant direct impact on ERBI, nevertheless, anticipated pride and anticipated guilt play crucial mediating roles between thermal stress and this intention. (2) Furthermore, environmental knowledge positively moderates the relationship between anticipated pride, anticipated guilt, and the ERBI. This research contributes to the understanding of how tourists’ anticipatory emotions affect their ERBI in desert climate regions while deepening our comprehension of the driving mechanisms behind such intentions among tourists. Moreover, it provides theoretical references for promoting environmentally responsible behaviors among tourists visiting desert climate regions. Full article

(This article belongs to the Special Issue Extreme Climate Events: Causes, Risk and Adaptation)

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15 pages, 3443 KiB

Open AccessArticle

Contact Hole Shrinkage: Simulation Study of Resist Flow Process and Its Application to Block Copolymers

by Sang-Kon Kim

Micromachines 2024, 15(9), 1151; https://doi.org/10.3390/mi15091151 (registering DOI) - 13 Sep 2024

Abstract

For vertical interconnect access (VIA) in three-dimensional (3D) structure chips, including those with high bandwidth memory (HBM), shrinking contact holes (C/Hs) using the resist flow process (RFP) represents the most promising technology for low- [...] Read more.

k_{1} (w h e r e C D = k_{1} λ / N A,

C D

is the critical dimension,

λ

is wavelength, and

N A

is the numerical aperture). This method offers a way to reduce dimensions without additional complex process steps and is independent of optical technologies. However, most empirical models are heuristic methods and use linear regression to predict the critical dimension of the reflowed structure but do not account for intermediate shapes. In this research, the resist flow process (RFP) was modeled using the evolution method, the finite-element method, machine learning, and deep learning under various reflow conditions to imitate experimental results. Deep learning and machine learning have proven to be useful for physical optimization problems without analytical solutions, particularly for regression and classification tasks. In this application, the self-assembly of cylinder-forming block copolymers (BCPs), confined in prepatterns of the resist reflow process (RFP) to produce small contact hole (C/H) dimensions, was described using the self-consistent field theory (SCFT). This research paves the way for the shrink modeling of the enhanced resist reflow process (RFP) for random contact holes (C/Hs) and the production of smaller contact holes. Full article

(This article belongs to the Special Issue Recent Advances in Micro/Nano-Fabrication)

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21 pages, 5674 KiB

Open AccessArticle

A Method for Estimating the SOH of Lithium-Ion Batteries Based on Graph Perceptual Neural Network

by Kang Chen, Dandan Wang and Wenwen Guo

Batteries 2024, 10(9), 326; https://doi.org/10.3390/batteries10090326 (registering DOI) - 13 Sep 2024

Abstract

The accurate estimation of battery state of health (SOH) is critical for ensuring the safety and reliability of devices. Considering the variation in health degradation across different types of lithium-ion battery materials, this paper proposes an SOH estimation method based on a graph perceptual neural network, designed to adapt to multiple battery materials. This method adapts to various battery materials by extracting crucial features from current, voltage, voltage–capacity, and temperature data, and it constructs a graph structure to encapsulate these features. This approach effectively captures the complex interactions and dependencies among different battery types. The novel technique of randomly removing features addresses feature redundancy. Initially, a mutual information graph structure is defined to illustrate the interdependencies among battery features. Moreover, a graph perceptual self-attention mechanism is implemented, integrating the adjacency matrix and edge features into the self-attention calculations. This enhancement aids the model’s understanding of battery behaviors, thereby improving the transparency and interpretability of predictions. The experimental results demonstrate that this method outperforms traditional models in both accuracy and generalizability across various battery types, particularly those with significant chemical and degradation discrepancies. The model achieves a minimum mean absolute error of 0.357, a root mean square error of 0.560, and a maximum error of 0.941. Full article

(This article belongs to the Special Issue State-of-Health Estimation of Batteries)

20 pages, 1731 KiB

Open AccessArticle

Parvovirus B19 Infection Is Associated with the Formation of Neutrophil Extracellular Traps and Thrombosis: A Possible Linkage of the VP1 Unique Region

by Bor-Show Tzang, Hao-Yang Chin, Chih-Chen Tzang, Pei-Hua Chuang, Der-Yuan Chen and Tsai-Ching Hsu

Int. J. Mol. Sci. 2024, 25(18), 9917; https://doi.org/10.3390/ijms25189917 (registering DOI) - 13 Sep 2024

Abstract

Neutrophil extracellular traps (NETs) formation, namely NETosis, is implicated in antiphospholipid syndrome (APS)-related thrombosis in various autoimmune disorders such as systemic lupus erythematosus (SLE) and APS. Human parvovirus B19 (B19V) infection is closely associated with SLE and APS and causes various clinical manifestations such as blood disorders, joint pain, fever, pregnancy complications, and thrombosis. Additionally, B19V may trigger the production of autoantibodies, including those against nuclear and phospholipid components. Thus, exploring the connection between B19V, NETosis, and thrombosis is highly relevant. An in vitro NETosis model using differentiated HL-60 neutrophil-like cells (dHL-60) was employed to investigate the effect of B19V-VP1u IgG on NETs formation. A venous stenosis mouse model was used to test how B19V-VP1u IgG-mediated NETs affect thrombosis in vivo. The NETosis was observed in the dHL-60 cells treated with rabbit anti-B19V-VP1u IgG and was inhibited in the presence of either 8-Br-cAMP or CGS216800 but not GSK484. Significantly elevated reactive oxygen species (ROS), myeloperoxidase (MPO), and citrullinated histone (Cit-H3) levels were detected in the dHL60 treated with phorbol myristate acetate (PMA), human aPLs IgG and rabbit anti-B19V-VP1u IgG, respectively. Accordingly, a significantly larger thrombus was observed in a venous stenosis-induced thrombosis mouse model treated with PMA, human aPLs IgG, rabbit anti-B19V-VP1u IgG, and human anti-B19V-VP1u IgG, respectively, along with significantly increased amounts of Cit-H3-, MPO- and CRAMP-positive infiltrated neutrophils in the thrombin sections. This research highlights that anti-B19V-VP1u antibodies may enhance the formation of NETosis and thrombosis and implies that managing and treating B19V infection could lower the risk of thrombosis. Full article

(This article belongs to the Special Issue Novel Advances on Systemic Lupus Erythematosus and Antiphospholipid Syndrome)

28 pages, 8134 KiB

Open AccessArticle

Enhancing Active Disturbance Rejection Control for a Vehicle Active Stabiliser Bar with an Improved Chicken Flock Optimisation Algorithm

by Zhenglin Tang, Qiang Zhao, Duc Truong Pham and Xuesong Zhang

Processes 2024, 12(9), 1979; https://doi.org/10.3390/pr12091979 (registering DOI) - 13 Sep 2024

Abstract

An active stabiliser bar significantly enhances the anti-roll capabilities of vehicles. The control strategy is a crucial factor in enabling the active stabiliser bar to function effectively. This paper investigates an active disturbance rejection control (ADRC) strategy. Given the numerous parameters of the ADRC and their significant mutual influence, optimising these parameters is challenging. To address this, an improved chicken flock optimisation algorithm is proposed to optimise the ADRC parameters and enhance its performance. First, a three-degree-of-freedom dynamic model of the vehicle is established, and an active disturbance rejection control-based optimisation model utilising a chicken flock optimisation algorithm is constructed. To tackle the issues of getting stuck in local optima and low precision when dealing with complex problems in the traditional chicken flock optimisation (CFO) algorithm, several strategies, including improved Lévy flight, have been adopted. Subsequently, the twelve parameters of the ADRC are optimised using the improved chicken flock optimisation algorithm. Comprehensive testing on multiple benchmark functions demonstrates that the improved chicken flock optimisation (ICFO) algorithm is distinctly superior to other advanced algorithms in terms of solution quality and robustness. Simulation results show that the ICFO-ADRC controller is significantly superior. In four different complex road condition tests, the ICFO-ADRC controller shows an average performance improvement of 8% compared to the fuzzy PI-PD controller, an average improvement of 82% compared to the non-optimised ADRC controller, and an average improvement of 18% compared to the CFO-ADRC controller. Our findings confirm that this paper was able to provide new solutions for vehicle stability control whilst opening up new possibilities for the application of metaheuristic algorithms. Full article

(This article belongs to the Section Advanced Digital and Other Processes)

32 pages, 648 KiB

Open AccessArticle

Top Management Team Heterogeneity, Top Management Incentives, and ESG Performance: Evidence from Chinese Listed Companies

by Shanshan Lyu, Mingzeng Yang and Qincheng Zhang

Sustainability 2024, 16(18), 8036; https://doi.org/10.3390/su16188036 (registering DOI) - 13 Sep 2024

Abstract

The challenge of balancing economic and social benefits has emerged as a critical issue for corporate sustainable development. Environmental, social, and governance (ESG) criteria are key considerations for enterprises aiming to enhance both social and economic benefits simultaneously. Based on the upper echelons theory, differences in cognitive foundations and values brought about by top management team heterogeneity can influence corporate decisions. Taking A-share listed companies in China from 2011 to 2022 as samples, we construct a two-way fixed-effects model by firm and year to explore the impact of top management team heterogeneity on corporate ESG performance, and we introduce top management incentives as a moderating variable to further analyze the underlying mechanisms. Our results demonstrate that the gender heterogeneity, functional background heterogeneity, and overseas background heterogeneity of top management teams have significant positive impacts on corporate ESG performance, and monetary compensation incentives and control incentives to top management teams play a positive moderating role, while equity incentives exhibits a negative moderating effect. These findings remain robust across alternative measures of corporate ESG ratings and monetary and control incentives, and through the SYS-GMM model test and instrumental variable approach to address endogeneity. This research contributes to the literature on corporate ESG by validating and extending the understanding of how top management team characteristics affect organizational outcomes, and it provides practical guidance for enhancing corporate ESG practices. The implications of this study suggest that to enhance corporate ESG performance, enterprises should prioritize the promotion of top management team heterogeneity and tailor their incentive mechanisms accordingly. Full article

(This article belongs to the Special Issue Sustainable Corporate Governance and Firm Performance)

17 pages, 3759 KiB

Open AccessArticle

A Multi-Scale Graph Attention-Based Transformer for Occluded Person Re-Identification

by Ming Ma, Jianming Wang and Bohan Zhao

Appl. Sci. 2024, 14(18), 8279; https://doi.org/10.3390/app14188279 (registering DOI) - 13 Sep 2024

Abstract

The objective of person re-identification (ReID) tasks is to match a specific individual across different times, locations, or camera viewpoints. The prevalent issue of occlusion in real-world scenarios affects image information, rendering the affected features unreliable. The difficulty and core challenge lie in how to effectively discern and extract visual features from human images under various complex conditions, including cluttered backgrounds, diverse postures, and the presence of occlusions. Some works have employed pose estimation or human key point detection to construct graph-structured information to counteract the effects of occlusions. However, this approach introduces new noise due to issues such as the invisibility of key points. Our proposed module, in contrast, does not require the use of additional feature extractors. Our module employs multi-scale graph attention for the reweighting of feature importance. This allows features to concentrate on areas genuinely pertinent to the re-identification task, thereby enhancing the model’s robustness against occlusions. To address these problems, a model that employs multi-scale graph attention to reweight the importance of features is proposed in this study, significantly enhancing the model’s robustness against occlusions. Our experimental results demonstrate that, compared to baseline models, the method proposed herein achieves a notable improvement in mAP on occluded datasets, with increases of 0.5%, 31.5%, and 12.3% in mAP scores. Full article

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37 pages, 8423 KiB

Open AccessArticle

Enhancing Clinical Decision Support for Precision Medicine: A Data-Driven Approach

by Nasim Sadat Mosavi and Manuel Filipe Santos

Informatics 2024, 11(3), 68; https://doi.org/10.3390/informatics11030068 (registering DOI) - 13 Sep 2024

Abstract

Precision medicine has emerged as a transformative approach aimed at tailoring treatment to individual patients, moving away from the traditional one-size-fits-all model. However, Clinical decision support systems encounter challenges, particularly in terms of data aspects. In response, our study proposes a data-driven framework rooted in Simon’s decision-making model. This framework leverages advanced technologies such as artificial intelligence and data analytics to enhance clinical decision-making in precision medicine. By addressing limitations and integrating AI and analytics, our study contributes to the advancement of optimal clinical decision-making practices in precision healthcare. Full article

(This article belongs to the Section Medical and Clinical Informatics)

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21 pages, 2245 KiB

Open AccessArticle

Egress Safety for STUDIO Residential Buildings

by Khaliunaa Darkhanbat, Inwook Heo, Kang Su Kim and Seung-Ho Choi

Buildings 2024, 14(9), 2901; https://doi.org/10.3390/buildings14092901 - 13 Sep 2024

Abstract

In recent years, the number of studio residential buildings has increased significantly in Korea, as well as in many other countries, due to changes in living patterns. In Korea especially, there have been many fire accidents in studio residential buildings, which have caused a huge number of casualties and property damages, because the buildings were not adequately equipped for firefighting. In this study, the egress safety of a typical studio residential building in Korea is analyzed. Fire simulations were performed with variables of the fire location and the capacity of the smoke exhaust system to estimate the available safe egress time (ASET); egress simulations were also performed with the variable of egress delay time, and the required safe egress time (RSET) was determined. Then, the egress safety was evaluated, and the criteria for egress safety evaluation were proposed based on the simulation results. A studio residential building with a floor plan different from the prototype was used to validate the proposed egress safety criteria. Finally, a simple evaluation model is presented to estimate the required safe egress time (RSET) without simulation and to examine the impact of bottlenecks. Full article

(This article belongs to the Special Issue Structural Safety Evaluation and Health Monitoring)

15 pages, 2623 KiB

Open AccessArticle

Inertial Motion Capture-Driven Digital Human for Ergonomic Validation: A Case Study of Core Drilling

by Quan Zhao, Tao Lu, Menglun Tao, Siyi Cheng and Guojun Wen

Sensors 2024, 24(18), 5962; https://doi.org/10.3390/s24185962 - 13 Sep 2024

Abstract

In the evolving realm of ergonomics, there is a growing demand for enhanced comfortability, visibility, and accessibility in the operation of engineering machinery. This study introduces an innovative approach to assess the ergonomics of a driller’s cabin by utilizing a digital human. Through the utilization of inertial motion capture sensors, the method enables the operation of a virtual driller animated by real human movements, thereby producing more precise and realistic human–machine interaction data. Additionally, this study develops a simplified model for the human upper limbs, facilitating the calculation of joint forces and torques. An ergonomic analysis platform, encompassing a virtual driller’s cabin and a digital human model, is constructed using Unity 3D. This platform enables the quantitative evaluation of comfortability, visibility, and accessibility. Its versatility extends beyond the current scope, offering substantial support for product development and enhancement. Full article

(This article belongs to the Special Issue Advances in Human Locomotion Using Sensor-Based Approaches)

9 pages, 817 KiB

Open AccessArticle

Efficient Study on Westervelt-Type Equations to Design Metamaterials via Symmetry Analysis

by Zehra Pinar Izgi, Pshtiwan Othman Mohammed, Ravi P. Agarwal, Majeed A. Yousif, Alina Alb Lupas and Mohamed Abdelwahed

Mathematics 2024, 12(18), 2855; https://doi.org/10.3390/math12182855 - 13 Sep 2024

Abstract

Abstract: Metamaterials have emerged as a focal point in contemporary science and technology due to their ability to drive significant innovations. These engineered materials are specifically designed to couple the phenomena of different physical natures, thereby influencing processes through mechanical or thermal effects. While much of the recent research has concentrated on frequency conversion into electromagnetic waves, the field of acoustic frequency conversion still faces considerable technical challenges. To overcome these hurdles, researchers are developing metamaterials with customized acoustic properties. A key equation for modeling nonlinear acoustic wave phenomena is the dissipative Westervelt equation. This study investigates analytical solutions using ansatz-based methods combined with Lie symmetries. The approach presented here provides a versatile framework that is applicable to a wide range of fields in metamaterial design. Full article

(This article belongs to the Special Issue Soliton Theory and Integrable Systems in Mathematical Physics)

18 pages, 3667 KiB

Open AccessArticle

An Improved Lightweight YOLOv8 Network for Early Small Flame Target Detection

by Hubin Du, Qiuyu Li, Ziqian Guan, Hengyuan Zhang and Yongtao Liu

Processes 2024, 12(9), 1978; https://doi.org/10.3390/pr12091978 - 13 Sep 2024

Abstract

The efficacy of early fire detection hinges on its swift response and precision, which allows for the issuance of timely alerts in the nascent stages of a fire, thereby minimizing losses and injuries. To enhance the precision and swiftness of identifying minute early flame targets, as well as the ease of deployment at the edge end, an optimized early flame target detection algorithm for YOLOv8 is proposed. The original feature fusion module, an FPN (feature pyramid network) of YOLOv8n, has been enhanced to become the BiFPN (bidirectional feature pyramid network) module. This modification enables the network to more efficiently and rapidly perform multi-scale fusion, thereby enhancing its capacity for integrating features across different scales. Secondly, the efficient multi-scale attention (EMA) mechanism is introduced to ensure the effective retention of information on each channel and reduce the computational overhead, thereby improving the model’s detection accuracy while reducing the number of model parameters. Subsequently, the NWD (normalized Wasserstein distance) loss function is employed as the bounding box loss function, which enhances the model’s regression performance and robustness. The experimental results demonstrate that the size of the enhanced model is 4.8 M, a reduction of 22.5% compared to the original YOLOv8n. Additionally, the mAP0.5 metric exhibits a 2.7% improvement over the original YOLOv8n, indicating a more robust detection capability and a more compact model size. This makes it an ideal candidate for deployment in edge devices. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence Technologies in Energy, Manufacturing and Automatic Control Processes)

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21 pages, 1400 KiB

Open AccessArticle

The Impact of Strategic Entrepreneurship Behaviors on Business Performance in Turkish SMES: The Role of Business Model Innovation and Competitive Intensity

by Jabril Ramadan, Ahmad Alzubi and Amir Khadem

Sustainability 2024, 16(18), 8035; https://doi.org/10.3390/su16188035 - 13 Sep 2024

Abstract

Strategic entrepreneurship behaviors enhance business performance and sustainability in Turkish SMEs by fostering innovation and leveraging competitive intensity for sustained growth. Employing strategic leadership theory, this study examines the effect of strategic entrepreneurial behaviors on business performance through the mediation role of business model innovation (BMI) and the moderation effect of competitive intensity. A quantitative approach was used, and data from 313 managers and business owners in Turkish small and medium enterprises (SMEs) were collected using a structured questionnaire. The results have shown that strategic entrepreneurial behavior significantly and positively impacts business performance and business model innovation. Business model innovation, in turn, positively affects business performance. Competitive intensity moderates the relationship between strategic entrepreneurial behavior and business model innovation, strengthening it under higher competitive pressure levels. However, competitive intensity does not moderate the direct link between strategic entrepreneurial behavior and business performance. At higher levels of competitive intensity, the conditional indirect effect of strategic entrepreneurial behavior on business performance through business model innovation becomes more prominent. The findings of this study offer actionable insights for enhancing SME performance through strategic entrepreneurship, innovative business models, and competitive strategy adaptation. Full article

(This article belongs to the Special Issue Advances in Business Model Innovation and Corporate Sustainability)

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31 pages, 3407 KiB

Open AccessReview

Glucose Metabolism-Modifying Natural Materials for Potential Feed Additive Development

by Wei-Chih Lin, Boon-Chin Hoe, Xianming Li, Daizheng Lian and Xiaowei Zeng

Pharmaceutics 2024, 16(9), 1208; https://doi.org/10.3390/pharmaceutics16091208 - 13 Sep 2024

Abstract

Glucose, a primary energy source derived from animals’ feed ration, is crucial for their growth, production performance, and health. However, challenges such as metabolic stress, oxidative stress, inflammation, and gut microbiota disruption during animal production practices can potentially impair animal glucose metabolism pathways. Phytochemicals, probiotics, prebiotics, and trace minerals are known to change the molecular pathway of insulin-dependent glucose metabolism and improve glucose uptake in rodent and cell models. These compounds, commonly used as animal feed additives, have been well studied for their ability to promote various aspects of growth and health. However, their specific effects on glucose uptake modulation have not been thoroughly explored. This article focuses on glucose metabolism is on discovering alternative non-pharmacological treatments for diabetes in humans, which could have significant implications for developing feed additives that enhance animal performance by promoting insulin-dependent glucose metabolism. This article also aims to provide information about natural materials that impact glucose uptake and to explore their potential use as non-antibiotic feed additives to promote animal health and production. Further exploration of this topic and the materials involved could provide a basis for new product development and innovation in animal nutrition. Full article

(This article belongs to the Special Issue Natural and Synthetic Compounds in Pharmaceutical Formulations and Drug Delivery Systems)

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24 pages, 2759 KiB

Open AccessArticle

Stability and Hopf Bifurcation Analysis of a Predator–Prey Model with Weak Allee Effect Delay and Competition Delay

by Yurong Dong, Hua Liu, Yumei Wei, Qibin Zhang and Gang Ma

Mathematics 2024, 12(18), 2853; https://doi.org/10.3390/math12182853 - 13 Sep 2024

Abstract

The purpose of this paper is to study a predator–prey model with Allee effect and double time delays. This research examines the dynamics of the model, with a focus on positivity, existence, stability and Hopf bifurcations. The stability of the periodic solution and the direction of the Hopf bifurcation are elucidated by applying the normal form theory and the center manifold theorem. To validate the correctness of the theoretical analysis, numerical simulations were conducted. The results suggest that a weak Allee effect delay can promote stability within the model, transitioning it from instability to stability. Nevertheless, the competition delay induces periodic oscillations and chaotic dynamics, ultimately resulting in the population’s collapse. Full article

(This article belongs to the Section Mathematical Biology)

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