Optimal Function Research Articles

'My Journey': A Qualitative Study of Recovery From the Perspective of Individuals With Chronic Mental Illness.

Due to the humanistic paradigm shift in recent years, mental health recovery has been approached through personal recovery beyond the limits of the biomedical perspective, emphasising the subjective perception and uniqueness of the individual. Therefore, approaching recovery perceptions from patients' perspectives has gained importance. This study aimed to examine in depth the recovery perceptions of individuals with chronic mental illness. It is a qualitative study conducted using a phenomenological design. The study group consisted of 12 patients who had been undergoing treatment for mental illness for at least 1 year and were selected by purposive sampling method. Data were collected face-to-face using a semi-structured interview form and analysed using the content analysis technique. The content analysis revealed three main themes and seven sub-themes. The themes were journey (a meaningful life, optimal functioning, new identity), journey ticket (resilience, support systems) and stones on the road (traditional perspective, barriers). In conclusion, the study results revealed that individuals in the recovery process required support and counselling to make sense of the process and adapt their identity. Employment should be used more effectively in the recovery process of individuals with chronic mental illness. Recovery can only be achieved by breaking away from the traditional perspective of healing and combating the perception of society towards patients. Accordingly, psychiatric nurses should provide effective guidance and counselling to show that individuals can create and live a meaningful life alongside their illnesses.

Exploring the Impact of 3D Printing Parameters on the THz Optical Characteristics of COC Material.

In terahertz (THz) optical systems, polymer-based manufacturing processes are employed to ensure product quality and the material performance necessary for proper system maintenance. Therefore, the precise manufacturing of system components, such as optical elements, is crucial for the optimal functioning of the systems. In this study, the authors investigated the impact of various 3D printing parameters using fused deposition modeling (FDM) on the optical properties of manufactured structures within the THz radiation range. The measurements were conducted on 3D printed samples using highly transparent and biocompatible cyclic olefin copolymer (COC), which may find applications in THz passive optics for "in vivo" measurements. The results of this study indicate that certain printing parameters significantly affect the optical behavior of the fabricated structures. The improperly configured printing parameters result in the worsening of THz optical properties. This is proved through a significant change in the refractive index value and undesirable increase in the absorption coefficient value. Furthermore, such misconfigurations may lead to the occurrence of defects within the printed structures. Finally, the recommended printing parameters, which improve the optical performance of the manufactured structures are presented.

Research on parameter optimization method of thrust vector/aerodynamic rudder composite control law based on singular value method

Advanced aircraft with thrust vectoring/aerodynamic rudder layout have significant control coupling problems. The traditional single-loop control parameter design method cannot take into account the performance of multiple control loops in this scenario. Therefore, a control law parameter optimization method based on the singular value of the hysteresis matrix is proposed. First, the control parameter optimization interval is determined using the time domain control performance index. Then, the singular value method is used to measure the stability margin of the multiple-input multiple-output (MIMO) system on this basis, and the corresponding optimal objective function is established to optimize the controller parameters. The feasibility of this method is verified by numerical simulation. The results show that the designed control parameter optimization algorithm has better time domain control performance and larger system stability margin than the traditional single-loop control parameter design method.

Directional differentiability of the optimal value function in quadratic programming problem on Hilbert spaces

Directional differentiability of the optimal value function in quadratic programming problem on Hilbert spaces

Global Stochastic Comprehensive Sensitivity Analysis Based on Robustness Grouping and Improved Pelican Algorithm-Optimized Radial Basis Function Neural Network

Abstract Modeling analysis is one of the important means to analyze practical engineering, and as technology continues to evolve, various models are getting closer and closer to reality, while at the same time, there are more and more parameters in the models. It is important to analyse the impact of these parameters on the project to assist engineers in making plans or decisions. Sensitivity analysis (SA) can describe the effect of changes in these parameters on the model. However, complex models often have dozens or even hundreds of parameters, and most current SA methods struggle to deal reliably and effectively with these high-dimensional problems. In addition, it is difficult to obtain the sensitivity of continuous points in the parameter space with traditional SA methods. Therefore, this paper proposes a method that combines adaptive grouping and an Improved Pelican Optimization Algorithm for an optimal radial basis function (IPOA-RBF) agent model to solve these problems. Firstly, a clustering grouping method considering grouping robustness is established to obtain objective and stable parameter grouping results in high-dimensional sensitivity analysis. Secondly, a proxy model based on radial basis function neural network and an improved Pelican optimization algorithm are proposed to capture the logic of the proxy model to obtain the parameter sensitivity of continuous points in the parameter space. Finally, the superiority and applicability of this method is verified using an arch dam simulation model.

Capturing autonomy, competence, and relatedness at work: further examining and validating an English language version of the work-related basic need satisfaction scale.

Self-Determination Theory (SDT) maintains that the satisfaction of the basic psychological needs for autonomy, competence, and relatedness is associated with optimal individual functioning, including in the workplace. A self-report instrument, the Work-related Basic Need Satisfaction scale (W-BNS), has previously been developed and validated in Dutch and Italian. We aimed to validate an English version of the W-BNS. We also evaluated a bifactor model to assess the extent to which the item responses could be explained by a single, underlying general latent trait. A Rasch calibration was also carried out to assess the extent to which the scores conformed to the assumptions of 'fundamental measurement' and could be converted to a common metric. We used data from 141 staff employed by a large UK-based mental health service provider. The postulated three-factor structure provided a good fit to the data. However, a bifactor model, introducing an underlying general factor, provided a superior fit. The items generally conformed to the Rasch measurement model. Evidence of convergent/divergent validity was observed via the correlations between the W-BNS scores and those for the Basic Psychological Needs Satisfaction and Frustration Scale (BPNFS). Regarding construct validity, both the separate needs and total W-BNS scores statistically significantly predicted an individual's reported intention to leave the current employer. Our findings foster research with the WBNS and have implications for how the W-BNS is optimally implemented in practice as a useful brief tool for assessing staff work-related need satisfaction.

The Role of Yoga in Enhancing the Proton, Electron, and Neutron of Human Life

This abstract explores the significance of yoga in enhancing the fundamental aspects of human life, symbolically represented by protons, electrons, and neutrons. By examining how yoga practices foster physical well-being, mental balance, and emotional stability, this study highlights the interconnectedness of these elements in promoting overall health. It emphasizes yoga's role in fostering resilience, enhancing cognitive function, and improving emotional regulation, ultimately contributing to a holistic sense of vitality and well-being. The findings suggest that integrating yoga into daily routines can profoundly impact individuals' lives, aligning their physical, mental, and emotional energies for optimal functioning and harmony.

Predictive Power of the Fried Phenotype in Assessing Postoperative Outcomes in Patients Undergoing Craniotomy for Tumor Resection.

Compared with the modified Frailty Index-11 (mFI-11) frailty tool, reflective of patient comorbidities, the Fried phenotype weighs functional patient variables. This study examined using the Fried phenotype in predicting postoperative outcomes in craniotomy for patients with tumor. This retrospective cohort analysis included patients with Current Procedural Terminology codes for supratentorial/infratentorial tumor resections and preoperative frailty scores. Chart review collected the remaining variables for the primary outcome, length of stay (LOS), and secondary outcomes, discharge disposition and postoperative complications. Basic descriptive statistics summarized patient demographics, clinical parameters, and postoperative outcomes. χ2 tests, t-tests, and ANOVA examined associations and mean differences. Logistic and Poisson regressions explored predictor-outcome relationships. Over 7 years, these 153 patients underwent Fried assessments. The Fried score was biased toward females being more frail (nonfrail 38.0% female, prefrail 50.0% female and frail 65.6% female, P = .027) but not by age, body mass index, or tumor type. The mFI-11 was biased by age (nonfrail 67.8 years vs frail 72.3 years, P < .001) and body mass index (nonfrail 27.5 vs frail 30.8, P < .001) but not sex or tumor type. The Fried score was significantly correlated with increased LOS's (odds ratio [OR] = 5.92, 95% CI = 1.66-21.13, P < .001) but the mFI-11 was not (OR = 0.82, 95% CI = 0.35-1.93, P = .64). The Fried phenotype was significantly correlated with discharge disposition location (P = .016), whereas the mFI-11 was not (P = .749). The Fried score was significantly correlated with postoperative complications (OR = 1.36, 95% CI = 1.08-1.71, P = .01), whereas the mFI-11 was not (OR = 1.10, 95% CI = 0.86-1.41, P = .44). The Fried phenotype more accurately correlates with postoperative outcomes including LOS, discharge disposition location, and complications than does the mFI-11 score. These findings can be used to guide preoperative planning, inform consent, and potentially identify patients who may benefit from functional optimization in the preoperative period to improve postoperative outcomes.

A metabolic modeling-based framework for predicting trophic dependencies in native rhizobiomes of crop plants.

The exchange of metabolites (i.e., metabolic interactions) between bacteria in the rhizosphere determines various plant-associated functions. Systematically understanding the metabolic interactions in the rhizosphere, as well as in other types of microbial communities, would open the door to the optimization of specific predefined functions of interest, and therefore to the harnessing of the functionality of various types of microbiomes. However, mechanistic knowledge regarding the gathering and interpretation of these interactions is limited. Here, we present a framework utilizing genomics and constraint-based modeling approaches, aiming to interpret the hierarchical trophic interactions in the soil environment. 243 genome scale metabolic models of bacteria associated with a specific disease-suppressive vs disease-conducive apple rhizospheres were drafted based on genome-resolved metagenomes, comprising an in silico native microbial community. Iteratively simulating microbial community members' growth in a metabolomics-based apple root-like environment produced novel data on potential trophic successions, used to form a network of communal trophic dependencies. Network-based analyses have characterized interactions associated with beneficial vs non-beneficial microbiome functioning, pinpointing specific compounds and microbial species as potential disease supporting and suppressing agents. This framework provides a means for capturing trophic interactions and formulating a range of testable hypotheses regarding the metabolic capabilities of microbial communities within their natural environment. Essentially, it can be applied to different environments and biological landscapes, elucidating the conditions for the targeted manipulation of various microbiomes, and the execution of countless predefined functions.

Protein kinase 2 of the giant sarcomeric protein UNC-89 regulates mitochondrial morphology and function

UNC-89 is a giant sarcomeric M-line protein required for sarcomere organization and optimal muscle function. UNC-89 contains two protein kinase domains, PK1 and PK2, separated by an elastic region. Here we show that PK2 is a canonical kinase expected to be catalytically active. C. elegans expressing UNC-89 with a lysine to alanine (KtoA) mutation to inactivate PK2 have normally organized sarcomeres and SR, and normal muscle function. PK2 KtoA mutants have fragmented mitochondria, correlated with more mitochondrially-associated DRP-1. PK2 KtoA mutants have increased ATP levels, increased glycolysis and altered levels of electron transport chain complexes. Muscle mitochondria show increased complex I and decreased complex II basal respiration, each of which cannot be uncoupled. This suggests that mutant mitochondria are already uncoupled, possibly resulting from an increased level of the uncoupling protein, UCP-4. Our results suggest signaling from sarcomeres to mitochondria, to help match energy requirements with energy production.

Hepcidin Modulators: A Potential Alternative Treatment of Iron Deficiency and Iron Deficiency Anemia in Heart Failure

Heart failure affects roughly 6.7 million Americans and is associated with many comorbidities and complications, including iron deficiency and anemia. Iron homeostasis is integral to optimal body function. Hepcidin, a hepatically produced peptide, is a major modulator in iron homeostasis. Its synthesis is induced in part by inflammatory states. Iron deficiency in heart failure is currently diagnosed with the assessment of serum ferritin and percent of transferrin saturation. Iron deficiency in the setting of hepcidin excess in inflammatory conditions, such as heart failure, is discussed in this literature review. Iron deficiency is associated with worse prognosis and poorer outcomes in heart failure patients. Targeted treatment of iron deficiency and iron deficiency anemia may provide improved quality of life and decrease mortality and morbidity among heart failure patients. Current treatment options for iron deficiency and anemia include intravenous and oral iron supplementation, as well as other erythropoiesis-stimulating agents. However, concerns regarding safety have been raised in regards to these options. An emerging possibility for the treatment of iron deficiency in the setting of hepcidin overload is hepcidin modulators, including hepcidin antagonists or inhibitors. This review has addressed iron deficiency and anemia in heart failure and suggests that hepcidin antagonists/inhibitors may provide a viable alternative to the classic treatment methods.

ISRSL0: compressed sensing MRI with image smoothness regularized-smoothed \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{\\ell}_{0}$$\\end{document} norm

The reconstruction of MR images has always been a challenging inverse problem in medical imaging. Acceleration of MR scanning is of great importance for clinical research and cutting-edge applications. One of the primary efforts to achieve this is using compressed sensing (CS) theory. The CS aims to reconstruct MR images using a small number of sampled data in k-space. The CS-MRI techniques face challenges, including the potential loss of fine structure and increased computational complexity. We introduce a novel framework based on a regularized sparse recovery problem and a sharpening step to improve the CS-MRI approaches regarding fine structure loss under high acceleration factors. This problem is solved via the Half Quadratic Splitting (HQS) approach. The inverse problem for reconstructing MR images is converted into two distinct sub-problems, each of which can be solved separately. One key feature of the proposed approach is the replacement of one sub-problem with a denoiser. This regularization assists the optimization of the Smoothed \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{\\ell}_{0}$$\\end{document} (SL0) norm in escaping local minimums and enhances its precision. The proposed method consists of smoothing, feature modification, and Smoothed \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{\\ell}_{0}$$\\end{document} cost function optimization. The proposed approach improves the SL0 algorithm for MRI reconstruction without complicating it. The convergence of the proposed approach is illustrated analytically. The experimental results show an acceptable performance of the proposed method compared to the network-based approaches.

Research on Optimization of Function and People Flow Organization of Large Space Buildings Based on the Needs of COVID-19 Nucleic Acid Test:

Research on Optimization of Function and People Flow Organization of Large Space Buildings Based on the Needs of COVID-19 Nucleic Acid Test:

An innovative quick-decision strategy for evaluating the applicability of extractive distillation process with a preconcentration column in separating high-concentration organic waste liquid

In the face of the high energy consumption challenge in separating high-concentration organic waste liquid through extractive distillation (ED), the concept of preconcentration provides a new strategic direction. Therefore, it has become crucial to determine the suitability of the ED process with a preconcentration column for processing such complex mixture. Taking this into consideration, the study proposes an innovative quick-decision method that utilizes thermodynamic analysis of ternary phase diagram to determine the ratio of theoretical product flowrate at the bottom of the preconcentration column to the feed flowrate, which serves as the basis for deciding whether to choose the ED process with a preconcentration column for handling such separation tasks. Three azeotropic systems with different thermodynamic characteristics are chosen as case studies: cyclohexane/ethyl acetate/ethanol (Case 1), ethyl acetate/ethanol/water (Case 2), and n-hexane/ethyl acetate/acetonitrile (Case 3). The task of separating the above three systems is accomplished through the application of both three-column extractive distillation configuration (TCED), and four-column extractive distillation configuration (FCED). The objective functions for multi-objective optimization of all separation configurations include the minimum total annual cost (TAC), the minimum entropy generation and the minimum CO2 emissions. The research findings indicate that when the ratio of theoretical product flowrate at the bottom of the preconcentration column to the feed flowrate is not less than 0.56, or when there is a significant concentration difference among the components in the feed stream, the FCED configuration exhibits clear advantages over the TCED configuration in terms of economic, environmental, and thermodynamic aspects.

Standardizing Identification of Cognitive Impairment in the Acute Hospital Setting: Toward a Common Language.

Although many hospitals universally screen their patients for functional mobility and activity impairment, there is no common method to do this for cognitive impairment. Establishing an interdisciplinary and standardized process to screen for cognitive impairment is essential for early identification of impairments, optimization of patients' function, and safe discharge planning. Previously, the reliability and validity of the Activity Measure for Post-Acute Care Applied Cognitive Inpatient Short Form "6-Clicks" (AM-PAC ACISF) were measured among occupational therapy practitioners and speech-language pathologists; however, its reliability among other team members, specifically nurses, is unknown. This study reports the interprofessional interrater reliability of the AM-PAC ACISF in the acute care hospital setting. To examine the nursing-therapist interrater reliability of the AM-PAC ACISF in the acute care hospital setting. A prospective study using a convenience sample. Acute care hospital. Fifty adult patients, six nurses, five occupational therapy practitioners, and one speech-language pathologist. Interrater reliability for the AM-PAC ACISF among therapists and nurses. Interrater reliability was good among nurses and therapists, with an intraclass correlation coefficient of .88 (95% confidence interval [.79, .93]) for the AM-PAC ACISF. Findings suggest that the AM-PAC ACISF is reliable for use among interdisciplinary teams, helps to establish a common language for early identification of cognitive impairment, and can be used for guiding clinical discussions around patient cognition in the acute care setting. Plain-Language Summary: Having an interdisciplinary and standardized process to screen patients early for cognitive impairment is essential for identifying impairments, optimizing patients' function, and facilitating safe discharge planning in the acute care setting. Early identification of cognitive impairment by the interdisciplinary team can also allow for timely occupational therapy and other rehabilitation service interventions. The findings of this study suggest that the Activity Measure for Post-Acute Care Applied Cognitive Inpatient Short Form "6-Clicks" (AM-PAC ACISF) can reliably be used by interdisciplinary teams, establishes a common language for early identification of cognitive impairment, and can be used for guiding clinical discussions around patient cognition in the acute care setting.

A hierarchical deep reinforcement learning method for solving urban route planning problems under large-scale customers and real-time traffic conditions

As urbanization and economic growth advance, large-scale customers and real-time traffic conditions have become crucial factors in urban route planning. Deep reinforcement learning is considered the most effective method for solving urban route planning problems involving large-scale customers and real-time traffic conditions. Due to memory usage limitations, existing deep reinforcement learning methods cannot identify candidate customers or determine optimal travel routes in large-scale and real-time environments. To tackle these problems, this study introduces a hierarchical deep reinforcement learning method utilizing an improved transformer model (HDRLITF) based on the divide-and-conquer concept. Graph attention networks and gate mechanisms are integrated into the transformer model to capture dynamic features and improve the model’s performance. A two-stage training method, based on the actor-critic algorithm, is proposed to determine the optimal policy function. To evaluate the HDRLITF method, experiments were conducted using datasets from the cities of Shenzhen and Chengdu in China. The experimental results suggest that the HDRLITF method can effectively interact with real-time traffic environments and obtain high-quality solutions compared to other deep reinforcement learning methods. The robustness and reliability of HDRLITF were further validated across multiple traffic scenarios and indicators.

Optimal Test Design for Estimation of Mean Ability Growth.

The design of an achievement test is crucial for many reasons. This article focuses on a population's ability growth between school grades. We define design as the allocating of test items concerning the difficulties. The objective is to present an optimal test design method for estimating the mean and percentile ability growth with good precision. We use the asymptotic expression of the variance in terms of the test information. With that criterion for optimization, we propose to use particle swarm optimization to find the optimal design. The results show that the allocation of the item difficulties depends on item discrimination and the magnitude of the ability growth. The optimization function depends on the examinees' abilities, hence, the value of the unknown mean ability growth. Therefore, we will also use an optimum in-average design and conclude that it is robust to uncertainty in the mean ability growth. A test is, in practice, assembled from items stored in an item pool with calibrated item parameters. Hence, we also perform a discrete optimization using simulated annealing and compare the results to the particle swarm optimization.

Luminance-based methodology for assessment of low level haze in glazing

AbstractVisual defects, in particular haze, in glass and façade technologies can significantly impact the aesthetic quality and human experience of daylight and views in buildings. The glass and façade industry increasingly requires methods that can objectively predict and measure the subjective user experience of haze. This is required to appropriately inform the manufacturing process, ensuring optimal functionality and performance, and avoiding material waste and economic losses due to the replacement of defective glazing. Existing methods for measuring haze are not appropriate for assessing large samples, either at manufacturing sites or in-situ. However, haze defects are often only exhibited and visible when glazing is produced in large samples or installed under real luminous conditions. This paper introduces a novel luminance-based method that measures haze by evaluating the halo around a light source that is observed through the glazing. This method is initially tested in a controlled laboratory setting on small glazing samples with varying level of haze. The halo serves as a proxy for haze severity and it is quantified by using luminance-based measurements. In this work, the newly-proposed method is verified by comparing the ranking in haze severity of different samples as performed by means of a standard haze meter and the newly proposed method. Additionally, the paper examines the dependence of this ranking on factors such as camera setup distance and light intensity. It was found that the proposed method was able to effectively ranks samples differing in haze intensity by more than 0.1 orders of magnitude. Positioning the camera closer to the glazing and using higher light intensity yielded more accurate results. However, for haze levels below 1% and differences smaller than 0.1 orders of magnitude, the accuracy is insufficient. To define the expected level of accuracy of methods for haze characterisation in-situ, the sensitivity of the human eye to haze under varying luminous environments and view content needs to be quantified.

Implementation of Support Vector Machine (SVM) for Classification of Heart Failure Patients (Case Study: Prabumulih City Hospital)

The heart is a vital organ that functions as a blood pump to meet the body's oxygen and nutrient needs. Disorders of the heart can result in disruptions to blood circulation, underscoring the importance of maintaining heart health to prevent heart disease. The optimal function of the heart depends on the condition of the cardiac muscles, valves, and proper pumping rhythm. Heart failure, as one of the heart diseases, refers to the heart's inability to meet the body's blood demands. The management of heart disease involves comprehensive medical services provided in hospitals. One effort to improve the quality of healthcare services is to enhance the performance of hospitals. The Regional General Hospital of Kota Prabumulih is one healthcare institution that addresses various diseases, including heart disease. However, the classification process for heart disease at the Regional General Hospital is still conducted manually, consuming time and inefficient. To address this issue, the development of an intelligent system capable of classifying heart failure based on patient symptoms is necessary. This research proposes the implementation of the Support Vector Machine (SVM) method as a classification model, which is a high-dimensional hypothesis learning system. It is hoped that this system can assist medical professionals in determining the initial steps in managing heart failure quickly and effectively.

Reconstructing confidence: Fiber reinforced reattachment of a fractured maxillary lateral incisor – A clinical case report

AbstractA broken smile can be more than just a physical injury—it can impact self-esteem and confidence. This case highlights the successful reattachment of a fractured upper front tooth in a 32-year-old female following a recent fall. The comprehensive treatment strategy involved immediate root canal therapy to relieve pain, followed by a gingivectomy and osteotomy to correct the subgingival fracture and restore the biologic width. Advanced techniques, including fiber post and core reconstruction reinforced with ribbond fiber, were used to stabilize and support the tooth structure. The final touch is done with a layer of composite for optimal function and aesthetics. This holistic approach not only revived the patient's smile but also rejuvenated her confidence and psychological well-being.