Actual Size Research Articles

Testing distributional assumptions in CUB models for the analysis of rating data

AbstractIn this paper, we propose a portmanteau test for misspecification in combination of uniform and binomial (CUB) models for the analysis of ordered rating data. Specifically, the test we build belongs to the class of information matrix (IM) tests that are based on the information matrix equality. Monte Carlo evidence indicates that the test has excellent properties in finite samples in terms of actual size and power versus several alternatives. Differently from other tests of the IM family, finite-sample adjustments based on the bootstrap seem to be unnecessary. An empirical application is also provided to illustrate how the IM test can be used to supplement model validation and selection.

Structural health monitoring of an onshore steel wind turbine

AbstractThe study presents the development of a structural monitoring system installed in a 45-m-high steel wind tower located in Italy. The installed monitoring system was composed by 16 strain gauges placed in the tower wall, in a pattern of four Wheatstone bridges at 45°, together with thermal couples, at 21 m from the ground (half-height of the tower). Moreover, several accelerometers were placed along the tower height (with one of them located next to the strain gauges). The wind velocity and directions were obtained directly from the turbine own monitoring system. Such a monitoring system was designed because, due to the decrement of the total height from the original design, the tower suffers from resonance problems. In fact, the investigated tower was originally designed with 65 m of height but then, to comply with local regulations, the height was decreased to the actual size. Therefore, to allow safe operation and avoid excessive fatigue due to the increased displacements, the velocity of the rotor has been manually limited causing an important reduction in the energy production. The results of the study show the importance of monitoring the resonance issue. The differences between the damage indexes obtained with two different working conditions are discussed: tower working with limited operational capacity and tower working at its maximum capacity (in resonance).

Image-based biomarkers for engineering neuroblastoma patient-specific computational models

AbstractChildhood cancer is a devastating disease that requires continued research and improved treatment options to increase survival rates and quality of life for those affected. The response to cancer treatment can vary significantly among patients, highlighting the need for a deeper understanding of the underlying mechanisms involved in tumour growth and recovery to improve diagnostic and treatment strategies. Patient-specific models have emerged as a promising alternative to tackle the challenges in tumour mechanics through individualised simulation. In this study, we present a methodology to develop subject-specific tumour models, which incorporate the initial distribution of cell density, tumour vasculature, and tumour geometry obtained from clinical MRI imaging data. Tumour mechanics is simulated through the Finite Element method, coupling the dynamics of tumour growth and remodelling and the mechano-transport of oxygen and chemotherapy. These models enable a new application of tumour mechanics, namely predicting changes in tumour size and shape resulting from chemotherapeutic interventions for individual patients. Although the specific context of application in this work is neuroblastoma, the proposed methodologies can be extended to other solid tumours. Given the difficulty for treating paediatric solid tumours like neuroblastoma, this work includes two patients with different prognosis, who received chemotherapy treatment. The results obtained from the simulation are compared with the actual tumour size and shape from patients. Overall, the simulations provided clinically useful information to evaluate the effectiveness of the chemotherapy treatment in each case. These results suggest that the biomechanical model could be a valuable tool for personalised medicine in solid tumours.

A statistical model for estimating size and productivity ratio in SDLC

During a software development life cycle, one has to estimate the effort and schedule required to produce a software unit. Estimates for The effort and schedule are derived from other measurements such as size and productivity Ratio. Size may be estimated using methods such as Function Point, WBS, LOC, etc., and the Productivity Ratio is expressed as Size / Time to complete the Unit of Software. Such estimates are static and do not consider real-time Parameters which cause variations from the estimate. In this paper, a statistical model for Size and Productivity Ratios is derived using Historical values after considering several factors influencing the size and the productivity ratio of produced software. Typically, an estimate of the effort and schedule is required to complete. A software work product an enhancement request or a software fix. We use Function Points or WBS to estimate the size of the software in consideration. We use baseline values for estimating productivity Ratios. Real-time values however differ from Baseline values as they are influenced by several factors. Here are some factors which would influence the Actual Size of the software Later on we will use Statistical techniques of Multivariable Parameter estimation and logistic Regression to derive run time equations of Size and Productivity Ratio.

A novel practical method to predict anterior cruciate ligament hamstring graft size using preoperative MRI

BackgroundPredicting hamstring graft size preoperatively for anterior cruciate ligament (ACL) reconstruction is important for preempting an insufficient diameter in graft size intraoperatively, possibly leading to graft failure. While there are multiple published methods using magnetic resonance imaging (MRI) picture archiving and communication systems (PACS), most are not feasible and practical. Our study aims to (1) practically predict the ACL hamstring graft size in a numerically continuous manner using the preoperative MRI from any native MRI PACS system, (2) determine the degree of correlation between the predicted and actual graft size, and (3) determine the performance of our prediction method if we define an adequate actual graft size as ≥ 8 mm.MethodsA retrospective review of 112 patients who underwent primary ACL reconstruction with quadrupled hamstring semitendinosus-gracilis grafts at a tertiary institution was conducted between January 2018 and December 2018. Graft diameter can be predicted in a numerically continuous manner as √[2*(AB + CD)], where A and B are the semitendinosus cross-sectional length and breath, respectively, and C and D are the gracilis cross-sectional length and breath, respectively.ResultsA moderately positive correlation exists between the predicted and actual graft diameter (r = 0.661 and p < .001). Our method yields a high specificity of 92.6% and a moderate sensitivity of 67.2% if we define an adequate actual graft size as ≥ 8 mm. An area under receiver-operating characteristic curve shows good discrimination (AUC = 0.856).ConclusionsWe present a practical method to predict the ACL hamstring graft size with high specificity using preoperative MRI measurements.

A virtual thermometer for ultrahigh-temperature–pressure experiments in a large-volume press

Ultrahigh-temperature–pressure experiments are crucial for understanding the physical and chemical properties of matter. The recent development of boron-doped diamond (BDD) heaters has made such melting experiments possible in large-volume presses. However, estimates of temperatures above 2600 K and of the temperature distributions inside BDD heaters are not well constrained, owing to the lack of a suitable thermometer. Here, we establish a three-dimensional finite element model as a virtual thermometer to estimate the temperature and temperature field above 2600 K. The advantage of this virtual thermometer over those proposed in previous studies is that it considers both alternating and direct current heating modes, the actual sizes of cell assemblies after compression, the effects of the electrode, thermocouple and anvil, and the heat dissipation by the pressure-transmitting medium. The virtual thermometer reproduces the power–temperature relationships of ultrahigh-temperature–pressure experiments below 2600 K at press loads of 2.8–7.9 MN (∼19 to 28 GPa) within experimental uncertainties. The temperatures above 2600 K predicted by our virtual thermometer are within the uncertainty of those extrapolated from power–temperature relationships below 2600 K. Furthermore, our model shows that the temperature distribution inside a BDD heater (19–26 K/mm along the radial direction and &amp;lt;83 K/mm along the longitudinal direction) is more homogeneous than those inside conventional heaters such as graphite or LaCrO3 heaters (100–200 K/mm). Our study thus provides a reliable virtual thermometer for ultrahigh-temperature experiments using BDD heaters in Earth and material sciences.

Meta-barriers for ventilated sound reduction via transformation acoustics

Resolving the trade-off problem between wave obstructing and flow transport is an intriguing but challenging task that may find potential applications in many noise control scenarios. Herein, we present a meta-barrier for achieving broadband sound reduction yet keeping efficient ventilation based on transformation acoustics. The proposed meta-barrier consists of an internal core and a coating made of double-negative acoustic metamaterial with high damping dissipation. Unlike conventional barriers, the meta-barrier has a magnified scattering cross-section far beyond its actual size, allowing it to provide the coherent interference effect in addition to the enhanced blocking effect. The coherent interference effect can directly reduce the sound radiation efficiency of sound waves radiating from an aperture. Moreover, the inherent material loss in the coating has a positive effect on the sound reduction performance of the meta-barrier over a wide frequency range. The robust sound reduction performance of the meta-barrier is demonstrated numerically for the cases of large placed distances and wide-range incident angles. Experiments were conducted to confirm the effectiveness of a meta-barrier sample constructed of a plate-type double-negative acoustic metamaterial with multiple constituent layers for sound reduction and ventilation. The fabricated meta-barrier sample, with the wind-speed ratio of 75 %, can achieve superior sound attenuation in the frequency range of 500–1000 Hz. This work may inspire the design and development of passive acoustic devices to suppress low-frequency airborne noise while supporting large-area ventilation.

Optimization and Simulation Parameters of the Resistance Spot Welding for Commercial Aluminium (AA1050) at Low Power Welding Machine

This study aims to find the best conditions of resistance spot welding in commercial Aluminium AA1050 to obtain the maximum tensile shear strength of the joint using a low-power supply welding machine. The chosen parameters for this study were welding current, welding time, and electrode force using a 90 kVA welding machine. The investigation used the DoE method with Taguchi’s technique to reduce the number of experiments where two sheet thicknesses (1 mm and 2 mm) were used in this work. The software program Minitab 18 analyzed the results using the main effects plots and the interaction plots to identify the most significant parameters and their effect on the joint strength. The best conditions for maximum tensile shear force were 14.85 kA welding current and 0.79 kN electrode force for both thicknesses and two cycles and 12 cycles welding time for 1 mm and 2 mm sheet thickness, respectively. The maximum tensile strength obtained was 250 N and 225 N for 1mm and 2mm sheet thickness, respectively. A mathematical equation was developed to predict the shear force with 19.9 % and 17.9 % error for 1 mm and 2 mm thicknesses, respectively. The best conditions were applied in ANSYS 2022R1 multi-physics to obtain the temperature distribution with time history, where the result shows the nugget size according to the molten temperature. The percentage of discrepancy between actual and numerical nugget size was 8 %.

Real-time measurements and modeling of sodium combustion aerosol dynamics in test chamber to improve the evaluation of SFR containment aerosol behaviour

The initial size distribution and morphological parameters of sodium aerosols are critical in evaluating the accidental suspended aerosol behaviour in Sodium-cooled Fast Reactor (SFR) containment. Mass-based measurements were more familiar in characterizing the sodium aerosols. Real-time number size distribution measurements are carried out in this study. The sensitivity analysis of sodium aerosol effective density (ρe) in deriving the actual number size distributions from the measured Aerodynamic Particle Size Distributions (APSD) and predicting suspended aerosol dynamics is presented. Tests are conducted in a 1 m3 chamber at 47 ± 3% RH for different initial mass concentrations (M0) of 0.1, 1, and 2.9 g/m3. The initial APSDs measured just after the generation completions are observed to be polydisperse with the count median aerodynamic diameter (CMAD) < 1 μm. The literature reported ρe values of sodium aerosols, 2.27, 1.362, and 0.61 g/cm3 are used to derive mobility equivalent PSDs from APSD in each test. The real-time number concentration decay and size growth for four different PSDs are measured and compared with the estimate using nodal method-based code to ascertain the actual parameters. The validated parameters CMD = 0.66 μm, σg = 1.96, ρe = 1 g/cm3 and χ = 1 are used for improved estimation of sodium aerosol dynamics in Indian SFR containment with M0 = 4 g/m3 for severe accident scenarios.

Introducing an Artificial Neural Network for Virtually Increasing the Sample Size of Bioequivalence Studies

Sample size is a key factor in bioequivalence and clinical trials. An appropriately large sample is necessary to gain valuable insights into a designated population. However, large sample sizes lead to increased human exposure, costs, and a longer time for completion. In a previous study, we introduced the idea of using variational autoencoders (VAEs), a type of artificial neural network, to synthetically create in clinical studies. In this work, we further elaborate on this idea and expand it in the field of bioequivalence (BE) studies. A computational methodology was developed, combining Monte Carlo simulations of 2 × 2 crossover BE trials with deep learning algorithms, specifically VAEs. Various scenarios, including variability levels, the actual sample size, the VAE-generated sample size, and the difference in performance between the two pharmaceutical products under comparison, were explored. All simulations showed that incorporating AI generative algorithms for creating virtual populations in BE trials has many advantages, as less actual human data can be used to achieve similar, and even better, results. Overall, this work shows how the application of generative AI algorithms, like VAEs, in clinical/bioequivalence studies can be a modern tool to significantly reduce human exposure, costs, and trial completion time.

Tanaka-Johnston’s Mixed Dentition Analysis among Orthodontic Patients in Tanzania

Background: Lower incisors are the first teeth to erupt, their sizes are reliable and little changes occur on the lower arch during mixed dentition. These teeth can be used to predict the size of remaining unerupted teeth. Tanaka Johnston's analysis is the most widely used method of mixed dentition analysis worldwide. The aim of the study was to assess the accuracy of Tanaka-Johnston’s methods of mixed dentition analysis among orthodontic patients in Tanzania. Methods: A cross-sectional study design was carried out at MUHAS dental clinic involving models from orthodontic patients. Data collection involved actual teeth size measurement and prediction using Tanaka-Johnston’s method. Comparison between the actual measurements and the predicted measurements was assessed using students' paired t-tests. Prediction equations for the present study were generated using a simple linear regression analysis. Results: Tanaka Johnston's method significantly overestimated the combined width of upper canines and premolars in the female group (p=0.000). However, the method underestimated the lower jaw measurements in both sexes (p&lt;0.05). New prediction equations for the present study were generated for each sex and arch separately. For the upper jaw, it was Y=11.97+ 0.56X for males and Y = 10.97+0.49X for females. While for the lower jaw, it was Y= 10.8 +0.51X for males and Y=11.39+0.47X for females. Conclusion: Tanaka-Johnston’s methods of mixed dentition analysis was found to be inaccurate in estimating the size of unerupted canines and premolars in the study sample. Therefore, new prediction formulas were generated.

Pothole Detection Using Machine Learning Models

Potholes are damage caused to the ground by the formation of water and wear and tear over time. According to statistical data, bad road conditions account for about one- third of the total road accidents which has been increasing exponentially. Potholes have become so common that it has become second nature for people to learn how to spot and avoid them, which causes further accidents. The need of the hour is to build a dependable pothole detection system to accurately detect potholes and warn the drivers and government officials in advance. The process to build such a system is divided into two steps i.e. collection of data and pothole identification. The first step is achieved by taking the data from already available data sets on the Internet. The other step includes labeling the potholes in the data set which is usually done manually. This paper focuses mainly on Visual-based techniques to identify the best detection method by comparing popular Machine Learning models and algorithms. The obtained data set is trained using various transfer learning techniques like You Only Look Once (YOLO)[1] and Single Shot Detector (SSD) [1]. Apart from transfer learning, this paper also focuses on some proposed techniques using Convolutional Neural Net- works (CNN) and classification algorithms like Support Vector Machine (SVM)[21] to identify and localize potholes. The actual size of potholes is calculated using morphological operations, which is a just a straightforward technique to analyze figures using set theory. To analyze every model and find the best model, each model is trained on different sizes of data sets and the obtained result is validated and examined by considering different aspects like speed and accuracy in mind.

[The diagnostic value of lung ultrasound in children with community-acquired pneumonia].

Objective: To investigate the diagnostic value of lung ultrasound in hospitalized children with community-acquired pneumonia (CAP). Methods: In the cross-sectional study, a total of 422 children with CAP who were hospitalized in the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, from February 2021 to August 2022 and completed lung ultrasound examination within 48 hours after admission were enrolled. The clinical characteristics, lung ultrasound and chest CT were collected. The patients were divided into two groups according to the signs of pneumonia indicated by chest CT, and the signs of lung ultrasound with diagnostic value were screened according to the signs of pneumonia indicated by chest CT by least absolute shrinkage and selection operator (Lasso) regression. According to severity of the disease, the children were divided into the severe group and the mild group, and the differences of lung ultrasound signs between the two groups were compared. Kruskal-Wallis test, Fisher's exact test was selected for comparison between groups. Random forest classifier wes used to evaluate the value of lung ultrasound in the diagnosis of CAP and prediction of severe pneumonia in children. The receiver operating characteristic curve was used to evaluate the prediction effect. Use DeLong test to compare the area under the curve. Results: Among the 422 cases of CAP, there were 258 males and 164 females, and the age of onset was 2.8 (1.3, 4.3) years. The confluent B-line, consolidation and pleural effusion detected by lung ultrasound were 309 cases (73.2%), 232 cases (55.0%) and 16 cases (3.8%), respectively, and the size of consolidation was 3.0 (0, 11.0) mm. One hundred and ten children (26.1%) with CAP completed chest CT. There were 90 cases with signs of pneumonia in chest CT and 20 cases without signs of pneumonia. Lasso was used for feature selection.Lung consolidation (OR=2.46), bilateral lung consolidation (OR=1.16) and confluent B-line (OR=1.34) were the main index. With random forest classifier, the accuracy of models using full variables and Lasso-selected variables were 0.79 (95%CI 0.70-0.86) and 0.79 (95%CI 0.70-0.86), the sensitivity were 0.81 and 0.81, and the specificity were 0.75 and 0.70, and the area under curve were 0.87 (95%CI 0.81-0.94, P<0.001) and 0.84 (95%CI 0.76-0.91, P<0.001), respectively. There were 97 cases in severe group and 325 cases in mild group. Compared with the mild group, the detection rate of consolidation, multiple consolidation, the size of consolidation and the size of consolidation was adjusted by body surface area (consolidation size/body surface area) in severe group were higher (66 cases (68.0%) vs. 166 cases (51.1%), 42 cases (43.3%) vs. 93 cases (28.6%), 8.0 (0, 17.0) vs. 1.0 (0, 9.0) mm, 12.5 (0, 24.6) vs. 2.1 (0, 17.6), χ2=8.59, 9.98, Z=14.40, 12.79, all P<0.05). Using lung ultrasound lung consolidation size and consolidation size/body surface area to predict the severe CAP, the optimal cut-off value were 6.7 mm and 10.2, the accuracy was 0.80 (95%CI 0.75-0.83) and 0.89 (95%CI 0.86-0.92), the sensitivity was 0.99 and 0.99, the specificity was 0.14 and 0.56, respectively, and the area under the curve was 0.66 (95%CI 0.60-0.72, P<0.001) and 0.76 (95%CI 0.70-0.83, P<0.001), respectively. The area under the curve of consolidation size/body surface area was higher than that of consolidation size (Z=5.50, P<0.001). Conclusions: Consolidation and confluent B-line, are important index for lung ultrasound diagnosis of CAP in children. The actual consolidation size adjusted by body surface area is superior to the size of consolidation in predicting severe CAP.

Multi-Type Traffic Conflict Identification at Signalized Intersections Based on LiDAR Point Cloud

Traffic conflicts have been widely used for proactive road safety evaluation, and this study develops methods to automatically identify different types of traffic conflict based on LiDAR point cloud data. With 10 h of data collected from a signalized intersection in Harbin, China, trajectories of motorized vehicles, bicycles, and pedestrians were extracted, and methods to handle the issues of trajectory discontinuity, type identification error, and same object with different trajectories were developed. Traffic conflicts between right-turn vehicles and through vehicles, between right-turn vehicles and left-turn vehicles, and between right-turn vehicles and pedestrians were considered, and detailed procedures for calculating the conflict indicators (i.e., time to collision [TTC] and post encroachment time [PET]) of different types were proposed. The identified traffic conflicts were also compared with the ones that were identified manually. A total of 5,352 and 1,366 traffic conflicts were identified by PET ≤ 4 s and TTC ≤ 4 s, respectively. The majority were during the through phase, and traffic conflicts between right-turn vehicles and through vehicles were the most common, followed by conflicts between right-turn vehicles and vulnerable road users (i.e., cyclists and pedestrians).The proposed automatic method considers the actual sizes of involved road users and thus leads to more accurate conflict indicator calculation, with an average accuracy over 90%.

Phytosynthesis of zinc oxide nanoparticles for enhanced antioxidant, antibacterial, and photocatalytic properties: A greener approach to environmental sustainability

Multifunctional nanoparticles (NPs) production from phytochemicals is a sustainable process and an eco-friendly method, and this technique has a variety of uses. To accomplish this, we developed zinc oxide nanoparticles (ZnONPs) using the medicinal plant Tinospora cordifolia (TC). Instruments such as UV–Vis, XRD, FTIR, FE-SEM with EDX, and high-resolution TEM were applied to characterize the biosynthesized TC-ZnONPs. According to the UV–vis spectra, the synthesized TC-ZnONPs absorb at a wavelength centered at 374 nm, which corresponds to a 3.2 eV band gap. HRTEM was used to observe the morphology of the particle surface and the actual size of the nanostructures. TC-ZnONPs mostly exhibit the shapes of rectangles and triangles with a median size of 21 nm. The XRD data of the synthesized ZnONPs exhibited a number of peaks in the 2θ range, implying their crystalline nature. TC-ZnONPs proved remarkable free radical scavenging capacity on DPPH (2,2-Diphenyl-1-picrylhydrazyl), ABTS (2,2-azino-bis-3-ethylbenzothiazoline-6-sulfonic acid), and NO (Nitric Oxide). TC-ZnONPs exhibited dynamic anti-bacterial activity through the formation of inhibition zones against Pseudomonas aeruginosa (18 ± 1.5 mm), Escherichia coli (18 ± 1.0 mm), Bacillus cereus (19 ± 0.5 mm), and Staphylococcus aureus (13 ± 1.1 mm). Additionally, when exposed to sunlight, TC-ZnONPs show excellent photocatalytic ability towards the degradation of methylene blue (MB) dye. These findings suggest that TC-ZnONPs are potential antioxidant, antibacterial, and photocatalytic agents.

Food security and the value of game animals—a study of Sweden

The food security value of wild meat is calculated by combining proxy methods for quantifying game animal abundance with shadow pricing techniques for assessing the unit values of food security. This study calculated the food security values of moose, roe deer, wild boar, and fallow deer for Sweden overall and for individual counties. The results showed that meat from these animal populations accounts for approximately 9% of meat consumption in Sweden and for 1.2% of the minimum energy food consumption during periods of crisis for the whole of Sweden, while in some counties it can be as much as 8%. The calculated unit value, or shadow price, of the minimum energy requirement ranged between € 0.1 and € 4.2/mcal, depending on the magnitude of the crisis scenario. At most, the total food security value of actual animal population sizes amounted to 0.50 billion euros, but this was unevenly distributed, with high values in counties that have an abundance of moose and wild boar.

Hydrated Formic Acid Clusters and their Interaction with Electrons.

We investigate ion formation in hydrated formic acid (FA) clusters upon collision with electrons of variable energy, focusing on electron ionization at 70 eV (EI) and low-energy (1.5-15 eV) electron attachment (EA). To uncover details about the composition of neutral clusters, we aim to elucidate the ion formation processes in FAM  ⋅ WN clusters initiated by interaction with electrons and determine the extent of cluster fragmentation. EI predominantly produces protonated [FAm +H]+ ions, and in FA-rich clusters, the stable ring structures surrounding H3 O+ ions are formed. In contrast, EA leads to a competition between the formation of intact [FAm  ⋅ Wn ]- and dissociated [FAm  ⋅ Wn -H]- fragment ions, influenced by the cluster size, level of hydration, and electron energy. Our findings reveal a predisposition of low-energy EA towards forming [FAm  ⋅ Wn ]- , while higher electron energies tend to favor the formation of [FAm  ⋅ Wn -H]- due to intracluster ion-molecule reactions. The comparison of positive and negative ion spectra suggests that the mass spectra of FA-rich clusters may indicate their actual size and composition. On the other hand, the more weakly bound water evaporation from the clusters depends strongly on the ionization. Thus, for the hydrated clusters, the neutral cluster size can hardly be estimated from the mass spectra.

Application and evaluation of artificial intelligence 3D preoperative planning software in developmental dysplasia of the hip

BackgroundAccurate preoperative planning is crucial for successful total hip arthroplasty (THA) for developmental dysplasia of the hip (DDH). The aim of this study was to compare the accuracy of an artificial intelligence-assisted three-dimensional (3D) planning system (AIHIP) with two-dimensional templates in predicting acetabular cup size in THA for DDH.MethodThis study retrospectively analyzed image data from 103 DDH patients who had THA between May 2019 and August 2023. AIHIP was used for 3D planning, and two-dimensional (2D) templates were used by two experienced surgeons. Accuracy was assessed by comparing predicted and actual cup sizes, and potential factors affecting accuracy were analyzed, including gender, side, BMI, and dysplasia classification.ResultsAIHIP had higher accuracy in predicting the acetabular cup size compared to the 2D template. Within ± 0 size, AIHIP’s accuracy was 84.1%, while the 2D template’s was 64.0% (p < 0.05). Within ± 1 size, AIHIP's accuracy was 95.1%, while the 2D template’s was 81.1% (p < 0.05). Accuracy was unaffected by gender, side, or BMI but was by DDH classification. In subgroup analysis, AIHIP's mean absolute error (0.21 ± 0.54) was significantly lower than the 2D template’s (0.62 ± 0.95) for Crowe II and Crowe III (p < 0.05).ConclusionAIHIP is superior to 2D templates in predicting the acetabular cup size accurately for THA in DDH patients. AIHIP may be especially beneficial for Crowe II and III DDH patients, as 2D templates may not accurately predict cup size in these cases.

Reduced body-image disturbance by body-image interventions is associated with neural-response changes in visual and social processing regions: a preliminary study.

Body-image disturbance is a major factor in the development of eating disorders, especially among young women. There are two main components: perceptual disturbance, characterized by a discrepancy between perceived and actual body size, and affective disturbance, characterized by a discrepancy between perceived and ideal body size. Interventions targeting body-image disturbance ask individuals to describe their own body without using negative expressions when either viewing it in a mirror or imagining it. Despite the importance of reducing body-image disturbance, its neural mechanisms remain unclear. Here we investigated the changes in neural responses before and after an intervention. We hypothesized that neuralresponses correlated with the degree of body-image disturbance would also be related to its reduction, i.e., a reduction in perceptual and affective disturbances would be related to changes in attentional and socio-cognitive processing, respectively. Twenty-eight young adult women without known psychiatric disorders underwent a single 40-min intervention. Participants completed tasks before and after the intervention, in which they estimated their perceived and ideal body sizes using distorted silhouette images to measure body-image disturbance. We analyzed the behavioral and neural responses of participants during the tasks. The intervention did not significantly reduce body-image disturbance. Analysis of individual differences showed distinct changes in neural responses for each type of disturbance. A decrease in perceptual disturbance was associated with bodily visuospatial processing: increased activation in the left superior parietal lobule, bilateral occipital gyri, and right cuneus. Reduced affective disturbance was associated with socio-cognitive processing; decreased activation in the right temporoparietal junction, and increased functional connectivity between the left extrastriate body area and the right precuneus. We identified distinct neural mechanisms (bodily visuospatial and socio-cognitive processing) associated with the reduction in each component of body-image disturbance. Our results imply that different neural mechanisms are related to reduced perceptual disturbance and the expression thereof, whereas similar neural mechanisms are related to the reduction and expression of affective disturbance. Considering the small sample size of this study, our results should be regarded as preliminary.

Colony size of amazonian stingless bees and its assessment through intrinsic parameters

Stingless bees live in populous colonies that vary in size from a few hundred individuals to tens of thousands, although information on actual colony size is limited. The objectives of this study were to determine the colony size of five Amazonian stingless bee species (Melipona flavolineata, Melipona fasciculata, Scaptotrigona aff. postica, Frieseomelitta longipes, and Plebeia minima), and to identify biological parameters that covary with colony size. The number of brood cells, adult bees, and food stocks were counted under laboratory conditions, alongside field assessments of egg-laying rate and external activity of adult workers. To identify covariates of colony size, the number of adult bees was regressed against the number of brood cells, egg-laying rate, external activity of adult workers, and food stocks, and the best candidate models were ranked using the Akaike Information Criterion. Mean (±s.d.) adult populations were: in M. flavolineata, 1046 ± 185; in M. fasciculata, 593 ± 300; in S. aff. postica, 7404 ± 1391; in F. longipes, 2425 ± 1000 and in P. minima, 405 ± 254. Thus, we showed that the external activity is the biological parameter, after the number of brood cells, that presents the best relationship with the number of adult bees, which can be easily evaluated in the field. Determining colony sizes using easily measurable biological parameters are important steps to understanding stingless bee’s life histories, ecology, and evolution, and to ease their keeping and utilization for pollination.