Ideal Point Research Articles

Evaluation of Sonicate Fluid Culture Cutoff Points for Periprosthetic Joint Infection Diagnosis.

Implant sonication is useful for recovery of periprosthetic joint infection (PJI) pathogens in culture, but exact cutoff points for definition of clinically significant sonicate fluid culture results vary from study to study. The aim of this study was to define ideal sonicate fluid culture cutoff points for PJI diagnosis. Sonicate fluid cultures from hip and knee prosthesis components removed between February 2007 and December 2020 were studied. Prosthesis components were placed in solid containers in the operating room; in the clinical microbiology laboratory, 400 mL Ringer's solution was added, and containers subjected to vortexing, sonication and then vortexing, followed by centrifugation. Concentrated sonicate fluid was plated on aerobic and anaerobic solid media, and culture results reported semiquantitatively, as no growth, <20, 20-50, 51-100, or >100 CFU/10 mL sonicate fluid. Sonicate cultures from cement spacers and cultures yielding more than 1 microorganism were excluded. Sensitivity and specificity of each cutoff point was evaluated. A total of 1448 sonicate fluid cultures were evaluated, 68% from knees and 32% from hips. PJI was present in 644 (44%) cases. Sensitivity of sonicate culture was 75.0% at <20 CFU/10 mL, 55.3% at ≥20 CFU/10 mL, 46.9% at >51 CFU/10 mL, and 39.8% at >100 CFU/10 mL. Specificity was 78.2%, 99.8%, 100%, and 100%, at the 4 cutoff points, respectively. A cutoff point for sonicate fluid culture positivity of ≥20 CFU/10 mL is suitable for PJI diagnosis.

From Crude Oil to Electricity: Examining the Shift in Indian Consumer Preferences and Behaviour Towards Electric Vehicles

The Industrial Revolution spurred mass production, making petrol vehicles affordable and accessible. The government invested in infrastructure to accommodate their widespread use, but environmental concerns arose. Efforts to address these led to the promotion of alternatives like CNG vehicles, with incentives driving their adoption. Despite emitting fewer greenhouse gases than gasoline vehicles, CNG still poses environmental challenges. Electric vehicles, emitting zero tailpipe emissions, offer superior ecological benefits. Electric vehicles (EVs) are one of the near-term practical solutions to vehicle technology providing solutions to growing concerns over environmental sustainability. The acceptance of a new product is always difficult in a market having people who are so traditionally driven and laggards. This paper aims to analyze the preferences through questionnaires learning various factors driving consumers' purchase decisions. The paper also discusses the importance of charging infrastructure in influencing customer’s product choices. To what extent has India inculcated ‘Aatmanirbhar Bharat’ in the brains of Indians that could drive consumers to delve into Indian-manufactured or foreign-manufactured EVs? Through the survey of 80+ vehicle consumers, the paper scrutinizes the customer’s ideal price point for both EV 2-wheelers and 4-wheelers to help manufacturers make pricing decisions. Lastly, we enquire about consumers’ optimism towards EV adoption in the future. The paper concludes that 71.6% of people desire to switch to EVs because of environmental concerns, 58% because of cost savings on fuel, and less emission of greenhouse gases. The data results show that customers' non-purchase decision is influenced by a lack of undeveloped charging infrastructure, lack of awareness of government incentives, high maintenance costs, uncertainty about the reliability of the technology, and higher prices of EVs as compared to petrol and diesel vehicles. Also, 81.5% of people expressed their view to have automobiles manufactured in India from the opposing belief that foreign manufactured goods have a superior quality. More than 60% of the market is optimistic towards electric vehicles (EVs) dominating the automotive sector in India within the next five years.

Integrated energy system evaluation and optimization based on integrated evaluation model and time-series optimization

Against the backdrop of global attention to climate change and environmental sustainability, the development timing and comprehensive cost of regional renewable energy power generation projects have become a focus of attention. By constructing effective models to evaluate them, it can help promote the healthy development of renewable energy projects. The research aims to quantitatively evaluate the development status of local renewable energy projects by constructing a comprehensive evaluation model, minimize information loss, and improve the accuracy of evaluation results. This study adopted a comprehensive evaluation model that combines Analytic Hierarchy Process (AHP) based on accelerated genetic algorithm, entropy weight method, and ideal point method. Firstly, the subjective weights of the development evaluation indicators for regional renewable energy power generation projects are calculated. Secondly, the entropy weight method is used to analyze the trend of each indicator and obtain objective weights. Finally, combined with the objective weights and the evaluation results calculated using the TOPSIS method, a comprehensive evaluation of renewable energy power generation projects in various regions is conducted. Through analysis, the core indicators of the development level of renewable energy power generation projects in various regions show specific performance, such as Hebei’s evaluation value of 0.4945 in the proportion of comprehensive energy development, and Inner Mongolia’s evaluation value of 0.4045 in the proportion of comprehensive energy installed capacity. Meanwhile, genetic optimization methods exhibit significant advantages in the calculation of optimization schemes compared to dynamic programming methods, possessing strong global search capabilities and high-precision solutions. This study provides a new research method and approach for the evaluation of regional renewable energy power generation projects, demonstrating the practical value and certain advantages of the research method.

Tetramerization is essential for the enzymatic function of the Pseudomonas aeruginosa virulence factor UDP-glucose pyrophosphorylase.

Multidrug-resistant bacteria such as the opportunistic pathogen Pseudomonas aeruginosa, which causes life-threatening infections especially in immunocompromised individuals and cystic fibrosis patients, pose an increasing threat to public health. In the search for new treatment options, P. aeruginosa uridine diphosphate-glucose pyrophosphorylase (PaUGP) has been proposed as a novel drug target because it is required for the biosynthesis of important virulence factors and linked to pathogenicity in animal models. Here, we show that UGP-deficient P. aeruginosa exhibits severely reduced virulence against human lung tissue and cells, emphasizing the enzyme's suitability as a drug target. To establish a basis for the development of selective PaUGP inhibitors, we solved the product-bound crystal structure of tetrameric PaUGP and conducted a comprehensive structure-function analysis, identifying key residues at two different molecular interfaces that are essential for tetramer integrity and catalytic activity and demonstrating that tetramerization is pivotal for PaUGP function. Importantly, we show that part of the PaUGP oligomerization interface is uniquely conserved across bacterial UGPs but does not exist in the human enzyme, therefore representing an allosteric site that may be targeted to selectively inhibit bacterial UGPs.IMPORTANCEInfections with the opportunistic bacterial pathogen Pseudomonas aeruginosa are becoming increasingly difficult to treat due to multidrug resistance. Here, we show that the enzyme uridine diphosphate-glucose pyrophosphorylase (UGP) is involved in P. aeruginosa virulence toward human lung tissue and cells, making it a potential target for the development of new antibacterial drugs. Our exploration of P. aeruginosa (Pa)UGP structure-function relationships reveals that the activity of PaUGP depends on the formation of a tetrameric enzyme complex. We found that a molecular interface involved in tetramer formation is conserved in all bacterial UGPs but not in the human enzyme, and therefore hypothesize that it provides an ideal point of attack to selectively inhibit bacterial UGPs and exploit them as drug targets.

Multi-objective optimization of residential building energy consumption, daylighting, and thermal comfort based on BO-XGBoost-NSGA-II

The energy consumption, daylighting, and thermal comfort of buildings directly affect the three key goals of residents. However, there is little research on the optimization of energy consumption, daylighting, and thermal comfort in residential buildings in China. Therefore, this study proposes an optimization framework that combines Bayesian optimization with extreme gradient boosting trees (BO-XGBoost) and non-dominated genetic algorithm-II (NSGA-II) to study the multi-objective optimization of residential building performance. This paper first uses Grasshopper to simulate and obtain a dataset through Latin hypercube sampling (LHS). BO-XGBoost is used to establish the regression relationship between building envelope design parameters and residential building performance. Then, the obtained regression model is used as the fitness function of NSGA-II to get the Pareto optimal solution set. Finally, the ideal point method is used to obtain the optimal combination of building envelope structure parameters for residential buildings. Taking a residential building in a hot summer and cold winter area as an example, the effectiveness of this method is verified. The results show that (1) BO-XGBoost has excellent predictive performance, with R2 values of 0.997, 0.960, and 0.994 for energy consumption, thermal comfort, and daylighting, respectively. (2) The proposed BO-XGBoost-NSGA-II can effectively achieve multi-objective optimization. Compared with the initial scheme of the case building, energy consumption is reduced by 44.1%, thermal comfort index is reduced by 10.9%, and daylighting performance is improved by 1.7%. Therefore, the proposed method can effectively optimize the performance goals of residential buildings and provide practical ideas for similar problems.

“Where are the pediatricians in all this?”: Family perspectives on the role of pediatricians in mental healthcare and suicide prevention

Rising rates of youth anxiety, depression, and suicide mean that pediatricians are increasingly likely to encounter children struggling with their mental health in their clinical practices. Despite pediatric professional organizations encouraging pediatricians to contribute more to mental healthcare and suicide prevention, research on the role of pediatricians and whether families consider them a resource is limited. Drawing on original survey (N = 1230) and interview data (N = 102), we investigate how families conceptualize and involve pediatricians in their children’s mental healthcare, including during suicidal crises. Our survey data show that while families considered mental health professionals the ideal point of contact, pediatricians were a close second, confirming that families view pediatricians as a mental health resource. Parent interviews clarify that parents most often turn to pediatricians for medication and referrals to other mental health professionals or because mental health professionals were inaccessible. We also examine how pediatricians helped (by connecting families promptly to appropriate care) or hurt (by stigmatizing suicide or by providing interventions associated with harm) during a child’s suicidal crisis. We conclude by emphasizing the importance of proper suicide prevention training for pediatricians, as well as suggesting directions for future research.

The kinetics of Torque Teno virus plasma load following calcineurin inhibitor dose change in kidney transplant recipients.

Torque Teno virus (TTV)is nonpathogenic, highly prevalent, and reflects the immune status of its host. Thus, TTV plasma load was suggested for the guidance of immunosuppression post solidorgan transplantation. The present study was designed to determine the kinetics of TTV following changes in calcineurin inhibitor (CNI)dose. A total of 48 adult recipients of a kidney graft transplanted at the Medical University of Vienna between 2018 and 2019 with isolated changes in CNI dose were selected from the prospective TTV-POETtrial. TTV plasma load was quantified by in-house PCR. At Day 30 following CNI dose adaptation (median 33% of daily dose) no changes in TTV load were noted. However, at Day 60, following CNI dose reduction a lower TTV load of 6.4 log10 c/mL (median; interquartile range[IQR] 4.9-8.1) compared with the baseline of 7.1 log10 c/mL (IQR5.3-8.9) was noted (p = 0.001); there was also a trend towarda higher TTV load following CNI increase (6.6 log10 c/mL, IQR 4.1-9.7 vs. 5.2 log10 c/mL, IQR 4.5-6.8; p = 0.09). The data suggested that TTV load changes become noticeable only 2 months after CNI dose adaptation, which might be the ideal time point for TTV load monitoring.

Deep learning-based low overlap point cloud registration for complex scenario: The review

Most studies on point cloud registration have established the problem in the case of ideal point cloud data. Although the state-of-the-art approaches have achieved amazing results on multiple public datasets, the issue of low overlap point cloud data invalidating state-of-the-art methods is acting as a latent challenge that has not been solved. Therefore, a profound analysis about why existing registration architectures break down in the low-overlap regime and how to select the appropriate strategies to improve the low overlap point cloud correspondence estimation is necessary and useful. Unfortunately, there are few survey works about low overlap cloud registration solving strategies and the corresponding datasets are very limited. This work briefly reviews mainstream deep learning-based point cloud registration and provides an in-depth analysis of the reasons why these architectures are not generalizable to scenarios with low overlapping areas. It is the first survey that mainly focuses on representative low overlap registration methods, their techniques, and related datasets for training/testing. It is worth noting that we also design and construct a large 3D dataset to eliminate the gap in Semantic-assisted point cloud registration with low overlap. Finally, challenges about low overlap point cloud registration and future directions in addressing these challenges are also pointed out. [dataset]

D-Dimer measurement as a predictor of severity and mortality in patients diagnosed with Covid-19 on admission

Introduction: The continuous spread of SARS-CoV-2 across the globe emphasizes the need for understanding the predictors of disease severity. A promising biomarker for this use is the D-dimer since its values are typically increased in SARS-CoV-2 infections. Objective: Verify D-dimer values at the moment of hospital admission to predict disease severity and mortality in SARS-CoV-2 (COVID-19) patients. Method: Cross-sectional study conducted with secondary data from 170 COVID-19 cases from a hospital in the Paraná state, Brazil, between 2020 and 2021. D-dimer levels at admission, disease severity, and mortality were evaluated. The chi-square test, the Student's t-test and the Mann-Whitney U test were used for comparison purposes. ROC curve analysis of the D-dimer parameter was performed to evaluate outcomes (mortality and severity), and the ideal ROC cutoff point was detected by the Youden Index (J). A p-value &lt;0.05 was considered significant in all analyses. Results: There was a statistical significant association between disease severity and the clinical signs of cough, arthralgia and myalgia, as well as D-dimer, leukocyte, and outcome variables. From the ROC curve, the effectiveness of the D-dimer level in identifying the probability of survival (cutoff point &lt;1059 µg/L, 75.44% sensitivity, 58.70% specificity, 95%CI 0.541-0.758, p-value &lt;0.009) and severity (cutoff point &lt;2244 µg/L, 85.9% sensitivity, 30.4% specificity, 95%CI 0.521-0.736, p-value &lt;0.024) was determined. Conclusion: Serum D-dimer levels are associated with COVID-19 severity and may be a promising tool for outcome prediction.

Multi-Criteria Optimization Conditions for the Recovery of Bioactive Compounds from Levisticum officinale WDJ Koch Roots Using Green and Sustainable Ultrasound-Assisted Extraction

Given that ultrasound-assisted aqueous extraction is gaining importance within “green technology” and to increase the efficiency of extracting bioactive compounds from Levisticum officinale root waste, optimization of its parameters was undertaken. Multi-objective (multi-criteria) optimization can be an extremely promising tool not only for designing and analyzing the extraction process, but also for making process-control decisions. Therefore, the main objective of this study was to develop and optimize an environmentally friendly ultrasound-assisted extraction methodology for the aqueous extraction of bioactive compounds from the roots of Levisticum officinale, which are considered a by-product. The focus was on determining the optimal extraction conditions of the independent variables, such as solid–liquid ratio, extraction time and ultrasound power, so that the optimized extracts present the highest bioactive potential expressed in terms of levels of phenolic compounds, flavonoids, sugars and antioxidant potential. Based on the Pareto-optimal solution sets, it was found that to maximize the criteria, aqueous extraction should be carried out at a Levisticum officinale biomass/solvent ratio of 0.0643 g/mL for a time of 8.1429 to 9.0000 min, with ultrasound assistance of 162.8571 to 201.4286 W. Among the compromise solutions, the so-called “best efficient solution” was indicated as the solution for which the Euclidean distance from the ideal point of Utopia was the smallest (among all analyzed points of the collection), which had coordinates x1comp = 0.0750 g/mL, x2comp = 9.0000 min and x3comp = 214.2857 W. The results obtained will provide a valuable tool to assist in the decision-making process of controlling such an extraction process.

Modified ideal point method for estimating minimum environmental flow of rivers

Modified ideal point method for estimating minimum environmental flow of rivers

Cutoff Point Measurement of the waist circumference for the diagnosis of Metabolic Syndrome in Iraqi university students

Background: Metabolic syndrome (MetS) is a collection of connected cardiovascular risk factors that characterizes the complicated illness. The waist circumference cutoff point fluctuation has so far defined Mets. Objective: This study aimed to determine the cutoff point for WC in healthy Iraqi adults. Methods: This cross-sectional survey establishes the standard value for WC among 300 healthy university students in Wasit city, Iraq. They are aged between 18-25 years. The receiver operator characteristic (ROC) curve was used WC to predict the presence of two or more risk factors for MetS, as defined by IDF. Results: The cutoff level yielding maximum sensitivity and specificity for predicting the presence of multiple risk factors was 82.5cm in females (sensitivity 65%, specificity 70%) and 91cm in males(sensitivity 50%, specificity 75%). Conclusion: Results propose that 82.5cm for females and 91cm for males represent the ideal WC cutoff points for diagnosing MetS in young adult participants from Iraq.

The Optimal Transportation Option in an Underground Hard Coal Mine: A Multi-Criteria Cost Analysis

The issue of transport in underground hard coal mines is very rarely described in the literature. The financial aspects of this issue are even less often analyzed. Publications in this area focus on technical issues and the safety of mining crews. More attention is paid to transport in open-pit mines. The above premises and practical needs imply the need to conduct economic analyses of transport systems in underground hard coal mines. This paper is a scientific communication, which presents the concept of a multi-criteria cost analysis as a tool to support the selection of the optimal transportation option in an underground hard coal mine. Considerations in this area have not been carried out in the relevant literature, and the problem of selecting a transportation option is a complex and necessary issue in the practice of underground mines with extensive mine workings. The methodology presented includes five cost criteria (costs of carrying out the transportation task; route expansion costs; rolling stock maintenance costs; depreciation costs; and additional personnel costs). The simultaneous application of criteria relating to utility properties in addition to cost criteria makes it possible to adopt a specific technical and organizational model of the transportation system based on the indication of the optimal solution, resulting from the mathematical construction of functions of objectives relating to utility and cost. The optimal variant of the designed system and configuration of the material transportation system in underground workings takes into consideration the following: (1) seven utility criteria (KU1—transportation task completion time; KU2—compatibility of transportation systems; KU3—continuous connectivity; KU4—co-use with other transportation tasks; KU5—safety; KU6—inconvenience; KU7—operation under overplanning conditions) and (2) five cost criteria (KK1—costs of implementing the transportation task; KK2—costs of route expansion; KK3—rolling stock maintenance costs; KK4—depreciation costs; KK5—additional personnel costs). Based on the aforementioned criteria, two objective functions are built for each option: utility and cost. They present divergent goals; therefore, they are non-cooperative functions. Both utility and costs strive for the maximum. In the developed methodology, an ideal point is usually a fictitious solution representing a set of maximum values among all the achievable values in a set of solutions, but it is impossible to achieve this simultaneously based on all the criteria. This point illustrates the maximum utility and lowest cost among the alternatives considered, which is obviously impossible for any of the variants to meet at the same time, although it indicates the possibilities of the technique and the range of costs. For the developed method, a so-called “PND” nadir point is also determined, representing the least-preferred level of achievement of all goals simultaneously, determined from the set of optimal points in the Pareto sense. The originality of the conceptual considerations undertaken stems from: filling the gap in the economic methodology of complex transportation systems evaluation; embedding considerations in the trend concerning complex transportation systems of underground mines; and focusing considerations on the pre-investment phase, making it possible to optimize costs before expenditures are incurred.

A comprehensive analysis and multi-objective optimization of a sustainable production system based on renewable energies

The adoption and promotion of clean and sustainable energy sources bring about significant benefits to the environment, economy, and society, both in the present and for future generations. This study introduces a pioneering solar-wind biomass multi-generation system, aimed at facilitating the sustainable production of electricity, fresh water, hydrogen, and heating. This system provides a comprehensive evaluation of the influence of environmental conditions and design factors on system efficiency, taking into account the crucial aspects of energy, exergy, economy, and environment (4E). Additionally, a meticulous sensitivity analysis is conducted to identify and prioritize key decision variables that exert the most significant impact on the overall system. Ultimately, the multi-objective optimization is conducted by taking into account various priorities in four distinct scenarios. This approach enables the simultaneous or individual optimization of the system's useful objective functions in relation to the total cost rate. Subsequently, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is employed to determine the ideal point that best fulfills the optimization criteria. By employing TOPSIS, this study aims to provide a robust and systematic decision-making framework for scenario selection in various domains. By evaluating the optimal points across various scenarios, the first scenario yields the most favorable outcomes in terms of the system's useful outputs. These include an exergy efficiency of 19.63 %, hydrogen production of 11.07 kg/h, fresh water production of 34.16 kg/s, and a total cost rate amounting to 213.80 $/h. Additionally, a considerable decrease of 22.65 % in the total cost rate is noted at the optimal point.

Multi-objective optimization of dew point indirect evaporative coolers for data centers

This study introduces a novel counter-flow dew point indirect evaporative cooler (DPIEC) tailored for data center cooling. A numerical model for DPIEC was developed in COMSOL and validated experimentally, followed by the formulation of regression models using response surface methodology. Eight critical design parameters (five operational and three geometrical) were linked to three performance evaluation indexes (outlet primary air temperature T1,out, coefficient of performance COP, and cooling capacity per unit volume Qv). The magnitude of parameter influence on the evaluation indexes was assessed. Employing the three regression models as objective functions, a genetic algorithm, implemented in MATLAB, was adopted to perform multi-objective optimization for DPIEC under four climatic conditions, yielding an array of optimal parameter combinations. The results underscored the robust predictive accuracy of the established regression models, with R2 and Adeq Precision exceeding 0.96 and 53.7, respectively. The ideal point method achieved design solutions of the DPIEC with T1,out below 21.6 °C, COP above 61.3, and Qv over 15.5 kW/m3. Compared to the original design, the optimized design featured reductions of over 13.84 %, elevations of over 23.50 %, and enhancements of over 104.10 % in these three indexes, respectively. In addition, the confirmation study of the Pareto front proved the reliability of the obtained DPIEC design reference.

Comprehensive analysis of quality and bioactive compounds in umbu-cajá fruit during different ripening stages

Umbu-cajá (Spondias tuberosa x S. mombin) is a tropical fruit with potential health benefits due to its bioactive compounds. The objective of this study was to identify the optimal harvest point for enhanced nutritional value and consumer appeal of umbu-cajá. Umbu-cajá fruit was collected at five ripening stages (S1 to S5). Immature fruit (S1) had a higher pulp yield but a lower seed yield, suggesting an inverse relationship between these fruit components. Stage 3 (S3) was identified as the optimal harvest time, as it maintained stable pulp and seed mass, corresponding to the ideal physiological maturity point for human consumption. The respiration rate increased significantly at stages S4 and S5, indicating that Spondias sp. could be classified as a climacteric fruit. Color analysis revealed increased yellowness during ripening, which is indicative of the development of desirable attributes such as sweetness and soft texture in hipened fruits. Compositional analysis demonstrated that moisture content decreased while ash and solids content increased during ripening, reflecting appropriate maturity for human consumption. The ratio of soluble solids to acidity increased, suggesting enhanced sweetness as the fruits ripened. The levels of antioxidants, except for anthocyanins, decreased during ripening, with the latter increasing up to stage S3, thereby providing the fruit with its vibrant colors. The compositional changes underscore the importance of considering both nutritional and sensory attributes in determining the optimal harvest time. Moreover, the increased antioxidant content at early ripening stages signifies the fruit's potential as a valuable source of health-promoting compounds. This study contributes to optimizing the quality of umbu-cajá and offers valuable information for sustainable production and improved consumption of these fruits. By identifying the ideal harvest time and evaluating the changes in bioactive compounds and quality attributes during ripening, this research aids in promoting healthier dietary choices and supporting the fruit industry. The findings have implications for producers, retailers, and consumers, providing a scientific basis for enhancing the nutritional value and flavor profile of umbu-cajá at the optimal stage of ripening.

American politics in 3D: measuring multidimensional issue alignment in social media using social graphs and text data

A growing number of social media studies in the U.S. rely on the characterization of the opinion of individual users, for example, as Democrat- or Republican-leaning, or in continuous scales ranging from most liberal to most conservative. Recent works have shown, however, that additional opinion dimensions, for instance measuring attitudes towards elites, institutions, or cultural change, are also relevant for understanding socio-informational phenomena on social platforms and in politics in general. The study of social networks in high-dimensional opinion spaces remains challenging in the US, both because of the relative dominance of a principal liberal-conservative dimension in observed phenomena, and because two-party political systems structure both the preferences of users and the tools to measure them. This article leverages graph embedding in multi-dimensional latent opinion spaces and text analysis to propose a method to identify additional opinion dimensions linked to cultural, policy, social, and ideological groups and preferences. Using Twitter social graph data we infer the political stance of nearly 2 million users connected to the political debate in the U.S. for several issue dimensions of public debate. We show that it is possible to identify several new dimensions structuring social graphs, non-aligned with the classic liberal-conservative dimension. We also show how the social graph is polarized to different degrees along these newfound dimensions, leveraging multi-modality measures in opinion space. These results shed a new light on ideal point estimation methods gaining attention in social media studies, showing that they cannot always assume to capture liberal-conservative divides in single-dimensional models.

Welfare, egalitarianism, and polarization: the politics of noncontributory social programs

AbstractThis study analyzes the link between egalitarian ideals and the rise in party polarization in Congress. To demonstrate how philosophical differences over conceptions of fairness, equality, and justice help explain the recent growth in partisanship over the past few decades, I argue one overlooked explanatory factor which assists in capturing this ideological rift is noncontributory welfare spending. Recovering annual ideal point estimates between 1947 and 2018 that are comparable with annual federal spending, I use multivariate time series models and find convincing evidence which suggests welfare outlays have a strong short- and long-run effect on polarization. Moreover, analysis of the roll call record also shows when ideal point estimates are recovered by specific policy area, lawmakers exhibit higher levels of ideological separation on welfare compared to, among others, policies such as defense and transportation. Robustness checks confirm these findings also hold even when controlling for income inequality.

Unveiling the frictional properties of brake pads developed from silver nanoparticles modified carbon nanotubes derived from rice husks

The development of brake pads using eco-friendly materials was spurred by the health risks associated with the use of brake pads based on asbestos. In this experiment, the frictional substance used to develop vehicle brake pads, which was based on asbestos, was replaced with carbon nanotubes made from rice husk. The impacts of the wear parameter were evaluated experimentally using Taguchi-Grey's (GRA) multi-response analysis. The wear rate, wear depth, and coefficients of friction were the three multi-response variables that were selected. The modified sol-gel and solution-stir-cast technique was used to develop the brake pads. The results show an ideal point of 1.5 wt% carbon nanotube made from rice husk, a 20 N applied load, a 3 m/s sliding speed, a 25 m sliding distance with 2.15 mg/m, a wear depth of 5 mm, and a COF of 0.42. At a 95% confidence level, the predicted and experimental GRA were fairly similar. The designed brake pad system's achieved frictional qualities meet industry requirements. It is known that silver nanoparticle modified carbon nanotubes made from rice husk biomass may be utilised to make high-friction brake pads using the advanced method.

Advancing Front Mesh Generation on Dirty Composite Surfaces

Computer-aided design (CAD) models usually contain many errors between neighboring surfaces, such as slivers, gaps, and overlaps. To clean up such models, virtual operations have been suggested to merge multiple neighboring CAD surfaces into a single composite surface. However, it remains a challenge to generate a quality mesh on thereby formed dirty composite surfaces. In this paper, we propose a novel advancing front technique (AFT) that can treat such composite surfaces by developing two new schemes to enhance the traditional AFT. Firstly, for each composite surface, we define a parametric plane by using a combined set of the tessellation on this composite surface. Simplicial complex augmentation framework reparameterization approach is suggested since it can treat tessellations containing gap and overlap after introducing a pre-processing step. Meanwhile, this approach can ensure a bijective mapping between the parametric and physical space. The front intersection check can thus be performed on the parametric plane robustly. Secondly, the indirect and direct approaches are alternatively employed to calculate ideal points in different circumstances. In the circumstance that the possible new element is completely contained in one single CAD surface, the ideal point is calculated on the intrinsic parametric plane of the surface; otherwise, the ideal point is directly calculated on the physical space. We avoid using the geometry defined on the tessellation since we prefer to getting a mesh respecting the original CAD model rather than its tessellation counterpart. Presently, the developed new schemes have been incorporated into our in-house surface mesher, and their efficiency and effectiveness have been demonstrated through a comparison with state-of-the-art commercial tools (e.g., COMSOL Multiphysics) and AFT algorithm, using CAD models of industry-level complexity.