Optimal Function Research Articles

Interplay of Glucose Metabolism and Hippo Pathway in Chondrocytes: Pathophysiology and Therapeutic Targets.

In this review, we explore the intricate relationship between glucose metabolism and mechanotransduction pathways, with a specific focus on the role of the Hippo signaling pathway in chondrocyte pathophysiology. Glucose metabolism is a vital element in maintaining proper chondrocyte function, but it has also been implicated in the pathogenesis of osteoarthritis (OA) via the induction of pro-inflammatory signaling pathways and the establishment of an intracellular environment conducive to OA. Alternatively, mechanotransduction pathways such as the Hippo pathway possess the capacity to respond to mechanical stimuli and have an integral role in maintaining chondrocyte homeostasis. However, these mechanotransduction pathways can be dysregulated and potentially contribute to the progression of OA. We discussed how alterations in glucose levels may modulate the Hippo pathway components via a variety of mechanisms. Characterizing the interaction between glucose metabolism and the Hippo pathway highlights the necessity of balancing both metabolic and mechanical signaling to maintain chondrocyte health and optimal functionality. Furthermore, this review demonstrates the scarcity of the literature on the relationship between glucose metabolism and mechanotransduction and provides a summary of current research dedicated to this specific area of study. Ultimately, increased research into this topic may elucidate novel mechanisms and relationships integrating mechanotransduction and glucose metabolism. Through this review we hope to inspire future research into this topic to develop innovative treatments for addressing the clinical challenges of OA.

ADPA Optimization for Real-Time Energy Management Using Deep Learning

The current generation of renewable energy remains insufficient to meet the demands of users within the network, leading to the necessity of curtailing flexible loads and underscoring the urgent need for optimized microgrid energy management. In this study, the deep learning-based Adaptive Dynamic Programming Algorithm (ADPA) was introduced to integrate real-time pricing into the optimization of demand-side energy management for microgrids. This approach not only achieved a dynamic balance between supply and demand, along with peak shaving and valley filling, but it also enhanced the rationality of energy management strategies, thereby ensuring stable microgrid operation. Simulations of the Real-Time Electricity Price (REP) management model under demand-side response conditions validated the effectiveness and feasibility of this approach in microgrid energy management. Based on the deep neural network model, optimization of the objective function was achieved with merely 54 epochs, suggesting a highly efficient computational process. Furthermore, the integration of microgrid energy management with the REP conformed to the distributed multi-source power supply microgrid energy management and scheduling and improved the efficiency of clean energy utilization significantly, supporting the implementation of national policies aimed at the development of a sustainable power grid.

An isogeometric approach to dynamic structures for integrating topology optimization and optimal control at macro and micro scales

In the context of active vibration suppression, an integrated topology optimization framework is proposed for the optimal control of dynamic structures. This article formulates a new composite objective function by considering the external harmonic excitations with different excitation frequencies and the complex-valued displacement field. Within the proposed optimal control performance function, the symmetric weighting matrix, the magnitudes of which are related to the importance of the control forces, is integrated into dynamic compliance to serve the state displacement. Utilizing the non-uniform rational B-splines (NURBS) basis functions, the material interpolation model defined at control points is described by the NURBS surface, which is incorporated into the optimization formulation for optimal structural and vibration control design. The sensitivity results are deduced in terms of the pseudo-densities and the corresponding weights at control points from both macro and micro perspectives of structural dynamics. To demonstrate the effectiveness of the integrated topology optimization of dynamic structures at macro and micro scales, several numerical examples, including the topology optimization of solid beams in plane stress and Mindlin plates with 3D microstructures, are provided and discussed in terms of structural shape and topology, frequency response curve, and modal displacement.

Regulatory Involvement of Kisspeptin in Energy Balance and Reproduction.

The hypothalamic-pituitary-gonadal axis, which regulates steroidogenesis and germ cell formation, closely regulates the reproduction process. Nonetheless, other chemical mediators, such as kisspeptin, influence this axis. Kisspeptin is a hypothalamic neuropeptide that modulates the function of this axis and also plays a central role in energy balance. The present study reviews the impact and associated mechanisms of kisspeptin on male and female reproduction based on available evidence in the literature. Kisspeptin and its neurons exert anorexigenic activity, thus maintaining adequate energy balance for optimal reproductive function. Also, they stimulate the release of GnRH, resulting in the optimal performance of gonadal physiological processes viz. production of steroid sex hormones and germ cells. However, studies linking kisspeptin to reproduction are yet scanty. Hence, studies exploring the upstream and downstream signaling pathways activated by kisspeptin concerning reproduction in an attempt to better understand the associated mechanisms of the regulatory activities of kisspeptin on reproduction are recommended. In addition, potential factors that may modulate kisspeptin activities may be useful in the management of infertility and perhaps, in the development of contraceptives for those who do not intend to achieve conception.

Data Lakehouse: A survey and experimental study

Efficient big data management is a dire necessity to manage the exponential growth in data generated by digital information systems to produce usable knowledge. Structured databases, data lakes, and warehouses have each provided a solution with varying degrees of success. However, a new and superior solution, the data Lakehouse, has emerged to extract actionable insights from unstructured data ingested from distributed sources. By combining the strengths of data warehouses and data lakes, the data Lakehouse can process and merge data quickly while ingesting and storing high-speed unstructured data with post-storage transformation and analytics capabilities. The Lakehouse architecture offers the necessary features for optimal functionality and has gained significant attention in the big data management research community. In this paper, we compare data lake, warehouse, and lakehouse systems, highlight their strengths and shortcomings, identify the desired features to handle the evolving challenges in big data management and analysis and propose an advanced data Lakehouse architecture. We also demonstrate the performance of three state-of-the-art data management systems namely HDFS data lake, Hive data warehouse, and Delta lakehouse in managing data for analytical query responses through an experimental study.

Recursive terminal sliding mode approximate optimal control strategy based on reconstructed nonlinear systems

For a class of nonlinear systems with unknown internal states and actuator failures, this paper proposes a recursive terminal sliding-mode fault-tolerant control method based on reconfigurable nonlinear systems and further designs an approximate optimal compensated controller based on adaptive dynamic programming, which employs a neural network (NN) to estimate the optimal value function. The system ensures that the tracking error converges to a small set around zero with faster speed and higher accuracy even when the actuator fails. Finally, the convergence of the expanded state observer and the single-evaluation NN weights is demonstrated based on the Lyapunov theory, and the stability of the whole closed-loop system is given. Simulation results and comparisons verify the effectiveness of the proposed control strategy.

Optimum noise mechanism for differentially private queries in discrete finite sets

The differential privacy (DP) literature often centers on meeting privacy constraints by introducing noise to the query, typically using a pre-specified parametric distribution model with one or two degrees of freedom. However, this emphasis tends to neglect the crucial considerations of response accuracy and utility, especially in the context of categorical or discrete numerical database queries, where the parameters defining the noise distribution are finite and could be chosen optimally. This paper addresses this gap by introducing a novel framework for designing an optimal noise probability mass function (PMF) tailored to discrete and finite query sets. Our approach considers the modulo summation of random noise as the DP mechanism, aiming to present a tractable solution that not only satisfies privacy constraints but also minimizes query distortion. Unlike existing approaches focused solely on meeting privacy constraints, our framework seeks to optimize the noise distribution under an arbitrary (ϵ,δ)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$(\\epsilon , \\delta )$$\\end{document} constraint, thereby enhancing the accuracy and utility of the response. We demonstrate that the optimal PMF can be obtained through solving a mixed-integer linear program. Additionally, closed-form solutions for the optimal PMF are provided, minimizing the probability of error for two specific cases. Numerical experiments highlight the superior performance of our proposed optimal mechanisms compared to state-of-the-art methods. This paper contributes to the DP literature by presenting a clear and systematic approach to designing noise mechanisms that not only satisfy privacy requirements but also optimize query distortion. The framework introduced here opens avenues for improved privacy-preserving database queries, offering significant enhancements in response accuracy and utility.

Effects of long-term exposure to high-altitude on episodic memory: The moderating role of daytime dysfunction

Chronic exposure to high-altitude hypoxic environments may influence short-term memory and working memory. However, its impact on long-term memory, specifically episodic memory, remains understudied. In this study, we systematically investigated the effects of long-term exposure to high altitude (3650 m) on episodic memory, including item memory and source memory. Moreover, we examined the moderating role of daytime dysfunction on the relationship between altitude and episodic memory. In total, 97 participants were enrolled in the study: 49 were from the high-altitude (HA) group, comprising those born and raised in low altitude (LA) areas (< 500 m) and had migrated to HA for 2∼3 years after turning 18; and 48 were from the LA group, who had never lived at high altitudes. Episodic memory was evaluated using a what-when-where task, whereas daytime dysfunction was measured by the Pittsburgh Sleep Quality Index. For item memory, hit rate and correct rejection rate were significantly lower in the HA group relative to the LA group. For source binding memory, the performance in what-where binding and what-when-where binding were decreased in the HA group, but the what-when binding did not differ between the two groups. Furthermore, the effects of altitude on hit rate, what-where binding, and what-when-where binding were significantly pronounced in individuals with higher levels of daytime dysfunction. These findings suggest that long-term exposure to high-altitude hypoxic environments influences episodic memory performance, including item recognition and source-binding memory. Specifically, spatial source memory is affected by high-altitude exposure, whereas temporal source memory remains unaffected. Moreover, these results highlight the importance of considering sleep quality, especially good daytime function, in maintaining optimal episodic memory function following chronic exposure to high altitudes.

On the optimal control function diagrams in the problem of the movement of a platform with oscillators

We consider the problem of the time-optimal movement of a rigid body moving translationally along a horizontal straight line and carrying n-linear oscillators. The only control force is applied to the platform and is limited in magnitude, there is no friction. The system is transferred from a state of rest to a specified distance with vibration damping. The evolution of optimal control functions depending on the distance of movement is investigated. A general approach to constructing a visual diagram reflecting such evolution is proposed. To do this, a geometric interpretation of the necessary optimality conditions is used as properties of an auxiliary “control” curve in n-dimensional space. Numerical examples of constructing diagrams of optimal control functions for a platform with three oscillators are given.

Separating Light Impurities from the Combed Heap in the Adapter Case

Introduction. It is possible to increase the separating of loose grain on the lattice bottom of the combine harvester feeder house by removing most of the light impurities from the combed heap using an inertial cleaning system built into the combing adapter body.Aim of the Study. The study is aimed at developing an inertial system for separating light impurities in the combing header body and optimizing its main parameters.Materials and Methods. The object of the study was a large-scale model of a pneumatic cleaning device to simulate the motion of air and components of a combed grain heap inside the combing adapter body. It was a two-factor experiment with three variations of the air flow velocity (4.5; 5.5; 6.5 m/s) and the width of the air channel (0.26; 0.29; 0.32 m). The studies were conducted on a combed heap of Moskovskaya 56 wheat with a moisture content of about 12%. As a parameter for optimization and response function, a portion of the combed heap was selected from the body of the installation with an air flow.Results. According to the results of the experimental studies, it was found that the air flow velocity has a greater effect on separating glumes than the width of the air channel. A simultaneous increase in both factors leads to an improved separation. At the same time, if increasing the air flow velocity ensures a stable increase in the proportion of the combed heap and is limited only by the air velocity at which grain appears in the combed heap along with the glumes, then changing the channel width allows achieving the parameter optimal value.Discussion and Conclusions. The use of an inertial cleaning system makes it possible to remove almost completely light impurities from the combed heap. The optimal parameters of the device are: channel width 0.28...0.3 m, air flow velocity 6...6.5 m/s.

Ecological flow research in response to hydrological variation: A case study of the Jinsha River Basin, China

Climate change and human activities have altered the natural state of rivers to a certain extent, leading to runoff variation and impacting the stability of riverine ecosystems. The ecological flow has emerged to balance the water relationship between humanity and riverine ecosystems and to safeguard the health and biodiversity of rivers. Presently, the scientific quantification of riverine ecological flow has become a focal point in water resource research. Addressing the quantification of ecological flow under hydrological variation conditions, the cumulative deviation method (CDM) and Pettitt methods were firstly employed to detect breakpoints in the runoff sequences. Then, the natural monthly runoff post-variation was restored using the Soil and Water Assessment Tool (SWAT) model, i.e., natural runoff restoration, and three testing methods were employed to determine the optimal hydrological distribution functions for natural monthly runoff. Subsequently, an improved frequency calculation method was used to calculate the ecological flow. The Jinsha River Basin was selected as the study area, and four crucial sections in the mainstream of the Jinsha River were studied, including Zhimenda (ZMD), Shigu (SG), Xiaodeshi (XDS), and Pingshan (PS). The results indicated: (1) the runoff sequences (1960–2017) at the four crucial sections exhibit different breakpoint times, with breakpoint years being 2004, 1986, 2005, and 1996, respectively; (2) the SWAT model, in simulating runoff at the four crucial sections, exhibited NSE values all exceeding 0.85, and r values all surpassing 0.9, demonstrating its effective ability to restore natural runoff in the Jinsha River basin; (3) the optimal hydrological distribution functions for natural monthly runoff at the four sections varied, and most natural monthly runoff conforms to the Generalized Extreme Value (GEV) distribution; (4) the average annual ecological flows for the ZMD, SG, XDS, and PS sections were 296 m3/s, 992 m3/s, 1238 m3/s, and 2755 m3/s, and the ecological flows derived using the improved frequency calculation method under the optimal hydrological distribution functions were more rational. This study can provide a scientific basis for the optimal allocation of water resources and the protection of riverine ecosystems in the Jinsha River basin.

FAStEN: An Efficient Adaptive Method for Feature Selection and Estimation in High-Dimensional Functional Regressions

Functional regression analysis is an established tool for many contemporary scientific applications. Regression problems involving large and complex data sets are ubiquitous, and feature selection is crucial for avoiding overfitting and achieving accurate predictions. We propose a new, flexible and ultra-efficient approach to perform feature selection in a sparse high dimensional function-on-function regression problem, and we show how to extend it to the scalar-on-function framework. Our method, called FAStEN, combines functional data, optimization, and machine learning techniques to perform feature selection and parameter estimation simultaneously. We exploit the properties of Functional Principal Components and the sparsity inherent to the Dual Augmented Lagrangian problem to significantly reduce computational cost, and we introduce an adaptive scheme to improve selection accuracy. In addition, we derive asymptotic oracle properties, which guarantee estimation and selection consistency for the proposed FAStEN estimator. Through an extensive simulation study, we benchmark our approach to the best existing competitors and demonstrate a massive gain in terms of CPU time and selection performance, without sacrificing the quality of the coefficients’ estimation. The theoretical derivations and the simulation study provide a strong motivation for our approach. Finally, we present an application to brain fMRI data from the AOMIC PIOP1 study. Complete FAStEN code is provided at https://github.com/IBM/funGCN. Supplementary materials for this article are available online.

Modification of the hopfield neural network model for solving the task of optimal task allocation in a group of mobile robots

In the context of group interaction among mobile robots, there arises the challenge of task distribution within the group, considering the robots' characteristics and the working environment. This study aims to modify the Hopfield neural network and develop methodologies for its application in solving the task allocation problem for an arbitrary number of tasks within a group of mobile robots. To achieve this, the Hopfield neural network is represented as a graph. An algorithm is presented, demonstrating the transition from the initial problem to the Traveling Salesman Problem (TSP). The application of the Hopfield model to the task distribution problem in a group of robots is described, along with the development of an optimization function calculation algorithm. An assessment is conducted to evaluate the impact of neural network parameters on the quality and speed of solving the optimization problem. By comparing it with other heuristic methods (genetic and ant colony algorithms), the domains of application for the modified algorithm are determined.

Multidisciplinary design optimization of an autonomous underwater vehicles based on random uncertainty

Herein, discipline decomposition was performed for an autonomous underwater vehicle (AUV) based on multidisciplinary design optimization (MDO), and its design parameters were determined. Parameterized analysis of the hull-form and structure disciplines was performed, and an approximate model of these disciplines was established with a high fitting accuracy. An analysis model of propulsion, energy, and system disciplines was developed based on the formula method. Based on collaborative optimization as well as discipline and system level models, a deterministic MDO framework for AUVs was established. Subsequently, an optimized solution was obtained. By considering the random uncertainty of design variables, an MDO framework for the uncertainty of AUVs was established and optimized solutions were obtained. The distribution of solutions obtained from uncertainty optimization was more concentrated and the distribution range was smaller than that obtained using deterministic optimization. Robustness analysis was performed on the initial scheme, typical deterministic optimization schemes, and typical uncertainty optimization schemes. Results showed that fluctuations in design variables may lead to constraints that exceed boundary conditions in the deterministic optimization design scheme. Using uncertainty and objective function optimization, the robustness of the overall scheme of AUVs was improved.

Automatic multi-storey building partitioning from point cloud via bidirectional cloth simulation

With the advancements in scanning technology, the rapid acquisition of point cloud spanning multiple storeys of buildings has become feasible. Accurately dividing the building into individual storeys deepens the understanding of the building environment, accelerates the processing of the vast point cloud, and is crucial for numerous fields such as model reconstruction, indoor navigation, and building management. However, it remains a challenge to handle the building with rooms across multiple storeys. This study presents a novel method to partition complex multi-storey building from cluttered point cloud. First, the non-vertical structural elements are detected by taking the difference of height and normal vector as growth conditions. Next, we iteratively employ bidirectional cloth with height variation constraints to simulate the floor and ceiling trend surface. In this step, candidate ceiling patches are obtained through the optimization of a cost function. Finally, the irregular boundary between adjacent storeys that used for partitioning the point cloud is rasterized in terms of the ceilings at lower storey and the floors at upper storey. Each point is orderly classified into the correspond storey with reference to the elevation of the estimated boundary. Through comprehensive evaluation and analysis, our method has demonstrated its effectiveness and adaptability across various datasets. Our method yields superior results compared to the advanced methods, achieving an approximate value of 99 % on the weighted recall, weighted precision and weighted F1-score. Besides, the proposed method is not limited to Manhattan world buildings and requires only the spatial coordinate of points.

The Minimum Patient Acceptable Symptom State for the ACL-Return to Sport after Injury Scale Among Patients Treated With Anterior Cruciate Ligament Reconstruction.

Despite most patients reporting optimal knee function after anterior cruciate ligament reconstruction (ACLR), not all return to their pre-injury level of sport, often due to psychological factors. The ACL-Return to Sport after Injury Scale (ACL-RSI) was developed to measure the emotions, confidence in performance, and risk appraisal among athletes returning to sport. The purpose of this study was to determine the Patient Acceptable Symptom State (PASS) threshold for the ACL-RSI in patients undergoing ACLR. Patients with an ACL injury that required surgical reconstruction were included in this prospective study. All patients underwent ACLR with a bone-patellar tendon-bone autograft and completed the ACL-RSI 12 months postoperatively. An anchor-based approach was used to generate a receiver operating characteristic curve and establish the PASS threshold. Multivariable regression analyses were used to evaluate the effect of age, sex, and baseline score on likelihood of achieving PASS. A total of 113 patients (37% female) with a mean age of 28.1±8.2 years and a mean body mass index of 24.7±3.5 kg/m2 were included. At 12 months postoperatively, the threshold value for the PASS of the ACL-RSI was 40 (robust area under the curve: 0.89; sensitivity: 81%; specificity: 85%). Baseline score, sex, and age had no significant influence on achieving PASS at 12 months postoperatively. In a group of patients undergoing ACLR with bone-patellar tendon-bone autograft, the PASS threshold value was 40 for the ACL-RSI at 12 months postoperatively. The PASS value for the ACL-RSI established from this study can be useful for designing future clinical trials. [Orthopedics. 202x;4x(x):xx-xx.].

Quaternary Biocomposite of Chitosan-Polyvinyl Alcohol/Food Grade Algae/ Montmorillonite Clay for Cationic Methyl Violet 2B Dye Removal: Optimization and Desirability Functions

Quaternary Biocomposite of Chitosan-Polyvinyl Alcohol/Food Grade Algae/ Montmorillonite Clay for Cationic Methyl Violet 2B Dye Removal: Optimization and Desirability Functions

Stakeholders' Perspectives on Current Issues in Data Monitoring Committees

ABSTRACTData Monitoring Committees (DMCs) are groups of experts that review accumulating data from one or more ongoing clinical studies and advise the Sponsor regarding the continuing safety of study subjects along with the continuing validity and scientific merit of the study. Although DMCs are widely used, considerable variability exists in their conduct. This paper offers recommendations, derived from sessions given at the 2023 Central European Network International Biometric and Statisticians in the Pharmaceutical Industry Conferences' and the authors' experiences. We focus on four topics that are part of the DMC process and where there is unclarity and inconsistency in current practices: (1) Communication with the DMC—We reflect on the importance of effective, proper communication channels between the DMC and relevant stakeholders to foster collaboration and exchange of critical information while retaining study integrity throughout. (2) Open sessions—We discuss the benefits of incorporating open sessions in DMC meetings to enhance transparency, inclusivity, and the consideration of diverse perspectives, as well as pitfalls of open sessions. (3) Access to efficacy data—We highlight the need for appropriate access to efficacy data by DMCs and discuss how to implement this in practice and how to address potential concerns regarding multiplicity. (4) Interactive data displays—We outline the utilization of interactive data displays to facilitate a more intuitive understanding of study results by the DMC. By addressing these topics, we aim to provide comprehensive practical recommendations that bridge the gap between current practices and optimal DMC functionality.

Deep Learning-Based Nonparametric Identification and Path Planning for Autonomous Underwater Vehicles

As the nonlinear and coupling characteristics of autonomous underwater vehicles (AUVs) are the challenges for motion modeling, the nonparametric identification method is proposed based on dung beetle optimization (DBO) and deep temporal convolutional networks (DTCNs). First, the improved wavelet threshold is utilized to select the optimal threshold and wavelet basis functions, and the raw model test data are denoising. Second, the bidirectional temporal convolutional networks, the bidirectional gated recurrent unit, and the attention mechanism are used to achieve the nonlinear nonparametric model of the AUV motion. And the hyperparameters are optimized by the DBO. Finally, the lazy-search-based path planning and the line-of-sight-based path following control are used for the proposed AUV model. The simulation shows that the prediction accuracy of the DBO-DTCN is better than other artificial intelligence methods and mechanical models, and the path following of AUV is feasible. The methods proposed in this paper can provide an effective strategy for AUV modeling, searching, and rescue cruising.

Addressing data sparsity and cold-start challenges in recommender systems using advanced deep learning and self-supervised learning techniques

ABSTRACT The efficacy of e-commerce conversion rates relies on precise and personalized product recommendations within recommendation systems (RS). While collaborative filtering-based RS has demonstrated success, challenges such as sparsity and cold-start issues in the user-item matrix can impede optimal functionality. To address these challenges, there is a need to integrate additional information sources, encompassing item/user profiles and textual reviews. This study introduces an innovative RS architecture that seamlessly combines self-supervised learning (SSL) and collaborative filtering techniques with BERT-DNN to surmount these obstacles. The distinctiveness of our approach lies in integrating self-supervised learning with collaborative filtering and contextualized data obtained from BERT-DNN, providing a profound understanding of item profiles to enhance comprehension of user preferences and item characteristics. This refined understanding, operational in conjunction with collaborative filtering models like ItemKNN and UserKNN, empowers the system to generate highly personalized recommendations. The proposed method entails several pivotal steps: developing the BERT language model for textual embeddings in item profiles, conducting dimensionality reduction, constructing a Deep Neural Network, implementing self-supervised learning with both UserKNN and ItemKNN CF methods, and employing an ensemble learning technique. Empirical results substantiate the efficacy of our approach, with a specific focus on the innovative fusion of BERT-DNN with self-supervised learning and KNN CF methodologies, showcasing substantial improvements across diverse performance metrics. This underscores the practical importance of leveraging contextualized BERT-DNN data, strengthening the recommendation mechanism, and ultimately enhancing the overall performance of the RS.