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Research on fuzzy evaluation of ecological safety of land resources in Pearl river Delta area based on DPSIR framework
Leveraging YOLO deep learning models to enhance plant disease identification
Integrated analysis of single-cell and bulk RNA-sequencing to predict prognosis and therapeutic response for colorectal cancer
Predicting spatial familiarity by exploiting head and eye movements during pedestrian navigation in the real world
Abstract Spatial familiarity has seen a long history of interest in wayfinding research. To date, however, no studies have been done which systematically assess the behavioral correlates of spatial familiarity, including eye and body movements. In this study, we take a step towards filling this gap by reporting on the results of an in-situ, within-subject study with $$N=52$$ pedestrian wayfinders that combines eye-tracking and body movement sensors. In our study, participants were required to walk both a familiar route and an unfamiliar route by following auditory, landmark-based route instructions. We monitored participants’ behavior using a mobile eye tracker, a high-precision Global Navigation Satellite System receiver, and a high-precision, head-mounted Inertial Measurement Unit. We conducted machine learning experiments using Gradient-Boosted Trees to perform binary classification, testing out different feature sets, i.e., gaze only, Inertial Measurement Unit data only, and a combination of the two, to classify a person as familiar or unfamiliar with a particular route. We achieve the highest accuracy of $$89.9\%$$ using exclusively Inertial Measurement Unit data, exceeding gaze alone at $$67.6\%$$ , and gaze and Inertial Measurement Unit data together at $$85.9\%$$ . For the highest accuracy achieved, yaw and acceleration values are most important. This finding indicates that head movements (“looking around to orient oneself”) are a particularly valuable indicator to distinguish familiar and unfamiliar environments for pedestrian wayfinders.
Semi-analytical dynamic modeling and impact mechanism analysis of a hard-coating cylindrical shell with arbitrary circular perforations
Physics-informed machine learning for automatic model reduction in chemical reaction networks
Abstract Physics-informed machine learning bridges the gap between the high fidelity of mechanistic models and the adaptive insights of artificial intelligence. In chemical reaction network modeling, this synergy proves valuable, addressing the high computational costs of detailed mechanistic models while leveraging the predictive power of machine learning. This study applies this fusion to the biomedical challenge of A $$\beta$$ fibril aggregation, a key factor in Alzheimer’s disease. Central to the research is the introduction of an automatic reaction order model reduction framework, designed to optimize reduced-order kinetic models. This framework represents a shift in model construction, automatically determining the appropriate level of detail for reaction network modeling. The proposed approach significantly improves simulation efficiency and accuracy, particularly in systems like A $$\beta$$ aggregation, where precise modeling of nucleation and growth kinetics can reveal potential therapeutic targets. Additionally, the automatic model reduction technique has the potential to generalize to other network models. The methodology offers a scalable and adaptable tool for applications beyond biomedical research. Its ability to dynamically adjust model complexity based on system-specific needs ensures that models remain both computationally feasible and scientifically relevant, accommodating new data and evolving understandings of complex phenomena.
Structural analysis of $$\hbox{Sn}$$ on $${\hbox{Au}(111)}$$ at low coverages: Towards the $${\hbox {Au}_{2}\hbox {Sn}}$$ surface alloy with alternating fcc and hcp domains
Abstract We report on the structural and chemical evolution of submonolayer $$\hbox{Sn}$$ on $${\hbox{Au}(111)}$$ up to the formation of the striped $${\hbox {Au}_{2}\hbox {Sn}}$$ surface alloy. Using Low-Energy Electron Diffraction (LEED) and Scanning Tunneling Microscopy (STM), we identify a previously unobserved hexagonal $$(2\times 2)$$ -reconstruction at a $$\hbox{Sn}$$ film thickness of $$\approx 0.28$$ monolayers (ML). X-ray Photoelectron Spectroscopy (XPS) analysis reveals that the $$(2\times 2)$$ -structure is not chemically bonded to the $${\hbox{Au}(111)}$$ substrate. With increasing $$\hbox{Sn}$$ coverage, the $$(2\times 2)$$ -reconstruction performs a structural transition into a mixed phase before forming a local $$(\sqrt{3} \times \sqrt{3})\text {R}{30}^{\circ }$$ -reconstruction at a $$\hbox{Sn}$$ film thickness of $$0.33\,\textrm{ML}$$ . This reconstruction is superimposed by a larger periodicity resembling the herringbone reconstruction of clean $${\hbox{Au}(111)}$$ . Our XPS analysis identifies this phase as an $${\hbox {Au}_{2}\hbox {Sn}}$$ -alloy. By combining high-resolution x-ray photoelectron diffraction (XPD) measurements of $$\hbox{Au}\,\hbox{4f}$$ and $$\hbox{Sn}\,\hbox{4d}$$ 4d core levels with simulations based on a genetic algorithm, we propose a structural model for the $${\hbox {Au}_{2}\hbox {Sn}}$$ -supercell, revealing an unusually large unit cell with $$\text {Rec}(26\times \sqrt{3})$$ -periodicity. This study advances the understanding of the structural evolution of $$\hbox{Sn}$$ surface reconstructions on $${\hbox{Au}(111)}$$ up to the formation of the $${\hbox {Au}_{2}\hbox {Sn}}$$ surface alloy. Furthermore, it provides insights into the structural arrangements emerging at higher submonolayer $$\hbox{Sn}$$ coverages on $${\hbox{Au}(111)}$$ , offering potential pathways towards realizing freestanding stanene.
An efficient cell micronucleus classification network based on multi-layer perception attention mechanism
Abstract Cellular micronucleus detection plays an important role in pathological toxicology detection and early cancer diagnosis. To address the challenges of tiny targets, high inter-class similarity, limited sample data and class imbalance in the field of cellular micronucleus image detection, this paper proposes a lightweight network called MobileViT-MN (Micronucleus), which integrates a multilayer perceptual attention mechanism. Considering that limited data and class imbalance may lead to overfitting of the model, we employ data augmentation to mitigate this problem. Additionally, based on domain adaptation, we innovatively introduce transfer learning. Furthermore, a novel Deep Separation-Decentralization module is designed to implement the reconstruction of the network, which employs attention mechanisms and an alternative strategy of deep separable convolution. Numerous ablation experiments are performed to validate the effectiveness of our method. The experimental results show that MobileViT-MN obtains outstanding performance on the augmented cellular micronucleus dataset. Avg_Acc reaches 0.933, F1 scores 0.971, and ROC scores 0.965. Compared with other classical algorithms, MobileViT-MN is more superior in classification performance.
Prediction model for nonlinear variation of bolt preload with tightening torque based on mechanism and data fusion
Identifying key factors in the mammography procedure for delineating the pectoral muscle using the analytic hierarchy process
Seasonal dynamics of thraustochytrids in mangrove rhizospheres for microbial interactions, PUFA production
Semantic lossless encoded image representation for malware classification
Revising the Nottingham Inversion Instability as a bifurcation between two branches of steady states solutions of thermo-field emission from micro-protrusions
Risk of hematologic malignancies in psoriasis and rheumatoid arthritis patients using long term TNF-α inhibitors: a retrospective nationwide study
Heterotic potential and combining ability of Coffea arabica L
Psychometric evidence of a science, technology, engineering, and mathematics career interest survey of Indonesian high school students
Abstract The disparity between the growth of science, technology, engineering, and mathematics (STEM) job demand and students graduating from STEM areas raises an issue regarding the reason for low interest in STEM careers. An assessment tool is required to investigate this issue. However, the generalizability of existing assessment tools to be conducted cross-culturally becomes a concern. This study aims to report the psychometric evidence of the STEM career interest survey (STEM-CIS) in the Indonesian context using a quantitative design with a stratified random sampling technique. Data from 738 high school students were analyzed using confirmatory factor analysis (CFA). The adapted STEM-CIS showed good psychometric evidence as a single measure, a discipline-specific measure, and a social cognitive career theory (SCCT)-specific subscale measure. The reliability values of the adapted STEM-CIS indicated high, confirming its robustness for assessing STEM career interest among Indonesian high school population. These findings support the use of the adapted STEM-CIS as a contextually relevant and validated tool for cross-cultural research on STEM career interest. This study contributes to the global need for culturally adaptable assessment tools.