Set Function Research Articles

Acceptability and utility of a broad test battery to evaluate physical functioning and frailty in community-based settings for people experiencing homelessness and addiction–Findings from the LEAP trials

BackgroundPhysical function deficits and frailty are commonly reported in people experiencing homelessness and addiction. Typically, geriatric, or general physical performance measures are employed to evaluate these constructs. Objectives and settingWe aimed to investigate the feasibility of a broad test battery to evaluate physical functioning and frailty in a non-geriatric community-based population of people experiencing homelessness and addiction. Design and methodsPhysical function and the presence of frailty was evaluated using combined data from two linked low-threshold exercise pre-post intervention studies (LEAP I and II). ParticipantsSeventy-four participants attending an open-access day-service centre (18 to 65 years) were evaluated. Participants with acute confusion or behavioural issues, or major medical condition precluding safe participation were excluded. InterventionA broad test battery was performed which evaluated physical functioning and frailty. Main outcome measuresOutcome measures included were the Short Physical Performance Battery, Single Leg Stance, Chair Stand Test, 10 m and 2 minute Walk Tests, limb circumference, dynamometry, Clinical Frailty Scale, SHARE-Frailty Instrument, numerical pain rating and Short Form-12. ResultsTest completion was 100% for all outcomes except pain rating (81% and 79% for LEAPI and II, respectively), and balance and walking tests (95% and 93% in LEAP II). A ceiling effect was detected for the Short Physical Performance Battery. ConclusionHigh feasibility of the test batteries was demonstrated. An optimised test battery was proposed based on findings. This has important implications for clinicians working in inclusion health settings. Clinical Trial Registration NumberClinical Trials.gov: NCT05700305. Contribution of the Paper•This paper proposes a suitable test battery to enable clinicians to evaluate physical functioning and frailty in people experiencing homelessness and addiction challenges in community settings.•There is a paucity of research evaluating outcome measures for this population and no core outcome set has been reported.•This test battery would allow the effect of treatment and targeted interventions to be evaluated in this marginalised population.

Strictly Conservative Neural Implicits

AbstractWe describe a method to convert 3D shapes into neural implicit form such that the shape is approximated in a guaranteed conservative manner. This means the input shape is strictly contained inside the neural implicit or, alternatively, vice versa. Such conservative approximations are of interest in a variety of applications, including collision detection, occlusion culling, or intersection testing. Our approach is the first to guarantee conservativeness in this context of neural implicits. We support input given as mesh, voxel set, or implicit function. Adaptive affine arithmetic is employed in the neural network fitting process, enabling the reasoning over infinite sets of points despite using a finite set of training data. Combined with an interior point style optimization approach this yields the desired guarantee.

Detecting muscle fatigue among community-dwelling senior adults with shape features of the probability density function of sEMG

BackgroundPhysical exercise is an important method for both the physical and mental health of the senior population. However, excessive exertion can lead to increased risks of falls, severe injuries, and diminished quality of life. Therefore, simple and effective methods for fatigue monitoring during exercise are highly desirable, particularly in community settings. The purpose of this study was to explore the possibility of real-time detection of exercise-induced fatigue using surface Electromyogram (sEMG) features, including the kurtosis and skewness of the Probability Density Function (PDF) in the community settings to solve the issues of low sensitivity and high computational complexity of commonly used sEMG features.MethodssEMG signals from six forearm muscles were recorded during hand grip tasks at 20% maximal voluntary contraction (MVC) task-to-failure contractions from 30 healthy community-dwelling elders at their respective community centers. PDF shape features of the sEMG, namely kurtosis and skewness, were computed from 25 s of non-fatigue stable phase and 25 s of fatigue data for comparison. Statistical tests were conducted to compare and test for the significance of these features. We further proposed a novel fatigue indicator, Temporal-Mean-Kurtosis (TMK) of channel-averaged kurtosis, to detect fatigue with relatively low computational complexity and adequate sensitivity in community settings. ANOVA and post-hoc analyses were performed to examine the performance of TMK.ResultsStatistically significant differences were found between the non-fatigue period and the fatigue period for both kurtosis and skewness, with increasing values when approaching fatigue. TMK was shown to be sensitive in detecting fatigue with respect to time with lower computational complexity than the Sample Entropy.ConclusionThis study investigated PDF shape features of sEMG signals during a handgrip exercise to identify muscle fatigue in older adults in community experiments. Results revealed significant changes in kurtosis upon fatigue, indicating that PDF shape features were suitable convenient detectors of muscle fatigue in community experiments. The proposed indicator, TMK, showed potential sensitivity in tracking muscle fatigue over time in community-based settings with limited computational complexity, highlighting the promise of sEMG’s PDF features in detecting muscle fatigue among the elderly.

Combinatorial reduction of set functions and matroid permutations through minor invertible product assignment

We introduce an algebraic model, based on the determinantal expansion of the product of two matrices, to test combinatorial reductions of set functions. Each term of the determinantal expansion is deformed through a monomial factor in d indeterminates, whose exponents define a Zd-valued set function. By combining the Grassmann-Plücker relations for the two matrices, we derive a family of sparse polynomials, whose factorisation properties in a Laurent polynomial ring are studied and related to information-theoretic notions.Under a given genericity condition, we prove the equivalence between combinatorial reductions and determinantal expansions with invertible minor products; specifically, a deformation returns a determinantal expansion if and only if it is induced by a diagonal matrix of units in C(t) acting as a kernel in the original determinant expression. This characterisation supports the definition of a new method for checking and recovering combinatorial reductions for matroid permutations.

A genetically encoded actuator boosts L-type calcium channel function in diverse physiological settings.

L-type Ca2+ channels (CaV1.2/1.3) convey influx of calcium ions that orchestrate a bevy of biological responses including muscle contraction, neuronal function, and gene transcription. Deficits in CaV1 function play a vital role in cardiac and neurodevelopmental disorders. Here, we develop a genetically encoded enhancer of CaV1.2/1.3 channels (GeeCL) to manipulate Ca2+ entry in distinct physiological settings. We functionalized a nanobody that targets the CaV complex by attaching a minimal effector domain from an endogenous CaV modulator-leucine-rich repeat containing protein 10 (Lrrc10). In cardiomyocytes, GeeCL selectively increased L-type current amplitude. In neurons in vitro and in vivo, GeeCL augmented excitation-transcription (E-T) coupling. In all, GeeCL represents a powerful strategy to boost CaV1.2/1.3 function and lays the groundwork to illuminate insights on neuronal and cardiac physiology and disease.

Analysis of the Effect of Active Breaks on Executive Function in Educational Settings. A Systematic Review and Meta-Analysis

Analysis of the Effect of Active Breaks on Executive Function in Educational Settings. A Systematic Review and Meta-Analysis

Distribution of microvascular endothelial function in different clinical and non-clinical settings in the united states and china

Abstract Background Despite the fact that microvascular (endothelial) dysfunction is associated with various diseases from cardiovascular to kidney, lung, liver, and other medical conditions, it has not been extensively studied in various clinical and non-clinical settings as blood pressure has. Digital Thermal Flowmetry (DTF) of microvascular endothelial function is a new and automated technique based on monitoring fingertip temperature fall and rebound during reactive hyperemia. Here we report distributions of microvascular function across (1) CVD patients, (2) wellness and internal medicine clinics, (3) college students, and (4) community-based healthy volunteers in China. Methods A total of 7,907 endothelial function test results were collected from various settings. Blood pressure and heart rate were measured before the tests. The tests were conducted using FDA-approved automated VENDYS devices (Endothelix Inc., Palo Alto, CA). Adjusted maximum temperature rebound was reported as Vascular Reactivity Index (VRI) and compared across different settings. Results VRI in CVD patients, wellness and internal medicine clinics, college students, and Chinese volunteers were (1.25±0.34) (1.53±0.5) (1.86±0.5) (1.95±0.44.) respectively P<0.01. Age was weakly correlated with VRI with the equation age=-0.01 VRI+2.01 (r=0.17 p<0.001) Conclusions To our knowledge, this is the largest database of finger-based endothelial function testing. VRI showed distinct distributions across various clinical and non-clinical settings with CVD patients exhibiting the lowest and Chinese healthy volunteers the highest values. The VRI trend mimics the statistically expected risk trend with CVD patients having the highest and Chinese healthy volunteers having the lowest CVD events risk.Figure 1.

Mathematical modeling for the efficiency function of the Retiro small hydroelectric power plant turbine-generator set

ABSTRACT Many authors use an efficiency constant for the turbine-generator set to calculate energy generation in hydroelectric projects. This often does not reflect reality because the efficiency of this system is relative and is related to the potential of the net head height and the flow of turbine discharge. The present work aims to perform a mathematical modeling for an analysis of the efficiency of the Turbine-Generator Small Hydroelectric Power Plant (SHPP) Retiro. To perform this analysis, a model based on a second-degree polynomial function was employed. A multivariate regression technique was applied by the Least Square Method (LSM) to obtain the coefficients of the function. To verify the behavior of the measured and adjusted data, the Determination Coefficient (R2) of Root Mean Square Error (RMSE) algorithm was applied. The RMSE Mean of 0.022 and the RMSE Std of 0.029 suggest that the second-degree polynomial function is more consistent and stable across different training and validation datasets. The R2 value of 0.993 indicates that 99.3% of the variability in the data is explained by the model. The Omnibus, Durbin-Watson, and Jarque-Bera tests were applied to diagnose potential issues related to normality, autocorrelation, and adequacy, thereby validating the accuracy and significance of the model. The Hessian matrix technique was also used to verify the critical points of the function. The critical point corresponding to a water head of 11.47 meters and a turbine flow of 145.1 m3/s presented the highest operational efficiency. This study allows presenting a turbine efficiency function, its Hill Curve and a generator efficiency function with its corresponding Efficiency Curve. For efficiency analysis in several flow, height and power settings. The technique has proved efficient and can be an applicable tool in energy parameter studies for other hydroelectric projects with the same characteristics as SHPP Retiro.

Impact of dual sensory impairment on the risk of incident dementia: a protocol for a systematic review and meta-analysis

IntroductionStrong evidence supports the importance of potentially modifiable risk factors for dementia, with sensory loss, particularly visual and hearing impairment, being prominent among them. While single sensory impairment has been...

Distance-regularized level set inversion of magnetic data

Inversion is one of the key and difficult issues in potential field data processing and interpretation, and high-precision inversion has long been a popular research topic. We develop the distance-regularized level set inversion of the magnetic data (DRLSI-M) method. A double-well function is introduced into the objective function of the level set inversion, and we take advantage of its mathematical properties to minimize the gradient of the level set function so that it reaches a minimum value at points 0 and 1. As a result, the distance-regularized term not only replaces the costly reinitialization process so that the level set function maintains a signed distance property but also stabilizes the evolution of the level set function. When solving for the minimum value of the objective function, we transform the optimization problem into an initial-boundary value problem and solve it with the finite-difference method. The distance-regularized level set inversion method and the reinitialization level set inversion method are tested using three models: a single-level-set double-inclined plate model, a double-level-set double-inclined plate model, and a multiple-level-set three-cuboid model. Through model tests, the soundness of our method is verified and compared with the reinitialization level set inversion method. Our method avoids the limitations such as low efficiency and instability caused by the reinitialization of the level set inversion method. Furthermore, we apply our method for the inversion of aeromagnetic data from the Jinchuan copper-nickel mine. The results are consistent with the known geologic information, thus validating the practicality and effectiveness of DRLSI-M. The distance-regularized level set inversion method provides a theoretical basis for deep mine exploration.

Enhancing Hopfield network performance for pattern retrieval using sparse recovery algorithm and Parzen estimator

An improved pattern recovery approach that integrates the Hopfield neural network (HNN) with the iterative signal reconstruction and Parzen window-based classification is proposed. The HNN is observed as a form of associative memory network, used for various pattern recognition and optimization tasks. However, when the input pattern is highly damaged with a very limited set of available samples, the Hopfield network fails to perform the retrieval. The convex optimization-based gradient descent algorithm is considered for pattern recovery of damaged inputs in order to provide an improved pattern approximation for further processing within the HNN, enabling successful network performance. Additionally, in the case of grayscale images, the Parzen window approach is used to classify the probability density functions (pdfs) of the training set and to choose those being comparable to the pdf of the input pattern, therefore refining the selection of patterns and providing better convergence to the exact retrieval. The theoretical considerations are verified experimentally, showing the high performance of the proposed approach when only 10 % of the pixels are available for binary patterns and 40 % of pixels for grayscale patterns.

Efficient algorithm for stochastic rumor blocking problem in social networks during safety accident period

Rumor sources spread negative information throughout the network, which may cause unbelievable results in real society especially for social safety field. Propagating positive information from several “protector” users is an effective method for rumor blocking once the rumor is detected. Based on the probability of each user being a rumor, “protector” nodes need to be selected in order to prepare for rumor blocking. Given a social network G=(V,E,P,Q), where P(u,v) is the probability that v is activated by u after u is activated, and Q is the weight function on node set V, Qv is the probability that v will be a rumor source. Stochastic Rumor Blocking (SRB) problem is to select k nodes as “protector” such that the expected eventually influenced users by rumor is minimized. SRB will be proved to be NP-hard and the objective function is supermodular. We present a Compound Reverse Influence Set sampling method for estimation of the objective value which can be represented as a compound set function. A randomized greedy algorithm with theoretical analysis will be presented and other two different “protector” selection strategies will be proposed for comparison. Finally, we evaluate our algorithm on real world data sets and do comparison among different strategies.

Unveiling the potential role of virus-encoded polyphosphate kinases in enhancing phosphorus removal in activated sludge systems

While microbial phosphate removal in activated sludge (AS) systems has been extensively studied, the role of viruses in this process remains largely unexplored. In this study, we identified 149 viral auxiliary metabolic genes associated with phosphorus cycling from 2,510 viral contigs (VCs) derived from AS systems. Notably, polyphosphate kinase 1 (ppk1) and polyphosphate kinase 2 (ppk2) genes, which are primarily responsible for phosphate removal, were found in five unclassified VCs. These genes exhibited conserved protein structures and active catalytic sites, indicating a pivotal role of viruses in enhancing phosphorus removal. Phylogenetic analysis demonstrated a close relationship between viral ppk genes and their bacterial counterparts, suggesting the occurrence of horizontal gene transfer. Furthermore, experimental assays validated that viral ppk genes enhanced host phosphate removal capabilities. VCs carrying ppk genes were observed across diverse ecological and geographical contexts, suggesting their potential to bolster host functions in varied environmental and nutrient settings, spanning natural and engineered systems. These findings uncover a previously underappreciated mechanism by which viruses enhance phosphate removal in wastewater treatment plants. Overall, our study highlights the potential for leveraging virus-encoded genes to improve the efficiency of biological phosphorus removal processes, offering new insights into the microbial ecology of AS systems and the role of viruses in biogeochemical cycling.

Design of concatenative complete complementary codes for CCC-CDMA via specific sequences and extended Boolean functions

A complete complementary code (CCC) consists of M sequence sets with size M. The sum of the auto-correlation functions of each sequence set is an impulse function, and the sum of cross-correlation functions of the different sequence sets is equal to zero. Thanks to their excellent correlation, CCCs received extensive use in engineering. In addition, they are strongly connected to orthogonal matrices. In some application scenarios, additional requirements are made for CCCs, such as recently proposed for concatenative CCC (CCCC) division multiple access (CCC-CDMA) technologies. In fact, CCCCs are a special kind of CCCs which requires that each sequence set in CCC be concatenated to form a zero-correlation-zone (ZCZ) sequence set. However, this requirement is challenging, and the literature is thin since there is only one construction in this context. We propose to go beyond the literature through this contribution to reduce the gap between their interest and our limited knowledge of CCCCs. This paper will employ novel methods for designing CCCCs and precisely derive two constructions of these objects. The first is based on perfect cross Z-complementary pair and Hadamard matrices, and the second relies on extended Boolean functions. Specifically, we highlight that optimal and asymptotic optimal CCCCs could be obtained through the proposed constructions. Besides, we shall present a comparison analysis with former structures in the literature and examples to illustrate our main results.

Level-set based shape optimization for plane elastic structures using radial basis functions and Hilbertian descent direction

Structural optimization problems are often associated with the so-called shape functionals depending on a shape through its geometry and the state being a solution of given partial differential equation. In such a framework it is convenient to work with the gradient-like method based on a concept of a shape derivative and level set method. The key idea of level set method is to represent the structural boundary with zero level set of given function (level set function—LSF). Now, changing the shape of a structure under optimization is equivalent to transport the LSF in such a direction that ensures decreasing the value of the objective functional. To this end, we make use of coercive bilinear form taken from the weak formulation of elasticity problem to obtain descent direction at each iteration. This descent direction is a solution of an additional variational problem, involving the bilinear form mentioned above and the volumetric expression of the shape derivative plays the role of a linear form. In this paper, we combine level set method with radial basis functions (RBFs) used to approximate LSF. We focus on the so-called multiquadric RBFs, but other classes of RBFs are also briefly considered. This eventually leads to transformation of partial differential equation (linear transport equation governing the evolution of shapes) to a system of linear ordinary differential equations which admits analytical formula for the solution. We apply our method to compliance minimization of a cantilever problem as well as to total potential energy minimization of a structure with kinematic loading. To run all the numerical experiments, we wrote our own code in Wolfram Mathematica environment.

Shape optimization with level set-based method using a reaction diffusion equation for 2D sound barrier

A level set-based method using a reaction diffusion equation is applied for optimization problems of two dimensional (2D) sound barriers. The level set method is employed to implicitly represent the sound barrier structure, which distinguishes the material and void domains by the value of the level set function. The boundary element method is employed to solve acoustic problems governed by Helmholtz equation. Topological derivatives are computed by the boundary integral equation combined with the adjoint variable method. The distribution of level set function is iteratively updated based on the reaction diffusion equation to find the optimal structure. For the existent floating scatterers in the optimization process and the sharp and narrow parts on the surface of the sound barrier, we propose a filtering algorithm to remove floating scatterers and develop a method to achieve a smooth surface of the sound barrier. The shape optimization of sound barriers is achieved using these techniques, integrating the level set-based topology optimization method. Numerical tests are provided to demonstrate the validity and effectiveness of the proposed methods.

Preventing mass loss in the standard level set method: New insights from variational analyses

For decades, the computational multiphase flow community has grappled with mass loss in the level set method. Numerous solutions have been proposed, from fixing the reinitialization step to combining the level set method with other conservative schemes. However, our work reveals a more fundamental culprit: the smooth Heaviside and delta functions inherent to the standard formulation. Even if reinitialization is done exactly, i.e., the zero contour interface remains stationary, the use of smooth functions lead to violation of mass conservation. We propose a novel approach using variational analysis to incorporate a mass conservation constraint. This introduces a Lagrange multiplier that enforces overall mass balance. Notably, as the delta function sharpens, i.e., approaches the Dirac delta limit, the Lagrange multiplier approaches zero. However, the exact Lagrange multiplier method disrupts the signed distance property of the level set function. This motivates us to develop an approximate version of the Lagrange multiplier that preserves both overall mass and signed distance property of the level set function. Our framework even recovers existing mass-conserving level set methods, revealing some inconsistencies in prior analyses. We extend this approach to three-phase flows for fluid-structure interaction (FSI) simulations. We present variational equations in both immersed and non-immersed forms, demonstrating the convergence of the former formulation to the latter when the body delta function sharpens. Rigorous test problems confirm that the FSI dynamics produced by our simple, easy-to-implement immersed formulation with the approximate Lagrange multiplier method are accurate and match state-of-the-art solvers.

Determination of the plastic J integral of ductile material using the XFEM with only Heaviside function and variable-node elements

In this paper, the extended finite element method (XFEM) with only Heaviside function is proposed for the elastic–plastic fracture mechanics (EPFM) modeling. The proposed method removes tip enrichment functions depending on the polar coordinates at the crack front, and a step function only determined by the level set function is utilized to track the crack front. To alleviate the volumetric locking phenomena caused by the plastic incompressibility, the B-bar method is incorporated into the three dimensional (3D) XFEM program. Therefore, the fully integration scheme can be chosen to ensure the accuracy when addressing large plastic deformation in EPFM analysis. Additionally, the material tangent stiffness matrix of Ramberg-Osgood constitutive is given, and the local refinement technique using variable-node elements is adopted to reduce the number of elements and nodes for efficient analysis. A Newton-Raphson iterative algorithm is developed to solve the nonlinear algebraic equations caused by material nonlinearity. Several numerical examples including the determination of crack opening displacement, and the fully plastic J integral in the ductile materials are presented to test the performance of the proposed method. Comparisons with the results from the existing methodologies show that the new enrichment scheme can save computational cost and obtain sufficient accuracy even in the case of 3D curved crack.

Mass minimization using the reaction-diffusion level set method by considering local stress constraints in an integral form

Research in topology optimization with stress constraints has shown that defining the stress constraint locally yields better performance compared to global methods. However, an examination of the formulas for local stress constraints reveals a limitation - the constraint is only satisfied at points where it is explicitly defined, failing to guarantee satisfaction across the entire design domain. To address this shortcoming, this paper proposes utilizing an integral form of the stress constraint. The integral formulation theoretically ensures that the stress constraint is satisfied over whole design domain. The objective is to minimize the mass of plane stress structures using reaction-diffusion level set method while incorporating local stress constraints in this integral form. The methodology utilizes finite element approximations for geometry and displacements, defining local stress constraints through an integral formulation. A Lagrangian function combines objective and constraint functions, with sensitivity analysis performed during optimization based on level set function changes. Structural boundaries are updated using the Hamilton-Jacobi equation. The paper presents numerical examples with varying loads and support conditions to demonstrate the effectiveness of this integral stress constraint approach. Results illustrate the capability of the proposed method to generate optimal topologies while satisfying stress constraints across the entire design space.

Induced pluripotent stem cell-derived macrophages as a platform for modelling human disease.

Macrophages are innate immune cells that are present in essentially all tissues, where they have vital roles in tissue development, homeostasis and pathogenesis. The importance of macrophages in tissue function is reflected by their association with various human diseases, and studying macrophage functions in both homeostasis and pathological tissue settings is a promising avenue for new targeted therapies that will improve human health. The ability to generate macrophages from induced pluripotent stem(iPS) cells has revolutionized macrophage biology, with the generation of iPScell-derived macrophages (iMacs) providing unlimited access to genotype-specific cells that can be used to model various human diseases involving macrophage dysregulation. Such disease modelling is achieved by generating iPScells from patient-derived cells carrying disease-related mutations or by introducing mutations into iPScells from healthy donors using CRISPR-Cas9 technology. These iMacs that carry disease-related mutations can be used to study the aetiology of the particular disease in vitro. To achieve more physiological relevance, iMacs can be co-cultured in 2D systems with iPScell-derived cells or in 3D systems with iPScell-derived organoids. Here, we discuss the studies that have attempted to model various human diseases using iMacs, highlighting how these have advanced our knowledge about the role of macrophages in health and disease.