Average Deviation Research Articles

Unraveling the molecular dissociation between aging and atherosclerosis: A bioinformatics approach.

In clinical practice, cardiologists frequently note substantial differences in coronary artery health among patients of the same age bracket. This observation led to our investigation into identifying genes that are shared between atherosclerosis and aging, as well as those that are specifically amplified in atherosclerosis alone. Our study leveraged existing gene expression datasets from the Gene Expression Omnibus (GEO), avoiding the need for new experimental research involving human or animal subjects. We focused on analyzing two specific datasets: one comprising artery samples from individuals with and without atherosclerosis, and the other featuring samples from people in middle age versus those in older age groups. To identify significant genes, we applied a technique known as the weighted average difference (WAD). Our analysis identified 14 genes that were upregulated in both aging and atherosclerosis, hinting at the involvement of the type I interferon response in both conditions. Conversely, 408 genes that exhibited heightened activity within atherosclerotic lesions indicated an augmentation in lysosome-related processes. While aging might create a groundwork that predisposes individuals to atherosclerosis, the progression of atherosclerosis also involves distinct factors such as type I interferon response and an increase in lysosomal activity. Aging encompasses more than just the senescence of vascular cells; it is significantly affected by extracellular factors such as type I interferon. The onset of atherosclerosis, therefore, cannot be attributed solely to aging. Instead, it likely involves enhanced mechanisms such as phagocytosis and heighted lysosomal activity. Geriatr Gerontol Int 2024; ••: ••-••.

Inflation-adjusted analysis of ICER’s Unsupported Price Increase reports: contextualizing drug spending changes

Objective The Institute for Clinical and Economic Review (ICER) publishes annual Unsupported Price Increase (UPI) reports on prescription drugs, which have gained attention from policymakers and healthcare stakeholders. These reports do not adjust for inflation in their analyses of net price changes. This study aimed to evaluate the economic context of reported drug price increases in ICER’s UPI reports by applying inflation adjustments to the estimated impact on healthcare spending. Methods Separate inflation adjustments using the Medical Consumer Price Index (Medical CPI) and the Consumer Price Index for All Urban Consumers (CPI-U) were applied to unadjusted UPI findings. The study analyzed data from four most recent UPI reports spanning pricing data from 2018 to 2019 (report year 2020) to 2021 to 2022 (report year 2023), focusing on drugs classified as having “unsupported” price increases. Results Inflation-adjusted analyses showed lower estimates of real changes in spending compared to ICER’s unadjusted figures across all drugs analyzed. Across the four UPI reports, the average report-year difference in Medical CPI inflation adjustment spending versus unadjusted findings was 53%, whereas the average report-year difference in CPI-U inflation adjustment spending versus unadjusted findings was 80%. In the most recent report year of 2023, ICER’s unadjusted estimate of a $1.3 billion increase in healthcare spending transformed to a $134 million decrease when adjusted by Medical CPI and a $1.51 billion decrease when adjusted by CPI-U. Conclusion This study demonstrates that inflation adjustment can alter not only the findings but also the interpretation of drug spending changes in UPI reports. The findings highlight the importance of considering economic context in evaluating drug pricing trends and suggest that unadjusted figures may overstate the impact of real price changes. Given the influence of these reports on policy discussions, incorporating inflation adjustments into analyses is one of many steps toward informing evidence-based policy decisions.

Deep learning based super-resolution for CBCT dose reduction in radiotherapy.

Cone-beam computed tomography (CBCT) is a crucial daily imaging modality in image-guided and adaptive radiotherapy. However, the use of ionizing radiation in CBCT imaging increases the risk of secondary cancers, which is particularly concerning for pediatric patients. Deep learning super-resolution has shown promising results in enhancing the resolution of photographic and medical images but has not yet been explored in the context of CBCT dose reduction. To facilitate CBCT imaging dose reduction, we propose using an enhanced super-resolution generative adversarial network (ESRGAN) in both the projection and image domains to restore the image quality of low-dose CBCT. An extensive projection database, containing 2997 CBCT scans from head and neck cancer patients, was used to train two different ESRGAN models to generate super-resolution CBCTs. One model operated in the projection domain, using pairs of simulated low-resolution (low-dose) and original high-resolution (high-dose) projections and yielded CBCTSRpro. The other model operated in the image domain, using pairs of axial slices from reconstructed low-resolution and high-resolution CBCTs (CBCTLR and CBCTHR) and resulted in CBCTSRimg. Super-resolution CBCTs were evaluated in terms of image similarity (MAE, ME, PSNR, and SSIM), noise characteristics, spatial resolution, and registration accuracy, using the original CBCT as a reference. To test the perceptual difference between the original and super-resolution CBCT, we performed a visual Turing test. Visually, both super-resolution approaches in the projection and image domains improved the image quality of low-dose CBCTs. This was confirmed by the visual Turing test, that showed low accuracy, sensitivity, and specificity, indicating almost no perceptual difference between CBCTHR and the super-resolution CBCTs. CBCTSRimg (accuracy: 0.55, sensitivity: 0.59, specificity: 0.50) performed slightly better than CBCTSRpro (accuracy: 0.59, sensitivity: 0.61, specificity: 0.57). Image similarity metrics were affected by varying noise levels and did not reflect the visual improvements, with MAE/ME/PSNR/SSIM values of 110.4HU/2.9HU/40.4dB/0.82 for CBCTLR, 136.6HU/-0.4HU/38.6dB/0.77 for CBCTSRpro, and 128.2HU/1.9HU/39.0dB/0.80 for CBCTSRimg. In terms of spatial resolution, quantified by calculating 10% levels of the task transfer function, both CBCTSRpro and CBCTSRimg outperformed CBCTLR and nearly matched the reference CBCTHR (CBCTLR: 0.66lp/mm, CBCTSRpro: 0.88lp/mm, CBCTSRimg: 0.95lp/mm, CBCTHR: 1.01lp/mm). Noise characteristics of CBCTSRimg and CBCTSRpro were comparable to the reference CBCTHR. Registration parameters showed negligible differences for all CBCTs (CBCTLR, CBCTSRpro, CBCTSRimg), with average absolute differences in registration parameters being below 0.4° for rotations and below 0.06mm for translations (CBCTHR as reference). This study demonstrates that deep learning can be a valuable tool for CBCT dose reduction in CBCT-guided radiotherapy by acquiring low-dose CBCTs and restoring the image quality using deep learning super-resolution. The results suggest that higher quality images can be generated when super-resolution is performed in the image domain compared to the projection domain.

Prediction of the thermal degradation–induced colour change of acrylonitrile butadiene styrene products as a function of temperature and titanium dioxide content

In this study, we examined the thermal degradation–induced colour change of acrylonitrile butadiene styrene (ABS)–based titanium dioxide (TiO2)–doped products as a function of TiO2 content and temperature. Based on the time–temperature superposition (tTS) principle and the CIELAB colour space, we proposed a methodology for developing a robust model to estimate the long-term colour change of polymers doped with TiO2 at elevated temperatures. We used 800 h of measurement data to develop the model and validated the colour change predictions of the model in 1600 h. The average colour difference between the measured and modelled results at the application temperature range (below 80 °C) was <1.5, smaller than the just noticeable difference (JND) in the CIELAB colour space. We found that above a certain TiO2 content (approximately 3 wt%), the colour retention of the ABS/TiO2 specimens can no longer be improved by increasing their TiO2 content. To show the applicability of the model, we present two simple case studies predicting the colour and appearance of a polymer product under constant and variable heat loads. The proposed methodology provides an excellent tool for design, as it can be used to determine the optimum amount of TiO2. Moreover, it accelerates and simplifies the design process.

Deepfake Detection Model Combining Texture Differences and Frequency Domain Information

In recent years, public security incidents caused by deepfake technology have occurred frequently around the world, which makes an efficient and accurate deepfake detection model crucial. The existing advanced methods use the manipulation features in the image to realize the binary classification of real and fake images by training complex neural network models. However, these models rely on a single manipulation feature, and the detection accuracy of these methods will be greatly reduced when the forgery technology or image quality of the training data set and the validation data set are different. Inspired by the existing work, we propose a two-stream collaborative learning framework that combines spatial texture differences and frequency information. The average difference convolution (ADC) is designed to extract the spatial texture difference information of the image, and the gray image frequency-aware decomposition (GFAD) is used to extract the artifact information of the image in the frequency domain. At the same time, the ViT idea is combined with cross attention mechanism for feature fusion to comprehensively mine forged features in forged images. Experimental results show that the proposed model has good detection effects on three benchmark data sets. In terms of cross-dataset evaluation, the AUC on Celeb-DF dataset reaches 82.86%, which is better than the existing advanced methods.

VALIDATION OF A NONLINEAR WAVE DECOMPOSITION METHOD INCLUDING SHOALING FOR IRREGULAR WAVES

It is important to decompose the signals when performing physical model experiments. In this paper, a nonlinear decomposition method is extended with the effects of a sloping bed. The approach is validated based on synthetic signals and numerical simulations. Verification with linear synthetic signals shows that the effect on the wave height is limited for intermediate water depth, but increases for shallow water depth with an error of up to 4%. Moreover, when not accounted for a sloping bed the wave height at the first wave gauge is overestimated in shallow water and underestimated in deep water. The results from the numerical simulations show that a shoaling coefficient for the bound waves based on the water depth ratio with a power 1 is reasonably accurate for the sub-harmonics but not valid for the higher harmonics. In addition, it is recommended to verify this assumption for a large range of test conditions. When applying both the method without and with the effects of a sloping bed on physical model experiments, the average differences are 1 % with a maximum up to 4 % in terms of the wave height.

The Effectiveness of Expressive Writing Therapy on Anxiety Levels in School-Age Children Undergoing Hospitalization

Introduction: Anxiety disorders are one of the psychological problems that arise when children undergo hospitalization. Anxiety that is not treated will make the length of hospitalization days and worsen conditions. One of the interventions that can be done to treat anxiety in school-age children undergoing hospitalization is expressive writing therapy. Objective: This study aims to determine the effectiveness of expressive writing therapy on the anxiety of school-age children undergoing hospitalization. Methods: This research design uses quantitative methods with a pre-experimental one group pretest-posttest design. The study sample consisted of 25 respondents selected using purposive sampling technique based on the inclusion criteria, namely school-age children aged 10-12 years who can write, children who are undergoing hospitalization with stable conditions (children who are willing to communicate well and are able to sit on the patient's bed, parents who are willing for their children to become respondents, and children who experience anxiety based on screening results. Anxiety was measured using an instrument (CSAS-C). Results: Data analysis with the Paired Sample t-test test showed a p value = 0.000 (p &lt;0.05) which means there is a difference in the average score of anxiety before and after the expressive writing therapy intervention. Conclusion: Based on the results of the analysis, it can be concluded that there is an effect of expressive writing therapy intervention on the average difference in anxiety of school-age children undergoing hospitalization before and after the intervention.

The Relationship Between eSRTs and Upper Stimulation Levels in a Large Cohort of Adult Cochlear Implant Recipients.

To compare electrically evoked stapedial reflex thresholds (eSRTs) measured at 1 month post-activation to upper stimulation levels used for programming adult cochlear implant (CI) recipients over time in a large clinical population. Review of prospectively collected clinical database. Large CI program at an academic medical center. Postlingually deafened adult CI recipients (n = 439). eSRTs recorded in the medical record and upper stimulation levels derived from the programming software at 1 and 6 months post-activation. The correlation between eSRTs and upper stimulation levels was strong for all three manufacturers (r = 0.80-0.86). On average, upper stimulation levels were set 15.4 clinical levels below eSRT for Cochlear using a pulse width of 25 microseconds, 13.4 clinical levels below eSRT for Cochlear using a pulse width of 37 microseconds, 11.3 clinical units below eSRT for Advanced Bionics, and 0.1 charge unit above eSRT for MED-EL. eSRTs were found to be elicited at similar levels for different electrodes/frequencies across the array. After upper stimulation levels were set based on eSRT at 1 month post-activation, there was no significant change in upper stimulation levels between 1 and 6 months post-activation. eSRTs and upper stimulation levels are highly correlated. Average differences between eSRTs and upper stimulation levels reported herein can be used to guide programming in the clinic. Further, when eSRTs are used to program upper stimulation levels, upper stimulation levels should be relatively similar across channels and stable over time.

Predicting soil properties for fertiliser recommendation in South Korea using MIR spectroscopy

The national fertiliser policies in South Korea aim to provide guidance to farmers for efficient fertiliser application and thus rely on the annual collection and analysis of soil samples. Providing timely soil analysis results remains a challenge, as wet laboratory analysis is time-consuming and expensive. This study represents a pioneering effort in South Korea, by investigating mid-infrared (MIR) spectroscopy for accurate soil properties prediction and its application in developing fertiliser recommendations for several crop types. Additionally, we examined the time efficiency of MIR spectroscopy compared to conventional analytical methods. A total of 567 soil samples from diverse soil and land use types (paddy, upland, orchard, and greenhouse fields) in South Korea (0–20 cm depth) were collected and scanned using an MIR spectrometer. Four machine learning algorithms (partial least squares regression, support vector machine, cubist, and random forest) were trialled and compared for their prediction accuracies using 15-fold cross-validation for eight essential soil properties: organic matter, total nitrogen (N), available phosphorus (P), pH, exchangeable calcium (Ca), potassium (K), magnesium (Mg), and available silica. Results demonstrated robust predictive performance (R2 > 0.70) across the selected soil properties, with organic matter and total nitrogen exhibiting excellent accuracy (R2 > 0.9). Compared with conventional analysis, the average difference in fertiliser application recommendation for seven crops using MIR prediction was 3.8 % for N, 13.9 % for P and 8.1 % for K. Based on the measurement of 11 soil properties, analysis using MIR spectroscopy was about 12 times faster than conventional methods. The study demonstrates the potential of this approach to revolutionise soil analysis protocols, offering a more efficient and cost-effective solution for sustainable agricultural practices in South Korea.

Lasting liver injury following COVID-19 infection characterized by ultrasound shear wave elastography

ObjectiveTo assess for lasting hepatic injury using ultrasound shear wave elastography (SWE) in patients following COVID-19 infection. MethodsIn this retrospective cohort study, patients with SWE exams between January 2019 and 2022 were categorized into three groups: 1) post-COVID-19 subjects with positive COVID-19 PCR assay, 2) random sample of contemporaneous unexposed patients with only negative prior COVID-19 PCR tests, and 3) random sample of pre-pandemic patients to address possible undiagnosed COVID-19 infection in the contemporaneous group. The average difference in median Young’s modulus between post-COVID-19 patients and controls was calculated using a linear regression model after controlling for confounders. Results130 patients (mean age ± SD, 56 years ± 13; 66 women) were evaluated, including 30 patients after COVID-19 infection (mean age ± SD, 53 years ± 11; 15 women), 50 contemporaneous unexposed patients (mean age ± SD, 55 years ± 13; 27 men), and 50 pre-pandemic patients (mean age ± SD, 58 years ± 13; 28 women). SWE scans were performed on General Electric LOGIQ E9 or E10 an average of 44 (range, 12-81) weeks after COVID-19 infection. COVID-19 infection was associated with an average increase in median Young’s modulus of 1.5 kPa (95% CI [0.44, 2.355], p=0.006) after controlling for age, sex, obesity, history of chronic liver disease, and time period. Post-COVID-19 patients had higher liver stiffness compared to contemporaneous controls (median = 7.58 vs 5.99 kPa, p=0.001) but not pre-pandemic controls (median = 7.00 kPa, p=0.51). ConclusionsCOVID-19 infection is associated with increased liver stiffness, which may reflect lasting hepatic injury such as ongoing inflammation or the development of fibrosis. US SWE may serve as a noninvasive tool for long-term liver health monitoring after COVID-19 infection.

Pasture and diurnal temperature are key predictors of regional Plains Spotted Skunk (Spilogale interrupta) distribution.

The Plains Spotted Skunk (Spilogale interrupta) is a small carnivore native to central North America that has experienced significant population reductions, and there is a lack of information about the species that could inform conservation. Our study aimed to address knowledge gaps about the distribution and habitat associations of the species in South Dakota using species distribution modeling. We used species location data collected from state natural resource managers, trappers, and members of online social media groups dedicated to hunting and wildlife conservation; environmental predictors; and 6 predictive modeling algorithms (i.e., artificial neural networks, artificial classification tree analysis, generalized boosting models, maximum entropy, multivariate adaptive regression splines, and random forests) to develop climate and landcover ensemble distribution models. The most important climate and landcover predictors were mean temperature diurnal range (i.e., average monthly differences between daily high and low temperatures) and proportion of area classified as pasture. Ensemble model concordance identified approximately 31,300 km2 of potential Plains Spotted Skunk habitat primarily in eastern South Dakota and between the watersheds of the Missouri and James rivers. Our results offer insights that can guide conservation and inform effective management strategies for conserving Plains Spotted Skunk populations in the northern Great Plains. The promotion of low-intensity agricultural practices such as maintaining pastures, farm buildings, fences rows, and the management of woodland encroachment may improve habitat suitability and facilitate the recovery of plains spotted skunks in the region.

Solar power plant 618.8 kWp monitoring correction on the Fusion application

Monitoring of energy produced by the solar power plant (SPP) with a capacity of 618.8 kWp at HKBP Nommensen University in Medan is carried out using the FusionSolar application. This SPP energy management application includes displaying yield, energy consumption, energy exports, and SPP energy imports. The amount of energy displayed using the FusionSolar application is compared with the amount of energy resulting from manual calculations based on power data displayed on the alternating current (AC) combiner panel from 3-phase inverters. This comparison was carried out over seven days of measurements. It produced an average difference of 5.779%, with a correction value of 0.942, so the energy reading resulting from the FusionSolar application must be multiplied by this correction value.

Electronic system to speckle phenomenon characterization for random movement on fiber optics

Peru is a country located in a telluric area. The early detection of earthquakes will alert the population and avoid human losses. There are different methods to detect it, mainly on mechanical movements and electronic sensors, which are currently used. This article presents the analysis and implementation of a repetitive motion generation and detection system based on the study of the speckle phenomenon through an optical fiber. The analysis is calculated by the technique of averaged difference that allows obtaining the intensity variation of two consecutive frames, as the speckle pattern changes and occupies different positions. Several tests are carried out that show the relationship of the controlled random movement and speckle characteristics obtained, the test system that can be used for the detection of random movements similar to P and S earthquakes waves.

Optimizing laser cutting of stainless steel using latin hypercube sampling and neural networks

Optimizing cutting parameters in fiber laser cutting of austenitic stainless steel is challenging due to the complex interplay of multiple variables and quality metrics. To solve this problem, Latin hypercube sampling was used to ensure a comprehensive and efficient exploration of the parameter space with a smaller number of trials (185), coupled with feedforward neural networks for predictive modeling. The networks were trained with a leave-one-out cross-validation strategy to mitigate overfitting. Different configurations of hidden layers, neurons, and training functions were used. The approach was focused on minimizing dross and roughness on both the top and bottom areas of the cut surfaces. During the testing phase, an average MSE of 0.063 and an average MAPE of 4.68% were achieved by the models. Additionally, an experimental test was performed on the best parameter settings predicted by the models. Initial modelling was conducted for each quality metric individually, resulting in an average percentage difference of 1.37% between predicted and actual results. Grid search was also performed to determine an optimal input parameter set for all outputs, with predictions achieving an average accuracy of 98.34%. Experimental validation confirmed the accuracy and robustness of the model predictions, demonstrating the effectiveness of the methodology in optimizing multiple parameters of complex laser cutting processes.

Minimally invasive balloon-reduction of acute Hill-Sachs-lesions and impression fractures after shoulder dislocation – an experimental approach

The shoulder is the joint most often affected by dislocations. It is known that bony defects of the glenoid and/or humerus can lead to recurrent dislocations even after arthroscopic shoulder stabilization. To prevent recurrent instability, it appears reasonable to reduce fresh and larger Hill-Sachs lesions (off-track lesions). In spine surgery, balloon kyphoplasty is widely used for fracture reduction. The aim of this study was to determine whether the balloon dilatation procedure is transferable to the humeral head and under which conditions it can be successful. In this biomechanical proof-of-concept study, a preformed "impression stamp" was used to create standardized Hill-Sachs impressions (Calandra grade III lesion) on 13 human humeral specimens. A kyphoplasty balloon was then placed intraosseously and close to the defect using a special monolateral trocar and the defect was reduced by balloon dilatation. CT imaging was performed on the native specimens, after defect placement and after reduction. Both the defect size and the extent of reduction were measured radiologically, as well as the bone density. Also, the pressure and volume necessary for reduction were measured. 13 proximal humerus specimens were examined. The mean age of the 7 body donors (3 female) was 85 years (SD ±8.1). The average bone density was 49.0 mg/ml (SD ±26.9) and therefore defined as osteoporotic. The average defect depth (difference to native, measured from a best-fit circle) was 4.1 mm (SD ±1.4), corresponding to an average defect volume of 1203 mm³ (SD ±949). After balloon dilatation, the average achieved reduction (difference to the defect) was 4.3 mm (SD ±1.9) or 1223 mm³ (SD ±1059). This results in an average difference from native to reduced of -0,2 mm (SD ±0.5) or 20 mm³ (SD ±212) and represents no significant difference compared to baseline values (p = 0.90 or p = 0,75, respective), but a slight overreduction in the volumetric measurements. In all humeral specimens, a reduction of at least 2.6 mm or 221 mm³ compared to the defect situation was achieved. An average volume of 3.4 ml (SD ±1,1) and pressure of 271 psi (SD ±81) were required for reduction. The present results demonstrate, that minimally invasive balloon reposition of large acute Hill-Sachs lesions appears methodologically feasible. The required pressure for reduction seems to be higher compared to previous studies. Further studies are necessary for the development of this technique and its reproducible translation into clinical practice.

Changing sea level, changing shorelines: integration of remote-sensing observations at the Terschelling barrier island

Abstract. Sea level rise is associated with increased coastal erosion and inundation. However, the effects of sea level change on the shoreline can be enhanced or counteracted by vertical land motion and morphological processes. Therefore, knowledge about the individual contributions of sea level change, vertical land motion and morphodynamics on shoreline changes is necessary to make informed choices for climate change adaptation, such as applying coastal defence measures. Here, we assess the potential of remote-sensing techniques to detect a geometrical relationship between sea level rise and shoreline retreat for a case study at the Terschelling barrier island at the northern Dutch coast. First, we find that sea level observations from satellite radar altimetry retracked with ALES can represent sea level variations between 2002 and 2022 at the shoreline when the region to extract altimetry time series is chosen carefully. Second, results for cross-shore time series of satellite-derived shorelines extracted from optical remote-sensing images can change considerably, depending on choices made for tidal correction and parameter settings during the computation of time series. While absolute shoreline positions can differ on average by more than 200 m, the average trend differences are below 1 m yr−1. Third, by intersecting the 1992 land elevation with time-variable sea level, we find that inundation through sea level rise caused on average −0.3 m yr−1 of shoreline retreat between 1992 and 2022. The actual shoreline movement in this period was on average between −2.8 and −3.2 m yr−1, leading to the interpretation that the larger part of shoreline changes at Terschelling is driven by morphodynamics. We conclude that the combination of sea level from radar altimetry, satellite-derived shorelines and land elevation provides valuable information about the influence of sea level rise, vertical land motion and morphodynamics on shoreline movements.

Thermal-mechanical behavior of deeply buried pipe energy pile group in sand obtained from model test

One practical and effective method for shallow geothermal development is energy piling. This research presents the deeply buried pipe energy pile (DBP-EP), which has a wide range of potential applications and can collect deeper geothermal heat from the pile toe. This work conducts a model test investigation of the thermal–mechanical behavior of the DBP-EP group in sandy soil because the construction of the former differs from that of the common inside buried pipe energy piles (IBP-EP). The findings indicate that the heat exchanger tube at the pile toe of DBP-EP will be extended outward for heat exchange with the soil, in contrast to IBP-EP, and that the temperature change at the pile toe is greater than that of the whole. The pile cross-section strain decreases gradually from inside to outside along the radial direction. The axial earth pressure change rule around the pile is larger at both ends and small in the middle. For every 1℃ that the inlet temperature raises, the pile top’s final displacement increases by roughly 0.11‰D. At various inlet temperatures, the DBP-EP group heat transfer rate per meter drops by 8% − 23% when compared to the single pile’s. The average axial earth pressure difference surrounding the pile gradually rises when the pile top is not loaded, while the pile side friction difference of the pile group reduces in comparison to that of the single pile. The variations in pile side friction, axial earth pressure surrounding the pile, and pile top displacement of the pile group are reduced when the pile top is loaded because of the dense effect between the pile and the soil. This study contributes to the theoretical understanding of the design and practical implementation of DBP-EP structures.

Thermal Changes During Clavicle Fracture Healing in Children

Introduction: Clavicle fractures are among the most common in children, typically treated conservatively, with standard radiographs used to diagnose and monitor healing. Recently, infrared thermography (IRT) has been proposed as an alternative method for fracture detection, but no study has correlated the temperature changes during callus formation. Materials and Methods: Children aged 4–18 with X-ray-diagnosed clavicle fractures were included in the study. IRT measured temperatures above the fracture and contralateral healthy side on the 1st, 4th, 8th, 15th, and 22nd day after the injury. Along with IRT, an ultrasound was used to assess callus formation. Results: The study included 27 patients with an average age of 12.4 years, mostly boys. The left side was more often affected than the right side (33%). We found a correlation between callus formation and the ∆T. A maximum temperature difference of an average of 0.7 °C was noted during the proliferative phase of callus formation. After the formation of the fibrocartilaginous callus (4th to 8th day), the temperature above the fracture declined until it was equal (22nd day) to that of the healthy side. The average temperature difference between the broken and the healthy sides was statistically significant on the 4th and 8th days (during callus formation). Conclusions: The increased skin temperature above the fracture correlates with the inflammatory phase of bone healing. After the callus is visible on ultrasound, the temperature linearly drops with no statistical difference between the injured and the healthy sides. The standard protocol for clavicle fracture treatment typically involves using X-rays to assess callus formation during follow-up. IRT has shown potential in diagnosing callus formation in children with clavicle fractures, potentially reducing the need for traditional X-rays.

A comparative analysis of car fleet efficiency evolution in Europe and Australia insights on policy influence

Regulators worldwide introduce measures to improve energy consumption and reduce greenhouse emissions of road vehicles. The present study focuses on the impacts of passenger car carbon dioxide (CO2) and fuel efficiency measures, attempting to evaluate their effectiveness using vehicle and fleet modelling. To obtain a robust counterfactual result, two regions are compared as a case study: the European Union (EU), where CO2 emissions have been regulated for over fifteen years, and Australia, a region that recently introduced such a policy element. The average difference in the certified CO2 emissions of new cars registered in the two regions increased from 50 g/km in 2018 to 60 g/km in 2021 due to accelerated electrification in the EU. To demonstrate the importance of mandatory targets, a sensitivity analysis of the 2020 EU registrations showed that lower by one-third sales of zero and low-emission vehicles (Z-L-EVs) would have resulted in seven out of ten manufacturer pools failing to meet the 2020 target of 95 g/km. The simulation framework was validated against certified values of 2021 registrations and was subsequently used to calculate real-world fleet performance to quantify the actual emissions savings. The real-world CO2 emissions in the registered fleets for 2021 were estimated to be 143 g/km and 204 g/km, for the EU and Australia, respectively. With these results as a reference, the share of ZEVs required to meet the targets set in both regions is calculated: 51% of the new registrations in 2030 in the EU for achieving 49.5 g/km, and 60% in Australia for recently set target of 58 g/km for 2029. The respective calculated average fleet-wide real-world tailpipe CO2 emissions were estimated to be 66 g/km (EU) and 77 g/km (Australia). As a last step, the study discusses the application of similar tools and analysis in the design of future policies.

Reaching medium entropy oxides with rocksalt structure based on cluster-plus-glue-atom model: A case study of Co-Ni-Cu-Zn-O system with tunable optoelectrical properties

Mg–Co–Ni–Cu–Zn–O is the firstly reported high entropy oxide (HEO) which has great promise in optoelectrical applications. However, the multicomponent and decreased configurational entropy by extracting one element from (Mg, Co, Ni, Cu, Zn) makes it difficult to synthesize single-phase medium entropy oxides (MEOs) especially when the cations are non-equimolar. In this paper, we synthesized different compositional-type of non-equimolar Co–Ni–Cu–Zn–O MEOs possessing single-phase rocksalt structure guided by the cluster-plus-glue-atom model. Interestingly, Co21Ni43Cu16Zn16O96 and Co17Ni47Cu16Zn16O96 have the lowest single-phase formation temperature (850 °C) when the total cation content is kept unchanged. Further increase or decrease of Co/Ni content (none, little, more) will increase the difficulty of single-phase formation ability and temperature (900–1200 °C). It is suggested the single-phase formation ability of Co–Ni–Cu–Zn–O MEOs is closely related to the Gibbs free energy (ΔG) and average cation-size difference (δ). The linear decrease of Co content and increase of Ni content can monotonously enlarge the energy band gap (Eg) from 1.52 eV to 1.93 eV and increase the electrical impedance with five orders of magnitude (∼103 Ω–∼108 Ω), which may be valuable in the fields of optoelectrical devices.