Model Of Mechanism Research Articles

Common issues of data science on the eco-environmental risks of emerging contaminants.

Data-driven approaches (e.g., machine learning) are increasingly used to replace or assist laboratory studies in the study of emerging contaminants (ECs). In the past ten years, an increasing number of models or approaches have been applied to ECs, and the datasets used are continuously enriched. However, there are large knowledge gaps between what we have found and the natural eco-environmental meaning. For most published reviews, the contents are organized by the types of ECs, but the common issues of data science, regardless of the type of pollutant, are not sufficiently addressed. To close or narrow the knowledge gaps, we highlight the following issues ignored in the field of data-driven EC research. Complicated biological and ecological data and ensemble models revealing mechanisms and spatiotemporal trends with strong causal relationships and without data leakage deserve more attention in the future. In addition, the matrix influence, trace concentration, and complex scenario have often been ignored in previous works. Therefore, an integrated research framework related to natural fields, ecological systems, and large-scale environmental problems, rather than relying solely on laboratory data-related analysis, is urgently needed. Beyond the current prediction purposes, data science can inspire the discovery of scientific questions, and mutual inspiration among data science, process and mechanism models, and laboratory and field research is a critical direction. Focusing on the above urgent and common issues related to data, frameworks, and purposes, regardless of the type of pollutant, data science is expected to achieve great advancements in addressing the eco-environmental risks of ECs.

Multiscale modeling of oxidation-fatigue damage mechanisms under compressive dwell-fatigue loading conditions for nickel-based single-crystal superalloy

Multiscale modeling of oxidation-fatigue damage mechanisms under compressive dwell-fatigue loading conditions for nickel-based single-crystal superalloy

Bond performance of ECC-NC interface under salt freeze-thaw cycles: Damage mechanism and degradation model

Bond performance of ECC-NC interface under salt freeze-thaw cycles: Damage mechanism and degradation model

Mechanism and force modeling by considering wiper edge effect during cutting process of wiper tools

Mechanism and force modeling by considering wiper edge effect during cutting process of wiper tools

The neural mechanisms of emotional flexibility influenced by the emotional valence shift direction and preparation effect.

Emotional flexibility refers to an individual's ability to change emotional responses in constantly changing environments to adapt to different situations. This study aims to use the Emotional Switching Task (EST) paradigm, combined with Electroencephalogram (EEG) technology and behavioral experiments, to explore the impact of emotional valence shift directions and preparation effects on the switching cost of emotional flexibility. The results found that when individuals switch from positive emotional valence to positive emotional valence, the switching cost is smaller than other transition directions. In addition, emotional flexibility exhibits a preparation effect, with a 2000 ms preparation time resulting in a smaller switching cost than a 600 ms preparation time. The electrophysiological data, more specifically the N2, CNV, SPN and LPP components, reveal the neural mechanisms by which emotional valence shift direction and preparation time influence emotional flexibility across the three neural computational stages of emotion regulation. This study is beneficial for establishing a potential mechanism model for the switching cost of emotional flexibility.

Comprehensive suitability assessment of fish habitat by integrating mechanism model and bayesian network model

Comprehensive suitability assessment of fish habitat by integrating mechanism model and bayesian network model

Rolling force modeling based on neural network and mechanism model

Rolling force modeling based on neural network and mechanism model

Tool wear prediction based on xLSTM combined with the ECA attention mechanism model

Tool wear prediction based on xLSTM combined with the ECA attention mechanism model

Prognostic models of immune-related cell death and stress unveil mechanisms driving macrophage phenotypic evolution in colorectal cancer

BackgroundTumor microenvironment (TME), particularly immune cell infiltration, programmed cell death (PCD) and stress, has increasingly become a focal point in colorectal cancer (CRC) treatment. Uncovering the intricate crosstalk between these factors can enhance our understanding of CRC, guide therapeutic strategies, and improve patient prognosis.MethodsWe constructed an immune-related cell death and stress (ICDS) prognostic model utilizing machine learning methodologies. Furthermore, we performed enrichment analyses and deconvolution algorithms to elucidate the complex interactions between immune cell infiltration and the processes of PCD and stress within a substantial array of transcriptomic data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus data base (GEO) related to CRC. Single-cell sequencing and biochemical experiments were used to validate the interaction between the model genes and programmed cell death in tumor cells.ResultsThe ICDS prognostic model exhibited robust predictive performance in seven independent cohorts, revealing an inverse correlation between model scores and patient prognosis. Meanwhile, the ICDS index was positively correlated with clinical stage. Model analysis indicated that patient subgroups with low ICDS index exhibited heightened immune activation features and elevated activity in PCD and stress pathways. Single-cell analysis further revealed that macrophages were the central drivers of immune characteristics underlying prognostic differences within the ICDS prognostic model. Pseudotime analysis and cellular experiments indicated that the model gene GAL3ST4 promotes the transition of macrophages toward an M2 pro-tumor phenotype. Furthermore, cell communication analysis and experimental validation revealed that the cuproptosis in tumor cells suppress GAL3ST4 expression, thereby inhibiting M2-like macrophage polarization.ConclusionIn summary, we constructed the ICDS prognostic model and uncovered the mechanism by which tumor cells downregulate GAL3ST4 expression via cuproptosis to inhibit M2-like macrophage polarization, providing new targets and biomarkers for CRC treatment and prognosis evaluation.

A Global-Scale Overlapping Pixels Calculation Method for Whisk-Broom Payloads with Multi-Module-Staggered Longlinear-Array Detectors

A multi-module staggered (MMS) long-linear-array (LLA) detector is presently recognized as an effective and widely adopted means of improving the field of view (FOV) of in-orbit optical line-array cameras. In particular, in terms of low-orbit whisk-broom payloads, the MMS LLA detector combined with the one-dimensional scanning mirror is capable of achieving both large-swath and high-resolution imaging. However, because of the complexity of the instantaneous relative motion model (IRMM) of the whisk-broom imaging mechanism, it is really difficult to determine and verify the actual numbers of overlapping pixels of adjacent detector sub-module images and consecutive images in the same and opposite scanning directions, which are exceedingly crucial to the instrument design pre-launch as well as the in-orbit geometric quantitative processing and application post-launch. Therefore, in this paper, aiming at addressing the problems above, we propose a global-scale overlapping pixels calculation method based on the IRMM and rigorous geometric positioning model (RGPM) of the whisk-broom payloads with an MMS LLA detector. First, in accordance with the imaging theory and the specific optical–mechanical structure, the RGPM of the whisk-broom payload is constructed and introduced elaborately. Then, we qualitatively analyze the variation tendency of the overlapping pixels of adjacent detector sub-module images with the IRMM of the imaging targets, and establish the associated overlapping pixels calculation model based on the RGPM. And subsequently, the global-scale overlapping pixels calculation models for consecutive images of the same and opposite scanning directions of the whisk-broom payload are also built. Finally, the corresponding verification method is presented in detail. The proposed method is validated using both simulation data and in-orbit payload data from the Thermal Infrared Spectrometer (TIS) of the Sustainable Development Goals Satellite-1 (SDGSAT-1), launched on 5 November 2021, demonstrating its effectiveness and accuracy with overlapping pixel errors of less than 0.3 pixels between sub-modules and less than 0.5 pixels between consecutive scanning images. Generally, this method is suitable and versatile for the other scanning cameras with a MMS LLA detector because of the similarity of the imaging mechanism.

CSpredR: A Multi-Site mRNA Subcellular Localization Prediction Method Based on Fusion Encoding and Hybrid Neural Networks

Current research widely acknowledges that the subcellular localization of mRNA is crucial for understanding its biological functions. However, current methods for mRNA subcellular localization based on k-mer frequency features may overlook the sequential information of the sequence, and a single encoding method may not adequately extract the sequence’s features. This paper proposes a novel deep learning prediction method, CSpredR, specifically designed for predicting the subcellular localization of multi-site mRNAs. Unlike previous methods, CSpredR first employs k-mer to tokenize the mRNA sequences, then converts the tokenized sequences into de Bruijn graphs, thereby enabling a more precise capture of the structural information within the sequences. To mitigate the impact of lost sequential information and better capture sequence features, we combine word2vec and fasttext models to extract the features of each node in the graph and retain the sequence order. They can encode the k-mer units in the sequence into word vectors, thus serving as the node feature vectors of the graph. In this way, each node in the graph is assigned a feature vector containing rich semantic information. Subsequently, we utilize multi-scale convolutional neural networks and bidirectional long short-term memory networks to capture sequence features, respectively, and fuse the results as input for a multi-head attention mechanism model. The information from these heads is integrated into the node representations, and finally, the attention-processed data are fed into an MLP (Multi-Layer Perceptron) for prediction tasks. Extensive experiments reveal that CSpredR achieves a 2% improvement over the best existing predictors, offering a more effective tool for mRNA subcellular localization prediction.

Sensitive Mechanism and Instability Modeling Methods of Flexible Sensing Films Based on 2D Materials

This paper examines sensing mechanisms and stability issues of flexible sensors used in morphing wing applications. A key challenge is the lack of theoretical frameworks that accurately predict sensor behavior during complex deformation. Current models struggle to fully capture the relationships between mechanical strain, electrical response, and material properties. We first analyze the microscopic mechanisms and macroscopic sensing characteristics of 2D material-based sensitive films, developing strain-sensitive models based on crack effects and pressure-sensitive models based on slip effects. Through power spectrum analysis, we establish a quantitative model linking microscopic cracks to macroscopic electrical properties. Using this model, we study the factors affecting flexible sensing stability and propose a quantitative description model using dual-layer multi-channel flexible sensors. After simulation validation, our model successfully guides the structural design of flexible sensing films, offering a clear approach to improve flexible sensor stability.

Reducing undesirable effects of revolute joint clearances on the dynamic performance of spatial parallel mechanism

To mitigate the adverse effects of joint clearances on the dynamic performance of mechanisms, it is essential to investigate the dynamic optimization design of spatial parallel mechanisms with clearances. Currently, there are many studies on the dynamic characteristics of mechanisms with clearances, but there are few studies on dynamic optimization design of mechanisms with clearances, especially on the optimization design of parallel mechanisms with clearances. Therefore, the dynamic optimization design of 3-RRPaR (R stands for revolute joint, Pa stands for quadrilateral structure) parallel mechanism with dry friction clearance of revolute joints is studied. The dynamic model of the parallel mechanism with revolute joint clearance is derived. On these grounds, the dynamic response of the end actuator, the contact collision force at the clearance joint, and their weighted combination are defined as objective functions, respectively. The mass and moment of inertia of the end actuator are taken as optimization variables. A mathematical model for the dynamic optimization design of the parallel mechanism considering dry friction clearances is developed, and the optimal mass and moment of inertia of the end actuator are obtained by G-MSRS method. This study comparatively analyzes the dynamic characteristics of parallel mechanisms with clearances before and after optimization. The results indicate that dynamic optimization significantly enhances the dynamic characteristics of the mechanism. This study lays a theoretical foundation for dynamic optimization design of parallel mechanisms with clearances.

Global Gac/Rsm regulatory system activates the biosynthesis of mupirocin by controlling the MupR/I quorum sensing system in Pseudomonas sp. NCIMB 10586.

The biosynthesis of mupirocin, a clinically significant antibiotic produced by Pseudomonas sp. NCIMB 10586, is activated by the N-acyl homoserine lactone (AHL) MupR/I quorum sensing (QS) system. However, to date, limited research has focused on the influence of global regulators such as the GacS/A two-component system (TCS) on the MupR/I QS system or mupirocin biosynthesis. In this study, we characterized the regulatory components of the Gac/Rsm transduction system in the mupirocin-producing model strain NCIMB 10586 and investigated their interconnection with the MupR/I QS circuit and subsequent mupirocin biosynthesis. The production of mupirocin was hampered by either gacS inactivation, gacA inactivation, or the double-mutant of the sRNAs ( RsmY and RsmZ). Similarly, the expressions of mupR and mupI, and AHL synthesis significantly decreased in gacS, gacA, or rsmY/Z mutants, indicating that the GacS/A system stimulates mupirocin biosynthesis via the MupR/I QS system. Five CsrA family proteins, RsmA/E/I/F/N, were found in strain NCIMB 10586, and the single and multiple mutants of rsmA/E/I/F/N showed different phenotypes with respect to mupirocin production. Our results revealed that mupirocin biosynthesis was likely to be negatively regulated by RsmA/E/I, but positively regulated by RsmF. Additionally, the RsmF protein was shown to interact with the 5' leader of mupR mRNA. In summary, the Gac/Rsm system positively regulates the biosynthesis of mupirocin mainly through the MupR/I QS system, and the model of the regulatory mechanism is proposed. The elucidation of the Gac/Rsm-MupR/I regulatory pathway could help devise ways for improving mupirocin production through genetic engineering.IMPORTANCEThe Gac/Rsm regulatory system plays a global regulatory role in bacterial physiology and metabolism, including secondary metabolism. Mupirocin is a clinically important antibiotic, produced by Pseudomonas sp. NCIMB 10586, whose biosynthesis is activated by the MupR/I quorum sensing system. Global regulators have important impacts on the gene expression of secondary metabolic gene clusters and QS genes, and the GacS/A two-component system is one of the main regulators across Pseudomonas species, which significantly influences antibiotic production. Our study presented that the expressions of QS genes and mup gene cluster were downregulated in gacS, gacA, or rsmY/Z mutants compared to the wild-type. The inactivation of rsmA/E/I/F/N in NCIMB 10586, encoding CsrA family proteins, showed different regulatory traits of mupirocin production, in which the RsmF protein could interact with the 5' UTR region of mupR mRNA. These findings provide the understanding of the regulatory role of Gac/Rsm on mupirocin biosynthesis and mupR/I QS system and lay foundations for further improving mupirocin production.

Simultaneous multimodal measures for aesthetic evaluation of furniture color and form

The tendency toward the aesthetic preference affects an individual’s intention to purchase furniture. Color and form are two fundamental elements of furniture appearance. However, there is a significant lack of human–computer interaction research on the aesthetic evaluation of furniture with various colors and forms, necessitating a comprehensive study to provide theoretical and empirical support to furniture designers and businesses. Thus, this study introduces a multimodal method by integrating various modalities, which allowed us to investigate the factors that significantly affect subjective preference evaluation and physiological feedback. The results indicated significant correlations between behavioral data, first fixation duration, and average pupil diameter. Meanwhile, the correlation between multimodal measurement data and product aesthetic ratings was analyzed, and an evaluation model of the leisure chair aesthetic correlation mechanism was proposed.

Optimum Design and Dynamics Analysis of Intelligent Prosthetic Knee Joint

Background: The intelligent prosthetic knee joint is an essential rehabilitation device for amputee patients to recover from mobility disorders. It must ensure excellent performance when installed on the human body. However, issues such as excessive energy consumption and poor stability during walking are challenges in the design of such prosthetic devices. Objective: To address the issues of wasteful energy expenditure and instability in intelligent prosthetic knee joints during walking, an optimized model of a double rocker prosthetic knee joint mechanism was established with the minimum peak driving torque of the active rod as the optimization goal. Methods: The optimal model was established with the minimum peak driving moment of the prosthetic knee joint mechanism as the optimization objective. Under the constraint conditions of performance and structure, the model was optimized by the composite method, and the optimal structural parameters of the prosthetic knee joint mechanism were obtained. Then, according to the trajectory of the instantaneous rotation center of the knee joint, the double rocker mechanism is optimized so that the prosthesis can better simulate the movement trajectory of normal people. Finally, according to the designed virtual prototype model, a physical prototype of the intelligent prosthetic knee joint test platform is built, the knee joint Angle data is collected, and the collected data is analyzed accordingly. Results: ADAMS software was used to simulate the virtual motion of the optimized model. The results showed that the optimized intelligent prosthetic knee joint mechanism has excellent bionic performance. Its peak driving torque is reduced by 40% and the range of driving torque is reduced by 36%, significantly improving the stability and endurance of the intelligent prosthetic knee joint. Through the experimental test of an intelligent prosthetic knee joint, it is verified that the optimized intelligent prosthetic knee joint meets the normal needs of the human body. Conclusion: The results confirm that the optimized prosthetic knee joint mechanism exhibits excellent bionic performance, and the reduction in driving torque enhances its stability and energy efficiency, validating the correctness and rationality of the proposed optimization model.

Appetite Self-Regulation in Childhood: A Narrative Review and Conceptual Model of Processes and Mechanisms With Implications for Research and Practice.

The purpose of the present narrative review was to propose a unifying generalized conceptual model of mechanisms and processes in appetite self-regulation (ASR) in childhood. Appetite self-regulation, along with other domains of self-regulation, develops across childhood and contributes to energy intake and balance, diet quality, weight, and therefore long-term health outcomes. There have been efforts to conceptualize and measure components of ASR and associated processes/mechanisms, but, at present, there is no unifying conceptualization of ASR in childhood. A search of key databases supplemented by snowballing was undertaken for definitions/conceptions and theoretical models of ASR with a focus on children. An interpretive synthesis approach was used to identify themes from the definitions and models. The themes formed the basis of the proposed unifying generalized model of ASR in childhood, which is the main contribution of the article. At the center of the model is bottom-up reactivity to food, food cues and hunger, satiation and satiety signals, together with top-down regulatory control. An additional contribution is the proposed 5 interacting and overlapping domains (biological, hedonics, cognitive, behavioral, and traits) that function in and influence both bottom-up reactivity and top-down regulation. The domains also contribute to ASR outcomes of enactment and competence. External contextual and intrapersonal factors are conceived as impacting the domains and the bottom-up, top-down processes. The relevance of the model for explanations of ASR phenomena in childhood and children's food choice and diet quality, as well as its implications for research directions and approaches to preventive intervention, including food parenting practices, are discussed. The model provides a framework for researchers and practitioners to support and interpret children's problems and competence in self-directing food choices, energy intake, and nutrition.

Thermal Decomposition of Calcium Carbonate at Multiple Heating Rates in Different Atmospheres Using the Techniques of TG, DTG, and DSC

To grasp the decomposition reaction rule of calcium carbonate in cement raw material, the thermogravimetric analyzer (TG), derivative thermogravimetric (DTG), and differential scanning calorimeter (DSC) were used for analysis. Calcium carbonate samples were heated linearly at multiple heating rates of 10, 20, 30, and 40 °C/min in the atmospheres of N2 and 70% N2 + 30% O2, respectively. The decomposition kinetics was investigated using a double extrapolation method. Kinetic parameters of the thermal decomposition and the most probable mechanism function were determined in two different atmospheres. The results show that TG, DTG, and DSC curves moved to a higher temperature with the increase in heating rate, and the addition of O2 in the reaction atmosphere had almost no effect on the change in the decomposition curve. Additionally, the activation energy of the initial state in the formation of the new nucleus obtained using the double extrapolation method was 232.13 kJ/mol in the N2 atmosphere, and the most probabilistic mechanistic function was G(α) = 1 − (1 − α)1/2. The chemical reaction process was consistent with the contracted cylinder mechanism model of phase boundary reaction. Moreover, the activation energy of the initial state in the formation of the new nucleus was 233.79 kJ/mol in the 70% N2 + 20% O2 atmosphere, and the chemical reaction process was consistent with that of the N2 atmosphere. Therefore, these results could determine the decomposition temperature and decomposition rate of calcium carbonate. This was important for understanding the thermal stability and processing temperature range of polymer materials, especially the application and potential in production and scientific research.

Bionic Modeling Study on the Landing Mechanism of Flapping Wing Robot Based on the Thoracic Legs of Purple Stem Beetle, Sagra femorata.

Flapping wing micro aerial vehicles (FWMAVs) are recognized for their significant potential in military and civilian applications, such as military reconnaissance, environmental monitoring, and disaster rescue. However, the lack of takeoff and landing capabilities, particularly in landing behavior, greatly limits their adaptability to the environment during tasks. In this paper, the purple stem beetle (Sagra femorata), a natural flying insect, was chosen as the bionic research object. The three-dimensional reconstruction models of the beetle's three thoracic legs were established, and the adhesive mechanism of the thoracic leg was analyzed. Then, a series of bionic design elements were extracted. On this basis, a hook-pad cooperation bionic deployable landing mechanism was designed, and mechanism motion, mechanical performance, and vibration performance were studied. Finally, the bionic landing mechanism model can land stably on various contact surfaces. The results of this research guide the stable landing capability of FWMAVs in challenging environments.

Modeling and analysis of a non-linear compliant rolling-sliding contact mechanism when subjected to a periodic motion input

Modeling and analysis of a non-linear compliant rolling-sliding contact mechanism when subjected to a periodic motion input