Advanced Model Research Articles

Global Sensitivity Analysis of the Advanced ORYZA-N Model with Different Rice Types and Irrigation Regimes.

Identifying important parameters in crop models is critical for model application. This study conducted a sensitivity analysis of 23 selected parameters of the advanced rice model ORYZA-N using the Extended FAST method. The sensitivity analysis was applied for three rice types (single-season rice in cold regions and double-season rice (early rice and late rice) in subtropical regions) and two irrigation regimes (traditional flood irrigation (TFI) and shallow-wet irrigation (SWI)). This study analyzed the parameter sensitivity of six crop growth outputs at four developmental stages and yields. Furthermore, we compared the variation in parameter sensitivity on model outputs between TFI and SWI scenarios for single-season rice, early rice, and late rice. Results indicated that parameters RGRLMX, FRPAR, and FLV0.5 significantly affected all model outputs and varied over developmental stages. Water stress in paddy fields caused by water-saving irrigation had more pronounced effects on single-season rice than on double-season rice.

Enhanced earth pressure determination with negative wall-soil friction using soft computing

Estimating active earth pressure in cohesionless backfill behind rigid retaining walls has been significantly improved through the application of cutting-edge soft computing techniques in this paper. This study delves into the intricate interplay of negative wall-soil friction with load transfer mechanisms, underpinned by a comprehensive dataset obtained from a conservative solution that leverages statically admissible stress fields. Utilizing a Bayesian regularization backpropagation neural network, we construct an explicit function for active earth pressure estimation, ensuring the model's reliability through its remarkable alignment with measured values. Furthermore, feature importance analysis and advanced mathematical modeling enrich the study, providing a practical tool for retaining wall design and analysis. This tool is adept at addressing complex scenarios, including those involving negative wall-soil friction, thereby advancing the state-of-the-art in geotechnical engineering.

Assessing heat vulnerability in Philadelphia using geographically weighted principal component analysis (GWPCA): A geospatial big data-driven approach

The impact of climate change, specifically more intense heat waves, has increased concerns about heat vulnerability, particularly among high-risk populations. This research utilizes multi-source geospatial big data and employs Geographically Weighted Principal Component Analysis (GWPCA) as well as Global Principal Component Analysis (GPCA) to analyze heat vulnerability in Philadelphia. Using GPCA, four key components are identified Sensitivity, Adaptive Capacity, proxy for Sensitivity, and Exposure, respectively. The subsequent GWPCA analysis reveals localized vulnerability differences, showing distinct patterns across the city. Notably, Sensitivity factors are prominent in the western and southwestern regions, whereas Exposure is dominant in the central and southern parts. This study underscores the significance of considering spatial heterogeneity when assessing heat vulnerability. It also highlights the potential of GWPCA to capture subtle disparities within specific areas and proposes targeted strategies to reduce heat vulnerability in affected communities. Therefore, the incorporation of an advanced spatial analysis model enables a comprehensive understanding of heat vulnerability in complex urban environments. This progress is crucial in enhancing resilience and adaptation to evolving climate conditions.

Machine learning-based techniques for land subsidence simulation in an urban area

Understanding and mitigating land subsidence (LS) is critical for sustainable urban planning and infrastructure management. We introduce a comprehensive analysis of LS forecasting utilizing two advanced machine learning models: the eXtreme Gradient Boosting Regressor (XGBR) and Long Short-Term Memory (LSTM). Our findings highlight groundwater level (GWL) and building concentration (BC) as pivotal factors influencing LS. Through the use of Taylor diagram, we demonstrate a strong correlation between both XGBR and LSTM models and the subsidence data, affirming their predictive accuracy. Notably, we applied delta-rate (Δr) calculus to simulate a scenario with an 80% reduction in GWL and BC impact, revealing a potential substantial decrease in LS by 2040. This projection emphasizes the effectiveness of strategic urban and environmental policy interventions. The model performances, indicated by coefficients of determination R2 (0.90 for XGBR, 0.84 for LSTM), root-mean-squared error RMSE (0.37 for XGBR, 0.50 for LSTM), and mean-absolute-error MAE (0.34 for XGBR, 0.67 for LSTM), confirm their reliability. This research sets a precedent for incorporating dynamic environmental factors and adapting to real-time data in future studies. Our approach facilitates proactive LS management through data-driven strategies, offering valuable insights for policymakers and laying the foundation for sustainable urban development and resource management practices.

Process parameters and their effect on metal transfer in gas metal arc welding: a driving force perspective

AbstractIn gas metal arc welding (GMAW), the optimization of process parameters and the control of metal transfer is pivotal for achieving superior welding outcomes. This study meticulously examines the roles of core process parameters in metal transfer: wire extension and arc geometry, which includes arc length and inter-cathode distance. Using a unique MIG-based non-transferred arc system with symmetrically arranged tungsten cathodes we have achieved distinct control over wire feed rate and arc geometry, independent of the arc current. We investigate the interplay of these parameters and their consequential influence on the driving forces governing metal transfer. While increasing the wire extension enhanced inertial forces, it did not lead to consistent transitions in the metal transfer modes. In contrast, adjusting arc length and inter-cathode distance effectively controlled the electromagnetic force acting on molten droplets, as quantified by the arc spread angle $$\theta$$ θ . Notably, an increase in the arc spread angle resulted in the elongation of molten droplets. This study clarifies the fundamental relationships between driving forces and process parameters in GMAW. Additionally, it introduces a more advanced evaluation model for electromagnetic force and offers a comprehensive strategy for refining welding control techniques.

Forward-Thinking for Sustainable Shared Mobility Solutions in Amman

This study presents a novel examination of shared mobility’s viability and impact in Amman, Jordan, framed within the context of sustainable urban transportation. A rigorous methodological approach that integrates advanced statistical models including Probit and Decision Tree analyses was utilized to evaluate the propensity of Amman’s residents to adopt shared mobility solutions. Notably, the Ordered Probit Model provided superior model prediction compared to the multinomial logit model, evidenced by a better goodness of fit measure. The results showed that public transportation users would highly use shared mobility services based on cost and reliability, with service convenience emerging as a pivotal factor. The classification tree identified the convenience of the service as the most important factor in adopting shared mobility. The survey data revealed an initial adoption rate of 25.4%, indicating a significant inclination towards shared mobility among respondents. This is pivotal in understanding the current readiness and potential growth of shared mobility in the city. This study is one of the first to quantify the readiness and potential growth of shared mobility in a Middle Eastern urban setting. Furthermore, the impact of this adoption rate on CO2 emissions was conducted. Emission analysis is crucial for assessing the environmental benefits of transitioning towards shared mobility options and aligning with global sustainability goals. Finally, the study extrapolates strategic guidelines for advancing sustainable transportation in Amman, identifying shared mobility options with the highest potential for successful adoption and proposing strategies to foster their implementation. This research contributes a unique perspective to the discourse on urban mobility, particularly in developing urban contexts like Amman, offering valuable insights for policymakers and urban planners.

Incorporating sustainability in organizational strategy: a framework for enhancing sustainable knowledge management and green innovation

PurposeThis study investigates the crucial link between sustainable practices and organizational development, focusing on sustainable knowledge management (SKM), green innovation (GI) and corporate sustainable development (CSD) in diverse Pakistani organizations.Design/methodology/approachThis study employs a comprehensive research methodology involving advanced statistical techniques, such as confirmatory factor analysis, structural equation modeling and hierarchical linear modeling. These methods are instrumental in exploring the complex interrelationships between SKM, GI, moderating factors and CSD.FindingsThis research generates significant findings and actively contributes to sustainable development. The following sections (Sections 4 and 5) delve into the specific findings and in-depth discussions, shedding light on how industry regulation, organizational sustainability priorities, workplace culture collaboration and alignment between green culture and knowledge management practices influence the relationships between SKM, GI and CSD. These findings provide valuable insights for the research community and organizations striving for sustainability.Practical implicationsThe study’s findings have practical implications for organizations seeking to enhance their sustainability efforts and embrace a socially and environmentally conscious approach to organizational growth.Originality/valueThis study contributes to the literature on sustainable practices and organizational development. Researchers and business people can learn a lot from it because it uses advanced econometric models in new ways and focuses on the link between knowledge management, GI and sustainable corporate development.

ChatGPT in medical writing: enhancing healthcare communication through artificial intelligence and human expertise

This study explores the capabilities and limitations of ChatGPT, an advanced language model, in medical writing. Leveraging the GPT-4 architecture, ChatGPT has shown potential in aiding various stages of medical article creation, including planning, drafting, revising, and even submission processes. It can summarize extensive literature, suggest research questions, and assist in multi-language research, making it a versatile tool for initial research and planning. During revisions, ChatGPT’s strengths lie in improving language, ensuring consistency, and enhancing readability. Despite its abilities, ChatGPT has several limitations. ChatGPT’s training data only updates with each new version release, which could result in outdated or incomplete research. It also lacks the critical thinking, domain expertise, and ethical considerations that human researchers bring to medical writing. While ChatGPT can be a useful tool for routine tasks and initial drafts, human expertise remains critical for generating high-quality, ethical, and insightful medical research articles. Therefore, a hybrid approach that combines the computational power of ChatGPT with the intellectual and ethical rigor of human experts is recommended for optimizing medical writing processes.

Hybrid CFRP-BFRP Rebars for Prestress Concrete Slabs: Abaqus Analysis and Experimental Validation

This study utilizes Abaqus finite element software to analyze concrete slabs reinforced with a novel hybrid of Carbon Fiber Reinforced Polymers (CFRP) and Basalt Fiber Reinforced Polymers (BFRP) rebars known as CBRP rebars. The investigation focuses on assessing the structural performance of these slabs under varying prestressing levels (PL) and FRP reduction factors (FR), along with the prestress index level (PI). The study defines the FR factor as the reduction in the transverse reinforcement area of FRP compared to the control slab. The PI is the ratio of prestressed CBRP rebars to the total FRP reinforcement in the transverse direction, aiming to determine a lower FRP reduction factor without compromising the FRP slabs' design requirements. Three FR values (0.45, 0.37, and 0.29) are considered, with the CBRP tendon tensioned at 35% and 50% of the ultimate strength, including 0.5 and 0.6 of the PI, for validation against experimental test data. The numerical analysis incorporates advanced finite element modeling techniques, integrating material properties, load conditions, and structural parameters to detail the behavior of CBR-reinforced concrete slabs. Various loading scenarios are simulated to evaluate the performance of CBRP rebars under diverse conditions. Comparison with experimental data validates the hybrid reinforcement approach's reliability. The study's findings contribute significantly to understanding the structural performance of concrete slabs reinforced with CBRP rebars. The hybridization of CFRP and BFRP rebars offers a promising alternative for enhancing concrete structure strength while reducing the carbon footprint associated with traditional reinforcement materials. The versatility of CBRP rebars allows customization based on specific structural requirements. Based on the Abaqus numerical analysis and its alignment with experimental data, the study recommends optimizing the CFRP-BFRP ratio to meet structural demands, evaluating long-term performance, and exploring environmental benefits associated with sustainable reinforcement materials. This research advances knowledge in the hybrid fiber-reinforced polymers field, offering insights for engineers and researchers seeking innovative solutions for concrete structure enhancement using Abaqus as a powerful analytical tool.

Graphene Oxide Nanoparticles and Organoids: A Prospective Advanced Model for Pancreatic Cancer Research.

Pancreatic cancer, notorious for its grim 10% five-year survival rate, poses significant clinical challenges, largely due to late-stage diagnosis and limited therapeutic options. This review delves into the generation of organoids, including those derived from resected tissues, biopsies, pluripotent stem cells, and adult stem cells, as well as the advancements in 3D printing. It explores the complexities of the tumor microenvironment, emphasizing culture media, the integration of non-neoplastic cells, and angiogenesis. Additionally, the review examines the multifaceted properties of graphene oxide (GO), such as its mechanical, thermal, electrical, chemical, and optical attributes, and their implications in cancer diagnostics and therapeutics. GO's unique properties facilitate its interaction with tumors, allowing targeted drug delivery and enhanced imaging for early detection and treatment. The integration of GO with 3D cultured organoid systems, particularly in pancreatic cancer research, is critically analyzed, highlighting current limitations and future potential. This innovative approach has the promise to transform personalized medicine, improve drug screening efficiency, and aid biomarker discovery in this aggressive disease. Through this review, we offer a balanced perspective on the advancements and future prospects in pancreatic cancer research, harnessing the potential of organoids and GO.

Opportunities and Challenges of Generative AI in Construction Industry: Focusing on Adoption of Text-Based Models

In the last decade, despite rapid advancements in artificial intelligence (AI) transforming many industry practices, construction largely lags in adoption. Recently, the emergence and rapid adoption of advanced large language models (LLMs) like OpenAI’s GPT, Google’s PaLM, and Meta’s Llama have shown great potential and sparked considerable global interest. However, the current surge lacks a study investigating the opportunities and challenges of implementing Generative AI (GenAI) in the construction sector, creating a critical knowledge gap for researchers and practitioners. This underlines the necessity to explore the prospects and complexities of GenAI integration. Bridging this gap is fundamental to optimizing GenAI’s early stage adoption within the construction sector. Given GenAI’s unprecedented capabilities to generate human-like content based on learning from existing content, we reflect on two guiding questions: What will the future bring for GenAI in the construction industry? What are the potential opportunities and challenges in implementing GenAI in the construction industry? This study delves into reflected perception in literature, analyzes the industry perception using programming-based word cloud and frequency analysis, and integrates authors’ opinions to answer these questions. This paper recommends a conceptual GenAI implementation framework, provides practical recommendations, summarizes future research questions, and builds foundational literature to foster subsequent research expansion in GenAI within the construction and its allied architecture and engineering domains.

Class-specific regularized joint distribution alignment for unsupervised domain adaptation

Unsupervised domain adaptation (UDA) aims to learn robust classifiers for the target domain by leveraging knowledge from annotated source domain. Existing methods concentrated on cross-domain distribution alignment or domain-invariant deep learning model construction. Recently, although the class-specific characteristics have been incorporated for numerous UDA methods, there remains a need for further exploration in capturing intra-class and inter-class characteristics between the source and target domains. In this paper, we propose a novel UDA method, referred to as Class-specific Regularized Joint Distribution Alignment (CRJDA), to simultaneously optimize the intra-class distance of source domain, the inter-class discriminability of target domain, and the joint distribution between domains. Specifically, our method involves an overall optimization process that minimizes the maximum mean discrepancy while incorporating dual penalized class-specific regularizations during joint distribution alignment. Extensive experiments conducted on several benchmark datasets demonstrate that the superiority of the proposed method compared to both state-of-the-art conventional UDA methods and advanced deep UDA models.

Fall Recognition Based on Time-Level Decision Fusion Classification

We propose a vision-based fall detection algorithm using advanced deep learning models and fusion methods for smart safety management systems. By detecting falls through visual cues, it is possible to leverage existing surveillance cameras, thus minimizing the need for extensive additional equipment. Consequently, we developed a cost-effective fall detection system. The proposed system consists of four modules: object detection, pose estimation, action recognition, and result fusion. Constructing the fall detection system involved the utilization of state-of-the-art (SOTA) models. In the fusion module, we experimented with various approaches, including voting, maximum, averaging, and probabilistic fusion. Notably, we observed a significant performance improvement with the use of probabilistic fusion. We employed the HAR-UP dataset to demonstrate this enhancement, achieving an average 0.84% increase in accuracy compared to the baseline, which did not incorporate fusion methods. By applying our proposed time-level ensemble and skeleton-based fall detection approach, coupled with the use of enhanced object detection and pose estimation modules, we substantially improved the robustness and accuracy of the system, particularly for fall detection in challenging scenarios.

Tree-Level Chinese Fir Detection Using UAV RGB Imagery and YOLO-DCAM

Achieving the accurate and efficient monitoring of forests at the tree level can provide detailed information for precise and scientific forest management. However, the detection of individual trees under planted forests characterized by dense distribution, serious overlap, and complicated background information is still a challenge. A new deep learning network, YOLO-DCAM, has been developed to effectively promote individual tree detection amidst complex scenes. The YOLO-DCAM is constructed by leveraging the YOLOv5 network as the basis and further enhancing the network’s capability of extracting features by reasonably incorporating deformable convolutional layers into the backbone. Additionally, an efficient multi-scale attention module is integrated into the neck to enable the network to prioritize the tree crown features and reduce the interference of background information. The combination of these two modules can greatly enhance detection performance. The YOLO-DCAM achieved an impressive performance for the detection of Chinese fir instances within a comprehensive dataset comprising 978 images across four typical planted forest scenes, with model evaluation metrics of precision (96.1%), recall (93.0%), F1-score (94.5%), and AP@0.5 (97.3%), respectively. The comparative test showed that YOLO-DCAM has a good balance between model accuracy and efficiency compared with YOLOv5 and advanced detection models. Specifically, the precision increased by 2.6%, recall increased by 1.6%, F1-score increased by 2.1%, and AP@0.5 increased by 1.4% compared to YOLOv5. Across three supplementary plots, YOLO-DCAM consistently demonstrates strong robustness. These results illustrate the effectiveness of YOLO-DCAM for detecting individual trees in complex plantation environments. This study can serve as a reference for utilizing UAV-based RGB imagery to precisely detect individual trees, offering valuable implications for forest practical applications.

Application of industry 4.0 technologies for mitigating humanitarian supply chains risks: the case of Covid-19

ABSTRACT The study aims at integrating and combining successful digital tools such as Blockchain technology, Internet of Things (IoT), and Cloud Computing (CC) to digitalise HSC. To show the enabling of these digital tools, we first conducted a real case study in collaboration with a major leader in real estate diagnostics and building technical data. Second, to mitigate the impact of various disturbances and risks of COVID-19 on supply chain (SC) performances, we modelled two logistics networks, i.e. before and after the COVID-19 pandemic. Finally, we proposed two efficient solutions. In the first solution, we proposed a risk mitigation matrix model based on the cause-effect diagram. In the second one, we developed an advanced digital model which is completely based on Industry 4.0 technologies. Results showed that the new digital model was more efficient since it enables to enhance HSC performances by mitigating risks and enabling tracking, monitoring, facilitating coordination, real-time information sharing, and flexibility during humanitarian operations.

Graph autoencoder with mirror temporal convolutional networks for traffic anomaly detection

Traffic time series anomaly detection has been intensively studied for years because of its potential applications in intelligent transportation. However, classical traffic anomaly detection methods often overlook the evolving dynamic associations between road network nodes, which leads to challenges in capturing the long-term temporal correlations, spatial characteristics, and abnormal node behaviors in datasets with high periodicity and trends, such as morning peak travel periods. In this paper, we propose a mirror temporal graph autoencoder (MTGAE) framework to explore anomalies and capture unseen nodes and the spatiotemporal correlation between nodes in the traffic network. Specifically, we propose the mirror temporal convolutional module to enhance feature extraction capabilities and capture hidden node-to-node features in the traffic network. Morever, we propose the graph convolutional gate recurrent unit cell (GCGRU CELL) module. This module uses Gaussian kernel functions to map data into a high-dimensional space, and enables the identification of anomalous information and potential anomalies within the complex interdependencies of the traffic network, based on prior knowledge and input data. We compared our work with several other advanced deep-learning anomaly detection models. Experimental results on the NYC dataset illustrate that our model works best compared to other models for traffic anomaly detection.

Comprehensive exploration of multi-modal and multi-branch imaging markers for autism diagnosis and interpretation: insights from an advanced deep learning model.

Autism spectrum disorder is a complex neurodevelopmental condition with diverse genetic and brain involvement. Despite magnetic resonance imaging advances, autism spectrum disorder diagnosis and understanding its neurogenetic factors remain challenging. We propose a dual-branch graph neural network that effectively extracts and fuses features from bimodalities, achieving 73.9% diagnostic accuracy. To explain the mechanism distinguishing autism spectrum disorder from healthy controls, we establish a perturbation model for brain imaging markers and perform a neuro-transcriptomic joint analysis using partial least squares regression and enrichment to identify potential genetic biomarkers. The perturbation model identifies brain imaging markers related to structural magnetic resonance imaging in the frontal, temporal, parietal, and occipital lobes, while functional magnetic resonance imaging markers primarily reside in the frontal, temporal, occipital lobes, and cerebellum. The neuro-transcriptomic joint analysis highlights genes associated with biological processes, such as "presynapse," "behavior," and "modulation of chemical synaptic transmission" in autism spectrum disorder's brain development. Different magnetic resonance imaging modalities offer complementary information for autism spectrum disorder diagnosis. Our dual-branch graph neural network achieves high accuracy and identifies abnormal brain regions and the neuro-transcriptomic analysis uncovers important genetic biomarkers. Overall, our study presents an effective approach for assisting in autism spectrum disorder diagnosis and identifying genetic biomarkers, showing potential for enhancing the diagnosis and treatment of this condition.

Crystal Engineering of Hydrogen Bonding for Direct Air Capture of CO2: A Quantum Crystallography Perspective

Rising atmospheric CO2 levels demand efficient and sustainable carbon capture solutions. Direct air capture (DAC) via crystallizing hydrogen-bonded frameworks such as carbonate salts has emerged as a promising approach. This review explores the potential of crystal engineering, in tandem with advanced quantum crystallography techniques and computational modeling, to unlock the full potential of DAC materials. We examine the critical role of hydrogen bonding and other noncovalent interactions within a family of bis-guanidines that governs the formation of carbonate salts with high CO2 capture capacity and low regeneration energies for utilization. Quantum crystallography and charge density analysis prove instrumental in elucidating these interactions. A case study of a highly insoluble carbonate salt of a 2,6-pyridine-bis-(iminoguanidine) exemplifies the effectiveness of these approaches. However, challenges remain in the systematic and precise determination of hydrogen atom positions and atomic displacement parameters within DAC materials using quantum crystallography, and limitations persist in the accuracy of current energy estimation models for hydrogen bonding interactions. Future directions lie in exploring diverse functional groups, designing advanced hydrogen-bonded frameworks, and seamlessly integrating experimental and computational modeling with machine learning. This synergistic approach promises to propel the design and optimization of DAC materials, paving the way for a more sustainable future.

Developing a Mechatronics-Twin Framework for Effective Exploration of Operational Behaviors of Prosthetic Sockets

A Stewart platform is a six-degree-of-freedom parallel manipulator widely used as the motion base for flight simulators and antenna positioning systems, among others. This work presents a novel mechatronics-twin framework that integrates such a manipulator with advanced biomechanical models and simulations for effective exploration of operational behaviors of prosthetic sockets with amputees. By means of the biomechanical models and simulations, the framework allows the users to first analyze the fundamental operational characteristics of individual amputees according to their specific body geometries, pelvis-femur structures, and sizes of transfemoral sockets. Such operational characteristics are then fed to one Stewart platform as the reference control signals for the generation of dynamic loads and behaviors of prosthetic sockets that are otherwise difficult to observe or realize with the real amputees. Experiments in form of integration testing show that the proposed control strategy is capable of generating expected dynamic operational conditions. Currently, the mechatronics-twin framework supports a wide range of biomechanical configurations and the quantification of the respective intra-socket load conditions for socket design optimization and anomaly detection.

Beyond Reality: Exploring the effect of different virtual reality environments on visual assessment of cakes

Visual assessment triggers physiological, emotional, and cognitive responses in consumer behavior. This confluence of signals can be influenced by context, which plays a crucial role in eating behavior. The strategies used to evoke scenarios that enhance ecological validity in sensory experiences have evolved in the last years to include immersive technologies and virtual reality (VR) to simulate the complexity of the real world and predict consumer preferences. This study explored VR's effect on visual liking and hedonic responses of five virtual cakes in two virtual contexts designed with advanced 3D modeling and photogrammetry techniques to ensure high realism and immersion. Although the virtual contexts themselves did not impact liking ratings, the variables “context-cake,“ ”age,“ and “subjective hunger” had a significant effect on the visual liking of cakes. A Check-All-That-Apply (CATA) questionnaire showed significant differences in responses for various terms related to the intrinsic and extrinsic characteristics of the five cakes. Finally, the internal preference map separated two consumer patterns of visual liking: traditional versus innovative.