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Salp Navigation and Competitive based Parrot Optimizer (SNCPO) for efficient extreme learning machine training and global numerical optimization
Abstract Metaheuristic optimization algorithms play a crucial role in solving complex real-world problems, including machine learning parameter tuning, yet many existing approaches struggle with maintaining an effective balance between exploration and exploitation, leading to premature convergence and suboptimal solutions. The traditional Parrot Optimizer (PO) is an efficient swarm-based technique; however, it suffers from inadequate adaptability in transitioning between exploration and exploitation, limiting its ability to escape local optima. To address these challenges, this paper introduces the Salp Navigation and Competitive based Parrot Optimizer (SNCPO), a novel hybrid algorithm that integrates Competitive Swarm Optimization (CSO) and the Salp Swarm Algorithm (SSA) into the PO framework. Specifically, SNCPO employs a pairwise competitive learning strategy from CSO, which divides the population into winners and losers. Winners are refined using SSA-inspired salp navigation, enabling enhanced global search in the early stages and a dynamic transition to exploitation. Meanwhile, losers are updated using PO’s communication strategy, reinforcing solution diversity and exploration. To validate the efficacy of SNCPO, rigorous experimental evaluations were conducted on CEC2015 and CEC2020 benchmark functions, four engineering design optimization problems, and Extreme Learning Machine (ELM) training tasks across 14 datasets. The results demonstrate that SNCPO consistently outperforms existing state-of-the-art algorithms, achieving superior convergence speed, solution quality, and robustness while effectively avoiding local optima. Notably, SNCPO exhibits strong adaptability to diverse optimization landscapes, reinforcing its potential for real-world engineering and machine learning applications.
X-ray crystallographic and hydrogen deuterium exchange studies confirm alternate kinetic models for homolog insulin monomers
Despite the crucial role of various insulin analogs in achieving satisfactory glycemic control, a comprehensive understanding of their in-solution dynamic mechanisms still holds the potential to further optimize rapid insulin analogs, thus significantly improving the well-being of individuals with Type 1 Diabetes. Here, we employed hydrogen-deuterium exchange mass spectrometry to decipher the molecular dynamics of newly modified and functional insulin analog. A comparative analysis of H/D dynamics demonstrated that the modified insulin exchanges deuterium atoms faster and more extensively than the intact insulin aspart. Additionally, we present new insights derived from our 2.5 Å resolution X-ray crystal structure of modified hexamer insulin analog at ambient temperature. Furthermore, we obtained a distinctive side-chain conformation of the Asn3 residue on the B chain (AsnB3) by operating a comparative analysis with a previously available cryogenic rapid-acting insulin structure (PDB_ID: 4GBN). The experimental conclusions have demonstrated compatibility with modified insulin’s distinct cellular activity, comparably to aspart. Additionally, the hybrid structural approach combined with computational analysis employed in this study provides novel insight into the structural dynamics of newly modified and functional insulin vs insulin aspart monomeric entities. It allows further molecular understanding of intermolecular interrelations driving dissociation kinetics and, therefore, a fast action mechanism.
Water footprint management in textile industry through Acid Blue 113 remediation using halloysite nanoclay as a sustainable adsorbent
CCR2 dependent recruited pro-inflammatory monocytes contribute to the development of left ventricular hypertrophy in mice upon transverse aortic constriction
C-C chemokine receptor type 2 positive monocytes are recruited from the circulation to infiltrate inflamed tissue. Left ventricular (LV) hypertrophy caused by pressure overload presents with a chronic myocardial inflammation in our mouse model of transverse aortic constriction (TAC). Recent analyses demonstrated that deficiency of fractalkine receptor CX3CR1 leads to a pro-inflammatory phenotype characterized by increased numbers of Ly6Chigh macrophages in the myocardium due to chemokine receptor CCR2 dependent monocyte recruitment from the circulation. Here, we analyzed the role of CCR2 in the development of left ventricular hypertrophy using Ccr2-/- mice. We were able to show that a lack of CCR2 dependent recruited Ly6Chigh monocytes in the myocardium reveled cardioprotective effects resulting in less hypertrophy and reduced brain natriuretic peptide (BNP) expression, as biomarker of heart failure, in the myocardium. CCR2-deficiency caused an increase in neutrophil and a reduced macrophage accumulation in the myocardium in response to pressure overload. The cytokine pattern measured in the LV tissue indicates a significantly reduced release of IL1-β whereas TNF-α concentrations are increased following TAC. IL-6 secretion is not altered by the lack of CCR2 and the pro-remodeling cytokine IL-10 is not increased either. This study highlights the importance of CCR2 in the pathogenesis of LV hypertrophy and the relevance of CCR2 dependent recruited monocytes for the orchestration of the cardiac immune response.
Optimization of the synthesis of highly pure calcium sulphate from dolomite ore
A method for site-specifically tethering the enzyme urease to DNA origami with sustained activity
Attaching enzymes to nanostructures has proven useful to the study of enzyme functionality under controlled conditions and has led to new technologies. Often, the utility and interest of enzyme-tethered nanostructures lie in how the enzymatic activity is affected by how the enzymes are arranged in space. Therefore, being able to conjugate enzymes to nanostructures while preserving the enzymatic activity is essential. In this paper, we present a method to conjugate single-stranded DNA to the enzyme urease while maintaining enzymatic activity. We show evidence of successful conjugation and quantify the variables that affect the conjugation yield. We also show that the enzymatic activity is unchanged after conjugation compared to the enzyme in its native state. Finally, we demonstrate the tethering of urease to nanostructures made using DNA origami with high site-specificity. Decorating nanostructures with enzymatically-active urease may prove to be useful in studying, or even utilizing, the functionality of urease in disciplines ranging from biotechnology to soft-matter physics. The techniques we present in this paper will enable researchers across these fields to modify enzymes without disrupting their functionality, thus allowing for more insightful studies into their behavior and utility.
The REDOX balance in the prefrontal cortex is positively modulated by aerobic exercise and altered by overfeeding
Factors shaping subjective financial well-being in emerging adults: A comparative study of Italy and Germany
Academics and policymakers recognize the growing importance of subjective financial well-being for emerging adults, yet little is known about the factors influencing the subjective financial well-being perceived by the emerging adult population. We aim to investigate the role of socio-demographic characteristics (job type, age, country of residence), individual differences in financial knowledge (financial literacy), skills (financial behavior), cognitive response styles (impulsiveness, future orientation, and maximization), and attitudinal components (trust in governmental institutions and financial professionals) in shaping subjective financial well-being among emerging adults in Italy and Germany, representing the Mediterranean and Northern models of the transitions to adulthood, respectively. A sample of 385 participants residing in Italy (n = 193) and Germany (n = 192) voluntarily participated in an online survey. Variables such as trust and maximization were incorporated into a prior financial well-being model to assess their relevance in predicting subjective financial well-being. In Model 1, variables from the prior theoretical model (socio-demographic characteristics, financial literacy, financial behavior, impulsiveness, and future orientation) were analyzed. In Model 2, trust and maximization were added as predictors of subjective financial well-being. Results revealed that the inclusion of these variables improved the model fit, and further confirmed the significant role of age, financial behavior (specifically, caring for financial matters), impulsiveness, future orientation, and trust in governmental institutions in subjective financial well-being.
Human RCC1L is involved in the maintenance of mitochondrial nucleoids and mtDNA
Research on simulation of permanent magnet synchronous motor in full speed range
This paper studies the speed regulation simulation of permanent magnet synchronous motor. First, this paper analyzes the mathematical model of permanent magnet synchronous motor, and studies the control strategy of Id = 0, maximum torque-current ratio and weak magnetic leading Angle under synchronous rotation coordinate system. Secondly, the control strategy model is established on MATLAB/SIMULINK platform, including coordinate transformation, space vector pulse width modulation (SVPWM), proportional integration regulator, three-phase inverter, MTPA, weak magnetic core control algorithm module. Meanwhile, an APP for real-time monitoring of motor simulation model operation, control and parameter adjustment is designed by using APP Designer toolbox. Finally, the start and stop, speed increase and decrease, load surge torque and motor high-speed operation of the electric motorcycle PMSM are simulated in the actual operation. The experimental results show that the system has strong response ability and anti-interference ability under the control of current and speed PI regulator. In the motor start-stop state, the MTPA control strategy can distribute large electromagnetic torque during start-up, effectively improve the efficiency of the inverter and save costs. Compared with MTPA and Id = 0 control strategies, weak magnetic control has excellent speed increase effect, up to 6000r/min, and has strong anti-interference ability. Through the control method of the leading Angle, the dynamic switching between MTPA and weak magnetic control strategy is realized, and the running speed range of PMSM is effectively extended.
Lineage analysis of human papillomavirus types 33 and 35 based on E6 gene in cervical samples from Tehran, Iran
Population and sexual fluctuation of Calliphoridae and Mesembrinellidae (Diptera: Oestroidea) in the Atlantic forest of Rio de Janeiro
Dipterans of the Calliphoridae and Mesembrinellidae families are of high relevance in the Atlantic Forest of Rio de Janeiro, and it is important to examine their diversity and abundance in the different ecological areas of this biome over a time interval. This study aimed to study the diversity and abundance of Calliphoridae and Mesembrinellidae by evaluating the sexual variation and the influence of abiotic factors (average temperature, relative humidity and total precipitation) on the capture of insects collected during the four seasons of the year. Four traps were installed in each ecological area containing 300 grams of beef liver as attractive bait, which remained exposed for 48 hours in each season during the period between autumn 2021 and summer 2022. The collected dipterans were sacrificed, sent to the Laboratório de Estudos de Dípteros (LED-UNIRIO), and taxonomically identified. The Kruskal-Wallis and Wilcoxon tests were used to examine the influence of the four seasons on the abundance, and the Spearman correlation was used to relate abundance to abiotic variables. A total of 2,826 dipterans were collected during the four seasons of the year, represented by nine species of the Calliphoridae family and ten of the Mesembrinellidae family. During the summer, a numerically larger amount of insects was collected, but the Kruskal-Wallis test (chi-square = 5.2781, p = 0.1525) showed there was no significant difference between the abundance of the species collected and the seasons. Spearman’s correlation showed that most species did not show a significant correlation between their respective abundances and the analyzed abiotic factors. The Wilcoxon test indicated that there is a significant difference between the abundance of females and males, with females being significantly more abundant than males, however the difference is statistically greater within the Calliphoridae family (W = 60.49, p = 5.8x10-12) in relation to the Mesembrinellidae family (W = 1231.5, p = 0.019).
An effective lossless compression method for attitude data with implementation on FPGA
Research and validation of forest carbon sequestration measurement model based on biomass method-A case study of Guizhou Province
With the establishment of China Certified Voluntary Emission Reduction (CCER) market, more and more people pay attention to the development of forestry carbon sequestration in Guizhou. Guizhou is rich in forestry resources, but at the current stage, there are a series of measurement difficulties in the development of forestry carbon sequestration. Some traditional research methods have the problems of heavy workload and over-reliance on manual work. In view of the above problems, this paper studies the construction of carbon sequestration measurement model based on biomass estimation model. In this paper, sampling design, project boundary, carbon pool selection and other factors are considered in the construction of the model. At the same time, in view of the problem that the calculation results of the actual net carbon sequestration caused by CH4, N2O and other gas emissions in forestry carbon sequestration projects are not accurate enough, this study deduces a specific calculation model based on the actual project development needs and the data of 10 forest farms that have formed forestry carbon tickets. Taking Chinese fir as the research object, the effect of carbon sequestration model was verified and analyzed. Compared with the existing forestry carbon sequestration project evaluation, the results showed that the average relative error of the model was 6. 09%, and the absolute error range was 0. 348-4. 262/hm2, and the model effect was good. The establishment of the model not only solves the problems of over-reliance on manpower, material resources and inefficiency in the development of forestry carbon sequestration in China, but also accelerates the rapid and vigorous growth of forestry carbon sequestration industry in Guizhou, and contributes China's wisdom and effective solutions to global climate governance.
Kinetic modeling and CFD simulation of in-situ heavy oil upgrading using batch reactors and porous media
Postpartum maternal bonding scale: Development and validation in a low- and middle- income country setting
Introduction The World Health Organization’s Nurturing Care Framework recommends promoting secure postpartum maternal-infant bonding practices through responsive caregiving for healthy child development. Various instruments exist to assess maternal-infant bonding, but they differ in theoretical foundations and constructs, limiting their broad application and comparability. Notably, there is a lack of bonding instruments developed for low- and middle-income countries (LMICs), where children under five are most at risk of not reaching their developmental potential. This paper describes the development and psychometric validation of a conceptually grounded postpartum maternal bonding scale in an LMIC context and highlights its potential applications in similar settings. Methods Based on a literature review of bonding concepts and measurement processes, we developed a postpartum maternal bonding scale using a cultural adaptation model for psychometric instruments for children and adolescents. This involved identifying and reviewing existing bonding-related tools, generating items, iterative rounds of expert reviews, and pretesting with postpartum women. We then conducted a final survey with a large sample of women at 42 days postpartum to establish the scale’s psychometric properties. The study was conducted in the Thatta and Sujawal districts of Sindh, Pakistan. Results An initial pool of 44 items was developed following a literature review and interviews with postpartum women. After multiple rounds of expert review and cognitive pretesting, a 30-item tool was selected for field testing. Using data from 310 postpartum women, we examined the tool’s structure through exploratory (EFA) and confirmatory factor analysis (CFA), leading to a refined 12-item tool. The EFA revealed three factors related to Emotional, Cognitive, and Behavioural bonding. Taking the four highest loading items from each domain, we performed CFA using three models: a first-order model with the three domains, a second-order model, and a bifactor model, which included an overall bonding construct. The bifactor model showed the best fit (comparative fit index = 0.951; root mean square error of approximation = 0.066; standardized root mean square residual = 0.045). This indicates that both an overall bonding construct and specific domains can be measured separately. Pairwise domain correlations were all below 0.67, and internal reliability statistics ranged from 0.63-0.72 (Cronbach’s Alpha) and 0.64-0.77 (global omega). Regression analysis showed associations between bonding scores and factors such as cesarean delivery (reduced behavioural bonding score for mothers having caesarean: -0.94, 95% Confidence Interval -1.86 to -0.01, p-value 0.047), maternal disability (reduced overall bonding score for mothers with severe disability -1.54, 95% CI -3.12 to 0.03, p-value 0.054), and probable postpartum depression (reduced overall bonding score in mothers with probable PPD -1.57, 95% CI -2.70 to -0.45, p-value 0.006). Conclusion The 12-item postpartum maternal bonding scale (PMBS) is a conceptually grounded instrument. It is a brief, easy-to-administer tool with potential cross-cultural use in low- and middle-income settings after cultural adaptation.
Synergizing NOMA and energy harvesting in full duplex mobile edge computing for optimized energy efficiency
Exploring similarity patterns in a large scientific corpus
Similarity-based analysis is a common and intuitive tool for exploring large data sets. For instance, grouping data items by their level of similarity, regarding one or several chosen aspects, can reveal patterns and relations from the intrinsic structure of the data and thus provide important insights in the sense-making process. Existing analytical methods (such as clustering and dimensionality reduction) tend to target questions such as “Which objects are similar?”; but since they are not necessarily well-suited to answer questions such as “How does the result change if we change the similarity criteria?” or “How are the items linked together by the similarity relations?” they do not unlock the full potential of similarity-based analysis—and here we see a gap to fill. In this paper, we propose that the concept of similarity could be regarded as both: (1) a relation between items, and (2) a property in its own, with a specific distribution over the data set. Based on this approach, we developed an embedding-based computational pipeline together with a prototype visual analytics tool which allows the user to perform similarity-based exploration of a large set of scientific publications. To demonstrate the potential of our method, we present two different use cases, and we also discuss the strengths and limitations of our approach.