Final Fit Research Articles

Multi-Buoy Deployment Method Based on an Improved Tuna Swarm Optimizer Enhanced with Fractional-Order Calculus Method for Marine Observation

Ocean buoys play a critical role in marine hydrological, water quality, and meteorological monitoring, with applications in navigation, environmental observation, and communication. However, accurately modeling and deploying a multi-buoy system in the complex marine environment presents significant challenges. To address these challenges, this study proposes an enhanced deployment strategy using the tuna swarm optimizer enhanced with the fractional-order calculus method for marine observation. The proposed method first introduces a detailed observation model that precisely captures the performance of buoys in terms of coverage and communication efficiency. By integrating the observation coverage ratio and communication energy consumption, we establish an optimal multi-buoy deployment model. The proposed method leverages tent chaotic mapping to improve the diversity of initial solution generation and incorporates fractional-order calculus to strengthen its search capabilities. Simulation experiments and statistical analysis verify the effectiveness of the proposed deployment model, with the proposed method achieving the best performance in deploying the multi-buoy system, reaching a final fitness value of 0.190052 at iteration 449, outperforming TSA, PSO, GWO, and WOA. These results highlight the potential of the proposed method in optimizing multi-buoy system deployment in marine observation.

Pediatric obesity and adverse outcomes following deformity correction surgery for adolescent idiopathic scoliosis: A cross-sectional analysis using 2015-2019 NIS data.

Obesity in the pediatric population has been a growing medical concern over the last few decades with a prevalence of 19.7% as of 2017-2020. Obesity is a risk factor for greater scoliotic curves and failure of conservative therapy for adolescent idiopathic scoliosis (AIS). Establishing a correlation between obesity and a wide variety of adverse outcomes following scoliosis surgery can assist in the preoperative consultation with the family and proper optimization of the patient for scoliosis fusion surgery. The National Inpatient Sample (NIS) was used to access inpatient data from 2015 to 2019. Pediatric patients with idiopathic scoliosis admitted for spinal deformity correction via posterior spinal fusion of over 8 levels were identified. Patients were stratified based on the comorbid diagnosis of obesity. Variables that were significantly associated with outcomes (p < 0.05) were used in a multivariable logistic regression to control for confounders. Backwards stepwise p-value removal was used to build the final model and model fit was assessed using the area under the curve. A total of 855 obese and 17,285 non-obese pediatric patients undergoing posterior instrumented fusion for scoliotic deformity correction were identified. The obese group was associated with a higher rate of SSI (0.6% vs 0.1%, p < 0.001), UTI (1.2% vs. 0.3%, p < 0.001), and AKI (0.6% vs 0.1%, p = 0.12) compared to the normal BMI group. Obese patients were also more likely to have a non-routine discharge when compared to non-obese (4.7% vs. 2.3%, p < 0.001). The rate of having more than one complication occurring postoperatively was higher in the obese group, however, this finding was not significant (0.6%, vs 0.4%, p = 0.385). On multivariate regression analysis, obesity was positively associated with SSI (OR = 2.758, CI = 0.999-7.614, p = 0.050), UTI (OR = 2.221, CI = 1.082-4.560, p = 0.030), non-routine discharge (OR = 1.515, CI = 1.070-2.147, p = 0.019), and an extended LOS (OR = 1.869, CI = 1.607-2.174, p < 0.001). Obesity was associated with postoperative blood transfusion, SSI, UTI, increased length of stay, and non-routine discharge after pediatric AIS deformity surgery. In addition to the increased morbidity seen in obese patients, we also identified the significantly increased cost of care for this group when compared to non-obese patients. These data should be used for a robust preoperative risk assessment and evidence for BMI optimization prior to deformity correction for AIS.

A Novel Accurate Peak Extraction Algorithm of Mass Spectrometry Based on Iterative Adaptive Curve Fitting.

In the analysis of mass spectrometry, the peak identification from the overlapped region is necessary yet difficult. Although various methods have been developed to identify these peaks, especially the continuous wavelet transformation, their applications are still limited and it is hard to deal with the complex overlapped peaks. In this study, a novel peak extraction algorithm of mass spectrometry based on iterative adaptive curve fitting is proposed to address these challenges. It fully utilizes the global optimization characteristics of adaptive curve fitting. Initial peak parameters are obtained using a window searching method, and the residuals between the adaptive fitting peak and the original data indicate the fit's effectiveness and provide information about the peaks in overlap. Using this information, we performed iterative adaptive fitting, continuously updating the overlapped peaks until the residuals met the completion criteria. All of the peaks within the overlapped region can be successfully extracted by the final fitting. The proposed method is evaluated by the simulated data, the real signal from a public data set, and the spectra of two different mass spectrometry instruments. The results demonstrate that this method can more effectively extract peaks with severe overlap and multiple overlapped peaks, resist noise interference, and offer the potential to process peaks with a high dynamic range. More importantly, the proposed method accurately identifies overlapped peaks in the actual spectra from various mass spectrometry instruments, which helps the qualitative and quantitative analyses to a great extent.

Optimizing a Hybrid Wind-Solar-Biomass System with Battery and Hydrogen Storage Using Generic Algorithm-Particle Swarm Optimization for Performance Assessment

This paper investigates the optimal design of a hybrid renewable energy system, integrating wind turbines, solar photovoltaic systems, biomass, and battery and hydrogen storage to ensure a reliable energy supply at the lowest annual cost for a residential load in Kern County, USA. The hybrid generic algorithm particle swarm optimization (GAPSO) algorithm was adopted to determine the optimal configuration of parameters and cost-effectiveness, considering technical, economic, environmental, and social performance indicators. The generic algorithm (GA) and particle swarm optimization (PSO) validate the effectiveness of the proposed technique, showcasing its efficiency in system optimization. The findings indicate that GAPSO outperforms GA and PSO due to its rapid convergence, lowest final fitness value, and stable optimization process. The hybrid GAPSO's performance, combined with the different capacities of wind turbines (4,561 kW), solar PV (8,480 kW), biomass (2,261 kW), battery banks (8,000 kWh), and fuel cells (2,392 kW), resulted in an annual cost of $6,239,193; energy cost and net present value of $0.48/kWh and $101,333,937. The system maintained a supply loss of 0.8%, achieved an availability index of 99.2%, a renewable energy fraction of 88.87%, GHGs emission of 953615 kg, land use of 3842875 m2, and water consumption 528678 L respectively. GAPSO achieved a 2.17% and 0.01% improvement in cost-effectiveness and 11.11% increase in reliability compared to GA and PSO.

Combined Prediction of Dust Concentration in Opencast Mine Based on RF-GA-LSSVM

Accurate prediction of dust concentration is essential for effectively preventing and controlling mine dust. The environment of opencast mines is intricate, with numerous factors influencing dust concentration, making accurate predictions challenging. To enhance the prediction accuracy of dust concentration in these mines, a combined prediction algorithm utilizing RF-GA-LSSVM is developed. Initially, the random forest (RF) algorithm is employed to identify key features from the meteorological and dust concentration data collected on site, ultimately selecting five indicators—temperature, humidity, stripping amount, wind direction, and wind speed—as the input variables for the prediction model. Next, the data are split into a training set and a test set at a 7:3 ratio, and the genetic algorithm (GA) is applied to optimize the least squares support vector machine (LSSVM) model for predicting dust concentration in opencast mines. Additionally, model evaluation metrics and testing methods are established. Compared with LSSVM, PSO-LSSVM, ISSA-LSSVM, GWO-LSSVM, and other prediction models, the GA-LSSVM model demonstrates a final fitting degree of 0.872 for PM2.5 concentration data and 0.913 for PM10 concentration data. The GA-LSSVM model clearly demonstrates a strong predictive performance with low error and high fitting. The research results can serve as a foundation for developing dust control measures in opencast mines.

A competency model for basic public health professionals in public health emergencies.

To develop and validate a comprehensive competency model for basic public health professionals to enhance their response to public health emergencies. Staff working in basic public health institutions such as the Centre for Disease Control, community health services and township health centres were selected as the study population. Through an integrative literature review, structured questionnaire survey (n = 1310), exploratory factor analysis and confirmatory factor analysis, we developed and validated a competency model. Exploratory factor analysis was utilized to extract common factors, and confirmatory factor analysis was employed to establish the model and ensure its robustness. Identified competencies by exploratory factor analysis encompass professional and technical skills, medical professionalism, specialized medical knowledge, cognitive and managerial aptitude, public health service competence, emergency response proficiency and physical and mental quality. The model displayed high validity, with a Kaiser-Meyer-Olkin score of 0.933, the χ2 value of Bartlett's test of sphericity was 4169.238 at 889 degrees of freedom (df) (p < 0.001) and the cumulative contribution rate was 60.7%. The confirmatory analysis yielded a final model fit (χ2/df = 2.461) with satisfactory adjusted fit indicators. This validated competency model provides a robust framework for selecting, training and evaluating basic public health professionals, potentially enhancing overall emergency response capabilities.

Translation, validity and reliability of the fall risk scale for older adults

IntroductionFalls in older adults are a common and serious threat to health and functional independence. It can cause psychological distress, inability to participate in activities of daily living, brain injury, fractures, and even death. The aim was to analyze the psychometric properties of the self-assessed fall risk scale (FRS) that measures the risk of falls in older adults in a central region of Chile, as well as to verify the concurrent validity against functional fitness tests.Materials and methodsA descriptive cross-sectional study was carried out in 222 older adults (OA) [34 males and 188 females] with an age range of 65 to 85 years. The 13-item self-perceived fall risk scale (FRS) was validated. Anthropometric measures (weight, height and waist circumference) were assessed. Five functional fitness tests were measured (right and left hand grip strength, biceps curl, up-and-go, agility and 6-minute walk test). Validation was performed by construct validation [(exploratory factor analysis (EFA) and confirmatory factor analysis (CFA)] and concurrent validity.ResultsThe EFA revealed 4 factors in the FRS scale [1: fear of falling (variance 27.1%), 2: use of assistive devices (variance 10.6%), 3: loss of sensation (variance 9.3%), and 4: limited mobility (variance 8.3%)]. Factor loadings ranged from ∼ 0.50 to 0.83 across the 4 components. The Kaiser-Meyer Olkin sample adequacy test (KMO) reflected adequate adequacy (KMO = 0.79, chi-square (X2) = 498.806, gl = 78, p = 0.00). The CFA showed a satisfactory final fit [chi-square (X2) = 126.748, Root mean squared error of approximation (RMSEA) = 0.042, Tucker-Lewis Index (TLI) = 0.946, Comparative fit index (CFI) = 0.935 y Normed fit index (NFI) = 0.90. The relationships between the FRS scale and functional fitness tests (right and left hand grip strength, biceps curl, up-and-go, agility and 6-minute walk test) ranged from low to moderate (r= -0.23 to 0.41).ConclusionThe FRS scale showed acceptable validity and reliability in older adults in central region of Chile. It is expected that this scale will be useful for assessing fall risk in clinical and epidemiological settings in the aging Chilean population.

Within-plant genetic drift to control virus adaptation to host resistance genes.

Manipulating evolutionary forces imposed by hosts on pathogens like genetic drift and selection could avoid the emergence of virulent pathogens. For instance, increasing genetic drift could decrease the risk of pathogen adaptation through the random fixation of deleterious mutations or the elimination of favorable ones in the pathogen population. However, no experimental proof of this approach is available for a plant-pathogen system. We studied the impact of pepper (Capsicum annuum) lines carrying the same major resistance gene but contrasted genetic backgrounds on the evolution of Potato virus Y (PVY). The pepper lines were chosen for the contrasted levels of genetic drift (inversely related to Ne, the effective population size) they exert on PVY populations, as well as for their contrasted resistance efficiency (inversely related to the initial replicative fitness, Wi, of PVY in these lines). Experimental evolution was performed by serially passaging 64 PVY populations every month on six contrasted pepper lines during seven months. These PVY populations exhibited highly divergent evolutionary trajectories, ranging from viral extinctions to replicative fitness gains. The sequencing of the PVY VPg cistron, where adaptive mutations are likely to occur, allowed linking these replicative fitness gains to parallel adaptive nonsynonymous mutations. Evolutionary trajectories were well explained by the genetic drift imposed by the host. More specifically, Ne, Wi and their synergistic interaction played a major role in the fate of PVY populations. When Ne was low (i.e. strong genetic drift), the final PVY replicative fitness remained close to the initial replicative fitness, whereas when Ne was high (i.e. low genetic drift), the final PVY replicative fitness was high independently of the replicative fitness of the initially inoculated virus. We show that combining a high resistance efficiency (low Wi) and a strong genetic drift (low Ne) is the best solution to increase resistance durability, that is, to avoid virus adaptation on the long term.

Study on Associations between Root and Aboveground Growth of Mixed-Planting Seedlings of Populus tomentosa and Pinus tabuliformis under Soil Nutrient Heterogeneity

Near-natural transformation can convert artificial monoculture forests into mixed forests with diverse ages, multi-layered structures, and enhanced ecological functions. This transformation optimizes stand structure, improves soil physical and chemical properties, and enhances stand productivity and species diversity. This study aimed to explore the relationship between the underground roots and aboveground growth of Pinus tabuliformis and Populus tomentosa under conditions of nutrient heterogeneity, with the goal of advancing plantation transformation. This research focused on 1-year-old Populus tomentosa and 5-year-old Pinus tabuliformis, employing two planting densities (25 cm and 50 cm) and three fertilization levels, low (50 g·m−2), medium (100 g·m−2), and high (200 g·m−2), using Stanley Potassium sulfate complex fertilizer (N:P:K = 15:15:15). Each treatment had three replicates, resulting in a total of nine experimental groups, all planted in circular plots with a radius of 1 m. Standard major axis (SMA) regression was used to analyze the allometric relationship between underground fine root biomass and aboveground organ biomass. This study further explored correlations between fine root length, root surface area, volume, biomass, and aboveground biomass, culminating in a mixed-effects model. The mixed-effects model quantified the relationships between underground roots and aboveground growth in varying soil nutrient environments. The results indicated optimal root growth in Populus tomentosa and Pinus tabuliformis, characterized by maximum root length, surface area, and volume, under conditions of 200 g·m−2 soil nutrient concentration and 50 cm planting distance; Populus tomentosa fine roots had a vertical center at a depth of 8.5 cm, whereas Pinus tabuliformis roots were centered at depths of 5–7.5 cm, indicating differing competitive strategies. Pinus tabuliformis exhibited competitive superiority in the soil’s surface layer, in contrast to Populus tomentosa, which thrived in deeper layers. The study of the allometric growth model revealed that under conditions where the nutrient gradient was 200 g·m−2 and the planting distance was 25 cm, Populus tomentosa demonstrated its highest allometric growth index (2.801), indicative of positive allometric growth. Furthermore, there was a notable inclination of resource allocation towards the aboveground, which enhances the accumulation of aboveground biomass. The mixed-effects model equation showed a clear linear relationship between underground roots and aboveground biomass. The final fitting coefficient of the model was high, providing a robust theoretical basis for future management practices. The mixed-effects model revealed the following hierarchy of fixed-effect coefficients for root system characteristics affecting aboveground biomass: fine root volume (132.11) &gt; fine root biomass (6.462) &gt; root surface area (−4.053) &gt; fine root length (0.201). In subsequent plantation reconstruction and forest management, increasing soil fertility and planting distance can promote the growth of underground roots and biomass accumulation. Appropriately increasing soil fertility and reducing planting distance can effectively promote aboveground biomass accumulation, achieving sustainable forest development.

Multi-Object Detection and Tracking with Modified Optimization Classification in Video Sequences

The paper presents a novel approach to enhancing multi-object detection and tracking in video sequences using a Modified Ant Swarm Optimization Deep Learning (ASO-DL) algorithm. The ASO-DL algorithm synergistically combines the optimization capabilities of ant swarm optimization with the powerful feature extraction abilities of deep learning models, resulting in a robust framework for realtime video analytics. Extensive simulations and experiments demonstrate significant improvements in key performance metrics, including accuracy, precision, recall, and F1 score, across various iterations. The proposed method consistently outperforms baseline models, achieving a final best fitness value of 0.96, with an accuracy of 0.98, precision of 0.99, and recall of 0.95. Additionally, classification results across different datasets such as CIFAR-10, IMDB, COCO, and ImageNet highlight the algorithm’s versatility and effectiveness. This research contributes to the field by providing a highly optimized solution for complex multi-object tracking tasks, offering substantial advancements in the accuracy and efficiency of real-time object detection systems. The findings hold significant potential for applications in surveillance, autonomous vehicles, and other domains requiring precise and reliable multi-object tracking.

Perceived teacher autonomy support and grit as predictors of ideal L2 self among Chinese EFL learners

Although some research attention has been given to second language (L2) affective and emotion constructs recently, this research agenda is still in its infancy. In an attempt to shed more light on this area, the present researchers tested a structural model of ideal L2 self in which perceived teacher autonomy support and L2 grit served as predictors. For this purpose, 346 Chinese English as a foreign language (EFL) learners were requested to participate in an electronic survey. The collected data were analyzed using structural equation modelling (SEM). The construct validity of the used scales was verified by performing confirmatory factor analysis. The SEM results indicated that although both perceived teacher autonomy support and L2 grit significantly predicted the ideal L2 self, the former was a more powerful predictor in the final fit model. Additionally, it was revealed that perceived autonomy support had a slight effect on EFL learners’ grit levels. The outcomes of this study can have implications for research and practice.

Pharmacokinetic/Pharmacodynamic modelling of Saxagliptin and its active metabolite, 5-hydroxy Saxagliptin in rats with Type 2 Diabetes Mellitus

Background and purposesIt is unclear whether the parent Saxagliptin (SAX) in vivo is the same as that in vitro, which is twice that of 5-hydroxy Saxagliptin (5-OH SAX). This study is to construct a Pharmacokinetic-Pharmacodynamic (PK-PD) link model to evaluate the genuine relationship between the concentration of parent SAX in vivo and the effect.MethodsFirst, we established a reliable Ultra Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS/MS) method and DPP-4 inhibition ratio determination method. Then, the T2DM rats were randomly divided into four groups, intravenous injection of 5-OH SAX (0.5 mg/kg) and saline group, intragastric administration of SAX (10 mg/kg) and Sodium carboxymethyl cellulose (CMC-Na) group. Plasma samples were collected at different time points for subsequent testing. Finally, we used the measured concentrations and inhibition ratios to construct a PK-PD link model for 5-OH SAX and parent SAX.ResultsA two-compartment with additive model showed the pharmacokinetic process of SAX and 5-OH SAX, the concentration-effect relationship was represented by a sigmoidal Emax model and sigmoidal Emax with E0 model for SAX and 5-OH SAX, respectively. Fitting parameters showed SAX was rapidly absorbed after administration (Tmax=0.11 h, t1/2, ka=0.07 h), widely distributed in the body (V ≈ 20 L/kg), plasma exposure reached 3282.06 ng*h/mL, and the elimination half-life was 6.13 h. The maximum plasma dipeptidyl peptidase IV (DPP-4) inhibition ratio of parent SAX was 71.47%. According to the final fitting parameter EC50, EC50, 5−OH SAX=0.46EC50, SAX(parent), it was believed that the inhibitory effect of 5-OH SAX was about half of the parent SAX, which is consistent with the literature.ConclusionsThe PK-PD link model of the parent SAX established in this study can predict its pharmacokinetic process in T2DM rats and the strength of the inhibitory effect of DPP-4 based on non-clinical data.

A novel methodology for map-based model fitting: A case study with a Dual Source Heat Pump experimental dataset

This paper presents a new methodology to adjust map-based models to experimental data and reports the main results of a comprehensive experimental campaign of a Dual Source Heat Pump (DSHP) prototype. The prototype tested incorporates variable speed components (compressor, circulation pumps, and fan). The novelty of this prototype lies in its ability to select two possible heat sources: air or ground. Thus, it can operate as a geothermal or aerothermal heat pump, as well as a chiller, thanks to the additional capacity to reverse the cycle. Thanks to this hybrid approach, several advantages can be obtained compared to conventional equipment, such as higher efficiency, the requirement of smaller borehole heat exchangers, or the absence of defrost cycles. In a prior study, polynomial models were developed to accurately characterize the DSHP’s performance (i.e., condenser and evaporator capacities and electrical energy consumption). These models were obtained considering the external variables to the unit as independent variables to facilitate their applicability using variables commonly measured in real installations. Due to the complexity of heat pump performance, which in current equipment can be influenced by up to 5 or 6 independent variables, the search for suitable polynomial models required the availability of a complete working map including more than 3000 working points. Thus, this previous work developed these models based only on simulation results. In this sense, this paper concludes the development of these models by focusing on two critical issues concerning empirical model development. The first aspect involves determining the minimum number and location of testing points needed to define the experimental sample for the model adjustment. The reported experimental data were obtained by analyzing the most suitable experimental design methodology to create the experimental matrices in each operating mode of the DSHP. The second aspect focuses on the final adjustment of models using experimental data. A novel fitting approach for empirical models is introduced in the last part of this study. The developed methodology enables the integration of simulation and experimental results for the final fitting of empirical models through a two-step adjustment. The first step involves analyzing and defining polynomial functionals from the complete working maps generated by simulation. Subsequently, in a second step, the polynomial models are refitted to a suitable experimental sample using the methodology presented in this work. The latter allows for the increase of the accuracy of the models and the minimization of experimental costs. This novel approach ensures a robust characterization of systems with many independent variables using a minimum amount of experimental data. Significant benefits can be obtained from its application, such as the reduction of experimental cost and an increase in the model’s accuracy through an effective combination of both experimental and simulated information. Furthermore, it can be considered of general applicability to other engineering problems where the characterization of physical systems influenced by a high number of independent variables is required.

Associations between sexual identity stigma and positive sexual identity, and depression among men who have sex with men in China: A mediation analysis using structural equation modelling

PurposeHigh levels of depression are common among men who have sex with men (MSM) in China, and there is limited research on the relationship between MSM stigma and depression. Guided by the psychological mediation framework, to explore the relationship between stigma and depression among MSM and how positive sexual identity plays a role in it, we tested this model whether: 1) enacted MSM stigma, perceived MSM stigma, and positive sexual identity are associated with depression; 2) their effects on depression are mediated through resilient coping and social support; and 3) perceived MSM stigma is a mediator of the effects of enacted MSM stigma and positive sexual identity on depression. MethodsWe conducted mediation analyses using structural equation modeling (SEM) on data from a cross-sectional survey conducted among 1014 MSM recruited using a non-probability sampling method in December 2022. The data passed general analyses such as normality and correlation, which allowed for modeling. The final SEM fit metrics indicated that the model was acceptable. ResultsStructural equation modeling results showed that enacted stigma directly and positively affected depressive symptoms (β = 0.558, 95 % confidence interval = 0.457 ～ 0.644, P < 0.001). Perceived stigma had a positive effect on depressive symptoms (β = 0.114, 95 %CI = 0.036 ～ 0.200, P = 0.002). Positive identity had a positive effect on depressive symptoms (β = 0.086, 95 %CI = 0.016 ～ 0.162, P = 0.013). In addition, the results of mediation effect analysis showed that the indirect effect of enacted stigma on the effect of depression through social support was (β = 0.040, 95 %CI = 0.003 ∼ 0.017, P = 0.049); The indirect effect of perceived stigma on the effect of depression through resilient coping was (β = —0.015, 95 %CI = —0.034 ∼ —0.002, P = 0.056); The indirect effects of positive sexual identity on the effect of depression through resilient coping and social support were (β = —0.025, 95 %CI = —0.056 ∼ —0.002, P = 0.052) and (β = —0.056, 95 %CI = —0.101 ∼ —0.023, P = 0.005). ConclusionResilient coping and social support mediated the relationship between stigma and depression, and both were influenced by positive identity. This study reveals that enhancing coping and social support may reduce the impact of stigma on depressive symptoms, both of which are potential targets for interventions for MSM. Guiding MSM to develop a positive sexual identity is conducive to reducing the prevalence of depressive symptoms among MSM.

The Technique and Material Used to Join Transfers Affect the Accuracy and Final Fit of Implant-Supported Prostheses-In Vitro Study.

This study evaluated the linear dimensional change of polymerization of three materials and two techniques of the union of molding transfers for implant-supported prostheses used in the open-tray technique. A nylon maxilla-shaped matrix was made, two osseous integrated implants were installed, and, over these two, straight conical mini-pillars were installed. Open-tray impression transfers were attached to the mini-pillars, and a silicone guide was made to standardize the connections between the transfers. The samples were divided into six groups (n = 20): PA (Pattern Resin LS, chemically activated acrylic resin in the single step technique); DU (Durallay, chemically activated acrylic resin in the single step technique); BI (Protemp4, bisacrylic resin in the single step technique); PAC (Pattern Resin LS in sectioning and joining of segments technique); DUC (Durallay, in sectioning and joining of segments technique); and BIC (Protemp4, in sectioning and joining of segments technique). The linear dimensional change values that occurred among these transfers were measured in a profile projector (VB300; Starret) coupled to the Quadra Check device, with a resolution of 0.001 mm, performed by a single calibrated operator. Data were submitted to a two-way analysis of variance and Tukey's test (p < 0.01). Statistically significant mean values were found in all comparisons. The PA showed the lowest mean values (µm) of linear dimensional change, both in the single-step technique and in the sectioning and joining technique, in the following order: BI 255.73 (3.81), DU 173.75 (2.30), PA 95.97 (3.20), BIC 23.82 (1.71), DUC 20.85 (2.53), and PAC 13.27 (2.09). The single-step technique showed the worst results, regardless of the material. The sectioning and joining technique reduced the dimensional change in all materials, and the Pattern Resin LS showed the lowest shrinkage mean values, followed by Durallay and Protemp4.

Assessment of large-diameter and small-diameter SoftK specialty contact lenses for early-stage keratoconus.

Soft contact lenses may be a good alternative for early-stage keratoconus (KC) patients who do not tolerate rigid gas permeable (RGP) lenses due to ocular discomfort or complications. This prospective study compared outcomes obtained after 2 weeks of wearing two types of soft silicone hydrogel contact lenses for keratoconus that varied in their diameter and central thickness (cc). Patients with Amsler-Krumeich grades I or II KC were fitted with small-diameter (14.2 or 14.8 mm) SoftK (SD-SoftK, cc = 0.48 mm) and large-diameter (17 mm) SoftK (LD-SoftK, cc = 0.60 mm) lenses, each worn for 2 weeks in a crossover design. Low (10%;10VA) and high (100%;100VA) contrast visual acuity, contrast sensitivity (CS, Pelli-Robson), higher order aberrations (HOAs, Visionix Vx130), the number of trial lens modifications during fitting and the subjectively preferred lens were compared using Friedman tests with post-hoc analysis. Forty eyes (N = 20, 10 males, mean age: 39.0 ± 9.9 years, range: 23-55 years) were examined. Their habitual median (interquartile1, interquartile3) 10VA (LogMAR), 100VA (LogMAR) and CS (LogCS) were 0.52 (0.30, 0.50), 0.14 (0.10, 0.15) and 1.35 (1.35, 1.50), respectively. For the SD-SoftK condition, the values were 0.23 (0.17, 0.30), 0.02 (0.00, 0.05) and 1.50 (1.50, 1.65), respectively. For the LD-SoftK condition, the respective values were 0.36 (0.27, 0.44), 0.09 (0.05, 0.13) and 1.50 (1.50, 1.60). SD-SoftK lenses significantly improved 10VA compared with habitual and LD-SoftK. SD-SoftK also significantly improved CS compared with habitual, but not LD-SoftK. LD-SoftK significantly improved spherical aberration compared with uncorrected (0.03 ± 0.10 μ vs. 0.07 ± 0.13 μ) but not SD-SoftK (0.04 ± 0.07 μ). Both lenses required a mean of 1.5 modifications prior to final lens fitting. Fewer adverse events were seen with SD-SoftK (N = 3) compared with LD-SoftK (N = 8), and 75% of participants preferred SD-SoftK lenses. SD-SoftK lenses were preferred by 75% of subjects, were associated with fewer adverse events and significantly improved 10VA compared with LD-SoftK lenses. SD-SoftK lenses also significantly improved CS compared with the habitual correction, but this did not differ significantly from the LD-SoftK lenses.

Two-phase optimization of binary sequences with low peak sidelobe level value

The search for binary sequences with low peak sidelobe level value represents a formidable computational problem. To locate better sequences for this problem, we designed a stochastic algorithm that uses two fitness functions. In these fitness functions, the value of the autocorrelation function has a different impact on the final fitness value. It is defined with the value of the exponent over the autocorrelation function values. Each function is used in the corresponding optimization phase, and the optimization process switches between these two phases until the stopping condition is satisfied. The proposed algorithm was implemented using the compute unified device architecture and therefore allowed us to exploit the computational power of graphics processing units. This algorithm was tested on sequences with lengths L=2m−1, for 14≤m≤20. From the obtained results it is evident that the two-phase optimization improved the efficiency of the algorithm significantly, the solver speed is increased significantly by using graphics processing units, new-best known solutions were achieved, and the achieved peak sidelobe level values were significantly less than L.

Near-neighbor Propagation Clustering Algorithm Based on Cuckoo Search

In this paper, a nearest neighbor propagation clustering algorithm (CSB-AP) based on cuckoo search is proposed to solve the problem of poor parameter setting of the AP algorithm. A population-intelligent optimized cuckoo search algorithm is introduced into the AP algorithm to find the appropriate parameters. Two important parameters in the nearest neighbor propagation algorithm are taken as the location of the bird’s nest. The BWP value index is introduced as the bird’s nest fitness in the process of intelligent search, and the minimum value is obtained as the final fitness through the reverse mechanism. The final parameters are substituted into the calculation as the best parameters. The CSB-AP algorithm is verified by the UCI data set, and compared with the traditional AP nearest neighbor propagation clustering algorithm, it is found that the CSB-AP algorithm proposed in this paper is better than the traditional AP nearest neighbor propagation clustering algorithm. By comparison, it can be found that the clustering result obtained by the CSB-AP algorithm is closer to the actual result, and it can be known according to the results of the BWP index, contour coefficient, Recall, and F-measure. The improved algorithm can significantly improve the clustering quality and clustering performance.

Investigation on Traffic Carbon Emission Factor Based on Sensitivity and Uncertainty Analysis

The premise for formulating effective emission control strategies is to accurately and reasonably evaluate the actual emission level of vehicles. Firstly, the active subspace method is applied to set up a low-dimensional model of the relationship between CO2 emission and multivariate vehicle driving data, in which the vehicle specific power (VSP) is identified as the most significant factor on the CO2 emission factor, followed by speed. Additionally, acceleration and exhaust temperature had the least impact. It is inferred that the changes in data sampling transform the establishment of subspace matrices, affecting the calculation of eigenvector components and the fitting of the final quadratic response surface, so that the emission sensitivity and final fitting accuracy are impressionable by the data distribution form. For the VSP, the best fitting result can be obtained when the VSP conforms to a uniform distribution. Moreover, the Bayesian linear regression method accounts for fitting parameters between the VSP and CO2 emission factor with uncertainties derived from heteroscedastic measurement errors, and the values and distributions of the intercept and slope α and β are obtained. In general, the high-resolution inventory of the carbon emission factor of the tested vehicle is set up via systematically analyzing it, which brings a bright view of data processing in further counting the carbon footprint.

Influence of Printing Angulation on the Accuracy (Trueness and Precision) of the Position of Implant Analogs in 3D Models: An In Vitro Pilot Study

Background: With CAD-CAM, dental models are often fabricated by additive manufacturing (AM) methods. Dental models for implant prostheses involve the manual placement of analogs, which could affect their final fit. Therefore, in this experimental in vitro study, the accuracy of the analogs’ position in models printed using AM methods was examined by comparing three different printing orientations. Methods: An STL file was obtained by scanning a master model with an implant placed in the maxillary left central incisor position. Three study groups (n = 10) with varying printing orientation parameters were obtained (0, 45, and 90 degrees). They were digitalized with a laboratory scanner and evaluated with an analysis metrology program. Differences between 3D datasets were measured using the RMS for trueness and SD RMS for precision. The data were statistically analyzed using the ANOVA test at a significance level of p &lt; 0.05, followed by the Bonferroni post hoc test. Results: The 45-degrees group showed the best results. Regarding trueness, statistically significant differences were found between the 45- and 90-degrees groups (p &lt; 0.005). In terms of precision, statistically significant differences appeared between the 45- and 0-degrees groups (p &lt; 0.011) and between the 45- and 90-degrees groups (0.003). Conclusions: The printing angulation parameter affects the accuracy of 3D-printed models. Implant models manufactured at 45 degrees of printing angulation are more accurate than those printed at 0 or 90 degrees.