Concise Model Research Articles

Coupled model for electro‐osmosis consolidation and ion transport considering chemical osmosis in saturated clay soils

AbstractThe electro‐osmosis approach efficiently facilitates the rapid dewatering of soil with high water content and contributes to reducing contaminant levels within the clay soil. However, the changes of chemical field caused by ion transport in the clay soil during electro‐osmosis process will also influence the clay soil consolidation effect. Existing theories predominantly tend to disregard this crucial physical process and its resultant effects, thereby restraining a comprehensive analysis of electro‐osmosis consolidation (EOC) behavior under intricate chemical conditions. This study introduces a concise model of EOC and ion transport considering chemical osmosis. The model considers the nonlinear variation of clay soil parameters such as compressibility, permeability, and effective diffusion coefficients, along with the interaction between EOC and ion transport. Meanwhile, the correctness of the model is verified from different aspects such as theoretical derivation and model comparison. Based on the proposed model, the impacts of the variation in electrical field intensity and chemical concentration on the coupled behaviors between EOC and ion transport are systematically investigated, with and without incorporating nonlinear consolidation characteristics. The results show that diffusion and electro‐migration exhibit a more pronounced effect on ion transport during EOC. Simultaneously, with the increase of ion concentration in clay soil pore solution, the effects of chemical osmosis become increasingly apparent, thereby enhancing clay soil settlement.

A refined and concise model of indices for quantitatively measuring lexical richness of Chinese university students’ EFL writing

In the existing literature, scholars have proposed various indices to measure the lexical richness (LR) of English as a foreign language (EFL) writing. However, there are currently issues of redundant indices and inconsistent usage. Attempting to address the research question of which indices are the most sensitive and effective ones to distinguish between different grade levels of Chinese university students’ EFL writing, this study aims to put forward a refined and concise model of indices that can truthfully reflect LR in EFL writing. A total of 180 compositions were selected from a Chinese EFL learner corpus: <i>Spoken and written English corpus of Chinese learners</i>. Scores of 28 LR indices of these compositions were computed using the software <i>Lexical Complexity Analyzer</i>,<i> MATTR</i>,<i> </i>and<i> Coh-Metrix</i>. One-way ANOVA or Welch’s ANOVA, depending on the variable’s homogeneity of variances, was conducted for each index. Two criteria were applied to determine which index of a measure should be included in the refined model: whether the difference of an index is significant among different grade levels and the effect size of ANOVA. Based on the quantitative results of ANOVAs and qualitative human judgment based on literature, six indices of the six LR measures were included in the refined model: lexical density, lexical sophistication-I, verb sophistication-II, number of different words-expected sequence 50, corrected TTR, and squared verb variation-I. This refined model addresses the issues of redundancy and inconsistency in previous studies, providing a more accurate and efficient tool for assessing LR in EFL writing.

Recategorization of Multicultural Policy Models and Its Implications

Purpose: This study explores its implications by categorizing existing models related to multicultural policies more concisely. There are four types of existing multicultural policy-related models. In other words, it is divided into Differential Exclusionary Model, Assimilation Model, Multiculturalism Model, Cultural Pluralism Model. If the common issues underlying the four models are analyzed and then structured based on the issues, it is expected that a more concise and clear multicultural policy-related model can be derived. Method: Four models are re-categorized, focusing on common issues. Based on the re-categorized results, it is named to match the characteristics of the model. After deriving a newly re-categorized and named model, its implications are explored. Results: A common issue lies at the base of the existing four models related to multicultural policy. A common issue is how to establish the relationship between diversity and integration. Differential Exclusionary Model and Assimilation Model share the view that diversity undermines integration. This point of view can be named ‘Conflict between Diversity and Integrity Model’. On the other hand, Multiculturalism Model and Cultural Pluralism Model share the view that diversity and integration can coexist. This perspective can be categorized as Coexistence between Diversity and Integrity Model. Conclusion: Existing multicultural policy-related models can be re-categorized into ‘Conflict between Diversity and Integrity Model’ and ‘Coexistence between Diversity and Integrity Model’. In the ‘Conflict between Diversity and Integrity Model’, diversity and integration are set as incommensurable domains. In the ‘Coexistence between Diversity and Integrity Model’, diversity and integration are set as commensurable domains. Furthermore, using a series of strategies, ‘Conflict between Diversity and Integrity Model’ can be transformed to ‘Coexistence between Diversity and Integrity Model’.

Insight from Boric Acid into Bioskeleton Formation: Inscribed Circle Effect on the Edge-Base Plate Growth.

Complex morphologies in nature often arise from the assembly of elemental building blocks, leading to diverse and intricate structures. Understanding the mechanisms that govern the formation of these complex morphologies remains a significant challenge. In particular, the edge-base plate growth of biogenic crystals plays a crucial role in directing the development of intricate bioskeleton morphologies. However, the factors and regulatory processes that govern edge-base plate growth remain insufficiently understood. Inspired by biological skeletons and based on the soluble property of boric acid (BA) in both water and alcohols, we obtained a series of novel BA morphologies, including coccolith, and anemone biological skeletons. Here, we unveil the "inscribed circle effect", a concise mathematical model that reveals the underlying causative factors and regulatory mechanisms driving edge-base plate growth. Our findings illuminate how variations in solvent environments can exert control over the edge-base plate growth pathways, thereby resulting in the formation of diverse and complex morphologies. This understanding holds significant potential for guiding the chemical synthesis of bioskeleton materials.

Deciphering COPS5 influence on immune infiltration and prognosis in head and neck squamous cell carcinoma

Head and Neck Squamous Cell Carcinoma (HNSCC) is a widespread malignancy originating from the mucous epithelium of the oral cavity, pharynx, and larynx. Despite advances in diagnostic and therapeutic modalities, the prognosis of HNSCC remains challenging. This study investigates the intricate relationship among COPS5, immune infiltration patterns, and prognostic implications in HNSCC. Through comprehensive analyses of 519 HNSCC cases from TCGA and single-cell data from the GEO database, we utilize the CIBERSORT algorithm to discern immune cell dynamics influenced by COPS5 expression. Notably, Treg cells emerge as a central point in the interplay between COPS5 and immune modulation. Further analyses, encompassing differential gene expression, immune-related gene set enrichment, and protein-protein interaction networks, elucidate the molecular landscape associated with COPS5 in HNSCC. A prognostic risk model, incorporating CD27, TNFRSF4, FADD, and PSMD14, is formulated and validated across diverse datasets. The model demonstrates robust predictive power, underscoring its potential as a valuable prognostic tool. These genes, essential for immune regulation and cell cycle control, provide insights into the intricate mechanisms influencing HNSCC progression. In conclusion, this study not only reveals the impact of COPS5 on immune dynamics in HNSCC but also introduces a concise and effective prognostic model.

Exploring highly concise and accurate text matching model with tiny weights

Exploring highly concise and accurate text matching model with tiny weights

A physics-based dimension reduction and modeling method for monitoring data and its application to tunnel engineering

The promotion of intelligent control of engineering equipment requires extracting vital information from numerous original features in the monitoring data. Data-driven methods solely rely on data distribution to extract dimensional information, facing difficulties in comprehensively understanding and scrutinizing system behavior. To satisfy the control and optimization requirements of intelligent systems, this paper introduces a physics-based dimension reduction and modeling method. According to scientific principles, original features are transformed into causal variables with physical meaning. Then important causal variables are selected through regularization in machine learning. Finally, a concise prediction model with physical representation is established. An application to tunnel engineering demonstrates that the generated low-dimensional space reveals the essential laws between the control parameter and its main influencing factors (i.e., the geological conditions, operating status, and structural characteristics of the tunneling equipment), which can further provide a better prediction model balancing accuracy and dimensionality.

Accurate Density Prediction of Sesquiterpenoid HEDFs and the Multiproperty Computing Server SesquiterPre.

Accurate and rapid evaluation of density is crucial for evaluating the packing and combustion characteristics of high-energy-density fuels (HEDFs). This parameter is pivotal in the selection of high-performance HEDFs. Our study leveraged a polycyclic compound density data set and quantum chemical (QC) descriptors to establish a correlation with the target properties using the XGBoost algorithm. We utilized a recursive feature elimination method to simplify the model and developed a concise and interpretable density prediction model incorporating only six QC descriptors. The model demonstrated robust performance, achieving coefficients of determination (R 2) of 0.967 and 0.971 for internal and external test sets, respectively, and root-mean-square errors (RMSE) of 0.031 and 0.027 g/cm3, respectively. Compared to the other two mainstream methods, the marginal discrepancy between the predicted and actual molecular densities underscores the model's superior predictive ability and more usefulness for energy density calculation. Furthermore, we developed a web server (SesquiterPre, https://sespre.cmdrg.com/#/) that can simultaneously calculate the density, enthalpy of combustion, and energy density of sesquiterpenoid HEDFs, which greatly facilitates the use of researchers and is of great significance for accelerating the design and screening of novel sesquiterpenoid HEDFs.

주택 매매 및 임대차 시장의 단기균형 모형

In this study, we introduce a concise short-run equilibrium model for the housing market, covering sales and two distinct rental markets - monthly rent and Jeonsei markets. This model enables us to analyze the interplay among these markets and examine the determinants of equilibrium sales prices, monthly rent, and Jeonsei prices. Unlike monthly rent, the actual cost to a tenant in Jeonsei (interest on the Jeonsei deposit) depends on the tenant’s equity size, and the cost of ownership in Jeonsei differs from that in monthly rent. We incorporate these distinctions between monthly rent and Jeonsei contracts into our model and observe that the housing market's equilibrium is influenced by the type of rental market. We also examine the effects of an increase in real estate holding tax and find that sales prices decrease irrespective of the type of rental market. However, the impact on monthly rental fees and Jeonsei prices depends on the rental market structure: monthly rental fees do not change only if monthly rental is available, Jeonsei prices increase if only Jeonsei is available, and both monthly rental fees and Jeonsei prices increase if both types of rents are available.

Experimental investigation on flexural performance of concrete beams strengthened with SMA-GFRP-ECC smart composite materials

The structural performance improvement of concrete members is by far a crucial theoretical issue for engineers, and the development of modern smart and composite materials makes it possible to gradually enhance the durability design of the concrete structure. In this study, six beams of the same size and reinforcement ratio, the proposed composite beam (SMA-GFRP-ECC) and five comparative beams (RC, R-ECC, SS-ECC, GFRP-ECC, SMA-ECC), were designed and tested under low-cycle unidirectional cyclic loading and unloading conditions. The energy dissipation capacity, displacement ductility, residual deformation, and self-repairing performance of each concrete beam were evaluated. Afterward, a concise calculation model for the studied composite beam is deduced and developed based on the existing relevant constitutive models and concrete assumptions. The test results indicate that compared with RC beams, the composite reinforced ECC beams show obvious multi-cracking and smaller crack width during the loading process and have good bending ductility. The innovative SMA-GFRP-ECC beam is capable of a high bearing capacity, ductility, and damage self-repairing. The new proposed beam has more than 80% of the maximum crack width recovery capacity during unloading. Hence, the proposed SMA-GFRP-ECC beam is a rather good first attempt of strengthened beams, combining the advantages of SMA, GFRP, and ECC.

Cohesive Online Education Model Using Emergent Technologies to Improve Accessibility and Impact

Cohesive Online Education Model Using Emergent Technologies to Improve Accessibility and Impact

Central Banks, Stock Markets, and the Real Economy

In this article, we summarize empirical research on the interaction between monetary policy and asset markets and review our previous theoretical work that captures these interactions. We present a concise model in which monetary policy impacts the aggregate asset price, which in turn influences economic activity with lags. In this context, the following occurs: (a) the central bank (the Fed, for short) stabilizes the aggregate asset price in response to financial shocks, using large-scale asset purchases if needed (the Fed put); (b) when the Fed is constrained, negative financial shocks cause demand recessions; (c) the Fed's response to aggregate demand shocks increases asset price volatility, but this volatility plays a useful macroeconomic stabilization role; (d) the Fed's beliefs about the future aggregate demand and supply drive the aggregate asset price; (e) macroeconomic news influences the Fed's beliefs and asset prices; ( f ) more precise news reduces output volatility but heightens asset market volatility; and (g) disagreements between the market and the Fed provide a microfoundation for monetary policy shocks and generate a policy risk premium.

The Effect of Situational Variables on Women’s Rink Hockey Match Outcomes

The main objective of the present study was to develop a concise predictive model to determine the likelihood of winning in female rink hockey based on various situational variables. Additionally, the study aimed to assess the individual impact of each predictor on match outcomes. The analysis encompassed a dataset of 840 matches during five consecutive seasons (from 2018–2019 to 2022–2023) in the Spanish first division (OkLiga). Employing the comprehensive method of all possible regressions, the most effective predictive logistic model for match outcomes was identified. This entire model featured five categorical predictor variables (match location, team level, opponent level, scoring first, and match status at halftime) and one binary outcome variable (match outcome). Subsequently, the final model, which exhibited a sensitivity and specificity surpassing 80% for a cut-off point of 0.439, emerged. This model was applied to predict winning a match in 18 frequent situations determined from a two-step cluster analysis. Within this predictive framework, match status at halftime emerged as the most influential predictor impacting the match outcome, followed by opponent level, team level, and match location. The implications of our findings extend to rink hockey coaches and practitioners. Recognizing the significant impact of situational variables on match outcomes empowers them to customize game plans and design more specific strategies, thereby enhancing game understanding and elevating the overall performance.

Theoretical model for high-rise solar chimneys and optimum shape for uniform flowrate distribution

Solar chimneys (SCs) are effective in inducing natural ventilation. However, extending SCs from low-rise to high-rise buildings causes non-uniform flowrate distribution among storeys, where the flowrates on the lower storeys can be 300 % higher than those on the higher storeys. This paper aims to improve the uniformity of flowrate distribution among storeys in high-rise SCs up to 40 storeys. A concise one-equation theoretical model was derived to predict the flowrate distribution among storeys. This theoretical model was well verified by an experimentally validated numerical model. Furthermore, based on this flowrate distribution equation, the optimum shape (i.e., square-root-law (SRL) structure) was derived and proposed for high-rise solar chimneys to achieve a highly uniform flowrate distribution. Numerical simulations showed that the SRL structure reduced the relative difference in flowrate among storeys from 330.6 % to 15.0 % in a 20-storey SC. The findings of this study can provide guidance on how to design optimum high-rise SCs and maximise its ventilation performance.

The value of machine learning based on CT radiomics in the preoperative identification of peripheral nerve invasion in colorectal cancer: a two-center study

BackgroundWe aimed to explore the application value of various machine learning (ML) algorithms based on multicenter CT radiomics in identifying peripheral nerve invasion (PNI) of colorectal cancer (CRC).MethodsA total of 268 patients with colorectal cancer who underwent CT examination in two hospitals from January 2016 to December 2022 were considered. Imaging and clinicopathological data were collected through the Picture Archiving and Communication System (PACS). The Feature Explorer software (FAE) was used to identify the peripheral nerve invasion of colorectal patients in center 1, and the best feature selection and classification channels were selected. Finally, the best feature selection and classifier pipeline were verified in center 2.ResultsThe six-feature models using RFE feature selection and GP classifier had the highest AUC values, which were 0.610, 0.699, and 0.640, respectively. FAE generated a more concise model based on one feature (wavelet-HLL-glszm-LargeAreaHighGrayLevelEmphasis) and achieved AUC values of 0.614 and 0.663 on the validation and test sets, respectively, using the “one standard error” rule. Using ANOVA feature selection, the GP classifier had the best AUC value in a one-feature model, with AUC values of 0.611, 0.663, and 0.643 on the validation, internal test, and external test sets, respectively. Similarly, when using the “one standard error” rule, the model based on one feature (wave-let-HLL-glszm-LargeAreaHighGrayLevelEmphasis) achieved AUC values of 0.614 and 0.663 on the validation and test sets, respectively.ConclusionsCombining artificial intelligence and radiomics features is a promising approach for identifying peripheral nerve invasion in colorectal cancer. This innovative technique holds significant potential for clinical medicine, offering broader application prospects in the field.Critical relevance statementThe multi-channel ML method based on CT radiomics has a simple operation process and can be used to assist in the clinical screening of patients with CRC accompanied by PNI.Key points• Multi-channel ML in the identification of peripheral nerve invasion in CRC.• Multi-channel ML method based on CT-radiomics can detect the PNI of CRC.• Early preoperative identification of PNI in CRC is helpful to improve the formulation of treatment strategies and the prognosis of patients.Graphical

CSR attribution: Is it the cornerstone of CSR success?

PurposeThe current study aims to propose and empirically investigate a conceptual model of the most relevant antecedents and consequences of Corporate Social Responsibility (CSR) attribution, thus providing a practical and concise model as well as examining brand attachment as a mediator explaining the relationship between CSR attribution and its consequences.Design/methodology/approachA between-subjects experimental design was employed. The study included two experimental conditions; intrinsic and extrinsic CSR attribution and a control condition. An online self-administered survey was utilised for data collection. The sample was a convenience sample of 336 university students. Both one-way between-groups ANOVA and Partial Least Squares-Structural Equation Modelling (PLS-SEM) were utilised for hypotheses testing.FindingsThe most significant antecedents of CSR attribution in order of importance are the firm's approach to CSR communication, past corporate social performance, CSR type and the firm's call for customers' participation in its CSR. CSR attribution exerted a significant direct positive impact on brand attachment and trust. Three significant indirect consequences of CSR attribution were PWOM intention, purchase intention and brand loyalty intention. Whereas trust played a significant mediating role between CSR attribution and its three indirect consequences, brand attachment exerted significant mediation only between CSR attribution and brand loyalty intention. Brand attachment might mediate the relationship between CSR attribution and purchase intention. However, brand attachment failed to play a mediating role between CSR attribution and PWOM intention.Originality/valueSeveral studies marginally investigated CSR attribution. Despite the vital role of CSR attribution in how consumers receive firms' CSR engagement, the availability of CSR attribution-centric studies is limited. By introducing a model of the most relevant antecedents and consequences of CSR attribution, this study aids in understanding the psychological mechanism underlying consumers' CSR attribution and provides valuable implications.

Modeling Shifting Workloads for Learned Database Systems

Learned database systems address several weaknesses of traditional cost estimation techniques in query optimization: they learn a model of a database instance, e.g., as queries are executed. However, when the database instance has skew and correlation, it is nontrivial to create an effective training set that anticipates workload shifts, where query structure changes and/or different regions of the data contribute to query answers. Our predictive model may perform poorly with these out-of-distribution inputs. In this paper, we study how the notion of a replay buffer can be managed through online algorithms to build a concise yet representative model of the workload distribution --- allowing for rapid adaptation and effective prediction of cardinalities and costs. We experimentally validate our methods over several data domains.

A comprehensive model for the biochemistry of ageing, senescence and longevity.

Various models for ageing, each focussing on different biochemical and/or cellular pathways have been proposed. This has resulted in a complex and non-coherent portrayal of ageing. Here, we describe a concise and comprehensive model for the biochemistry of ageing consisting of three interacting signalling hubs. These are the nuclear factor kappa B complex (NFκB), controlling the innate immune system, the mammalian target for rapamycin complex, controlling cell growth, and the integrated stress responses, controlling homeostasis. This model provides a framework for most other, more detailed, biochemical pathways involved in ageing, and explains why ageing involves chronic inflammation, cellular senescence, and vulnerability to environmental stress, while starting with the spontaneous formation of advanced glycation end products. The totality of data underlying this model suggest that the gradual inhibition of the AMPK-ISR probably determines the maximal lifespan. Based on this model, anti-ageing drugs in general, are expected to show hormetic dose response curves. This complicates the process of dose-optimization. Due to its specific mechanism of action, the anti-aging drug alkaline phosphatase is an exception to this rule, because it probably exhibits saturation kinetics.

Numerical properties of solutions of LASSO regression

The determination of a concise model of a linear system when there are fewer samples m than predictors n requires the solution of the equation Ax=b, where A∈Rm×n and rankA=m, such that the selected solution from the infinite number of solutions is sparse, that is, many of its components are zero. This leads to the minimisation with respect to x of f(x,λ)=‖Ax−b‖22+λ‖x‖1, where λ is the regularisation parameter. This problem, which is called LASSO regression, yields a family of functions xlasso(λ) and it is necessary to determine the optimal value of λ, that is, the value of λ that balances the fidelity of the model, ‖Axlasso(λ)−b‖≈0, and the satisfaction of the constraint that xlasso(λ) be sparse. The aim of this paper is an investigation of the numerical properties of xlasso(λ), and the main conclusion of this investigation is the incompatibility of sparsity and stability, that is, a sparse solution xlasso(λ) that preserves the fidelity of the model exists if the least squares (LS) solution xls=A†b is unstable. Two methods, cross validation and the L-curve, for the computation of the optimal value of λ are compared and it is shown that the L-curve yields significantly better results. This difference between stable and unstable solutions xls of the LS problem manifests itself in the very different forms of the L-curve for these two solutions. The paper includes examples of stable and unstable solutions xls that demonstrate the theory.

Development of a short-term nutritional risk prediction model for hepatocellular carcinoma patients: a retrospective cohort study

Malnutrition in patients is associated with reduced tolerance to treatment-related side effects and higher risks of complications, directly impacting patient prognosis. Consequently, a pressing requirement exists for the development of uncomplicated yet efficient screening methods to detect patients at heightened nutritional risk. The aim of this study was to formulate a concise nutritional risk prediction model for prompt assessment by oncology medical personnel, facilitating the effective identification of hepatocellular carcinoma patients at an elevated nutritional risk. Retrospective cohort data were collected from hepatocellular carcinoma patients who met the study's inclusion and exclusion criteria between March 2021 and April 2022. The patients were categorized into two groups: a normal nutrition group and a malnutrition group based on body composition assessments. Subsequently, the collected data were analyzed, and predictive models were constructed, followed by simplification. A total of 220 hepatocellular carcinoma patients were included in this study, and the final model incorporated four predictive factors: age, tumor diameter, TNM stage, and anemia. The area under the ROC curve for the short-term nutritional risk prediction model was 0.990 [95% CI (0.966–0.998)]. Further simplification of the scoring rule resulted in an area under the ROC curve of 0.986 [95% CI (0.961, 0.997)]. The developed model provides a rapid and efficient approach to assess the short-term nutritional risk of hepatocellular carcinoma patients. With easily accessible and swift indicators, the model can identify patients with potential nutritional risk more effectively and timely.