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The structure of calcium aluminosilicate glasses (CaO)x(Al2O3)y(SiO2)1−x−y with the near tectosilicate compositions x ≃ 0.19 and 1 − x − y ≃ 0.61 or x ≃ 0.26 and 1 − x − y ≃ 0.49 was investigated by in situ high-pressure neutron diffraction and 27Al nuclear magnetic resonance (NMR) spectroscopy. The results show three distinct pressure regimes for the transformation of the aluminum coordination environment from tetrahedral to octahedral, which map onto the deformations observed in the production of permanently densified materials. The oxygen packing fraction serves as a marker for signaling a change to the coordination number of the network forming motifs. For a wide variety of permanently densified aluminosilicates, the aluminum speciation shares a common dependence on the reduced density ρ′ = ρ/ρ0, where ρ is the density and ρ0 is its value for the uncompressed material. The observed increase in the Al–O coordination number with ρ′ originates primarily from the formation of six-coordinated aluminum Al(VI) species, the fraction of which increases rapidly beyond a threshold ρ thr ′ ∼ 1.1. The findings are combined to produce a self-consistent model for pressure-induced structural change. Provided the glass network is depolymerized, one-coordinated non-bridging oxygen atoms are consumed to produce two-coordinated bridging oxygen atoms, thus increasing the network connectivity in accordance with the results from 17O NMR experiments. Otherwise, three-coordinated oxygen atoms or triclusters appear, and their fraction is quantified by reference to the mean coordination number of the silicon plus aluminum species. The impact of treating Al(VI) as a network modifier is discussed. [ABSTRACT FROM AUTHOR]

Musculoskeletal modeling, typically implemented using marker-based systems in laboratory environments, is commonly used for noninvasive estimations of loads. Inertial measurement units (IMUs) have become an alternative for the evaluation of kinematics. However, estimates of spine joint contact forces using IMUs have yet to be thoroughly evaluated. Dynamics tasks and static postures from activities of daily living were captured on 11 healthy subjects using both systems simultaneously. Spine kinematics obtained from IMU- and marker-based systems and L4–L5 joint contact forces were compared. Lateral bending resulted in a weak agreement with significant differences between the 2 systems (P =.02, average root mean-squared error = 4.81), whereas flexion–extension and axial rotation exhibited the highest agreement with no significant differences (P <.05, average root mean-squared error = 5.51 and P <.31, average root mean-squared error = 5.08, respectively). All tasks showed excellent correlations (R2 =.76–.99) in estimated loads between systems. Differences in predicted loads at the L4–L5 were only observed during flexion–extension (1041 N vs 947 N, P =.0004) and walking with weights (814 N vs 727 N, P =.004). Different joint reaction force outcomes were obtained in 2 of the 8 tasks between systems, suggesting that IMUs can be robust tools allowing for convenient and less expensive evaluations and for longitudinal assessments inside and outside the laboratory setting. [ABSTRACT FROM AUTHOR]

High-entropy oxides (HEOs) are single phase solid solutions where five or more metals share the same sublattice, giving rise to unexpected features in various fields of applications. Recently, HEOs have emerged as an alternative conversion electrode anode material for next-generation Li-ion batteries, where the combination of several different elements in a single solid solution can synergistically act to overcome some of its main drawbacks, improving performance. Due to their chemical complexity, x-ray absorption spectroscopy (XAS) emerges as an appropriate technique to study the electronic (x-ray absorption near edge structure, XANES) and local structure (extended x-ray absorption fine structure, EXAFS) of these compounds as a function of cycling. This work aims to highlight the capabilities of XAS as an element-specific probe to understand a material's structure at the atomistic level through EXAFS modeling of (MgFeCoNiCuZn)O high-entropy system and how to extract valuable information about the bond distance, number of near neighbors, and local disorder, which are crucial to a full understanding of the electrochemical reaction mechanisms of such battery electrodes. [ABSTRACT FROM AUTHOR]

Product consumption is an important yet understudied aspect of marketing. Over-the-counter medicines present a unique consumption context because consumers are expected to follow product label instructions. For acetaminophen, a widely consumed over-the-counter drug, the authors study which consumers tend to deviate from the label instructions, why they do so, and the interventions that are most promising to mitigate such deviations. They develop a dynamic structural model of consumption that enables them to investigate the probability of different types of label deviations (e.g., >4 g per day) and drivers of such behaviors. Label deviations are infrequent "tail area" behaviors, and the model uncovers them well. The analysis is based on a unique online consumption diary in which consumers select from two classes of acetaminophen products (single ingredient or a combination of ingredients) to treat their pain or non-pain-related symptoms. Consumers have a higher probability of taking >4 g of acetaminophen when they have multiple symptoms, and a variety of observable factors explain the individual propensity to deviate from the label. The authors propose two interventions—a new label instruction and consumer education—to mitigate the observed label deviations and assess the expected impact of each. [ABSTRACT FROM AUTHOR]

In this work, we investigate how exploiting symmetry when creating and modifying structural models may speed up global atomistic structure optimization. We propose a search strategy in which models start from high symmetry configurations and then gradually evolve into lower symmetry models. The algorithm is named cascading symmetry search and is shown to be highly efficient for a number of known surface reconstructions. We use our method for the sulfur-induced Cu (111) ( 43 × 43 ) surface reconstruction for which we identify a new highly stable structure that conforms with the experimental evidence. [ABSTRACT FROM AUTHOR]

We present a novel formalism to characterize elastic heterogeneities in amorphous solids. In particular, we derive high-order strain-energy expansions for pairwise energies under athermal quasistatic dynamics. We then use the presented formalism to study the statistical properties of pairwise expansion coefficients and their link with the statistics of soft, quasilocalized modes, for a wide range of formation histories in both two- and three-dimensional systems. We further exploit the presented framework to access local yield stress maps by performing a non-linear stress–strain expansion within a cavity embedded in a frozen matrix. We show that our "bond micromechanics" compare well with the original "frozen matrix" method, with the caveat of overestimating large stress activations. We additionally show how local yield rules can be used as an input for a scalar elasto-plastic model (EPM) to predict the stress response of materials ranging from ductile to brittle. Finally, we highlight some of the limits of simple mesoscale models in capturing the aging dynamics of post-yielding systems. Intriguingly, we observe subdiffusive and diffusive shear band growths for particle-based simulations and EPMs, respectively. [ABSTRACT FROM AUTHOR]

We present structural models for three different amyloid fibril polymorphs prepared from amylin20–29 (sequence SNNFGAILSS) and amyloid-β25–35 (Aβ25–35) (sequence GSNKGAIIGLM) peptides. These models are based on the amide C=O bond and Ramachandran ψ-dihedral angle data from Raman spectroscopy, which were used as structural constraints to guide molecular dynamics (MD) simulations. The resulting structural models indicate that the basic structural motif of amylin20–29 and Aβ25–35 fibrils is extended β-strands. Our data indicate that amylin20–29 forms both antiparallel and parallel β-sheet fibril polymorphs, while Aβ25–35 forms a parallel β-sheet fibril structure. Overall, our work lays the foundation for using Raman spectroscopy in conjunction with MD simulations to determine detailed molecular-level structural models of amyloid fibrils in a manner that complements gold-standard techniques, such as solid-state nuclear magnetic resonance and cryogenic electron microscopy. [ABSTRACT FROM AUTHOR]

Purpose: To compare the 3D accuracy of three scanning strategies and conventional impressions using an edentulous model with six implants. Materials and Methods: An edentulous maxillary master model was fabricated with six equigingival internal connection implants at 0 degrees of angulation. Ten conventional open-tray splinted implant-level impressions were made and poured in stone. A master model and conventional casts were digitized with a reference scanner. Digital impressions were made by calibrated clinicians with a TRIOS 3 intraoral scanner ([IOS] 3Shape) according to three scanning strategies: DIG1 (occlusal-palatal-lingual), DIG2 (S-type motion from buccal to palatal), and DIG3 (scanning two half arches and connecting them at the midline). Each technique was repeated 10 times on the master model. Deviations from the STL datasets (N = 40) were compared to those of the reference master model using the Hexagon Metrology software system PC-DMIS CAD++. Linear distortions (dX, dY, dZ), global linear distortion (dR), and angular distortions (Absdθx, Absdθy) were calculated. Kruskal-Wallis test and mixed linear and logistic regression models were used to compare the original and binary distortion measures between the techniques. Results: The mean dR ranged from 91 μm (conventional method) to 183 μm (DIG1). The mean angular distortion ranged from 0.20 degrees (Absdθx for DIG2) to 0.69 degrees (Absdθy for DIG3). No scan pattern resulted in a more accurate reproduction in any of the measured parameters than the conventional impression method. There were significant differences between the methods for all distortion measures. Conclusions: No group reproduced the 3D position of the six-implant master model below the thresholds for both global linear and angular distortions. All the digital strategies tested were less accurate than the conventional open-tray splinted implant-level impression technique. [ABSTRACT FROM AUTHOR]

Physical aging is the process of a system evolving toward a new equilibrium, and thus the response to a change in external parameters such as temperature T, pressure p, or static electric field E. Using a static electric field has been shown to access physical aging above the glass transition temperature Tg, in the regime of milliseconds or faster, but the relation to its temperature jump counterpart has not been investigated to date. This work compares temperature and field induced physical aging in the limit of small perturbations for supercooled tributyl phosphate. It is found that both structural recovery dynamics are very similar, and that they match the collective reorientational dynamics as observed by dielectric relaxation. The results facilitate expanding the range of aging experiments to well above Tg, where a comparison with structural relaxation in equilibrium is straightforward, thus improving models of structural recovery and physical aging. [ABSTRACT FROM AUTHOR]

The study focuses on understanding the structural model of safety and health practices, organizational commitment, and socio-psychosocial attributes in relation to compliance with safety standards in an academic institution. It emphasizes the importance of providing a secure environment for employees and students, given the risks associated with campus activities such as construction, lab work, and other hazardous operations. The study investigates how demographic factors such as gender, employment status, and educational background influence safety compliance, alongside organizational commitment and employee attitudes towards health and safety standards. Using a comprehensive survey methodology, data was collected from the teaching and non-teaching staff of Bukidnon State University. The study utilized Structural Equation Modeling (SEM) to analyze the relationships between variables, assessing factors like the physical environment, use of facilities and electrical equipment, and hazard control. Key findings suggest a strong commitment to safety standards, with high compliance rates attributed to effective safety training, management commitment, and employee responsibility. The research identifies the use of electrical equipment, physical environment, and management commitment as the most significant predictors of safety compliance, while employee commitment, educational background, and hazard control showed negative correlations. The study concludes by recommending the adoption of a holistic safety model, known as Miranda's Model, which integrates organizational commitment, safety practices, and socio-psychosocial factors to enhance compliance. The results underscore the need for continuous safety audits, effective communication of safety protocols, and the involvement of all organizational levels in fostering a safety-conscious culture. [ABSTRACT FROM AUTHOR]

Aims: Based on the two‐factor model of caregiving appraisals, this study aims to (1) investigate the relationships between competence in dementia care and job satisfaction (work attitudes in positive aspect) and turnover intention (work attitudes in negative aspect) among formal caregivers in nursing homes, and (2) examine the mediating roles of positive aspects of caregiving (caregiving appraisals in positive aspect) and caregiving burden (caregiving appraisals in negative aspect) in the above associations. Design: A cross‐sectional design was used. Structural equation modelling was employed to test the proposed model. STROBE checklist guides the reporting of this study. Methods: Using the multistage cluster sampling method, 407 formal caregivers were selected from 43 nursing homes across 7 districts/counties in China. Results: The proposed model shows a good model fit. Competence in dementia care shows significant positive associations with increased job satisfaction and decreased turnover intention among formal caregivers in nursing homes. Caregiving burden and positive aspects of caregiving mediate the relationships between competence in dementia care and both job satisfaction and turnover intention. Furthermore, the relationship between positive aspects of caregiving and job satisfaction is stronger than that with turnover intention, but the relationship between caregiving burden and turnover intention is not stronger than that with job satisfaction. Conclusions: The results demonstrate that the two‐factor model of caregiving appraisals effectively elucidates the associations and underlying mechanisms between competence in dementia care and formal caregivers' work attitudes. Specifically, two crucial conclusions are drawn: (1) competence in dementia care is associated with higher levels of job satisfaction and lower levels of turnover intention among formal caregivers in nursing homes; (2) caregiving burden and positive aspects of caregiving serve as parallel mediators in these associations. Implications for the Profession and/or Patient Care: Policymakers and nursing homes managers should implement a series of measures aimed at formal caregivers in nursing home. Specifically, allocating more resources to enhance the competence in dementia care of formal caregivers is crucial, given its significant association with elevated job satisfaction and reduced turnover intention. Besides, positive and negative caregiving appraisals emerge as proximal factors influencing work attitudes of formal caregivers. Therefore, intervention projects focused on formal caregivers in nursing home should target enhancing positive experiences and alleviating caregiving burden. Patient or Public Contribution: No patient or public contribution. [ABSTRACT FROM AUTHOR]

With mental health needs consistently increasing in our communities and medical centers, we want to ensure that institutions are aware of the benefit and value that psychologists bring to their system and provide several pathways for consideration and structure to understand how to support the salaries and careers of psychologists working within AHCs. Leadership and administration within Academic Health Centers (AHC) often do not understand the value and measurement of productivity for psychologists being added to the medical teams. The current article aims to present varied structural models and demonstrate how productivity is reviewed for psychologists across different institutions and departments. The authors will outline the many roles that psychologists serve within academic health centers as well as the value those roles bring to the system. The overarching goal is to provide an educational article that serves as a tool for recruitment of psychologists that leaders and faculty can refer to when approaching administration in AHCs to understand the systems and roles of psychologists within medical settings. This information can be utilized to help create new positions for psychologists, aid in recruitment efforts and provide transparency for faculty currently working within AHCs who may not be aware of the varied opportunities. [ABSTRACT FROM AUTHOR]

This paper investigates the estimation of a linear regression model with a possible structural break at a known point. We analytically derive the exact formulae of the MSE for the restricted SR and PSR estimators, which shrinks the OLS estimator toward the restriction of no structural break. We compare the MSE performance of restricted/unrestricted SR, PSR, and least squared estimators. We analytically show that the unrestricted SR estimator can have a smaller MSE than the restricted SR estimator even when the restriction is correct. Further, our numerical results show that the unrestricted PSR estimator has the best MSE performance over a wide region of parameter space. These results indicate that the use of the unrestricted PSR estimator is recommended even when a structural break may not exist, although the unrestricted PSR estimator does not take the possibility of no structural break into consideration. [ABSTRACT FROM AUTHOR]

Evaluating track irregularities is crucial for ensuring the safe operation of trains since they serve as the primary source of excitation for vehicle systems and track structures. In China's high-speed railway networks, bridges are frequently employed as alternatives to traditional track foundations. Operational experience has revealed that bridge segments frequently display track irregularities closely linked to their structural characteristics. Nonetheless, there is presently limited research on track irregularities, particularly on bridges. This paper systematically investigates the factors that influence the vibration response of the vehicle-track-bridge coupled system, with a specific focus on track irregularities on bridges. We study the influence of various critical parameters on the vibration response of the vehicle-track-bridge coupled system, utilizing finite element analysis software and multibody dynamics software analysis models and incorporating structural design data. Furthermore, we analyze the spectral characteristics of track irregularities on bridges, revealing the patterns of variation in dynamic vertical and lateral irregularities influenced by both bridge and track structures. Lastly, leveraging the correlation between track irregularities on bridges and the dynamic response of the coupled system, we introduce an assessment methodology and corresponding indicators for track irregularities on bridges. This methodology can establish a theoretical basis for the scientific assessment and maintenance management guidance of track irregularities on high-speed railway bridges. [ABSTRACT FROM AUTHOR]

Existing lithospheric velocity models exhibit similar structures typically associated with the first-order tectonic features, with dissimilarities due to different data and methods used in model generation. The quantification of model structural similarity can help in interpreting the geophysical properties of Earth's interior and establishing unified models crucial in natural hazard assessment and resource exploration. Here we employ the complex wavelet structural similarity index measure (CW-SSIM) active in computer image processing to analyze the structural similarity of four lithospheric velocity models of Chinese mainland published in the past decade. We take advantage of this method in its multiscale definition and insensitivity to slight geometrical distortion like translation and scaling, which is particularly crucial in the structural similarity analysis of velocity models accounting for uncertainty and resolution. Our results show that the CW-SSIM values vary in different model pairs, horizontal locations, and depths. While variations in the inter-model CW-SSIM are partly owing to different databases in the model generation, the difference of tomography methods may significantly impact the similar structural features of models, such as the low similarities between the full-wave based FWEA18 and other three models in northeastern China. We finally suggest potential solutions for the next generation of tomographic modeling in different areas according to corresponding structural similarities of existing models. [ABSTRACT FROM AUTHOR]

This research investigates the tourists' attitudes and preferences regarding street food and how their quest for food authenticity and customization affects their culinary experiences. The study will also reveal the impact of food customization on their perceived food authenticity, perceived memorable experience and behavioral intentions in the Fiji Islands. Using the location-intercept approach and a questionnaire survey, data was gathered from 279 foreign visitors who visited the Fiji Islands and consumed street foods at numerous food vending outlets across Fiji. The PLS approach was employed to evaluate the structural and measurement model's dual estimation simultaneously. Findings suggest that tourists' perceived food authenticity and cultural differences positively impact how they perceive their memorable destination experiences. Additionally, it has been discovered that the extent of customization in street food reduces tourists' perceptions of their authenticity. At the same time, street food authenticity significantly impacts their perceived memorable experiences at the destination. This study attempts to investigate how the perceived street food authenticity and the degree of their customization affect the perceived memorable experiences of foreign tourists in Fiji Islands. This may assist the street food vendors and other stakeholders in aligning the culinary offerings and enhancing travelers' perceived destination experiences. [ABSTRACT FROM AUTHOR]

Purpose: Despite their significant economic impact, small and medium-sized enterprises (SMEs) remain underrepresented in public procurement. While previous research has identified the barriers facing SMEs in public procurement markets, a knowledge gap still exists on how these barriers influence one another in a specific context. Design/methodology/approach: This study developed a hierarchical structural model that reveals the contextual interrelationships between 14 notable barriers to SME involvement in public procurement, based on evidence from the publicly funded healthcare sector in the UK. The data were analyzed using interpretive structural modeling (ISM) and matrix impact of cross-multiplication applied to categorization (MICMAC). Findings: The results systematically classify barriers based on their hierarchical relationships, offering a robust theoretical foundation for understanding their nature and interconnections. Practical implications: The findings can help scholars, government administrators and politicians in government-funded healthcare systems determine the most influential barriers to SME involvement and their causes, enabling them to build viable remedies. Originality/value: This is the first empirical study to examine the relationship between the barriers to SMEs' public health procurement, using evidence from the public health procurement context. [ABSTRACT FROM AUTHOR]

How will structural change unfold beyond the rise of services? Motivated by the observed dynamics within the service sector we propose a model of structural change in which productivity is endogenous and output is produced with two intermediate substitutable capital goods. In the productive sector the accumulation of specialized skills leads to an unbounded increase in TFP, as sector becoming asymptotically dominant. We are then able to recover the increasing shares of workers, the increasing real and nominal shares of the output observed in productive service and IT sectors in the US. Interestingly, the economy follows a growth path converging to a particular level of wealth that depends on the initial price of capital and knowledge. As a consequence, countries with the same fundamentals but lower initial wealth will be characterized by lower asymptotic wealth. [ABSTRACT FROM AUTHOR]

The study aims to enhance resilience in Indian automobile supply chains by identifying enablers and developing structural model to explain their leads-to relationship with business outcomes amidst disruptions. The study is particularly useful for Indian automobile supply chains as they function amidst great deal of turbulence making them vulnerable to disruptions. Building resilience helps them recover from disruptions while ensuring business continuity, customer satisfaction and profitability. The study identifies the resilience enablers through a Delphi survey and applies Fuzzy ISM and Fuzzy MICMAC to model the structural linkages between them. The methods result in identification of eight resilience enablers for Indian automobile supply chains and essential drivers in achieving the business outcomes amidst disruptions. The structural model establishes that supply chain collaboration, resilience infrastructure, strong relationships with supply chain partners and orientation are essential in enhancing flexibility, agility, preparedness and responsiveness. Together they help in achieving financial performance, business continuity and market share growth amidst disruptions. The enablers would guide automobile managers in better adoption and resource allocation of resilience enablers in their existing supply chains. [ABSTRACT FROM AUTHOR]

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A new biomimetic model complex of the active site of acireductone dioxygenase (ARD) was synthesized and crystallographically characterized ([Ni(II)(N-(ethyl-N′Me 2)(Py)(2-t-ButPhOH))(OTf)]- 1). 1 displays carbon–carbon oxidative cleavage activity in the presence of O2 towards the substrate 2-hydroxyacetophenone. This reactivity was monitored via UV-Visible and NMR spectroscopy. We postulate that the reactivity of 1 with O2 leads to the formation of a putative Ni(III)-superoxo transient species resulting from the direct activation of O2via the nickel center during the oxidative reaction. This proposed intermediate and reaction mechanism were studied in detail using DFT calculations. 1 and its substrate bound derivatives display reactivity toward mild outer sphere oxidants, suggesting ease of access to high valent Ni coordination complexes, consistent with our calculations. If confirmed, the direct activation of O2 at a nickel center could have implications for the mechanism of action of ARD and other nickel-based dioxygenases and their respective non-traditional, enzymatic moonlighting functions, as well as contribute to a general understanding of direct oxidation of nickel(II) coordination complexes by O2. [ABSTRACT FROM AUTHOR]

ABSTRACT The modelling of rock structure is of great significance in characterizing rock characteristics and studying the failure laws of rock samples. In order to construct a high‐fidelity model of the rock structure efficiently, this paper proposes an adaptive mesh dissection algorithm based on the Voronoi structure. Image processing techniques, including greyscale, threshold segmentation and edge detection, are applied to simplify the original rock image into a feature edge image. Then, a probability density diagram of the feature image is generated, which provides a probabilistic basis for the subsequent spreading of mesh seed points. Moreover, the concept of polygonal representation rate and the mesh quality evaluation system of four‐dimensional metrics are established to suggest values for the seed point parameters of the initial mesh. The initial mesh is continuously optimized and iterated by barycentric iteration and gradient descent optimization methods to form mesh structural models with high representational performance efficiently. The model tests on particle, fracture and multi‐phase rock images show that the optimized mesh model is highly similar to the original image in terms of similarity and edge fit, and the algorithm significantly reduces the short‐edge rate and improves the shape regularity of the mesh structure. Finally, numerical tests of uniaxial compression are carried out based on the optimized mesh model. The results show that the model has computational potential in numerical calculations. This method builds a procedural structure from digital images to numerical models, which can provide a reliable model basis for simulating the physico‐mechanical behaviour of heterogeneous rocks. [ABSTRACT FROM AUTHOR]

ABSTRACT Language mindsets influence learners' behavior, motivation, and strategic approaches to studying a language. These mindsets impact how learners perceive control and value in achievement activities, which, in turn, affect their achievement emotions and ultimately their learning strategies and academic performance. This study investigates the effects of growth and fixed language mindsets on strategic approaches to studying English, with perceived academic control, task value, and positive/negative achievement emotions serving as mediators. Questionnaire surveys were administered to 517 university students in Taiwan. The structural models revealed that students with growth language mindsets exhibited stronger academic control and task value, experienced more positive emotions, and employed more study strategies. These factors partially mediated the relationship between growth language mindsets and study strategies. In contrast, fixed language mindsets did not predict task value or study strategies, but they negatively predicted academic control. No mediating effects were found between fixed language mindsets and study strategies. [ABSTRACT FROM AUTHOR]

The bar and hinge framework uses truss elements and rotational springs to efficiently model the structural behaviour of origami. The framework is especially useful to investigate origami metamaterials as they have repeating geometry, which makes conventional finite element simulations very expensive due to a large number of degrees of freedom. This work proposes improvements to the parameters of bar and hinge model within the context of structural dynamics, specifically modal analysis under small deformations, which has not been carried out previously in the literature. A range of low-frequency modes involving origami folding and panel bending deformations that can be accurately captured by the bar and hinge framework are identified. Within this range, bar and hinge parameters like the lumped masses and the rotational spring stiffness values are derived using conservation laws and finite element tests. The best among the proposed schemes is found to predict natural frequencies of the considered origami structures to within 10% maximum error, improving the accuracy by more than three times from existing schemes. In most cases, the errors in natural frequencies are less than 5%. This article is part of the theme issue 'Origami/Kirigami-inspired structures: from fundamentals to applications'. [ABSTRACT FROM AUTHOR]

The integrity of large crossing cables is frequently compromised by inland river ultra‐high sand dredgers, leading to severe safety incidents. To mitigate these risks, height‐limiting mechanisms, known as river‐blocking lines, are essential on both sides of the large crossing cable. Traditional river‐blocking lines are typically rigid structures, posing challenges for both the river‐blocking line and passing ships. To address this issue and enhance safety measures, this study introduces a novel flexible blocking device. Initially, the fundamental principles of suspension cable theory are employed to design the river‐blocking line. By leveraging energy conversion principles, heavy objects are suspended at the cable ends to convert the kinetic energy of oncoming ships into potential energy during collisions. Subsequently, a structural model for a three‐suspension cable river‐blocking line is developed. Finite element simulations confirm the model's efficacy, aligning with theoretical expectations. Furthermore, a scaled prototype of the model is constructed to assess project feasibility and reliability. The results affirm that the flexible blocking device can serve as an effective height‐limiting mechanism, proficiently impeding passing ships, mitigating impact loads, safeguarding vessels, and offering direct physical protection for ultra‐high voltage (UHV) large‐span cables. [ABSTRACT FROM AUTHOR]

China's operational highway subgrades exhibit a trend of diversifying types and an increasing number of defects, leading to more frequent urban road safety incidents. This paper starts from the non-destructive testing of urban road subgrade defects using geological radar, aiming to achieve intelligent identification of subgrade pathologies with geological radar. The GprMax forward simulation software is used to establish multi-layer composite structural models of the subgrade, studying the characteristics of geological radar images for different types of subgrade diseases. Based on the forward simulation images of geological radar for subgrade defects and field measurement data, a geological radar subgrade defect image database is established. The Faster R-CNN deep learning algorithm is applied to achieve target detection, recognition, and classification of subgrade defect images. By comparing the loss value, total number of identified regions, and recognition accuracy as metrics, the study compares four improved versions of the Faster R-CNN algorithm. The results indicate that the faster_rcnn_inception_v2 version is more suitable for the intelligent identification of non-destructive testing of urban road subgrade defects. [ABSTRACT FROM AUTHOR]

Musculoskeletal simulations are a useful tool for improving our understanding of the human body. However, the physiological validity of predicted kinematics and forces is highly dependent upon the correct calibration of muscle parameters and the structural integrity of a model's internal skeletal structure. In this study, we show how ill-tuned muscle parameters and unphysiological deformations of a model's skeletal structure can be detected by using muscle elements as sensors with which modelling and parameterization inconsistencies can be identified through muscle and tendon strain injury assessment. To illustrate our approach, two modelling issues were recreated. First, a model repositioning simulation using the THUMS AM50 occupant model version 5.03 was performed to show how internal model deformations can occur during a change of model posture. Second, the muscle material parameters of the OpenSim gait2354 model were varied to illustrate how unphysiological muscle forces can arise if material parameters are inadequately calibrated. The simulations were assessed for muscle and tendon strain injuries using previously published injury criteria and a newly developed method to determine tendon strain injury threshold values. Muscle strain injuries in the left and right musculus pronator teres were detected during the model repositioning. This straining was caused by an unphysiologically large gap (12.92 mm) that had formed in the elbow joint. Similarly, muscle and tendon strain injuries were detected in the modified right-hand musculus gastrocnemius medialis of the gait2354 model where an unphysiological reduction of the tendon slack length introduced large pre-strain of the muscle-tendon unit. The results of this work show that the proposed method can quantify the internal distortion behaviour of musculoskeletal human body models and the plausibility of Hill-type muscle parameter choice via strain injury assessment. Furthermore, we highlight possible actions to avoid the presented issues and inconsistencies in literature data concerning the material characteristics of human tendons. [ABSTRACT FROM AUTHOR]

Despite the prevalence of health communication on social media, the mechanisms by which social media affect health information exchange have received less than adequate investigation. By integrating the lenses of affordance and social support, this study examines the effect of social media affordances on health information exchange, with a particular focus on the mediation effect of social support and the boundary condition of health consciousness. Results of an online survey indicated that the affordances of information accessibility and metavoicing are positively related to both general and experiential informational support. Moreover, the affordance of association is positively related to experiential informational support and emotional support. Additionally, three types of social support have positive effects on health information exchange, and health consciousness positively moderates the effect of experiential informational support on health information exchange. This study enriches existing literature by revealing the underlying mechanisms and boundary conditions of the relationship between social media affordances and health information exchange. Furthermore, it provides new insights for practitioners to promote health information exchange on social media. [ABSTRACT FROM AUTHOR]

The finite‐element analysis plays a crucial role in enhancing the quality and predicting the safety of steel cord. The conventional parametric model for finite‐element analysis often overlooks the inhomogeneity of steel cord, making it difficult to achieve precise simulation. This article utilizes microcomputed tomography to track the mesoscopic deformation of real steel cord during stretching, reconstructing 3D digital images and quantitatively analyzing the corresponding rule for an optimized finite‐element modeling method. In comparison to the parametric model's experimental deviation of 28.37% in strain, the actual structural tensile model aligns with the tensile test with a deviation of only 2.88%, indicating high processing quality. In addition, when simulating the impact of layered nonuniformity on the fracture strain of cords, it is observed that a small degree of nonuniformity leads to an increase in compression between the cord filaments and subsequently enhances the fracture strain. This study provides a practical and dependable quantitative analysis on a mesoscopic scale to optimize the design and production of cords. [ABSTRACT FROM AUTHOR]

There are only a few studies on the function of neuronal axon guidance molecules during low brain pH conditions. We previously reported that roundabout (ROBO) 2, a receptor for the axon guidance molecule SLIT, can bind to the neural epidermal growth factor‐like‐like (NELL) ligands in acidic conditions by conformational change of its ectodomain. Here, we show that the ROBO3 receptor also exhibits a pH‐dependent increase in binding to the NELL2 ligand. We found that the Glu592 residue of ROBO3 at the binding interface between NELL2 and ROBO3 is a pH sensor and that the formation of a new hydrogen bonding network, due to protonation of the Glu592, leads to increased binding in acidic conditions. These results suggest that NELL2–ROBO3 signaling could be regulated by extracellular pH. [ABSTRACT FROM AUTHOR]

Thailand is one of the most popular outbound destinations for Chinese tourists. However, the conspiracy theory about trafficking Chinese in Thailand has negatively affected the resilience of the Chinese outbound market after the COVID-19 pandemic. The issue of how beliefs in the conspiracy theory affect Chinese tourists' intention to visit Thailand is investigated in this study. An online survey with a valid sample of 452 Chinese was conducted in July and August 2023. The measurement model and structural model were tested via WarpPLS 8.0. The results of this study indicate that beliefs in the conspiracy theory arouse Chinese tourists' distrust towards Thailand and signify that tourism boycott is a form of negative political consumption. Furthermore, the respondents react differently to the conspiracy theory based on their age and past visit experience. This study highlights conspiracy beliefs as a psychological factor affecting tourist behaviour. It also suggests that relevant conspiracy theories overshadow tourism resilience in destinations. [ABSTRACT FROM AUTHOR]

Corporate disclosures convey crucial information to financial market participants. While machine learning algorithms are commonly used to extract this information, they often overlook the use of idiosyncratic terminology and industry-specific vocabulary within documents. This study uses an unsupervised machine learning algorithm, the Structural Topic Model, to overcome these issues. Our findings illustrate the link between machine-extracted risk factors discussed in corporate disclosures (10-Ks) and the corresponding pricing behavior by investors, focusing on a previously unexplored US REIT sample from 2005 to 2019. Surprisingly, when disclosed, most risk factors counterintuitively lead to a decrease in return volatility. This resolution of uncertainties surrounding known risk factors or the provision of additional facts about these factors contributes valuable insights to the financial market. [ABSTRACT FROM AUTHOR]

The parasitic nematode Heligmosomoides polygyrus bakeri secretes the HpARI family, which bind to IL-33, either suppressing (HpARI1 and HpARI2) or enhancing (HpARI3) responses to the cytokine. We previously showed that HpARI2 also bound to DNA via its first complement control protein (CCP1) domain. Here, we find that HpARI1 can also bind DNA, while HpARI3 cannot. Through the production of HpARI2/HpARI3 CCP1 domain-swapped chimeras, DNA-binding ability can be transferred, and correlates with in vivo half-life of administered proteins. We found that HpARI1 and HpARI2 (but not HpARI3) also binds to the extracellular matrix component heparan sulphate (HS), and structural modelling showed a basic charged patch in the CCP1 domain of HpARI1 and HpARI2 (but not HpARI3) which could facilitate these interactions. Finally, a mutant of HpARI2 was produced which lacked DNA and HS binding, and was also shown to have a short half-life in vivo. Therefore, we propose that during infection the suppressive HpARI1 and HpARI2 proteins have long-lasting effects at the site of deposition due to DNA and/or extracellular matrix interactions, while HpARI3 has a shorter half-life due to a lack of these interactions. [ABSTRACT FROM AUTHOR]

Structural Path Analysis (SPA) and the Hypothetical Extraction Method (HEM) are both established methods for studying CO2 emissions. However, their combined application to investigate emission linkages in specific sectors, such as construction, is relatively novel. This research integrates SPA and HEM to explore the CO2 emissions linkages within the construction sectors of China and the United States, providing a comprehensive understanding of how these emissions are interlinked. The findings show that construction sector of Untied States and China is the largest production-based CO2 emissions of construction sector in the world, but the consumption-based emissions of construction sector in China contributes 29.81% of total CO2 emissions, compared to 5.63% in the U.S. This suggests that the carbon footprint of the construction sector is a significant consideration, irrespective of whether it is assessed from the standpoint of production or consumption dynamics. Meanwhile, the development of construction sector has driven the electricity, gas, steam, and air conditioning supply sectors to emit a large amount of CO2 emission in both countries. By analyzing the differences in the main emission linkages and pathways of the construction sectors between China and the U.S., this study provides insights for reducing CO2 emissions in the construction sector and assists policymakers in developing future strategies. [ABSTRACT FROM AUTHOR]

Protein glycosylation is a fundamental modification crucial for numerous intra- and extracellular functions in all eukaryotes. The phosphorylated dolichol (Dol-P) is utilized in N-linked protein glycosylation and other glycosylation pathways. Dolichol kinase (DK) plays a key role in catalyzing the phosphorylation of dolichol. The glycosylation patterns in the Kluyveromyces lactis DK mutant revealed that the yeast well tolerated a minor deficiency in Dol-P by adjusting protein glycosylation. Comparative analysis of sequences of DK homologs from different species of eukaryotes, archaea and bacteria and AlphaFold3 structural model studies, allowed us to predict that DK is most likely composed of two structural/functional domains. The activity of predicted K. lactis DK C-terminal domain expressed from the single copy in the chromosome was not sufficient to keep protein glycosylation level necessary for survival of K. lactis. However, the glycosylation level was partially restored by additionally provided and overexpressed N- or C-terminal domain. Moreover, co-expression of the individual N-and C-terminal domains restored the glycosylation of vacuolar carboxypeptidase Y in both K. lactis and Saccharomyces cerevisiae. Despite the differences in length and non-homologous sequences of the N-terminal domains the human and nematode Caenorhabditis elegans DKs successfully complemented DK functions in both yeast species. Additionally, the N-terminal domains of K. lactis and C. elegans DK could functionally substitute for one another, creating active chimeric enzymes. Our results suggest that while the C-terminal domain remains crucial for DK activity, the N-terminal domain may serve not only as a structural domain but also as a possible regulator of DK activity. [ABSTRACT FROM AUTHOR]

Background: Research to date suggests that physical activity (PA) can buffer menopausal symptoms and support well-being, but there is limited evidence on the link between PA and menopausal symptoms in the United Kingdom, and no study has assessed how PA affects well-being through menopausal symptoms and three psychosomatic factors (i.e. depression, anxiety and stress). Objectives: This study investigated whether PA affects well-being through menopausal symptoms and psychosomatic factors. This study focuses on how PA influences working women with menopause. Design: A cross-sectional design based on the STROBE (i.e. Strengthening the Reporting of Observational Studies in Epidemiology) checklist was adopted. Methods: The participants were 324 working women in England. An online self-reported questionnaire was utilised to gather data through Qualtrics. The data were analysed with path analysis through structural equation modelling, and sensitivity analyses were performed to avoid or reduce statistical bias. Results: PA had a negative effect on menopausal symptoms (β = −0.21; p < 0.001) but a positive effect on well-being (β = 0.19; p < 0.001). Menopausal symptoms had a negative indirect effect on well-being, but PA had a positive indirect effect on well-being through menopausal symptoms and the three psychosomatic factors. Conclusion: PA was positively associated with well-being but negatively associated with menopausal symptoms. Menopausal symptoms may lower well-being through anxiety, depression and stress, but PA can be associated with better well-being through depression, anxiety and stress. Plain language summary: The influence of physical activity on well-being through menopausal symptoms, depression, anxiety, and stress Why was the study done? Studies have assessed the potential influence of physical activity on menopausal symptoms among women, but no study in the United Kingdom has examined this relationship. Although the positive effect of physical activity on well-being is well researched and documented, no study has explored how it is mediated by menopausal symptoms and psychosomatic factors (i.e. stress, anxiety, and depression). Evaluation of this potential mediation is needed to improve stakeholders' understanding of the best ways to manage menopause at work. What did the researchers do? The researchers designed an online survey and used Qualtrics to distribute it among 324 working women with menopause in England. They used the appropriate statistical analysis technique to ascertain how the effect of physical activity on well-being is mediated by menopausal symptoms and the psychosomatic factors. What did the researchers find? Women who reported higher physical activity experienced fewer menopausal symptoms. Women with higher physical activity reported better well-being through fewer menopausal symptoms and lower stress, anxiety, and depression. Physical activity may help to improve well-being among working menopausal women by lowering menopausal symptoms, stress, anxiety, and depression. What do the findings mean? Working women can maintain well-being and experience less menopausal symptoms, stress, anxiety, and depression if they participate in physical activities. Organizations can manage menopause by rolling out workplace programmes encouraging employees to regularly participate in physical activities. [ABSTRACT FROM AUTHOR]

When a researcher proposes an SEM model to explain the dynamics among some latent variables, the real question in model evaluation is the fit of the model's structural part. A composite index that lumps the fit of the structural part and measurement part does not directly address that question. The need for more attention to structural-level fit has been noted for a long time, but the literature has been limited to conceptual issues such as how to define the structural-level RMSEA or CFI. How to estimate those structural-level fit indices, however, have been overlooked. Traditional estimation methods for composite-fit RMSEA and CFI are not applicable to structural-level fit indices, even if data are complete and normally distributed. In this article, we give correct point estimators and confidence intervals for structural-level RMSEA and CFI. Simulation results suggest that the new methods have satisfactory performance in realistic situations. [ABSTRACT FROM AUTHOR]

ProjectAnatomy combines tangible objects with video projections that show the anatomy of the human forearm intended for learning through its eye-catching appearance. The prototype was shown to three target groups: non-experts, trainee medical students, and anatomy experts to provide qualitative and quantitative feedback. The study and observation of the users in the first two groups were carried out 'in the wild' where the users would come in contact with this type of visualisation to collect data on how it should be adjusted to their needs. The results have shown that the non-experts find it most helpful in learning anatomy, contrary to the experts who find it less useful yet very interesting and see its educational potential. The trainee medical students scored it the lowest; however, they would find it helpful to complement dissection classes. We conclude that this visual installation could be used in science centres and, when enriched with more details, for beginning medical students. Our study contributes specifically to the field of physical visualisation using 3D printed objects combined with projections that can be adjusted to the level of detail suitable for the target audience. [ABSTRACT FROM AUTHOR]

Purpose: As a highly knowledge-intensive activity, digitalization is changing the construction industry landscape and is encouraging construction firms to explore the transformation. This study establishes a new theoretical model aimed at examining the impact of three types of intellectual capital (IC) on digitalization through the lens of knowledge-based view and explores how IC and digitalization influence sustainability performance from the triple bottom line principles. Design/methodology/approach: A questionnaire survey was conducted to collect data from Chinese construction firms using convenience sampling. A total of 181 valid responses were obtained. Then, a partial least squares structural equation modelling (PLS-SEM) technique was executed through Smart PLS 3.0 software. The measurement model was assessed to ensure reliability and validity, and the structural model was analysed to test the proposed hypotheses. Findings: The empirical results confirm the positive impact of IC on digitalization and digitalization on sustainability performance. Moreover, digitalization plays a significant mediating role in the relationship between IC and sustainability performance. Originality/value: The results provide empirical evidence supporting the different roles of IC and digitalization in improving sustainability. The findings contribute to enhancing the understanding of digitalization practices from the perspective of IC and provide theoretical and managerial implications for sustainability issues in the context of the construction industry. [ABSTRACT FROM AUTHOR]

The Latent Dirichlet Location (LDA) model is a popular method for creating mixed-membership clusters. Despite having been originally developed for text analysis, LDA has been used for a wide range of other applications. We propose a new formulation for the LDA model which incorporates covariates. In this model, a negative binomial regression is embedded within LDA, enabling straight-forward interpretation of the regression coefficients and the analysis of the quantity of cluster-specific elements in each sampling units (instead of the analysis being focused on modeling the proportion of each cluster, as in Structural Topic Models). We use slice sampling within a Gibbs sampling algorithm to estimate model parameters. We rely on simulations to show how our algorithm is able to successfully retrieve the true parameter values and the ability to make predictions for the abundance matrix using the information given by the covariates. The model is illustrated using real data sets from three different areas: text-mining of Coronavirus articles, analysis of grocery shopping baskets, and ecology of tree species on Barro Colorado Island (Panama). This model allows the identification of mixed-membership clusters in discrete data and provides inference on the relationship between covariates and the abundance of these clusters. HIGHLIGHTS: We propose a new formulation for the Latent Dirichlet Allocation (LDA) model which incorporates covariates. Our extension enables a straight-forward interpretation of the regression coefficients and the analysis of the quantity of cluster-specific elements in each sampling unit - including the prediction of these quantities in a new sample through covariates. We illustrate the benefits of this formulation using three data sets: text-mining of Coronavirus articles, analysis of grocery shopping baskets, and ecology of tree species in Barro Colorado Island. We provide an R package that enables users to readily apply our model. [ABSTRACT FROM AUTHOR]

Within this manuscript, we provide a novel Fano resonance-driven micro-nanosensor. Its primary structural components are a metal-insulator-metal (MIM) waveguide, a shield with three disks, and a T-shaped cavity (STDTC). The finite element approach was used to study the gadget in theory. It is found that the adjustment of the structure and the change of the dimensions are closely related to the sensitivity (S) and the quality factor (FOM). Different model structural parameters affect the Fano resonance, which in turn changes the transmission characteristics of the resonator. Through in-depth experimental analysis and selection of appropriate parameters, the sensor sensitivity finally reaches 3020 nm/RIU and the quality factor reaches 51.89. Furthermore, the installation of this microrefractive index sensor allows for the quick and sensitive measurement of glucose levels. It is a positive contribution to the field of optical devices and micro-nano sensors and meets the demand for efficient detection when applied in medical and environmental scenarios. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to evaluate the feasibility and performance of deep transfer learning (DTL) networks with different types and dimensions in differentiating thymomas from thymic cysts in a retrospective cohort. Materials and Methods: Based on chest‐enhanced computed tomography (CT), the region of interest was delineated, and the maximum cross section of the lesion was selected as the input image. Five convolutional neural networks (CNNs) and Vision Transformer (ViT) were used to construct a 2D DTL model. The 2D model constructed by the maximum section (n) and the upper and lower layers (n − 1, n + 1) of the lesion was used for feature extraction, and the features were selected. The remaining features were pre‐fused to construct a 2.5D model. The whole lesion image was selected for input and constructing a 3D model. Results: In the 2D model, the area under curve (AUC) of Resnet50 was 0.950 in the training cohort and 0.907 in the internal validation cohort. In the 2.5D model, the AUCs of Vgg11 in the internal validation cohort and external validation cohort 1 were 0.937 and 0.965, respectively. The AUCs of Inception_v3 in the training cohort and external validation cohort 2 were 0.981 and 0.950, respectively. The AUC values of 3D_Resnet50 in the four cohorts were 0.987, 0.937, 0.938, and 0.905. Conclusions: The DTL model based on multiple different dimensions can be used as a highly sensitive and specific tool for the non‐invasive differential diagnosis of thymomas and thymic cysts to assist clinicians in decision‐making. [ABSTRACT FROM AUTHOR]

The use of high‐dimensional fixed‐effects estimation has become customary with the estimation of gravity models of bilateral trade, migration, or commuting as outcome. However, fixed‐effects methods can be used without incidental‐parameter bias in a very small set of stochastic models. Alternatives to fixed‐effects estimation are iterative‐structural model estimation or linearizations of the structural model. Baier and Bergstrand deployed such a linearization. While easy to implement, the approach has drawbacks related to the approximation point and lack of observability of ingredients needed for the linearization. This compromises empirical work. The present paper provides a remedy to this problem by linearizing at the observed trade equilibrium. [ABSTRACT FROM AUTHOR]

We develop a rheological model to approximate the nonlinear rheology of wormlike micelles using two constitutive models to represent a structural transition at high shear rates. The model is intended to describe the behavior of semidilute wormlike micellar solutions over a wide range of shear rates whose parameters can be determined mainly from small-amplitude equilibrium measurements. Length evolution equations are incorporated into reactive Rolie-Poly entangled-polymer rheology and dilute reactive-rod rheology, with a kinetic exchange between the two models. Although the micelle length is remarkably reduced during flow, surprisingly, we propose that they are not shortened by stress-enhanced breakage, which remains thermally driven. Instead, we hypothesize that stretching energy introduces a linear potential that decreases the rate of recombination and reduces the mean micelle length. This stress-hindered recombination approach accurately describes transient stress-growth upon start-up shear flow, and it predicts a transition of shear viscosity and alignment response observed at high shear rates. The proposed mechanism applies only when self-recombination occurs frequently. The effect of varying the relative rate of self-recombination on the rheology of wormlike micelles at high shear rates is yet to be explored. [ABSTRACT FROM AUTHOR]

Biomedical event causal relation extraction (BECRE), as a subtask of biomedical information extraction, aims to extract event causal relation facts from unstructured biomedical texts and plays an essential role in many downstream tasks. The existing works have two main problems: i) Only shallow features are limited in helping the model establish potential relationships between biomedical events. ii) Using the traditional oversampling method to solve the data imbalance problem of the BECRE tasks ignores the requirements for data diversifying. This paper proposes a novel biomedical event causal relation extraction method to solve the above problems using deep knowledge fusion and Roberta-based data augmentation. To address the first problem, we fuse deep knowledge, including structural event representation and entity relation path, for establishing potential semantic connections between biomedical events. We use the Graph Convolutional Neural network (GCN) and the predicated tensor model to acquire structural event representation, and entity relation paths are encoded based on the external knowledge bases (GTD, CDR, CHR, GDA and UMLS). We introduce the triplet attention mechanism to fuse structural event representation and entity relation path information. Besides, this paper proposes the Roberta-based data augmentation method to address the second problem, some words of biomedical text, except biomedical events, are masked proportionally and randomly, and then pre-trained Roberta generates data instances for the imbalance BECRE dataset. Extensive experimental results on Hahn-Powell's and BioCause datasets confirm that the proposed method achieves state-of-the-art performance compared to current advances. • A deep knowledge fusion method for biomedical event causal relation extraction is proposed. • A Roberta-based data augmentation method for data imbalance problem is proposed. • Competitive with state-of-the-art performance on standard benchmarks. [ABSTRACT FROM AUTHOR]

Paper-based materials are being alternative candidates to build load-bearing components for the high demanding building sector to be committed to the green transition, but more knowledge of structural mechanics of such components is needed. In this work, three categories of innovative load-bearing sandwich beams with cup-box core fully made of different paper materials were produced, tested and analyzed in four-point bending. The first failure mode was observed at the top facesheets due to the low compressive strength of paper materials; Beams with thin facesheets had premature buckling failure, whereas those with thicker facesheets exhibited ductility reaching higher deflection before the compressive failure. The developed finite element model, calibrated with the experiments for the equivalent bending and shear rigidities, provided figures of the modulus of the facesheet as well as the properties of the core. Furthermore, the compressive plasticity behavior of the facesheets was assessed by fitting the model with load-deflection curves from the tests. Using the model for the structural optimization of the thickness and height of the core, the work suggests optimal values 3 times higher than the original ones. [ABSTRACT FROM AUTHOR]

The hysteresis model simplifies the relationship between resilience and deformation in seismic performance analysis of structural components. However, accurately capturing structural performance poses a challenge due to the intricate nature of hysteresis data. To enhance precision, this study introduces a multi-physics-guided neural network model. This innovative approach integrates explicit hysteresis models with an implicit network to create a robust hybrid model. The network architecture is grounded in physical principles, with the loss function incorporating physical parameter relationships for effective training. Through quasi-static testing, this approach demonstrates its robust adaptability and precision to the unique hysteretic curves of diverse components. [ABSTRACT FROM AUTHOR]

Due to the advantages of low cost, high flexibility, and large workspace, industrial robot has been considered to be the most promising plan to replace traditional CNC machine tool. However, the low absolute positioning accuracy of robot is a key factor that restricts further application in high-precision metal-cutting scenarios. In order to improve the absolute positioning accuracy of robot, a positioning error compensation method based on the stacked ensemble learning is proposed. Firstly, the sources of positioning errors and compensation strategies are clarified by analyzing the kinematic model and structural composition of industrial robot. Then, based on the stacked ensemble learning algorithm, robot positioning error prediction model containing multi-layer learners is constructed. And a discrete grid optimization method is presented for model hyper-parameters optimization calculation. Next, predicted positioning errors are adopted to realize the positioning compensation by offline compensation method. Finally, by setting up a robotic milling platform based on MOTOMAN ES165D robot, a series of error compensation experiments have been implemented to verify the proposed method. After compensation, the maximum absolute position error and average position error have decreased by 83% and 89%, respectively, in the compensation experiments of a single point. Moreover, the error compensation of the end milling experiments has also brought significant accuracy improvement, which proved the effectiveness of the proposed method in robotic machining. [ABSTRACT FROM AUTHOR]

Exploratory structural equation modelling (ESEM) is an alternative to the well‐known method of confirmatory factor analysis (CFA). ESEM is mainly used to assess the quality of measurement models of common factors but can be efficiently extended to test structural models. However, ESEM may not be the best option in some model specifications, especially when structural models are involved, because the full flexibility of ESEM could result in technical difficulties in model estimation. Thus, set‐ESEM was developed to accommodate the balance between full‐ESEM and CFA. In the present paper, we show examples where set‐ESEM should be used rather than full‐ESEM. Rather than relying on a simulation study, we provide two applied examples using real data that are included in the OSF repository. Additionally, we provide the code needed to run set‐ESEM in the free R package lavaan to make the paper practical. Set‐ESEM structural models outperform their CFA‐based counterparts in terms of goodness of fit and realistic factor correlation, and hence path coefficients in the two empirical examples. In several instances, effects that were non‐significant (i.e., attenuated) in the CFA‐based structural model become larger and significant in the set‐ESEM structural model, suggesting that set‐ESEM models may generate more accurate model parameters and, hence, lower Type II error rate. [ABSTRACT FROM AUTHOR]