Types Of Functions Research Articles

Hierarchical communities in the larval Drosophila connectome: Links to cellular annotations and network topology

One of the longstanding aims of network neuroscience is to link a connectome's topological properties-i.e., features defined from connectivity alone-with an organism's neurobiology. One approach for doing so is to compare connectome properties with annotational maps. This type of analysis is popular at the meso-/macroscale, but is less common at the nano-scale, owing to a paucity of neuron-level connectome data. However, recent methodological advances have made possible the reconstruction of whole-brain connectomes at single-neuron resolution for a select set of organisms. These include the fruit fly, Drosophila melanogaster, and its developing larvae. In addition to fine-scale descriptions of connectivity, these datasets are accompanied by rich annotations. Here, we use a variant of the stochastic blockmodel to detect multilevel communities in the larval Drosophila connectome. We find that communities partition neurons based on function and cell type and that most interact assortatively, reflecting the principle of functional segregation. However, a small number of communities interact nonassortatively, forming form a "rich-club" of interneurons that receive sensory/ascending inputs and deliver outputs along descending pathways. Next, we investigate the role of community structure in shaping communication patterns. We find that polysynaptic signaling follows specific trajectories across modular hierarchies, with interneurons playing a key role in mediating communication routes between modules and hierarchical scales. Our work suggests a relationship between system-level architecture and the biological function and classification of individual neurons. We envision our study as an important step toward bridging the gap between complex systems and neurobiological lines of investigation in brain sciences.

6G in medical robotics: development of network allocation strategies for a telerobotic examination system.

Healthcare systems around the world are increasingly facing severe challenges due to problems such as staff shortage, changing demographics and the reliance on an often strongly human-dependent environment. One approach aiming to address these issues is the development of new telemedicine applications. The currently researched network standard 6G promises to deliver many new features which could be beneficial to leverage the full potential of emerging telemedical solutions and overcome the limitations of current network standards. We developed a telerobotic examination system with a distributed robot control infrastructure to investigate the benefits and challenges of distributed computing scenarios, such as fog computing, in medical applications. We investigate different software configurations for which we characterize the network traffic and computational loads and subsequently establish network allocation strategies for different types of modular application functions (MAFs). The results indicate a high variability in the usage profiles of these MAFs, both in terms of computational load and networking behavior, which in turn allows the development of allocation strategies for different types of MAFs according to their requirements. Furthermore, the results provide a strong basis for further exploration of distributed computing scenarios in medical robotics. This work lays the foundation for the development of medical robotic applications using 6G network architectures and distributed computing scenarios, such as fog computing. In the future, we plan to investigate the capability to dynamically shift MAFs within the network based on current situational demand, which could help to further optimize the performance of network-based medical applications and play a role in addressing the increasingly critical challenges in healthcare.

Unveiling the Roles of Immune Function and Inflammation in the Associations Between Dietary Patterns and Incident Type 2 Diabetes

Objective To investigate the associations between data-driven dietary patterns, immune function, and incident type 2 diabetes (T2D) and the mediating effects of immune function. Methods This study included 375,665 participants without diabetes at baseline in the UK Biobank study. Dietary patterns were derived through principal component analysis of food frequency questionnaire data. Immune function was assessed using 14 individual inflammatory markers and an integrated low-grade inflammation score (INFLA-score). Cox proportional hazard models were used to estimate the associations of dietary patterns or immune function with incident T2D. Linear regressions were used to estimate the associations of dietary patterns with immune function. Mediating effects of immune function were quantified. Results During a median 14.6-year follow-up, 13,932 participants developed T2D. Four dietary patterns were identified: prudent diet (high in whole grains, vegetables, fruits, fish), wheat/dairy/eggs restrictive diet (limiting these foods), meat-based diet (high in red/processed meat, salt), and full-cream dairy diet (preference for full cream milk or dairy products). The prudent diet was negatively (HRQ4 vs Q1, 0.69 [95% CI, 0.65–0.72]), while the wheat/dairy/eggs restrictive diet (HRQ4 vs Q1, 1.08 [95% CI, 1.03–1.13]), meat-based diet (HRQ4 vs Q1, 1.12 [95% CI, 1.06–1.17]), and full-cream dairy diet (HRQ4 vs Q1, 1.08 [95% CI, 1.03–1.12]) were positively associated with incident T2D (all p for trend ≤0.04). The prudent diet was negatively and the full-cream dairy diet was positively associated with most inflammatory markers. Most inflammatory markers, especially INFLA-score (HR, 1.18 [95% CI, 1.16–1.20]), were positively associated with incident T2D. INFLA-score mediated 13% of the association with incident T2D for the prudent diet and 34% for the full-cream dairy diet. Conclusions This study identified four distinct dietary patterns and a range of inflammatory markers associated with incident T2D. A notable proportion of the associations between dietary patterns and T2D was mediated by immune function.

Sharp maximal function estimates for linear and multilinear pseudo-differential operators

In this paper, we study pointwise estimates for linear and multilinear pseudo-differential operators with exotic symbols in terms of the Fefferman-Stein sharp maximal function and Hardy-Littlewood type maximal function. Especially in the multilinear case, we use a multi-sublinear variant of the classical Hardy-Littlewood maximal function introduced by Lerner, Ombrosi, Pérez, Torres, and Trujillo-González [16], which provides more elaborate and natural weighted estimates in the multilinear setting.

Non-differentiable second-order symmetric multiobjective fractional variational programming with cones constraints

In this article, we build a pair non-differentiable second-order symmetric multiobjective fractional variational programming models with cone constraints, where each objective function component has a support function for a compact convex set. The (C, ρ, θ)-convexity/(C, ρ, θ)-pseudo-convexity/(C, ρ, θ)-quasiconvexity functions are defined and also, constructed concrete numerical examples for existing such type of functions. The duality results are established by using these aforesaid assumptions.

Different types of continuous functions and their applications

Different types of continuous functions and their applications

The differential impact of early graft dysfunction in kidney donation after brain death and after circulatory death: Insights from the Dutch National Transplant Registry

Kidneys donated after circulatory death (DCD) perform similarly to kidneys donated after brain death (DBD). However, the respective incidences of delayed graft function (DGF) differ. This questions the donor type-specific impact of early graft function on long-term outcomes. Using competing risk and Cox-regression analysis, we compared death-censored graft loss between types of early graft function: DGF (temporary dialysis dependency started within 7 days after transplantation), slow graft function (3-day plasma creatinine decline less than 10% per day), and immediate graft function. In 1061 DBD and 1605 DCD graft recipients (January 2014 until January 2023), graft survival was similar. DGF was associated with death-censored graft loss in DBD and DCD (adjusted hazard ratios: DGF in DBD: 1.79 [1.04-2.91], P = .027, DGF in DCD: 1.84 [1.18-2.87], P = .008; Reference: no DGF). Slow graft function was associated with death-censored graft loss in DBD, but not significantly in DCD (adjusted hazard ratios DBD: 2.82 (1.34-5.93), P = .007, and DCD: 1.54 (0.72-3.35), P = .262; Reference: immediate graft function). Early graft dysfunction has a differential impact on graft outcome in DBD and DCD. The differences between DBD and DCD should be accounted for in research and the clinic.

The association between ‘weekend warrior’, regular exercise, and cognitive function in elderly individuals with and without depressive symptoms: A cross-sectional analysis from NHANES 2011–2014

BackgroundWhile previous studies have suggested that both ‘Weekend Warrior’ (WW) and Regular Exercise (RE) patterns confer health benefits, the relationship between different types of physical activity (PA) and cognitive function (CF) in elderly individuals with and without depressive symptoms remains unclear. MethodsOur study leveraged the NHANES 2011–2014 dataset, focusing on older adults. We explore the relationship between PA and CF, stratifying by depressive status. Our statistical approach included multivariable regression analysis to identify relationships between PA levels and cognitive outcomes, along with advanced techniques such as smoothed curve fitting and threshold effect analysis to examine potential nonlinear associations and identify optimal PA pattern for cognitive health. ResultsAnalysis revealed a positive correlation between PA time and CF across all participants (β-depressive symptoms = 0.03, 95 % CI: 0.01–0.05; β-non-depressive symptoms = 0.01, 95%CI: 0.00–0.02). When comparing against the inactive, non-depressed participants partaking in WW showed improved cognitive scores (β-WW = 0.22, 95 % CI: 0.05–0.39), similar to those engaging in RE (β-RE = 0.15, 95 % CI: 0.09–0.21). However, among the depressed participants, significant cognitive improvements were observed in the RE (β-RE = 0.15, 95 % CI: 0.04–0.25), with the WW showing less definitive results (β-WW = 0.22, 95 % CI: −0.02-0.47). LimitationThe cross-sectional nature limits causal inferences. ConclusionOur findings affirm the potential role of PA in enhancing CF among older subjects without depressive symptoms. However, only RE was associated with improved CF in those with depressive symptoms. These results are critical for crafting personalized PA guidelines to enhance cognitive health in the aging population.

Optimized Selective Assembly using Hungarian Algorithm

Assembly of discrete parts guided by hardware design specification constitutes the final phase in product manufacturing. In the course of mass production of components, mating parts with geometric or dimensional deviation from their intended design can be made acceptable by the identification of suitable pairs after analysing design fit and tolerance limits. By transforming this application-specific problem into a unified mathematical model, an optimal solution can be achieved that minimizes the rejection of non-conforming fabricated parts. Regardless of the type and range of a design fit, the problem can be mapped into a matrix using a ranking function defined by the user. The ranking function is modifiable as per the user requirements and may vary based on the selection criteria for an assembly. Based on the type of ranking function used, the tabulated matrix is solved using the Hungarian minimization/maximization algorithm, which is a powerful combinatorial optimization algorithm that solves the classical assignment problem in mathematics. This approach ensures maximum number of suiting pairs as well as nominal suiting of parts with each other resulting in high-quality products and maximum utilization of fabricated resources.

Evolutionary Optimization of Deep Learning Models: Enhance Performance through Hyperparameter and Structural Tuning with Evolutionary Algorithms

Evolutionary optimization is a strong way to improve the performance of deep learning models by using evolutionary algorithms (EAs) to fine-tune both structure elements and hyperparameters. This method is based on biological evolution and uses processes like crossing, mutation, and selection to make model setups better over time. When the model and hyperparameter space get more complicated, it can be hard to use traditional methods for adjusting hyperparameters, like grid search or random search, because they need a lot of computing power and don't work as well. Evolutionary algorithms, on the other hand, can quickly move through these high-dimensional spaces and find the best or almost best designs that make the model work better. This research looks into how evolutionary algorithms can be used to improve deep learning models. It focuses on two main areas: setting hyperparameters and making changes to the models' structures. Hyperparameters, like learning rate, batch size, and the number of layers, have a big effect on how the model learns and how accurate it is in the end. The evolutionary method encodes these hyperparameters into chromosomes and then iteratively evolves a population of models, picking the best ones based on a fitness function that usually shows how accurate or lost the models are. Random changes are made by mutations, and traits from high-performing models are combined in crosses. This creates variety and stops things from convergent too soon. Changing the model's design, such as the amount of neurons in each layer, the type of activation functions, and the patterns of connections, is called structural tweaking. These building blocks can be changed on the fly by evolutionary algorithms, which can find new, efficient designs that human creators might miss. This feature is especially helpful when making complicated networks like convolutional neural networks (CNNs) or recurrent neural networks (RNNs), where the best structure isn't always clear. Results from real life show that evolutionary optimization not only makes models more accurate, but it also makes them more stable and able to generalize. This method gives a scalable and adaptable strategy for optimizing deep learning models. This makes it a very useful tool for students and professionals who want to get the best performance in a wide range of situations.

Analysis of Allocative Efficiency of Watermelon Farming in Kapuas Regency, Central Kalimantan Province

The objective of this research is to analyze the costs, revenues, and profits of watermelon farming, to examine the impact of the use of production factors in watermelon farming, and to evaluate the allocative efficiency level of watermelon farming in Kapuas Regency, Central Kalimantan Province. The population of this research consists of 113 farmers, from which 55 were selected using simple random sampling techniques. The data processing method used to analyze the costs, revenues, and profits of watermelon farming includes using a multiple linear regression model, specifically the Cobb-Douglas function type. Additionally, to analyze the allocative efficiency of production factors used by farmers in watermelon farming, the comparison of a production factor’s Marginal Product Value (MPVx) with the price of the production factor (Px) is set to be equal to 1. Based on the results of the research and data processing, the average revenue obtained from watermelon farming in Kapuas Regency is IDR 75,619,181.82 per farm, and the average profit earned by watermelon farmers is IDR 45,541,259 per farm. The statistical test results of the Cobb-Douglas production function show that collectively (simultaneously), the production factors (land area, number of seeds, organic fertilizer, inorganic fertilizer, pesticides, and labor) significantly affect watermelon production. However, the partial test results indicate that the production factors of land area, seeds, organic fertilizer, inorganic fertilizer, and labor significantly affect watermelon production, except for the production factor of pesticides. The statistical test results for allocative efficiency (price efficiency) show that three production factors, namely organic fertilizer, labor, and pesticides, are used efficiently, meaning the marginal product equals the price of the production factor. The use of land area, seeds, and inorganic fertilizer is not yet efficient and needs to be increased to reach an efficient point.

A novel behavioral penalty function for interval goal programming with post-optimality analysis

Goal programming (GP) is a multi-objective extension of linear programming. Interval GP (IGP) is one of the earliest methods to expand the range of preferred structures in GP. The decision maker’s (DM’s) utility or preference in IGP is investigated by incorporating a widening range of underlying utility functions, commonly known as penalty functions. The basic idea of these functions is that undesirable deviations from the target levels of the goals are penalized regarding a constant or variable penalty value. The main concern with introducing the penalty functions is providing a wide range of a priori preference structures. Yet, the evaluation of how undesirable deviations are penalized based on DM’s behavioral preferences is not sufficiently addressed in the penalty function types developed in the GP literature. In real-world scenarios involving risk, the achievement levels of decision-making attributes are typically associated with the behavior of the DM. In such scenarios, the DM’s unavoidable attitude toward risk should be integrated into the decision-making process. We introduce the concept of behavioral penalty functions into the IGP approach, incorporating a risk aversion parameter tailored to the nature of each attribute to address this gap. This concept offers an innovative framework for capturing the preferences of the DMs and their various attitudes toward risk within the IGP approach. In this paper, we first introduce the concept of behavioral penalty functions. Next, we develop a behavioral utility-based IGP model. Finally, we present a portfolio selection case study to demonstrate the applicability and efficacy of the proposed procedure, followed by a post-optimality analysis and comparisons with other GP approaches.

Comparison of Extremely Randomized Survival Trees and Random Survival Forests: A Simulation Study

Abstract. Purpose: This simulation study investigates the Extremely Randomized Survival Trees (EST) model, a machine learning technique expected to handle survival analysis, particularly in large survival datasets, effectively. The study compares the performance of the EST model with that of the Random Survival Forest (RSF) model, focusing on the C-index value to determine which model performs better. Methods: The analysis begins with the generation of 540 simulated datasets, created by combining three levels of sample sizes, two levels of censoring proportions, three types of hazard functions, and 30 repetitions for each scenario. The simulation data were split into 80% training and 20% testing data. The training data were used to build the EST and RSF models, while the test data were used to evaluate their performance. The model with the highest C-index value was deemed the best performer, as a higher C-index indicates superior model performance. Result: The results indicate that the sample size, type of hazard function, and the method used influence that model performance. The EST model significantly outperformed the RSF model when the sample size was large, though no significant difference was observed when the sample size was small or medium. Additionally, the EST model consistently demonstrated faster computation times across all simulation scenarios. Novelty: This study provides a pioneering exploration into applying decision tree algorithms, specifically EST and RSF, in survival analysis. While these methods have been extensively studied in regression and classification contexts, their application in survival analysis remains relatively unexplored.

Retarded and advanced Green functions for first-order differential equations

Retarded and advanced Green functions are examined through the lens of linear first-order ordinary differential equations. They are constructed for a finite interval with emphasis on the imposition of homogeneous Dirichlet boundary conditions at the interval ends. It is shown that both types of Green functions exhibit the property of facilitating solutions.

Generalized Fejér monotone sequences and their finitary content

We provide quantitative and abstract strong convergence results for sequences from a compact metric space satisfying a certain form of generalized Fejér monotonicity where (1) the metric can be replaced by a much more general type of function measuring distances (including, in particular, certain Bregman distances), (2) full Fejér monotonicity is relaxed to a partial variant and (3) the distance functions are allowed to vary along the iteration. For such sequences, the paper provides explicit and effective rates of metastability and even rates of convergence, the latter under a regularity assumption that generalizes the notion of metric regularity introduced by Kohlenbach, López-Acedo and Nicolae, itself an abstract generalization of many regularity notions from the literature. In the second part of the paper, we apply the abstract quantitative results established in the first part to two algorithms: one algorithm for approximating zeros of maximally monotone and maximally ρ-comonotone operators in Hilbert spaces (in the sense of Combettes and Pennanen as well as Bauschke, Moursi and Wang) that incorporates inertia terms every other term and another algorithm for approximating zeros of monotone operators in Banach spaces (in the sense of Browder) that is only Fejér monotone w.r.t. a certain Bregman distance.

A Benchmarking Method to Rank the Performance of Physics-Based Earthquake Simulations

Abstract Physics-based earthquake simulators are an increasingly popular modeling tool in earthquake forecasting for seismic hazard as well as fault rupture behavior studies. Their popularity comes from their ability to overcome completeness limitations of real catalogs, and also because they allow reproducing complex fault rupture and interaction patterns via modeling the physical processes involved in earthquake nucleation and propagation. One important challenge of these models revolves around selecting the physical input parameters that will yield the better similarity to earthquake relationships observed in nature, for instance, the frictional parameters of the rate-and-state law—a and b—or the initial normal and shear stresses. Because of the scarcity of empirical data, such input parameters are often selected by trial–error exploration and predominantly manual model performance analyses, which can overall be time consuming. We present a new benchmarking approach to analyze and rank the relative performance of simultaneous earthquake simulation catalogs by quantitatively scoring their combined fit to three reference function types: (1) earthquake-scaling relationships, (2) the shape of the magnitude–frequency distributions, and (3) the rates of the surface ruptures from paleoseismology or paleoearthquake occurrences. The approach provides an effective and potentially more efficient approximation to easily identify the models and input parameter combinations that fit more closely to earthquake relations and behavior. The approach also facilitates the exhaustive analysis of many input parameter combinations, identifying systematic correlations between parameters and model outputs that can potentially improve the overall understanding of the physics-based models. Finally, we demonstrate how the method results agree with the published findings in other earthquake simulation evaluations, a fact that reinforces its overall usefulness. The model ranking outputs can be useful for subsequent analyses, particularly in seismic hazard applications, such as the selection of appropriate earthquake occurrence rate models and their weighting for a logic tree.

Simple closed-form estimation of a binary latent variable model

Abstract This paper develops a closed-form nonparametric estimator of the conditional distribution function for a binary outcome variable given an unobserved latent variable. This type of function is commonly used in models that involve measurement error and dynamic models with agent-specific unobserved heterogeneity. This paper presents a consistent extremum sieve estimator with the following advantages: i) it has a closed-form expression for all the sieve coefficients; ii) it is computationally straightforward, equivalent to computing eigenvalues and eigenvectors of matrices without the use of iterative optimization algorithms. While as flexible as the sieve maximum likelihood estimator (MLE) previously proposed for this model, our estimator proves computationally simpler. The finite sample properties of the estimator are investigated through a Monte Carlo study, and the developed estimator is applied to a probit model to assess the targeting performance of a social welfare program.

Static analysis driven enhancements for comprehension in machine learning notebooks

Jupyter notebooks have emerged as the predominant tool for data scientists to develop and share machine learning solutions, primarily using Python as the programming language. Despite their widespread adoption, a significant fraction of these notebooks, when shared on public repositories, suffer from insufficient documentation and a lack of coherent narrative. Such shortcomings compromise the readability and understandability of the notebook. Addressing this shortcoming, this paper introduces HeaderGen, a tool-based approach that automatically augments code cells in these notebooks with descriptive markdown headers, derived from a predefined taxonomy of machine learning operations. Additionally, it systematically classifies and displays function calls in line with this taxonomy. The mechanism that powers HeaderGen is an enhanced call graph analysis technique, building upon the foundational analysis available in PyCG. To improve precision, HeaderGen extends PyCG’s analysis with return-type resolution of external function calls, type inference, and flow-sensitivity. Furthermore, leveraging type information, HeaderGen employs pattern matching techniques on the code syntax to annotate code cells. We conducted an empirical evaluation on 15 real-world Jupyter notebooks sourced from Kaggle. The results indicate a high accuracy in call graph analysis, with precision at 95.6% and recall at 95.3%. The header generation has a precision of 85.7% and a recall rate of 92.8% with regard to headers created manually by experts. A user study corroborated the practical utility of HeaderGen, revealing that users found HeaderGen useful in tasks related to comprehension and navigation. To further evaluate the type inference capability of static analysis tools, we introduce TypeEvalPy, a framework for evaluating type inference tools for Python with an in-built micro-benchmark containing 154 code snippets and 845 type annotations in the ground truth. Our comparative analysis on four tools revealed that HeaderGen outperforms other tools in exact matches with the ground truth.

Gaussian process regression to predict dryout incipience quality of saturated flow boiling in mini/micro-channels

In the present study, a Gaussian process regression (GPR) model is developed to predict the dryout incipience quality for flow boiling in mini / micro – channels based on a consolidated database obtained from Purdue University Boiling and Two – Phase Flow Laboratory (PU – BTPFL) consisting of 997 points from 26 sources. The database includes 13 different working fluids over a wide range of operating conditions: hydraulic diameter (0.51 – 6.0 mm), mass velocities (29 – 2303 kg/ m2 s), liquid – only Reynolds number (125 – 53,770), boiling number (0.31 – 44.3 × 10-4), and reduced pressure (0.005 – 0.78). The inputs to the model were liquid – only Weber number (Wefo), reduced pressure (PR), boiling number (Bo), heated to frictional perimeter ratio(PH/PF), capillary number (Ca) and density ratio (ρg/ρf), and the output was dryout incipience quality. The database was randomly divided into training data to learn GPR kernel (covariance) parameters using maximum likelihood estimation, and test data to evaluate the prediction accuracy of the outputs based on mean absolute error (MAE). Six – different types of covariance functions were tested, and GPR model with automatic relevance detection (ARD) rational quadratic covariance function showed the best overall performance. A performance comparison of approved GPR model was made with an existing highly reliable universal correlation to predict dryout incipience quality for mini / micro – channels. Results show that the developed GPR model exhibits superior generalization ability with an overall MAE of 6.03 %, and a significant reduction of 51.76 % in MAE compared with the universal correlation. Overall, the GPR model was found to be a data efficient machine learning technique for predicting dryout incipience quality for flow boiling in mini / micro – channels based on a consolidated database.

Construction of Ecological Security Patterns Incorporating Multiple Types of Ecological Service Functions for Developed Coastal Regions: A Case Study in Jinjiang Watershed, China

Construction of Ecological Security Patterns Incorporating Multiple Types of Ecological Service Functions for Developed Coastal Regions: A Case Study in Jinjiang Watershed, China