Variable Set Research Articles

Bitonic st-Orderings for Upward Planar Graphs: Splits and Bends in the Variable Embedding Scenario

Bitonic st-orderings for st-planar graphs were introduced as a method to cope with several graph drawing problems. Notably, they have been used to obtain the best-known upper bound on the number of bends for upward planar polyline drawings with at most one bend per edge in polynomial area. For an st-planar graph that does not admit a bitonic st-ordering, one may split certain edges such that for the resulting graph such an ordering exists. Since each split is interpreted as a bend, one is usually interested in splitting as few edges as possible. While this optimization problem admits a linear-time algorithm in the fixed embedding setting, it remains open in the variable embedding setting. We close this gap in the literature by providing a linear-time algorithm that optimizes over all embeddings of the input st-planar graph. The best-known lower bound on the number of required splits of an st-planar graph with n vertices is n-3. However, it is possible to compute a bitonic st-ordering without any split for the st-planar graph obtained by reversing the orientation of all edges. In terms of upward planar polyline drawings in polynomial area, the former translates into n-3 bends, while the latter into no bends. We show that this idea cannot always be exploited by describing an st-planar graph that needs at least n-5 splits in both orientations. We provide analogous bounds for graphs with small degree. Finally, we further investigate the relationship between splits in bitonic st-orderings and bends in upward planar polyline drawings with polynomial area, by providing bounds on the number of bends in such drawings.

Masking approaches to analyse plantar pressure data of new and confident walking infants

BackgroundDue to its easy and straightforward use, regional analysis with the “standard” mask is the most common approach for quantifying plantar pressures in infancy. Such a mask, however, identifies foot regions based on typical foot proportions and pressure gradients. Alternatively, the use of a customised mask retaining infants’ feet proportions has not been explored. Research questionDoes a customised mask scaled on infants’ feet improve processing of pressure data collected during walking development compared with a standard mask? MethodsThirteen infants walked across an EMED xl platform. Steps were grouped applying eight foot-regions standard and customised masks. To evaluate masks’ performance, peak pressure (PP) and contact area (CA) were extracted from each region, and mask. Intra-individual coefficients of variation were then calculated for each variable, and compared between masks using a Mann-Whitney U test (p < 0.05). Unsuccessful masks application was reported, expressed as percentage of data loss. ResultsFor CA variation, significant differences were found in all the regions but the lateral toes in new (Z = −0.184, p = 0.8540) and confident walking (Z = −1.562, p = 0.118). For PP variation, a significant difference was found in confident walking within the lateral midfoot (Z = −2.598, p = 0.009). With the standard mask, 22–27 % of data was lost in new and confident walking respectively, compared to 1.6–0 % with the customised. As a result, the customised mask characterised the more variable steps, demonstrating higher variation compared to the standard mask. SignificanceIdentifying foot regions using a mask based on infants’ feet proportions yielded an improved performance compared to the standard mask. With the customised mask, we retained almost all the steps and characterised the variability of the data, thereby providing an appropriate approach for infants’ pressure data processing. Application of the customised mask could therefore be beneficial in future studies analysing highly variable data sets.

(O, G)-granular variable precision fuzzy rough sets based on overlap and grouping functions

Since Bustince et al. introduced the concepts of overlap and grouping functions, these two types of aggregation functions have attracted a lot of interest in both theory and applications. In this paper, the depiction of (O, G)-granular variable precision fuzzy rough sets ((O, G)-GVPFRSs for short) is first given based on overlap and grouping functions. Meanwhile, to work out the approximation operators efficiently, we give another expression of upper and lower approximation operators by means of fuzzy implications and co-implications. Furthermore, starting from the perspective of construction methods, (O, G)-GVPFRSs are represented under diverse fuzzy relations. Finally, some conclusions on the granular variable precision fuzzy rough sets (GVPFRSs for short) are extended to (O, G)-GVPFRSs under some additional conditions.

Spatial Downscaling of Forest Above-Ground Biomass Distribution Patterns Based on Landsat 8 OLI Images and a Multiscale Geographically Weighted Regression Algorithm

Forest above-ground biomass (AGB) is an excellent indicator for the health status and carbon sink potential of forest ecosystems, as well as the effectiveness of sustainable forest management practices. However, due to the strong heterogeneity of forest structures, acquiring high-accuracy and high-resolution AGB distributions over wide regions is often prohibitively expensive. To fill the resulting gap, this paper uses part of Lishui city, Zhejiang province as the study area, based on 168 forest sample observations, and proposes a novel integrated framework that combines a multi-scale geographically weighted regression (MGWR) with the co-kriging algorithm to refine the spatial downscaling of AGB. Specifically, optimal predictor variable sets identified by random forest importance ranking, multiple stepwise regression, and Pearson VIF methods were first assessed based on their total explanatory power (R square), followed by reconfirmation of the optimal predictor variable set based on the non-stationarity impact of each variable’s action scale (bandwidth) on the output pattern of AGB downscaling. The AGB downscaling statistical algorithms included MGWR, GWR, random forest (RF), and the ordinary least square (OLS), and their downscaling performances were quantitatively compared to determine the best downscaling method. Ultimately, the downscaled AGB pattern was produced using the best method, which was further refined by considering the spatial autocorrelation in AGB samples by implementing a co-kriging interpolation analysis of the predicted AGB downscaling residuals. The results indicated that the variable set selected by random forest importance ranking had the strongest explanatory power, with a validation R square of 0.58. This was further confirmed by the MGWR analysis which showed that the set of variables produced a more spatially smooth downscaled AGB pattern. Among the set of optimal variables, elevation and aspect affected AGB at local scales, representing a strong spatial heterogeneity. Some textural features and spectral features showed a smooth action scale relative to AGB, showing insignificant spatial scale processes. In the study area with complex terrain, using aspect as a covariant, the co-kriging (CK) model achieved a higher simulation accuracy for the MGWR-predicted AGB residuals than the ordinary kriging model. Overall, the proposed MGWR-CK model, with a final validation R square value of 0.62, effectively improved the spatial distribution characteristics and textural details of AGB mapping without the additional costs of procuring finer satellite images and GIS-based features. This will contribute to the accurate assessment of carbon sinks and carbon stock changes in subtropical forest ecosystems globally.

Information transmission with continuous variable quantum erasure channels

Quantum capacity, as the key figure of merit for a given quantum channel, upper bounds the channel&amp;apos;s ability in transmitting quantum information. Identifying different types of channels, evaluating the corresponding quantum capacity, and finding the capacity-approaching coding scheme are the major tasks in quantum communication theory. Quantum channel in discrete variables has been discussed enormously based on various error models, while error model in the continuous variable channel has been less studied due to the infinite dimensional problem. In this paper, we investigate a general continuous variable quantum erasure channel. By defining an effective subspace of the continuous variable system, we find a continuous variable random coding model. We then derive the quantum capacity of the continuous variable erasure channel in the framework of decoupling theory. The discussion in this paper fills the gap of a quantum erasure channel in continuous variable setting and sheds light on the understanding of other types of continuous variable quantum channels.

Hazard Assessment of Concrete Dam Cracks Based on Variable Fuzzy Sets and the Modified Analytic Hierarchy Process

Hazard Assessment of Concrete Dam Cracks Based on Variable Fuzzy Sets and the Modified Analytic Hierarchy Process

EXISTING TRENDS OF HERITAGE CONSERVATION IN SHIMLA: A HILL TOWN IN INDIA

India is bestowed with rich cultural diversity manifested by a wide range of tangible heritage resources. The formal inception of the Archaeological Survey of India (ASI) during colonial rule paved way for heritage conservation protecting the National Monuments pan India that are less than 0.01% of the total existing heritage resources. Though the State Archaeology Department and the National Monuments Authority have taken various initiatives at the state levels, the adopted conservation process lacks an integrated approach due to the involvement of a wide array of stakeholders ranging from the Central Government to the local authorities to the various conservation groups. Each of these agencies employs vivid funding sources and mechanisms and variable sets of guidelines and norms with diversity in delivery models for various heritage conservation projects limiting the conservation process to piecemeals. Shimla, a colonial hill town in the north of India, and the state capital of Himachal Pradesh is home to vast stock of tangible and intangible heritage resources. Amidst rapid growth and urbanization, over the past two decades, several initiatives have been taken by the state government of Himachal Pradesh (GoHP) in resonance with the urban local bodies of Shimla but only a few of the conservation projects have seen success. However, the findings of the research bring out that though the GoHP has been proactive in protecting its heritage resources, however, a large part of the heritage resources in Shimla remain in a state of neglect and lack maintenance and management. This is because of the complexity involved in the conception and implementation of heritage conservation because of the vastness of heritage resources and the involvement of diverse groups of stakeholders in the process, as the role of urban local bodies directly impacts the efficiency of the conservation program. The study brings out the heritage conservation process adopted in Shimla by various government agencies.

Rooted Stereotypical Thinking on Gender and SexDiscussion on Notions Against Homosexuality in Chinese Sociocultural Context

The rooted paradigms of dichotomic thinking in modern societies have greatly limited human cognition and self-identification of gender identification and sexual inclination. It is against these biased yet prevalent pattern of dualistic tradition, a great number of scholars have contributed to the formation of non-dualistic theoretical stances, accentuating that the cognition of gender should based on the emphasis of gender fluidity. Michel Foucaults power-knowledge theory has been long taken by the concerned academia as the fundamental theory embedding the non-dichotomic thinking paradigm. On the premise of applying dichotomous cognition to the analysis of the human gender cognition, this paper will firstly include an ethnographic analysis of the differences in people's cognition and definition of gender in modern society and particular small-scaled societies. This paper will also discuss the influence of the rigid impression of dichotomous cognition on the setting of control variables in many medical and psychological scientific researches. The concurrent scientific studies into sexual orientation and gender identification have largely neglected the influence of gender spectrum on humans concerned cognition and self-recognition. The author would also include discussion of eth-nographic data from Chinese sociocultural context. In Chinese society, LGBT communities and homosexuality face various discrimination and inequality depends on peoples dualistic thinking and traditional Chinese social cultures so that LGBT people have to live in shadows. Through this study, a solid theoretical foundation can be established for relevant researchers to determine the theoretical framework of future empirical and experimental data.

Associations of potential ADRD plasma biomarkers in cognitively normal volunteers.

This study examined the relationships between 13 novel blood-plasma biomarkers and dementia-related demographic and health factors in a cohort of 237 cognitively normal research volunteers whose average age was ≈82 years and who were 63% female. We regressed each biomarker on selected covariates to explore the associations between the biomarkers and selected factors to assess whether they may contribute to biomarker values. Post hoc sensitivity analyses were done with updated data and consistent variable sets for robustness and batch effects. Biomarker concentrations were largely not associated with demographics or health conditions, but some expected associations (e.g., apolipoprotein E [APOE] status with amyloid beta [Aβ]42/Aβ40) were observed. Post hoc results remained similar to those of the main analysis. The absence of strong associations between the biomarkers with age, gender, or medical conditions suggests that changes in these biomarkers, when observed, may be attributable to neuropathological changes. Among N = 237 cognitively normal adults, we studied candidate Alzheimer's disease and related dementia (ADRD) plasma biomarkers. Biomarkers were largely not associated with demographic or health factors. Apolipoprotein E (APOE) status was associated with amyloid beta (Aβ)42/Aβ40 ratio. These results support hypotheses that plasma biomarkers are informative for ADRD.

Influence of Socio-personal and Management Factors to Generate the Knowledge Level of the Farmers Associated with Sericulture of West Bengal, India

Knowledge level is assessed in respect to a total of twenty-one socio-management variables associated with sericultural practices by the farmers in West Bengal, India. The study was conducted using a total of 126 respondents under three districts. The independent fourteen societal and seven management factors were statistically analyzed using correlation, path, factor and regression models. Consistency and dependency level in between the independent variables were assessed and found discrete and/or random in nature. Various model values by path analysis showed the direct substantial effect on x10, x12 and x19 where they occupied the 1st, 2nd and 3rd positions respectively. All the variables were justified by a total of seven factors extracted form a homophyllus group and identified with their variation and cumulative percentages. Regression equations was extracted for knowledge level (y) separately as dependent variable and Minimum Data Set as predictor set. The probability of ‘F’ value ranged from 0.05 - 0.10 and found in variables x10 and x16, critically effected on the predicted character knowledge level (y). Considering all these socio-management factors, if the identified variables can be taken care in knowledge level increment, the number of sericulture farmers will be more in future across West Bengal.

The Influence of Director Expertise and Executive Expertise on Firm Performance (An Empirical Study of the Two-tier Board System in Indonesia)

This research aimed to assess the influence of the expertise of boards of directors and executives on manufacturing companies' performances in Indonesia. The independent variable set was Director and Executive Expertise measured by the proportion of board members who have academic degrees (graduate and postgraduate) and professional certifications in business and finance, while the dependent variable was Firm Performance measured by Return on Assets, Return on Equity, and Tobin's Q. The objects of research were manufacturing companies in Indonesia, whose data were obtained from the website of the Indonesia Stock Exchange in 2014-2018. Separate regression analysis of data was performed for each group of board members. Return on Assets, Return on Equity, and Tobin's Q functioned for measuring Firm Performance. The study found that academic degrees and professional certifications in business or finance held by directors and executives did not affect Firm Performance across all proxies because, among others, most companies in Indonesia were family-owned, where educational background and certification were not the main factors for the companies' financial performances. As the implications, companies can consider Director Expertise as a necessary thing for improving their performances. Members of boards who have more relevant expertise, experiences, knowledge, and skills can develop decision-making and drive more strategy in making more decisions to solve problems and improve their firms' performances.

Ant Colony Optimized Controller for Fast Direct Torque Control of Induction Motor

Induction motor (IM) drives have tremendous applications as high-performance drives in things such as mine winders, machine tools, electric vehicles, and elevators. Usually, IM drives controlled by direct torque control are preferred for these applications due to their fast torque control and simplicity compared with IM drives with field-oriented control. Proportional–integral–derivative (PID) controllers are commonly used to control IM drives using DTC. Though these controllers are simple and provide excellent response for linear systems with constant set points, they perform poorly with variable set points and IM motor parameter uncertainties. Hence, many control techniques and optimization algorithms have been applied to improve IM drive performance. This paper proposes an IM drive controlled using direct torque control principles, but with the power converter operation optimized to give fast torque performance. The IM drive speed response is improved using an optimized fuzzy PID (FPID). The FPID optimization is accomplished by the ant colony optimization (ACO) algorithm. All components of the IM drive with the optimized control system were simulated using the MATLAB/Simulink platform. The responses of the introduced drive using three different controllers—conventional PID, FPID, and optimized FPID—were compared. The simulation results indicate that the optimized FPID controller provided the best performance in terms of speed and torque. Additionally, the performance of the IM with the proposed optimized FPID under parameter uncertainties was studied. The simulation results indicated the robustness of the optimized FPID controller against parameter uncertainties.

RAAV Manufacturing: The Challenges of Soft Sensing during Upstream Processing

Recombinant adeno-associated virus (rAAV) is the most effective viral vector technology for directly translating the genomic revolution into medicinal therapies. However, the manufacturing of rAAV viral vectors remains challenging in the upstream processing with low rAAV yield in large-scale production and high cost, limiting the generalization of rAAV-based treatments. This situation can be improved by real-time monitoring of critical process parameters (CPP) that affect critical quality attributes (CQA). To achieve this aim, soft sensing combined with predictive modeling is an important strategy that can be used for optimizing the upstream process of rAAV production by monitoring critical process variables in real time. However, the development of soft sensors for rAAV production as a fast and low-cost monitoring approach is not an easy task. This review article describes four challenges and critically discusses the possible solutions that can enable the application of soft sensors for rAAV production monitoring. The challenges from a data scientist's perspective are (i) a predictor variable (soft-sensor inputs) set without AAV viral titer, (ii) multi-step forecasting, (iii) multiple process phases, and (iv) soft-sensor development composed of the mechanistic model.

A microworld simulation of dynamic cognition as a test of executive function

ABSTRACT Introduction The lack of consensus regarding the nature or composition of executive functioning (EF) has led to a proliferation of executive tasks to assess the concept. Many do agree however that the theoretical concept of EF is a holistic one, leading us to consider whether it would be beneficial to assess EF in a more holistic manner. We explore how well a computerized simulation of dynamic cognition – that reproduces the context of real-world complex decision-making – can predict performance on nine classical neuropsychological tasks of EF. Methods A sample of 121 participants completed all tasks, and canonical correlations were used to assess the nine tasks as predictors of the three simulation performance metrics to evaluate the multivariate-shared relationship between the two variable sets: executive functions and dynamic cognition. Results Results show that a substantial amount of variance in two indices of dynamic cognition can be explained by a linear combination of three key types of neuropsychological tasks (planning, inhibition, working memory), with a larger contribution from the planning tasks. Conclusion Our findings suggest that tasks of dynamic cognition could augment traditional, separate tests of EF, offering benefits in terms of parsimony, ecological validity, sensitivity, and computerized delivery.

Hybrid Gray Wolf Optimization–Proportional Integral Based Speed Controllers for Brush-Less DC Motor

For Brush-less DC motors to function better under various operating settings, such as constant load situations, variable loading situations, and variable set speed situations, speed controller design is essential. Conventional controllers including proportional integral controllers, frequently fall short of efficiency expectations and this is mostly because the characteristics of a Brush-less DC motor drive exhibit non linearity. This work proposes a hybrid gray wolf optimization and proportional integral controller for management of the speed in Brush-less DC motors to address this issue. For constant load conditions, varying load situations and varying set speed situations, the proposed controller’s efficiency is evaluated and contrasted with that of PID controller, PSO-PI controller, and ANFIS. In this study, two PI controller are used to get the more stability of the system based on tuning of their coefficients with meta heuristic method. The simulation findings show that Hybrid GWO-PI-based controllers are in every way superior to other controllers under consideration. In this study, four case studies are presented, and the best-case study was obtained 0.18619, 0.01928, 0.00030, and 0.01233 for RMSE, IAE, ITAE, and ISE respectively.

Novel variable precision fuzzy rough sets and three-way decision model with three strategies

Variable precision (fuzzy) rough sets are interesting generalizations of Pawlak rough sets and can handle uncertain and imprecise information well due to their error tolerance capability. The comparable property (CP for short), i.e., the lower approximation is included in the upper approximation, is fundamental in Pawlak rough sets since many theories and applications rely on this property. However, the CP is lost in many existing variable precision (fuzzy) rough sets. Therefore, a novel variable precision fuzzy rough set model with CP is proposed and an associated three-way decision model is developed. Firstly, a pair of variable precision fuzzy upper and lower approximation operators are defined and studied through fuzzy implication and co-implication operators. We show that this pair of approximation operators satisfy various properties (including CP) that the existing variable precision fuzzy approximation operators do not possess. Secondly, new methods for constructing loss functions and conditional probabilities are given by using CP, and then a new three-way decision model with three strategies (optimistic, pessimistic, and compromise strategies) is established. Finally, an explanatory example is provided to examine validity, stability and sensitivity of the proposed three-way decision model.

Renin-Angiotensin System Antagonism Protects the Diabetic Heart from Ischemia/Reperfusion Injury in Variable Hyperglycemia Duration Settings by a Glucose Transporter Type 4-Mediated Pathway.

Diabetes mellitus (DM) is a risk factor for cardiovascular diseases, specifically, the ischemic heart diseases (IHD). The renin-angiotensin system (RAS) affects the heart directly and indirectly. However, its role in the protection of the heart against I/R injury is not completely understood. The aim of the current study was to evaluate the efficacy of the angiotensin-converting enzyme (ACE) inhibitor and Angiotensin II receptor (AT1R) blocker or a combination thereof in protection of the heart from I/R injury. Hearts isolated from adult male Wistar rats (n = 8) were subjected to high glucose levels; acute hyperglycemia or streptozotocin (STZ)-induced diabetes were used in this study. Hearts were subjected to I/R injury, treated with Captopril, an ACE inhibitor; Losartan, an AT1R antagonist; or a combination thereof. Hemodynamics data were measured using a suitable software for that purpose. Additionally, infarct size was evaluated using 2,3,5-Triphenyltetrazolium chloride (TTC) staining. The levels of apoptosis markers (caspase-3 and -8), antioxidant enzymes, superoxide dismutase (SOD) and catalase (CAT), nitric oxide synthase (eNOS), and glucose transporter type 4 (GLUT-4) protein levels were evaluated by Western blotting. Pro-inflammatory and anti-inflammatory cytokines levels were evaluated by enzyme-linked immunosorbent assay (ELISA). Captopril and Losartan alone or in combination abolished the effect of I/R injury in hearts subjected to acute hyperglycemia or STZ-induced diabetes. There was a significant (p < 0.05) recovery in hemodynamics, infarct size, and apoptosis markers following the treatment with Captopril, Losartan, or their combination. Treatment with Captopril, Losartan, or their combination significantly (p < 0.05) reduced pro-inflammatory cytokines and increased GLUT-4 protein levels. The blockade of the RAS system protected the diabetic heart from I/R injury. This protection followed a pathway that utilizes GLUT-4 to decrease the apoptosis markers, pro-inflammatory cytokines, and to increase the anti-inflammatory cytokines. This protection seems to employ a pathway which is not involving ERK1/2 and eNOS.

Addressing heterogeneous populations in latent variable settings through robust estimation.

Individuals routinely differ in how they present with psychiatric illnesses and in how they respond to treatment. This heterogeneity, when overlooked in data analysis, can lead to misspecified models and distorted inferences. While several methods exist to handle various forms of heterogeneity in latent variable models, their implementation in applied research requires additional layers of model crafting, which might be a reason for their underutilization. In response, we present a robust estimation approach based on the expectation-maximization (EM) algorithm. Our method makes minor adjustments to EM to enable automatic detection of population heterogeneity and to recognize individuals who are inadequately explained by the assumed model. Each individual is associated with a probability that reflects how likely their data were to have been generated from the assumed model. The individual-level probabilities are simultaneously estimated and used to weight each individual's contribution in parameter estimation. We examine the utility of our approach for Gaussian mixture models and linear factor models through several simulation studies, drawing contrasts with the EM algorithm. We demonstrate that our method yields inferences more robust to population heterogeneity or other model misspecifications than EM does. We hope that the proposed approach can be incorporated into the model-building process to improve population-level estimates and to shed light on subsets of the population that demand further attention. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Environmental quality or behavioural cognitions? Identifying variables that can be used to classify intended civic engagement against transportation noise exposure

The European Environmental Noise Directive opens the door for the public to participate in the development and review of noise action plans. Regarding health inequities related to environmental noise exposure, we aimed to identify determinants that influence residents' civic engagement against transportation noise exposure. We defined four variable groups representing either environmental quality with indicators of (1) noise exposure and (2) environmental resources or behavioural cognitions with indicators related to (3) civic engagement and (4) generalised emotions and cognitions. We used data collected from 3,743 participants in the population-based KORA study in the Augsburg region, Germany, and employed Conditional Inference Trees (CIT) to identify variable sets within these four groups that are most likely to classify civic engagement intentions. A final CIT model encompassed all variables. Participants' social characteristics were included in each of our CIT models. Results suggest interactions between environmental quality (related to sound/atmosphere in the sleeping room) and income and between income and emotions (positive affectivity) with respect to engagement intentions. When considered simultaneously, engagement-specific cognitions appeared as more relevant for engagement intentions than environmental quality. This finding indicates that noise action planning should take behavioural cognitions into account when designing participation processes.

Digital Breast Tomosynthesis: A Decade of Practice in Review

Digital breast tomosynthesis (DBT), introduced in 2011, was thought to be a further evolution of full-field digital mammography (DM). Assessing DBT presents unique challenges. The widely variable settings in which DBT has been applied affect outcomes. In initial studies comparing DM-DBT with DM, outcomes for cancer detection rates (CDRs) and recall rates have been mixed. CDR improves most in biennial screening settings, with little or no improvement in annual screening. Recall rates improve most where rates are highest; no improvement is seen in European prospective trials. Adoption of synthesized mammography (SM), derived from the tomosynthesis acquisition and intended to avoid the DM exposure, has been slow because of inferior image quality. Despite this, SM-DBT has shown equivalent outcomes measures to DM-DBT. An important exception is the To-Be randomized controlled trial, which showed that SM-DBT was equivalent to DM in CDR, not better. To date, interval cancer rate, a surrogate for mortality reduction, has not been shown to be improved by DBT. We may have reached the limit of morphological assessment in x-ray technique. Its use may evolve with advancements in technology as use of contrast agents expands, algorithms for SM progress, and tomosynthesis-guided biopsy proliferates. Our expectations of the contributions of DBT will evolve as well.