Composite Approach Research Articles

Epistemic Class Injustice: Class Composition and Industrial Action

ABSTRACT Writings on epistemic injustice have assessed how people can be harmed in their capacity as knowers when they are a racial minority, a woman, disabled and so on. But what about when they belong to the working class? This paper is an initial attempt to understand why class has so far received limited attention within writings on epistemic injustice and to respond to these reasons. It focuses on how testimonial and hermeneutic injustices specifically harm workers in ways distinctive from the harm one might suffer due to other social identities. It does this by drawing attention to the special case of industrial strike action and the play of conceptual resources and credibility assessments that influence the action’s success. Additionally, it provides a first-time exposition for social epistemologists on what I term the ‘class compositional approach’, derived from 1960-70s Italian labour struggles. This approach, I argue, succeeds in evading the criticism of class reductionism while developing recent philosophical work on class-based injustice.

Composition and application prospect of several novel quantum dot-polymer composites

Composition and application prospect of several novel quantum dot-polymer composites

Finite-time fault-tolerant control of a spacecraft using composite learning in the presence of moment of inertia uncertainty and external disturbances

In this paper, a neuro-adaptive finite-time fault-tolerant control for a class of nonlinear systems is proposed based on the backstepping method, considering model uncertainties and external disturbances. To compensate for model uncertainties, a neural network is employed, while external disturbances and the estimation error of the neural network are handled using a disturbance observer. Additionally, actuator faults are estimated and compensated for distinctly using two adaptive elements in the proposed control system. Moreover, to enhance the learning of the neural network, disturbance observer, and other adaptive elements, particularly in the lack of persistently exciting signals, a composite learning approach is utilized. To this end, a state-observer is introduced, and its estimation error along with the tracking error is used in the updating rules of all the adaptive elements in the design. Additionally, to prevent the explosion of terms in the backstepping method, a command filter with practical finite-time stable auxiliary variables is incorporated into the proposed controller. Finite-time convergence of the tracking error to zero is rigorously proved using the Lyapunov method. To demonstrate the effectiveness of the proposed control method, the problem of attitude control of a rigid spacecraft with moment of inertia uncertainty, external disturbances, and multiple actuator faults is considered since precise attitude control of a spacecraft is of great importance in different maneuvers. The results show that using the introduced control system, the spacecraft is able to track the desired trajectory under different types of uncertain dynamics.

Thermochemistry of amino-1,2,4-triazole derivatives

AbstractThe present work is focused on determining the enthalpy of formation of several derivatives of amino-1,2,4-triazoles. Experimentally, the enthalpies of formation of the crystalline phase and the enthalpies of sublimation of 3-amino- and 3,5-diamino-1H-1,2,4-triazole were derived, respectively, from static-bomb combustion calorimetry and Calvet microcalorimetry or Knudsen effusion measurements. For 4-amino-4H-1,2,4-triazole, only the enthalpy of sublimation was measured. Gas-phase standard molar enthalpies of formation were also estimated using theoretical calculations performed with the G3(MP2) composite approach. The very good agreement of these estimates with the experimental results, support the extension of this study to the estimate of this property for the remaining compounds not studied experimentally. The results obtained are interpreted in terms of structural contributions.

Estimation for generalized linear cointegration regression models through composite quantile regression approach

This paper introduces a meaningful approach employing composite quantile regression (CQR) to estimate generalized linear cointegration regression models. We elucidate the fundamental structure of the proposed model by presenting its underlying expressions and derive the asymptotic distribution of the estimates of model parameters. Through extensive simulations, our findings demonstrate the superior robustness and precision of the CQR method compared to ordinary least squares (OLS) and quantile regression (QR) approaches. The application of the model to economic and financial variables highlights its significant academic and practical value.

Shifting from sustained to delayed drug delivery systems: Encapsulated mesoporous silica-chitosan grafted polylactic acid-based composite approach

The current study demonstrates a desired drug delivery-controlled system with a wide range of release parameters. It illustrates the feasibility of carriers in the systems for extended and short drug release. Herein, mesoporous silica nanoparticles (MSNs) coated with chitosan (Cs) grafted polylactic acid (PLA) composite were fabricated by a sol-gel method in the form of micrometric porous particles (∼270 nm) with ζ-potential of -17.7±1.7 mV and a specific surface area and total pore volume of 122700 cm2/g and 0.057 cm3/g, respectively. The prepared template of polymer-silica composite pores proved to be essential in the incorporation of Doxorubicin (Dox) drug molecules with adsorption capacity of 83.33 mg/g and encapsulation efficiency of 88%. Dox was incorporated via the solvent diffusion method into the polymer-silica composites at suitable pH and concentration, resulting in solid drug dispersions. The silica-Cs-g-PLA composites showed greater effectiveness in the delayed release of the Dox with complete release after 10 days as compared to pristine MSNs and silica-Cs, which were completed after 12 h and 24 h, respectively. The drug release profiles were studied at different pH (2, 7, and 8), with maximum results obtained at pH 2. Both pristine MSNs and silica-Cs suffered from the burst effect, which was overcome by the grafting of PLA. The functionalized silica-Cs with PLA shows a significant change in the drug release properties from a sustained release process for pristine MSNs and silica-Cs to an enhanced delayed process for the silica-Cs-g-PLA composite, which makes this composite attractive for oral multi-particulate formulations of modified release.

Geospatial Insights into Greece’s Desertification Vulnerability: A Composite Indicator Approach

The Environmentally Sensitive Areas Index (ESAI) is a comprehensive tool for assessing the susceptibility of areas to desertification. This index analyzes various parameters that are vital for environmental health. Through this index, factors such as human activities, geology, soil quality, vegetation and climate patterns are scrutinized. The analysis assigns weights to each participating factor. Thus, the index is derived from the aggregation of four categories (vegetation, climate, soil quality and management practices), and each of them is independently assessed to understand ecological health. In this way, the level of vulnerability to desertification is effectively measured. The application of the index in Greece (for a period of 20 years, 1984–2004) showed signs of environmental degradation and identified many areas with a high risk of desertification. Notably, there was a substantial increase in cultivated land within rural areas, contributing to shifts in the environmental landscape. Furthermore, this period is distinguished as the driest in the last century, with a peak between 1988 and 1993. The consequential rise in irrigation demand, driven by the simultaneous growth of crops and the intensification of agricultural practices, underscores the intricate interplay between human activities and environmental vulnerability.

Current approaches to planning (with) sound

While sound plays a critical role in our experience of the built environment, professionals feel ill-equipped to design and plan with sound in mind. Through a document analysis for 22 planning projects from Quebec, we aim to better understand how sound considerations are integrated into planning in practice. We identify and characterize the observed strategies and propose a typology of sound approaches in planning along two axes related to 1) the integration of the project into the pre-existing sound environment (from continuity to disruption) and 2) the nature and extent of sound considerations (from minimal to composite). This mapping revealed four main approaches to planning with sound, namely insufficient, sufficient, necessary, and extensive. The analysis further highlights a disconnect between planning and sound considerations, partly related to the abstract nature of planning considerations that exert an inherent but rarely acknowledged influence on sound. This disconnect is clearly visible at a tipping point between (flexible but vague) planning considerations and (concrete but technical) sound considerations when projects tend toward a more difficult integration into the pre-existing environment. We conclude with suggestions on how to move toward composite approaches to better integrate sound into planning.

Development and preliminary validation of a prediction formula of sodium and sodium-to-potassium ratio based on multiple regression using 24-h urines

Accurate measurement of sodium intake in the diet is challenging, and epidemiological studies can be hampered by the attenuation of associations due to measurement error in sodium intake. A prediction formula for habitual 24-h urine sodium excretion and sodium-to-potassium ratio might lead to more reliable conclusions. Five 24-h urinary samples and two Food Frequency Questionnaires (FFQs) were conducted among 244 Japanese participants aged 35–80 years. We conducted multivariate linear regression analysis with urinary excretion as dependent variables and eating behaviour and food frequency as independent variables. Empirical weights of sodium excretion and sodium-to-potassium ratio were extracted. Preliminary validity was also assessed by randomly dividing the subjects into development and validation groups based on the correlation coefficient between estimates by the prediction formula and urinary excretion. Taste preference, soy sauce use at the table, frequency of pickled vegetables intake and number of bowls of miso soup were extracted as determinants of sodium excretion. Correlation coefficients between the estimates and urinary excretion for men and women were 0.42 and 0.43, respectively, for sodium and 0.49 and 0.50, respectively, for sodium-to-potassium ratio. This prediction formula may provide more accurate estimation of sodium intake and sodium-to-potassium ratio than the food composition approach.

Assessment of chromite ore wastes for methylene blue adsorption: Isotherm, kinetic, thermodynamic studies, ANN, and statistical physics modeling

This research investigates the adsorption potential of chrysotile and lizardite, two minerals derived from chromite ore wastes, for the uptake of Methylene Blue (MB) dye from waste streams. The characterization of these minerals involves XRD, XRF, FTIR, and SEM. Results confirm the dominance of polymorphic magnesium silicate minerals, specifically chrysotile and lizardite, in the samples. The FTIR spectra reveal characteristic vibration bands confirming the presence of these minerals. The SEM analysis depicts irregular surfaces with broken and bent edges, suggesting favorable morphologies for adsorption. N2 adsorption-desorption isotherms indicate mesoporous structures with Type IV pores in both adsorbents. The Central Composite Design approach is employed to optimize MB adsorption conditions, revealing the significance of contact time, adsorbent mass, and initial MB concentration. The proposed models exhibit high significance, with F-values and low p-values indicating the importance of the studied factors. Experimental validation confirms the accuracy of the models, and the optimum conditions for MB adsorption are determined. The influence of solution acidity on MB uptake is investigated, showing a significant enhancement at higher pH values. Isothermal studies indicate Langmuir and Freundlich models as suitable descriptions for MB adsorption onto chrysotile and lizardite. The maximum adsorption capacities of MB for chrysotile and lizardite were found to be 352.97 and 254.85, respectively. Kinetic studies reveal that the pseudo-first-order model best describes the adsorption process. Thermodynamic analysis suggests an exothermic and spontaneous process. Statistical physics models further elucidate the monolayer nature of adsorption. Additionally, an artificial neural network is developed, exhibiting high predictive capability during training and testing stages. The reusability of chrysotile and lizardite is demonstrated through multiple regeneration cycles, maintaining substantial adsorption potential. Therefore, this research provides comprehensive insights into the adsorption characteristics of chrysotile and lizardite, emphasizing their potential as effiective and reusable sorbents for MB uptake from wastewater.

Characterizing land use/land cover change dynamics by an enhanced random forest machine learning model: a Google Earth Engine implementation

Land use and land cover (LULC) analysis is crucial for understanding societal development and assessing changes during the Anthropocene era. Conventional LULC mapping faces challenges in capturing changes under cloud cover and limited ground truth data. To enhance the accuracy and comprehensiveness of the descriptions of LULC changes, this investigation employed a combination of advanced techniques. Specifically, multitemporal 30 m resolution Landsat-8 satellite imagery was utilized, in addition to the cloud computing capabilities of the Google Earth Engine (GEE) platform. Additionally, the study incorporated the random forest (RF) algorithm. This study aimed to generate continuous LULC maps for 2014 and 2020 for the Shrirampur area of Maharashtra, India. A novel multiple composite RF approach based on LULC classification was utilized to generate the final LULC classification maps utilizing the RF-50 and RF-100 tree models. Both RF models utilized seven input bands (B1 to B7) as the dataset for LULC classification. By incorporating these bands, the models were able to influence the spectral information captured by each band to classify the LULC categories accurately. The inclusion of multiple bands enhanced the discrimination capabilities of the classifiers, increasing the comprehensiveness of the assessment of the LULC classes. The analysis indicated that RF-100 exhibited higher training and validation/testing accuracy for 2014 and 2020 (0.99 and 0.79/0.80, respectively). The study further revealed that agricultural land, built-up land, and water bodies have changed adequately and have undergone substantial variation among the LULC classes in the study area. Overall, this research provides novel insights into the application of machine learning (ML) models for LULC mapping and emphasizes the importance of selecting the optimal tree combination for enhancing the accuracy and reliability of LULC maps based on the GEE and different RF tree models. The present investigation further enabled the interpretation of pixel-level LULC interactions while improving image classification accuracy and suggested the best models for the classification of LULC maps through the identification of changes in LULC classes.

Revisiting the hydrological legacy of revegetation on China's Loess Plateau using Eagleson's ecohydrological perspective

Revegetation has resulted in a trend of increasing vegetation greenness on the Chinese Loess Plateau. However, it remains unclear whether the regional vegetation coverage exceeds hydroclimatic limitations in the context of revegetation, and the hydrological effects of greening are controversial. Eagleson's optimality hypothesis can explain some of the hydrological effects on the Loess Plateau. Here, building on previous research, the geospatial vegetation states were estimated for pre- and post-revegetation periods on the Loess Plateau from 1982 to 2015 using Eagleson's ecological optimality theory. Additionally, a drought composite analysis approach was utilized to investigate the hydrological effects related to drought (including sensitivity and partitioning) under various vegetation states. It was found that revegetation increased the proportion of catchments in the equilibrium state and decreased the proportion in the disturbed state, owing to a wetter climate compared with the pre-revegetation period. Root-zone soil drought, driven by precipitation (P) deficit, asymmetrically triggered hydrological effects for both the pre- and post-revegetation periods, with reduced runoff (Q) for both periods and a decrease in evapotranspiration (ET) during the pre-revegetation period but an increase in ET during the post-revegetation period. Moreover, catchments in an equilibrium state exhibited lower sensitivity between ET and P, and more stable partitioning of ET with regards to P, compared with those in a disturbed state. These results underscore the theoretical framework that an equilibrium state is crucial for maintaining ecosystem ET. Our results highlight the necessity of considering the hydrologic regulation of vegetation states when assessing the hydrological effects of vegetation change.

Extended Composite Approach to Deep Plane Face Lifting with Deep Contouring of the Neck and the Nuances of Secondary and Tertiary Facelifting. Principles for restoration of natural anatomy and aesthetically attractive face and neck contour.

Patients seek second facelifts either due to dissatisfaction with the primary procedure or years later after continued face and neck aging following successful first surgery. Previous surgical anatomical disruption, deformity of tension and vector, scar formation and skin excision compounded by the proliferation of historical energy based skin treatments, thread lift procedures and injectables makes revision rhytidectomy a complex process of causal identification and surgical repair. Patients seeking revision due to dissatisfaction with a primary procedure, or worse visible deformity, have a heightened sense of anxiety which necessitates accurate diagnosis, careful examination, comprehensive documentation and confidence in any planned secondary intervention as well as conservative expectation management. This article outlines the authors approach to identify the challenges posed and successfully perform secondary and revision rhytidectomy using a modified extended deep plane approach.

Alkalinity Regulation and Optimization of Cementitious Materials Used in Ecological Porous Concrete.

Ecological porous concrete (EPC) is one of the novel formulations of concrete with unique phytogenic properties. However, achieving both low alkalinity and high strength in EPC proves challenging due to the inherently high alkalinity of the pore environment, which hinders the growth of the plant and affects its ecological benefits significantly. This research investigated the utilization of 15 types of chemical admixtures and diatomaceous earth as alkali-reducing agents to optimize the properties of silicate cementitious materials for the applications of EPC. To identify the most effective agents, the pH value and compressive strength of the cement paste were adopted as the screening criteria for the selection of the essential alkali-reducing ingredients. Subsequently, a composite approach combining chemical admixtures and DE was employed to explore the synergistic effects on the pH and strength of silicate cementitious materials. The results revealed that a combination of 8% DE, 5% oxalic acid, and 5% iron sulfate functioned effectively and resulted in desirable performance for the concrete. This synergistic blend effectively consumed a large amount of Ca(OH)2, reducing the pH of cement paste to 10.48 within 3 days. Furthermore, the hydration reaction generated C-S-H with a low Ca/Si ratio, leading to a remarkable increase in the compressive strength of the concrete, reaching 89.7 MPa after 56 days. This composite approach ensured both low alkalinity and high strength in silicate cementitious materials, providing a theoretical basis for the application and promotion of EPC in the ecological field.

Composite Plot for Visualizing Aminotransferase and Bilirubin Changes in Clinical Trials of Subjects with Abnormal Baseline Values.

On-treatment excursions of liver laboratory test values in clinical trials involving subjects with underlying liver disease are relevant for the efficacy and safety assessment of drug products and biologics. Existing visualization and analysis tools do not efficiently provide an integrated view of these excursions when baseline liver tests are abnormal. The aim of this study was to develop a composite plot that enables visualization of on-treatment changes in liver test results both as multiples of the upper limit of normal defined by each laboratory's reference population (×ULN) and multiples of the subjects' baseline (×BLN) values. The composite plot approach combines biochemical evaluation for drug-induced severe hepatotoxicity (eDISH) plots sequentially applied to subjects' baseline and peak on-treatment liver test results normalized by ULN and integrates them into a four-panel shift plot of peak on-treatment values normalized by BLN. The composite plot enabled efficient assessment of improvement in liver test values during treatment compared with pretreatment in subjects treated with the investigational drug (or the natural history of placebo-treated subjects) and identified outlier subjects for potential drug-induced liver injury. For studies in subjects with abnormal baseline values, the composite plot has potential application in the assessment of beneficial and concerning on-treatment modifications in liver test values in reference to the individual subject's baseline and population threshold values.

An interpretable multi-scaled agent hierarchy for time series prediction

The traditional time series analysis treats the time series as a dynamic system of sequential entries, leading to complex models and a lack of interpretability. Time series, however, can be understood simply as a composition of correlated information hierarchies in various time scales. Rising from this perspective, here we propose Interpretable multi-time-Scaled Hierarchical Agent-based Time Series Prediction (ISHA-TSP) with linear AutoRegressive (AR) architecture. These time scales, determined based on the instantaneous dominant frequencies of the time series or domain of knowledge, down-sample time series for each agent. Accordingly, a trend prediction signal is offered from a higher time-scale agent to a lower time-scale agent in a hierarchical manner. This hierarchy can also be interpreted reversely, where the lower time-scale agent offers a direction gradient signal to the higher-scale agent to adjust its estimated trend. The optimal learning rate for ISHA-TSP is determined based on a composite Lyapunov function that guarantees convergence. The proposed ISHA-TSP is evaluated with seven datasets in various prediction horizons for accuracy and stop-learning and stop-forecasting steps for robustness. Results show the ISHA-TSP is superior over an equivalent single agent in prediction error. Also, the proposed method adapts against agent failure and data-missing situations. This strategy also leads to simpler models with fewer parameters than a single complex monolithic model. Furthermore, it shows higher interpretability due to its compositional approach.

Method for wind power forecasting based on support vector machines optimized and weighted composite gray relational analysis

This study proposes a weighted composite approach for grey relational analysis (GRA) that utilizes a numerical weather prediction (NWP) and support vector machine (SVM). The approach is optimized using an improved grey wolf optimization (IGWO) algorithm. Initially, the dimension of NWP data is decreased by t-distributed stochastic neighbor embedding (t-SNE), then the weight of sample coefficients is calculated by entropy-weight method (EWM), and the weighted grey relational of data points is calculated for different weather numerical time series data. At the same time, a new weighted composite grey relational degree is formed by combining the weighted cosine similarity of NWP values of the historical day and to be measured day. The SVM’s regression power prediction model is constructed by the time series data. To improve the accuracy of the system’s predictions, the grey relational time series data is chosen as the input variable for the SVM, and the influence parameters of the ideal SVM are discovered using the IGWO technique. According to the simulated prediction and analysis based on NWP, it can be observed that the proposed method in this study significantly improves the prediction accuracy of the data. Specifically, evaluation metrics such as root mean squared error (RMSE), regression correlation coefficient (r2), mean absolute error (MAE) and mean absolute percent error (MAPE) all show corresponding enhancements, while the computational burden remains relatively low.

Decipher syntrophies within C2-C4 organic acids-degrading anaerobic microbiomes: A multi-omic exploration

In methanogenic ecosystems, carboxylic acid degradation is a crucial process facilitated by specialized bacteria working in syntrophy with methanogens. This study explores the transcriptomic responses in 178 metagenome-assembled genomes upon changes in feeding composition, specifically lactate, butyrate, propionate, and acetate, in four lab-scale bioreactors. Methanothrix soehngenii and Methanoculleus sp. emerged as key biomethanation contributors. Through metatranscriptomics, machine learning-based functional annotation, and flux balance analysis the underlying microbial interactions and dynamics were deciphered. Syntrophomonadaceae and Mesotoga species maintained mutualistic metabolite exchanges with hydrogenotrophic methanogens, degrading primarily butyrate and lactate, respectively. Acetate was mainly consumed by Smithellaceae sp., in competition with M. soehngenii in all reactors. Furthermore, the acetoclastic archaeon exhibited a previously undocumented capability to metabolize lactate, thereby confirming the prevalence of acetotrophic pathway. Transcriptomic profiles revealed an additional layer of complexity, where propionate dismutation and beta-oxidation pathways are interconnected by the exchanges of butyrate between putative syntrophs, including Syntrophomonadaceae and Smithellaceae species. Integrating multi-omics data with genome-scale metabolic modeling enabled the accurate reconstruction of dynamics within the controlled ecosystem. This composite novel approach was applied to the AD system to unveil the intricate relationships operating within the microbiome to promote thriving. Results elucidated the still poorly explored organization of the so-called microbial dark matter.

Enhancing Breast Cancer Detection with Ensemble Machine Learning Models

Breast cancer remains a significant public health issue worldwide, underlining the need for accurate and efficient diagnostic methods. In this paper, we propose a new technique to enhance breast cancer diagnosis through the integration of multiple machine-learning models. Our strategy employs a combination of the Naive Bayes classifier, Stochastic Gradient Descent (SGD), Bagging, and the ZeroR classifier, alongside Bayes Network learning. The cornerstone of our approach is Bayes Network learning, a probabilistic graphical model designed to map out the intricate interconnections among various diagnostic factors. This is significant in the way that it can help to uncover complex relationships in the data for the sake of leading to more accurate predictions. Added to the above, we use the Naïve Bayes classifier, a classifier showing good validity in classification tasks and based on probabilistic reasoning, for the screening of breast cancer. Further, a refined model's parameter is included using the SGD and leads to enhancement of the generalization and overall performance of the model. In addition, as part of controlling overfitting, one can also use Bagging. It is an ensemble method in the sense that it considers several models. ZeroR classifier is a very basic classifier and is just used to compare its performance with our composite approach. We are comparing complex ensemble results to its simplicity. We will validate the ability of our proposed methodology to compare the performance of our integrated models against ZeroR.

Bad times keep us together: Policy priorities and economic shocks

AbstractObjectiveWe analyze how economic shocks affect the partisan nature of budgetary trade‐offs and use data from the U.S. Census Annual Survey of Government Finance to illustrate it.MethodsWe propose a compositional approach to model trade‐offs among 10 budgetary categories across both time and space in U.S. states.ResultsWe find support for the notion that partisanship drives the allocation of budgetary expenditures. However, during times of negative economic shocks, either within a state or in neighboring states, Democratic and Republican governors have a similar budgetary response.ConclusionsThe results show the effects of economic and political shifts, as well as the implications of spillovers from other states, on partisan decisions about trade‐offs in government budgets.