Optimal Function Research Articles

Physical information-guided multidirectional gated recurrent unit network fusing attention to solve the Black-Scholes equation

Reasonable option pricing is crucial in the financial derivatives market. Finding analytical solutions for the Black-Scholes (BS) equation, particularly for American options or with fluctuating volatility and interest rates, is challenging. BS equations exhibit strong time-series characteristics, with asset prices typically adhering to geometric Brownian motion. To address the BS equations, we propose a sequence-to-sequence model guided by physical information (PI), called PiMGA. The PiMGA fuses a multidirectional gated recurrent unit (GRU) network with an attention module, where multidirectional GRU enhances the coding performance of the input sequences and the attention module balances the feature weights of the hidden variables. Prior physical knowledge in BS equations is jointly used as a constraint, forming the penalty function for objective optimization. This allows PiMGA to serve as an efficient approximation function in the learning paradigm of physically informed machine learning to solve BS equations. BS equations with various complexities illustrate the accuracy and feasibility of PiMGA for numerical solutions. Furthermore, the out-of-distribution generalization ability of PiMGA is verified by predicting the Nasdaq 100 index.

Optimal scheduling of battery energy storage system operations under load uncertainty

This paper investigates the optimal scheduling of battery energy storage system operations considering energy load uncertainty. We develop a novel two-stage distributionally robust optimization model to determine an optimal battery usage schedule that minimizes the worst-case energy costs considering peak load costs. The model leverages deep-learning-based probabilistic forecasting in the construction of the ambiguity set. Specifically, we develop a Deep Autoregressive Recurrent Networks model to generate a probabilistic forecast of energy loads over a time horizon. The output of the forecasting model is then used to construct a marginal-moment ambiguity set for the distributionally robust optimization model. To solve the proposed model, we establish a closed-form characterization of the optimal second-stage objective function value. Leveraging this closed-form expression and using second-order conic duality, we derive an exact single-level mixed integer second-order conic reformulation of the problem. Extensive computational experiments, conducted on a real dataset, demonstrate the value of our proposed model and the effectiveness of the resulting battery schedule. The results show that the proposed model outperforms several benchmarks, including two-stage stochastic programming. Furthermore, the accuracy of the load forecast significantly impacts the effectiveness of the optimal battery schedule in eliminating peak loads by achieving up to 18% reduction in the maximum energy load.

Level of prooxidant-antioxidant status in highly productive cows with comorbid obstetric, gynecological and orthopedic pathology

The lipid peroxidation and antioxidant defense system is a balanced system responsible for the processing and utilization of lipids in the body’s cells. It plays an important role in lipid metabolism, cell protection and overall health of the body. Its proper functioning is necessary to ensure optimal functioning of cells and organs throughout the body. The article clinically and experimentally substantiates the pathogenetic role of lipid peroxidation products and the level of antioxidant protection in cows with endometritis and purulent-necrotic processes in the finger area, as well as with the comorbid course of endometritis and orthopedic pathology. It has been shown that multimorbid manifestation is accompanied by a more severe course than individual diseases. It has been established that the development of postpartum endometritis and purulent-necrotic lesions of the limbs in cows is accompanied by a highly significant increase in lipid peroxidation products in the blood serum against the background of a decrease in the amount of antioxidant enzymes, with the exception of ceruloplasmin. Moreover, these changes are accompanied by a sharp jump in the comorbid course of endometritis and orthopedic pathology in highly productive animals.

Performance Optimization of Convolutional Neural Networks in Image Classification

With the development of deep learning, CNNs have become mainstream in image classification. However, CNN performance is affected by factors like loss function choice and optimization algorithm. This paper aims to improve CNN performance in image classification by optimizing SGD. Firstly, we analyze different loss functions and propose a suitable optimization strategy. Second, we explore SGD and its variants' roles in CNN training, focusing on accelerating convergence and improving accuracy. Experiments show the proposed scheme effectively enhances CNN performance, providing a reliable solution for image classification. This study supports CNN applications in image recognition and lays a foundation for exploring more efficient optimization methods.

A Spectral-Based Blade Fault Detection in Shot Blast Machines with XGBoost and Feature Importance

The optimal functionality and dependability of mechanical systems are important for the sustained productivity and operational reliability of industrial machinery, and have a direct impact on its longevity and profitability. Therefore, the failure of a mechanical system or any of its components would be detrimental to production continuity and availability. Consequently, this study proposes a robust diagnostic framework for analyzing the blade conditions of shot blast industrial machinery. The framework explores the spectral characteristics of the vibration signals generated by the industrial shot blast for discriminative feature excitement. Furthermore, a peak detection algorithm is introduced to identify and extract the unique features present in the peak magnitudes of each signal spectrum. A feature importance algorithm is then deployed as the feature selection tool, and these selected features are fed into ten machine learning classifiers (MLCs), with extreme gradient boosting (XGBoost (version 2.1.1)) as the core classifier. The results show that the XGBoost classifier achieved the best accuracy of 98.05%, with a cost-efficient computational cost of 0.83 s. Other global assessment metrics were also implemented in the study to further validate the model.

Optimization of Load-transfer Functions for a Large-diameter Pile in Multi-layered Geological Conditions via a Stochastic Search Algorithm

The paper presents the combination of the load transfer method (computational model) and the genetic algorithm (optimization model) for the automated inverse analysis of instrumented pile load tests. The output of this process are the input parameters governing the shape of the load-transfer functions and thus the prediction accuracy when the load-transfer method is used as a design tool for deep foundations. The optimization problem is converted into an unconstrained task by applying the static penalty approach. Two types of measurements are considered in a newly proposed objective function: the load-displacement curve monitored in a pile head and the axial force profiles along a pile derived from strain gauges. Firstly, the local sensitivity analysis via the Design of Experiments is carried out to identify the parameters of the genetic algorithm which significantly influences the rate of convergence during the optimization process. Subsequently, a fully automated inverse analysis of the loading test of the large-diameter bored pile in multilayered geological conditions is presented. The simultaneous combination of two instrumentation sources leads to a stable unique solution with a sufficient match between the prediction and measurements despite a larger number of unknown – optimized variables.

Mitophagy in Cell Death Regulation: Insights into Mechanisms and Disease Implications.

Mitophagy, a selective form of autophagy, plays a crucial role in maintaining optimal mitochondrial populations, normal function, and intracellular homeostasis by monitoring and removing damaged or excess mitochondria. Furthermore, mitophagy promotes mitochondrial degradation via the lysosomal pathway, and not only eliminates damaged mitochondria but also regulates programmed cell death-associated genes, thus preventing cell death. The interaction between mitophagy and various forms of cell death has recently gained increasing attention in relation to the pathogenesis of clinical diseases, such as cancers and osteoarthritis, neurodegenerative, cardiovascular, and renal diseases. However, despite the abundant literature on this subject, there is a lack of understanding regarding the interaction between mitophagy and cell death. In this review, we discuss the main pathways of mitophagy, those related to cell death mechanisms (including apoptosis, ferroptosis, and pyroptosis), and the relationship between mitophagy and cell death uncovered in recent years. Our study offers potential directions for therapeutic intervention and disease diagnosis, and contributes to understanding the molecular mechanism of mitophagy.

Remaining useful life prediction of high-capacity lithium-ion batteries based on incremental capacity analysis and Gaussian kernel function optimization

Remaining useful life (RUL) is a key indicator for assessing the health status of lithium (Li)-ion batteries, and realizing accurate and reliable RUL prediction is crucial for the proper operation of battery systems. As high-capacity Li batteries have more complex chemical properties, most of the current RUL prediction methods rely mainly on a priori knowledge to make judgments. As a result, prediction accuracy is not high. In this study, we developed a health indicator-capacity (HI-C) dual Gaussian process regression (GPR) model based on incremental capacity analysis (ICA) and optimized its kernel function to achieve accurate RUL prediction for 280 Ah high-capacity Li batteries. Validation against the United States National Aeronautics and Space Administration’s four battery test datasets showed that the use of the HI-C dual GPR model resulted in a mean absolute percentage error and root mean square error of less than 0.02 and 0.04, respectively, for the four battery-rated capacity predictions. Additionally, this model achieved an absolute error of less than five battery failure turns. Compared with a single model, the HI-C dual GPR model not only had high accuracy but also solved the problem that the HI was not measurable in the actual battery operation, which made it more suitable for RUL prediction of Li batteries.

Performance analysis of meta-heuristic optimization techniques for multi-objective VLSI circuit partitioning

Abstract The efficiency of various stages of physical design in the Very Large Scale Integration (VLSI) circuit can be enhanced by using multi-objective optimization techniques. The circuit is partitioned into a set of modules at the beginning of the physical design phase. This step is known as circuit partitioning. All subsequent physical design stages, like floor planning, placement, pin configuration, and routing etc, are influenced by the outcome of the circuit partitioning method being used. The efficiency of the partitioning method controls the quality of ultimate architecture. The efficiency achieved by the final product may be decreased by incorrect partitioning. This study compares the effectiveness of four Meta-heuristic optimization methods include the Genetic Algorithm (GA), Satin Bowered Bird (SBO), Particle Swarm Optimization (PSO), Simulated Annealing (SA) — in terms of number of net cut, duration of sleeping mode, and efficiency in power consumption for addressing the VLSI circuit partitioning problem. This experiment achieves K-way partitioning of the circuit that minimizes net cuts as well as maximizes sleep duration simultaneously, by evaluating optimization function. Solutions developed using GA, SBO and PSO consistently surpass those acquired using SA with respect to quality of optimal solution. The average optimum net cut and sleep duration achieved by this experiment are 10.7 and 9.9 in GA ; 11.5 and 12.3 in SBO, 11.80 and 9.10 in PSO; and 16.8 and 6.7 in SA based method respectively. This research resulted in a considerable reduction in power consumption, with the system’s average power efficiency achieved by 21.6%, 26.84%, 19.85%, and 14.62%, respectively, in GA, SBO , PSO, and SA based optimizations. The experimental results are compared with a recent publication and proposed GA, SBO, and PSO based methods achieve 18.75%, 4.17%, and 1.04% improvement in terms of average net cut respectively. The proposed GA, SBO, PSO and SA based methods also achieve 44.90%, 51.30%, 40.05%, and 18.60% improvement in terms of average power efficiency.

Machine learning model‐based optimal tracking control of nonlinear affine systems with safety constraints

AbstractThis work focuses on the development of a machine learning (ML) model‐based framework for safe optimal tracking control of a class of nonlinear control‐affine systems to ensure simultaneous closed‐loop stability and safety. Specifically, a novel multilayer feedforward neural network (FNN) with a control‐affine architecture is designed to model nonlinear dynamic systems. Subsequently, a model‐based reinforcement learning (RL) framework is presented, utilizing a novel cost function with Control Lyapunov‐Barrier Function (CLBF) properties, to learn both the control policy and the optimal value function for an infinite‐horizon optimal tracking control problem for nonlinear systems with safety constraints. The efficacy of the proposed methodology is demonstrated through simulations of a one‐link robot manipulator and a chemical process example.

Sleep Quality in School-Aged Children: The Role of Environmental Factors and Screen Time

Sleep plays a vital role in the optimal functioning of our body and is crucial for overall well-being. However, many parents are unaware of the negative consequences of sleep deprivation in children. This study examines factors affecting sleep in children and their correlation with screen time. The aim is to evaluate the relationship between screen time and environmental factors impacting sleep quality among school-aged children.A cross-sectional observational study was conducted on children aged 6 to 12 years. Sleep quality was assessed using a 22-item, pre-validated, parent-administered Children’s Sleep Habits Questionnaire (CSHQ). Contributing factors, including a screen time questionnaire, were gathered from parents. Children with pre-existing sleep disorders, neurobehavioral disorders, and chronic illnesses were excluded. A CSHQ score above 41 was considered abnormal, indicating a sleep problem. Data were analyzed using the Chi-square test with SPSS version 20.0.In this study, 40.3% of children experienced sleep disturbances. The total mean sleep score was 39.56 ± 14.84, with private school children showing greater disruptions (41.78 ± 12.96) compared to public school children (37.14 ± 16.33; p = 0.001). Significant correlations were found between sleep quality, screen time exposure, and environmental factors such as lower socio-economic status, bed sharing, and room sharing.This study highlights that both excessive screen time and adverse environmental conditions negatively impact children's sleep quality. Efforts to increase awareness among parents, educators, and healthcare providers are needed to reduce sleep disturbances.

Functional Optimization in Distinct Tissues and Conditions Constrains the Rate of Protein Evolution.

Understanding the main determinants of protein evolution is a fundamental challenge in biology. Despite many decades of active research, the molecular and cellular mechanisms underlying the substantial variability of evolutionary rates across cellular proteins are not currently well understood. It also remains unclear how protein molecular function is optimized in the context of multicellular species and why many proteins, such as enzymes, are only moderately efficient on average. Our analysis of genomics and functional datasets reveals in multiple organisms a strong inverse relationship between the optimality of protein molecular function and the rate of protein evolution. Furthermore, we find that highly expressed proteins tend to be substantially more functionally optimized. These results suggest that cellular expression costs lead to more pronounced functional optimization of abundant proteins and that the purifying selection to maintain high levels of functional optimality significantly slows protein evolution. We observe that in multicellular species both the rate of protein evolution and the degree of protein functional efficiency are primarily affected by expression in several distinct cell types and tissues, specifically, in developed neurons with upregulated synaptic processes in animals and in young and fast-growing tissues in plants. Overall, our analysis reveals how various constraints from the molecular, cellular, and species' levels of biological organization jointly affect the rate of protein evolution and the level of protein functional adaptation.

The role of Imp and Syp RNA-binding proteins in precise neuronal elimination by apoptosis through the regulation of transcription factors.

Neuronal stem cells generate a limited and consistent number of neuronal progenies, each possessing distinct morphologies and functions, which are crucial for optimal brain function. Our study focused on a neuroblast (NB) lineage in Drosophila known as Lin A/15, which generates motoneurons (MNs) and glia. Intriguingly, Lin A/15 NB dedicates 40% of its time to producing immature MNs (iMNs) that are subsequently eliminated through apoptosis. Two RNA-binding proteins, Imp and Syp, play crucial roles in this process. Imp+ MNs survive, while Imp-, Syp+ MNs undergo apoptosis. Genetic experiments show that Imp promotes survival, whereas Syp promotes cell death in iMNs. Late-born MNs, which fail to express a functional code of transcription factors (mTFs) that control their morphological fate, are subject to elimination. Manipulating the expression of Imp and Syp in Lin A/15 NB and progeny leads to a shift of TF code in late-born MNs toward that of early-born MNs, and their survival. Additionally, introducing the TF code of early-born MNs into late-born MNs also promoted their survival. These findings demonstrate that the differential expression of Imp and Syp in iMNs links precise neuronal generation and distinct identities through the regulation of mTFs. Both Imp and Syp are conserved in vertebrates, suggesting that they play a fundamental role in precise neurogenesis across species.

Optimizing solarized desalination unit through the implementation of 4-step MED method using energy, exergy, economic and environmental analysis

The consumption of thermal energy in thermal desalination plants leads to a higher price for the fresh water they produce compared to other methods. By utilizing optimization techniques, it is possible to lower both energy consumption and price. The focus of this paper is on optimizing a solarized desalination unit through the implementation of the 4-step MED method with a PTC collector. To achieve this objective, the NSGA II algorithm was implemented in MATLAB using a function for optimization. This algorithm is known for its cost-effectiveness and high energy efficiency. According to the results, there has been an improvement in the fresh water flow rate, desalination efficiency, and GOR, with values reaching 126.87, 53.6%, and 3.66 respectively, compared to the previous values of 116.5, 49.21%, and 3.32. In the ideal condition, the power generated is 6089 kW, priced at 3.28 cents per kilowatt, and the cost of producing fresh water is 8.49 dollars per cubic meter, which decreases as the process lifespan increases. Solar collectors and thermal tanks account for the largest portion (64%) of exergy destruction, as indicated by the exergy analysis. Optimization of the process has led to energy and exergy efficiencies of 59.8% and 58%, respectively, representing a notable enhancement of around 10% in the system's lifespan. The optimal mode also includes the completion of the sensitivity analysis. The process was subjected to LCA analysis, and the results indicated that the largest impact is on human health, with the collectors and thermal storage tanks being responsible for most of the pollution. As a result, the optimized process has delivered outstanding results while also being environmentally conscious.

Postoperative bowel function in children operated for Hirschsprung's disease in a low-income setting: Institution-based cross-sectional study.

Hirschsprung's disease is a common cause of lower intestinal obstruction in newborns. It has variable postoperative outcomes affecting quality of life. The study was aimed at assessing postoperative bowel function in children with Hirschsprung's disease. It was conducted on 120 children operated for Hirschsprung's disease. A structured questionnaire for bowel function score was used and analyzed using relevant statistical tests. Of the 120 children in the study, 97 (80.8%) were male with 49 (40.8%) diagnosed during neonatal age and others by 2years of age. Ninety-three (77.5%) of them had the classic type. Diversion colostomy was done in 104 (86.6%), and two-staged endorectal pullthrough was performed in 62 (72.5%) of cases with a 16% rate of retained aganglionosis. Postoperative continence was excellent in 46 (57%) and good in 26 (32%) with an incontinence rate of 11%. None of the outcome predictor showed significant influence. Optimal postoperative bowel function was obtained in the majority of patients with two-stage procedures, and the overall outcome of bowel function in children was not influenced by age, gender, level of aganglionosis, and type of procedure. Longer follow-up periods are required for definitive information.

Engineering synthetic agonists for targeted activation of Notch signaling.

Notch signaling regulates cell fate decisions and has context-dependent tumorigenic or tumor suppressor functions. Although there are several classes of Notch inhibitors, the mechanical force requirement for Notch receptor activation has hindered attempts to generate soluble agonists. To address this problem, we engineered synthetic Notch agonist (SNAG) proteins by tethering affinity-matured Notch ligands to antibodies or cytokines that internalize their targets. This bispecific format enables SNAGs to "pull" on mechanosensitive Notch receptors, triggering their activation in the presence of a desired biomarker. We successfully developed SNAGs targeting six independent surface markers, including the tumor antigens PDL1, CD19, and HER2, and the immunostimulatory receptor CD40. SNAGs targeting CD40 increase expansion of central memory γδ T cells from peripheral blood, highlighting their potential to improve the phenotype and yield of low-abundance T cell subsets. These insights have broad implications for the pharmacological activation of mechanoreceptors and will expand our ability to modulate Notch signaling in biotechnology.

Intelligent computing framework to analyze the transmission risk of COVID-19: Meyer wavelet artificial neural networks

The optimum control methods for the epidemiology of the COVID-19 model are acknowledged using a novel advanced intelligent computing infrastructure that joins artificial neural networks with unsupervised learning-based optimizers i.e., Genetic Algorithms (GA) and sequential quadratic programming (SQP). Unsupervised learning strategy is provided which depends on the wavelet basis's sequential deconstruction of stochastic data. The weights or selection values of neural networks are utilizing cumulative algorithms of Meyer wavelet artificial neural networks (MWANNs) optimized with global search Genetic Algorithms (GAs) and Sequential Quadratic Programming (SQP), referred to as MWANNs-GA-SQP and the design technique is utilized to determine the COVID-19 model for five different scenarios employing different step sizes and input intervals. The findings of this research article examined that in order to minimize the total disease transmission at the lowest cost and complexity, safety, focused medical care, and exterior sterilization methods applicability. The provided data is validated through various graphical simulations, which surely authenticate the effectiveness and robustness of the proposed solver. The suggested solver, MWANNs-GA-SQP, is tested in a variety of circumstances to examine that how reliable, safe, and tolerant. Using the proposed MWANNs hubristic intelligent approach, an objective optimization function is created in feed forward neural networking to minimize the mean square error. An investigation of the hybrid GA-SQP is used to confirm the accuracy and dependability of the MWANNs model results. Mean absolute graphs have been constructed to assess the integrity and efficiency of the proposed methodology. The accuracy and reliability of the suggested method are demonstrated by constantly achieving maximum variables of analytical assessment criteria computed for a large appropriate variety of distinct trials.

Eukaryotic cell size regulation and its implications for cellular function and dysfunction.

Depending on cell type, environmental inputs, and disease, the cells in the human body can have widely different sizes. In recent years, it has become clear that cell size is a major regulator of cell function. However, we are only beginning to understand how the optimization of cell function determines a given cell's optimal size. Here, we review currently known size control strategies of eukaryotic cells and the intricate link of cell size to intracellular biomolecular scaling, organelle homeostasis, and cell cycle progression. We detail the cell size-dependent regulation of early development and the impact of cell size on cell differentiation. Given the importance of cell size for normal cellular physiology, cell size control must account for changing environmental conditions. We describe how cells sense environmental stimuli, such as nutrient availability, and accordingly adapt their size by regulating cell growth and cell cycle progression. Moreover, we discuss the correlation of pathological states with misregulation of cell size and how for a long time this was considered a downstream consequence of cellular dysfunction. We review newer studies that reveal a reversed causality, with misregulated cell size leading to pathophysiological phenotypes such as senescence and aging. In summary, we highlight the important roles of cell size in cellular function and dysfunction, which could have major implications for both diagnostics and treatment in the clinic.

Silicone sling surgery for congenital ptosis: Tarsal tunnel technique.

This was a prospective, interventional, comparative study conducted on patients with congenital simple severe ptosis. A novel method of silicone rod fixation to the tarsus (tarsal tunnel technique, group 1) was done compared with a conventional technique of silicone rod fixation (suture fixation technique, group 2) in frontalis suspension surgery. A total of 30 patients were randomized into two groups of 15 patients each. Postoperatively, MRD1, vertical palpebral aperture, and eyelid fold height were comparable in both groups till the last follow-up with stability in eyelid position. Patient satisfaction scores showed similar results with good-fair satisfaction grading in 13 patients in group 1 and 11 patients in group 2 at 6 months follow-up. No significant complications occurred in either group. Tarsal tunnel fixation of silicone rods is a novel method in sling surgery with optimal cosmetic and function outcomes. Further long-term studies are needed to validate the results of the technique.

THE EFFECTIVENESS OF THE FUNCTIONING OF RECONSTRUCTED HEPATIC VEINS USING VARIOUS TYPES OF MATERIALS IN TRANSPLANTATION OF THE RIGHT LOBE OF THE LIVER FROM A LIVING DONOR

Background. In adult living donor liver transplantation with the right lobe, the venous outflow of the anterior sector is typically restored during the procedure to form a neo-middle hepatic vein. Restoring the middle hepatic vein for the drainage of the anterior sector is critically important for achieving optimal graft function. Various conduits are used for this reconstruction, such as synthetic and biological grafts (e.g., the recipient's portal vein, vessels from a deceased donor, and modified great saphenous vein). However, the selection of the best option remains a topic of discussion. This study evaluates the effectiveness of using biological and synthetic grafts for MHV reconstruction. Materials and methods. A retrospective analysis of outcomes was conducted in patients who underwent transplantation of a modified right liver lobe due to end-stage liver disease from 2011 to 2024. Results. A transplantation of modified right liver lobes was performed on 80 patients. In 69 cases, the reconstruction of the hepatic veins was carried out using a biological graft, while in 11 cases, a synthetic graft was used. Statistically significant differences were noted in mortality and the postoperative period. No significant differences were found in the frequency of intraoperative complications. Conclusion. Our study demonstrates that the use of a biological graft for the reconstruction of the hepatic veins of segments 5 and 8 is more effective than the application of a vascular prosthesis.