Small Differences In Performance Research Articles

Comparative assessment and QA measurement array validation of Monte Carlo and Collapsed Cone dose algorithms for small fields and clinical treatment plans.

Many studies have demonstrated superior performance of Monte Carlo (MC) over type B algorithms in heterogeneous structures. However, even in homogeneous media, MC dose simulations should outperform type B algorithms in situations of electronic disequilibrium, such as small and highly modulated fields. Our study compares MC and Collapsed Cone (CC) dose algorithms in RayStation 12A. Under consideration are 6 MV and 6 MV flattening filter-free (FFF) photon beams, relevant for VMAT plans such as head-and-neck and stereotactic lung treatments with heterogeneities, as well as plans for multiple brain metastases in one isocenter, involving highly modulated small fields. We aim to investigate collimator angle dependence of small fields and performance differences between different combinations of ArcCHECK configuration and dose algorithm. Several verification tests were performed, ranging from simple rectangular fields to highly modulated clinical plans. To evaluate and compare the performance of the models, the agreements between calculation and measurement are compared between MC and CC. Measurements include water tank measurements for test fields, ArcCHECK measurements for test fields and VMAT plans, and film dosimetry for small fields. In very small or narrow fields, our measurements reveal a strong dependency of dose output to collimator angle for VersaHD with Agility MLC, reproduced by both dose algorithms. ArcCHECK results highlight a suboptimal agreement between measurements and MC calculations for simple rectangular fields when using inhomogeneous ArcCHECK images. Therefore, we advocate for the use of homogeneous phantom images, particularly for static fields, in ArcCHECK verification with MC. MC might offer performance benefits for more modulated treatment fields. In ArcCHECK results for clinical plans, MC performed comparable to CC for 6 MV. For 6 MV FFF and the preferred homogeneous phantom image, MC resulted in consistently better results (13%-64% lower mean gamma index) compared to CC.

Comparison of the effects of different ageing tests on flexural properties of flax fibre composites

This study investigates the changes in the flexural properties and microstructure of flax fibre thermoset composites induced by accelerated and natural ageing methods. The reductions in flexural strength and modulus of composites, ranging from 14 %–48 % and 6 %–57 %, respectively or even an increase in flexural properties after the cyclic ageing test, show the diverse effects of the tests on the flax composites. Only small differences in the composites' performance were found between the natural ageing under the shade and natural ageing exposed to sunlight. The long-term moisture cycling did not cause progressive damage; instead, the composite's flexural properties increased, likely due to an improvement in both the fibre's and matrix's mechanical properties. The water immersion test seems to be predictive of the long-term durability of uncoated flax composites in natural conditions. Microstructure analysis supports composites' flexural test results, with flax/epoxy showing superior properties compared to flax/polyester. The results suggest that uncoated flax composites can withstand extreme humidity changes, but perhaps not in applications wherein composites come in contact with liquid water for long periods, likely due to the effects of leaching. The varied impacts of the different ageing tests on flax fibre composites underscore the need to thoroughly select testing protocols that simulate service conditions relevant to the composites' intended application.

“Anything you can do, I can do”: Examining the use of ChatGPT in situational judgement tests for professional program admission

We explored the transformative impact of ChatGPT on applicants' responses and performance in situational judgement tests (SJTs), as well as the role played by faking-prevention mechanisms, in two complementary studies. Study 1 examined how the availability of ChatGPT influenced response content and performance of real applicants (N = 107,805), who completed an SJT for admission before vs. after the release of the technology. We found only small differences in content (e.g., slightly less “authentic” words used) and performance (slight score improvements when controlling for response length, no differences otherwise). In Study 2, we used an experimental approach with (N = 138) Prolific participants completing a mock SJT, while being instructed to use ChatGPT when responding (vs. use online resources or no resources). We found only slightly higher SJT scores for the ChatGPT users, but no difference in response content. Additionally, GPTZero (i.e., a popular AI detection tool) struggled to detect ChatGPT content, and generated many false positives, in both studies. This research advances our understanding of how the release and popularization of ChatGPT can influence applicant behaviors. Given the “arms race” nature of applicant selection, they also highlight the importance of designing assessments to prevent or limit faking. Yet, the ever-evolving nature of AI calls for continuous research on the topic.

Using automated machine learning for the upscaling of gross primary productivity

Abstract. Estimating gross primary productivity (GPP) over space and time is fundamental for understanding the response of the terrestrial biosphere to climate change. Eddy covariance flux towers provide in situ estimates of GPP at the ecosystem scale, but their sparse geographical distribution limits larger-scale inference. Machine learning (ML) techniques have been used to address this problem by extrapolating local GPP measurements over space using satellite remote sensing data. However, the accuracy of the regression model can be affected by uncertainties introduced by model selection, parameterization, and choice of explanatory features, among others. Recent advances in automated ML (AutoML) provide a novel automated way to select and synthesize different ML models. In this work, we explore the potential of AutoML by training three major AutoML frameworks on eddy covariance measurements of GPP at 243 globally distributed sites. We compared their ability to predict GPP and its spatial and temporal variability based on different sets of remote sensing explanatory variables. Explanatory variables from only Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data and photosynthetically active radiation explained over 70 % of the monthly variability in GPP, while satellite-derived proxies for canopy structure, photosynthetic activity, environmental stressors, and meteorological variables from reanalysis (ERA5-Land) further improved the frameworks' predictive ability. We found that the AutoML framework Auto-sklearn consistently outperformed other AutoML frameworks as well as a classical random forest regressor in predicting GPP but with small performance differences, reaching an r2 of up to 0.75. We deployed the best-performing framework to generate global wall-to-wall maps highlighting GPP patterns in good agreement with satellite-derived reference data. This research benchmarks the application of AutoML in GPP estimation and assesses its potential and limitations in quantifying global photosynthetic activity.

Comparative analysis of compact and benchtop near-infrared spectrometers for forage nutritional trait measurements

We compared near-infrared (NIR) partial least squares models of a compact and a benchtop spectrometer to measure six nutritional traits of Brachiaria forages, including in-vitro dry matter digestibility (IVDMD), crude protein (CP), crude fiber (CF), acid detergent fiber (ADF), neutral detergent fiber (NDF), and lignin (LG). The relative prediction errors (RMSE/mean) for both instruments varied between 1.5% (for NDF) and 13% (for CP). Additionally, we obtained multivariate figures of merit and used a robust randomization t-test to comprehensively compare the two instruments. Our results show that, despite its lower analytical sensitivity, the compact instrument performs sufficiently well compared to the benchtop instrument and can be used for routine measurement of all six forage nutritional traits. The small performance difference between the two spectrometers can primarily be attributed to the narrower spectral range measured by the compact instrument.

The Effects and Reproducibility of 10, 20, and 30% Velocity Loss Thresholds on Acute and Short-Term Fatigue and Recovery Responses.

Weakley, J, Johnston, RD, Cowley, N, Wood, T, Ramirez-Lopez, C, McMahon, E, and García-Ramos, A. The effects and reproducibility of 10, 20, and 30% velocity loss thresholds on acute and short-term fatigue and recovery responses. J Strength Cond Res 38(3): 465-473, 2024-This study aimed to establish the effects and reproducibility of implementing 10, 20, and 30% velocity loss thresholds (VLTs) during the free-weight barbell back squat on acute and short-term perceived soreness, neuromuscular fatigue, and physical performance. Using a repeated, counterbalanced, crossover design, 12 team-sport athletes completed on separate sessions 5 sets of the free-weight barbell back-squat until reaching VLTs of either 10, 20, or 30%. Outcomes were measured immediately postexercise and 24 hours after each session. To assess reproducibility, the same sessions were repeated after 4 weeks. Immediately postexercise, small differences in countermovement jump (CMJ) and 10-m sprint performance were observed between VLT conditions, whereas small to moderate differences in differential ratings of perceived exertion were reported (10% < 20% < 30%). At 24 hours, trivial differences in CMJ outcomes were found but small differences in 10-m sprint performance were detected between conditions (10% < 20% < 30%). In addition, at 24 hours, a single small difference in radial deformation using tensiomyography was found between 10 and 30% conditions, whereas large to very large differences in perceived soreness were reported between conditions (10% < 20% < 30%). Finally, the standard error of measurement of all outcome measures at 24 hours were of a similar magnitude to those reported in tightly controlled, short-term studies. Collectively, these findings demonstrate that VLTs help control the fatigue outcomes that occur as a response to resistance training and that they are reproducible. Therefore, for practitioners who wish to prescribe resistance training and be confident in the subsequent fatigue response, it is strongly advised that VLTs are implemented.

Overview of Noninvasive Prenatal Testing (NIPT) for the Detection of Fetal Chromosome Abnormalities; Differences in Laboratory Methods and Scope of Testing.

Although nearly all noninvasive prenatal testing is currently based on analyzing circulating maternal cell-free DNA, the technical methods usedvary considerably. We review the different methods. Based on validation trials and clinical experience, there are mostly relatively small differences in screening performance for trisomies 21, 18, and 13 in singleton pregnancies. Recent reports show low no-call rates for all methods, diminishing its importance when choosing a laboratory. However, method can be an important consideration for twin pregnancies, screening for sex chromosome abnormalities, microdeletion syndromes, triploidy, molar pregnancies, rare autosomal trisomies, and segmental imbalances, and detecting maternal chromosome abnormalities.

Performance analysis of innovative cleaning and soiling mitigation solutions in the semi-arid climate of Benguerir Morocco

The objective of this paper is to evaluate the performance of the hydrophobic coatings and detergent cleaning & antistatic protection for photovoltaic solar panels in semi-arid weather conditions in Benguerir Morocco. Various coating and cleaning strategies were tested on five photovoltaic (PV) systems with the same PV panels and electrical configurations. The first PV system (uncleaned) was not subject to any coatings or cleaning solutions. The second PV system (Water Cleaned) was periodically cleaned with raw water. The third PV system: solar wash protects (SWP) made use of a cleaning solution. The fourth:D solar defender (DSS) and fifth: industrial glass protect (IGP) PV systems each had a unique combination of the two hydrophobic coatings. The results demonstrated that after 9 months of operation, in the first 3 months (cleaning period) the average efficiency gain of the coated PV panels is around 10% compared to the reference system. Whereas in the non-cleaning period after 6 months of exposure, the efficiency gain is around 5%. After the outdoor exposure period, the cumulative energy gain of the coated systems compared to the water-cleaned reference reaches an average of 3%. It has been found that the SWP used 50% less water to clean the PV panels than the system cleaned without a cleaning solution, which made the panels harder to clean. The SWP is more successful at dust removal during the dry season (August–February) with low rain rates. However, during the rainy season (March–April), IGP outperformed SWP and DSD, with a small difference in PV performance. This study demonstrates the significance of using new cleaning strategies such as anti-soling coatings in dry areas to enhance the performance of photovoltaic systems, which may be useful for investors, researchers, and engineers interested in grid-connected photovoltaic and self-cleaning technology.

GaN surface acoustic wave filter with low insertion loss

Surface acoustic wave (SAW) filter with a low insertion loss (IL) of 4.415 dB has been demonstrated on Carbon-doped semi-insulating c-plane bulk GaN without external lumped element matching. The center frequency, 3 dB bandwidth, out-of-band attenuation, return loss of the filter are 477.05 MHz, 0.308 MHz, 32.5 dB, and −9.72 dB, respectively. The electromechanical coupling coefficient (Kt2), and temperature coefficient of frequency (TCF) of the filter are 0.21 % and −26.0 ppm/°C, respectively. The impact of the number of interdigital transducers (NIDT) and acoustic propagation direction on filter performance has been studied. The IL of filters reduces from 16.07 dB to 4.415 dB with the increase of NIDT from 50 to 150 due to the enhanced acoustic superposition. The numerical distribution of elastic stiffness ([cij]), and piezoelectric constants ([eik]) of GaN has been calculated in Euler angle space, indicating that they are isotropic on c-plane. The small performance difference of filters along the m- and a-direction on c-plane bulk GaN can be attributed to the small offset angle of 0.5° of the bulk GaN wafer or IDT quality variation.

Numerical Method for Predicting Emissions from Biodiesel Blend Fuels in Diesel Engines of Inland Waterway Vessels

The use of alternative fuels in ships faces the dual challenge of emission regulations and cost of use. In this paper, the impact of biodiesel blends from cooking waste as a carbon-neutral fuel for inland waterway vessels was investigated. The software AVL FIRE was used to simulate the detailed chemical combustion process of a marine diesel engine running on D100 (pure diesel), B5 (5% biodiesel by volume), B10 (10% biodiesel by volume), and B15 (15% biodiesel by volume). The results showed that B5, B10, and B15 all provided a better air-fuel mixture and significantly reduced soot production. Based on the performance and emission values, B5, B10, and B15 cause relatively small differences in engine performance compared to diesel and are readily applicable in practice. Optimizing exhaust gas recirculation (EGR) and varying injection timing can further optimize biodiesel fuel combustion while reducing NOx and soot emissions. The results of this study are helpful for the application of waste cooking oil biodiesel fuel and reducing exhaust gas emissions from ships.

The Effects of Cannabidiol on the Driving Performance of Healthy Adults: A Pilot RCT

A common side effect of cannabidiol is drowsiness, which could impact safe driving. This study's purpose was to determine the feasibility and whether cannabidiol impacts simulated driving performance. This was a randomized, parallel-group, sex-stratified, double-blind, pilot trial that consisted of a volunteer sample of healthy, currently driving college students. Participants were randomized and allocated to receive a placebo (n=19) or 300 mg cannabidiol (n=21) by oral syringe. Participants completed a ~40-minute driving simulation. A post-test survey assessed acceptability. The primary outcomes were mean SD of lateral position, total percent time the individual drove outside travel lanes, total collisions, time to initial collision, and mean brake reaction time. Outcomes were compared between groups using Student's t-tests and Cox proportional hazards models. None of the relationships were statistically significant, but the study was underpowered. Those receiving cannabidiol experienced slightly more collisions (0.90 vs 0.68, p=0.57) and had slightly higher mean SD of lateral position and slower brake reaction times (0.60 vs 0.58 seconds, p=0.61) than those who received placebo. Participants were satisfied with their experiences. The design was feasible. Larger trials may be warranted because it is unclear whether the small differences in performance seen in the cannabidiol group were clinically relevant.

Automated monitoring of online news accuracy with change classification models

In the past decade, news consumption has shifted from printed news media to online alternatives. Although these come with advantages, online news poses challenges as well. Notable here is the increased competition between online newspapers and other online news providers to attract readers. Hereby, speed is often favored over quality. As a consequence, the need for new tools to monitor online news accuracy has grown. In this work, a fundamentally new and automated procedure for the monitoring of online news accuracy is proposed. The approach relies on the fact that online news articles are often updated after initial publication, thereby also correcting errors. Automated observation of the changes being made to online articles and detection of the errors that are corrected may offer useful insights concerning news accuracy. The potential of the presented automated error correction detection model is illustrated by building supervised classification models for the detection of objective, subjective and linguistic errors in online news updates respectively. The models are built using a large news update data set being collected during two consecutive years for six different Flemish online newspapers. A subset of 21,129 changes is then annotated using a combination of automated and human annotation via an online annotation platform. Finally, manually crafted features and text embeddings obtained by four different language models (TF-IDF, word2vec, BERTje and SBERT) are fed to three supervised machine learning algorithms (logistic regression, support vector machines and decision trees) and performance of the obtained models is subsequently evaluated. Results indicate that small differences in performance exist between the different learning algorithms and language models. Using the best-performing models, F2-scores of 0.45, 0.25 and 0.80 are obtained for the classification of objective, subjective and linguistic errors respectively.

Both real-time and long-term environmental data perform well in predicting shorebird distributions in managed habitat.

Highly mobile species, such as migratory birds, respond to seasonal and interannual variability in resource availability by moving to better habitats. Despite the recognized importance of resource thresholds, species‐distribution models typically rely on long‐term average habitat conditions, mostly because large‐extent, temporally resolved, environmental data are difficult to obtain. Recent advances in remote sensing make it possible to incorporate more frequent measurements of changing landscapes; however, there is often a cost in terms of model building and processing and the added value of such efforts is unknown. Our study tests whether incorporating real‐time environmental data increases the predictive ability of distribution models, relative to using long‐term average data. We developed and compared distribution models for shorebirds in California's Central Valley based on high temporal resolution (every 16 days), and 17‐year long‐term average surface water data. Using abundance‐weighted boosted regression trees, we modeled monthly shorebird occurrence as a function of surface water availability, crop type, wetland type, road density, temperature, and bird data source. Although modeling with both real‐time and long‐term average data provided good fit to withheld validation data (the area under the receiver operating characteristic curve, or AUC, averaged between 0.79 and 0.89 for all taxa), there were small differences in model performance. The best models incorporated long‐term average conditions and spatial pattern information for real‐time flooding (e.g., perimeter‐area ratio of real‐time water bodies). There was not a substantial difference in the performance of real‐time and long‐term average data models within time periods when real‐time surface water differed substantially from the long‐term average (specifically during drought years 2013–2016) and in intermittently flooded months or locations. Spatial predictions resulting from the models differed most in the southern region of the study area where there is lower water availability, fewer birds, and lower sampling density. Prediction uncertainty in the southern region of the study area highlights the need for increased sampling in this area. Because both sets of data performed similarly, the choice of which data to use may depend on the management context. Real‐time data may ultimately be best for guiding dynamic, adaptive conservation actions, whereas models based on long‐term averages may be more helpful for guiding permanent wetland protection and restoration.

Preliminary tests show bisphenol-A reduces memory and scent discrimination in tests of rat olfactory performance

Over 10% of the 260 million tons of plastic produced each year ends up in Earth's oceans. Despite worldwide efforts to limit plastic waste, many organisms have already been exposed to plastic waste for prolonged periods. Plastic degradation releases biologically active compounds (e.g. bisphenol-A (BPA)). Studies have demonstrated that BPA can affect brain development and function, but little is known about its impact on physiological activity or brain function. The aim of this research is to assess the effect of BPA on olfaction in order to understand how BPA might affect marine organisms that are critically dependent on olfaction for survival. Operant conditioning was used to train two groups of laboratory rats (Rattus norvegicus) to detect the scent of lemon juice. Rats in the experimental group received water containing 0.05 mg/kg/day of BPA for five weeks, and then 0.10 mg/kg of BPA for nine weeks. Performance variables measured included sensitivity, scent discrimination, and memory. Data analysis with a general linear mixed model found a small significant difference (based on p-value) in performance between the two groups of rats before BPA administration, and a stronger difference in scent discrimination afterward. After BPA administration, repeated measures tests found significant differences within the experimental group in the rats' ability to remember the target scent. These preliminary results suggest a need for expanded testing. If these results are then confirmed, we recommend additional research examining the effects of plastic waste on the olfactory capabilities and survival of terrestrial and marine species in their native habitats.

Biochar from food waste: a sustainable amendment to reduce water stress and improve the growth of chickpea plants

The application of biochar in agriculture is a developing means to improve soil water retention, fertility, and crop yield. The present work focuses on biochar preparation from mixed vegetable and fruit wastes, using cauliflower, cabbage, banana peels, corn leaves, and corn cobs. The biochar produced at 400 °C was applied to the soil as an amendment to observe the qualitative changes of soil quality, plant growth, and water retention capacity of the soil based on screening in a previous study. Pot experiments were conducted at a laboratory scale having 0%, 2%, and 6% biochar mixed with sand. Each pot was sown with seeds of chickpeas (Cicer arietinum L.) and monitored over 60 days. Two biochar application rates improved soil quality by increasing soil porosity from 49.3 to ≥ 53.4%, more than doubling cation exchange capacity to ≥ 21.1 cmolc.kg−1, providing a small reduction in bulk density of approximately 10% and decreasing electrical conductivity of the extract by at least 40% in comparison to control condition. The biochar application also increased key soil nutrients K, Mn, S, and P by a factor of 2–9 times. Application of biochar at 2% and 6% improved water retention from 55 to 77 and 91 mL respectively over the study and, more importantly, more than doubled the biomass yield for the same water application. The lower biochar application rate of 2% led to more germinated seeds (p = 0.0001), leaves (p = 0.0001), flowers, and fruiting chickpeas than the control condition. The 6% biochar application rate slightly improved plant height (p = 0.01) and provided a small reduction in water loss compared with the 2% biochar. Both biochar loadings increased the root and shoot biomass (p = 0.005) and nutrient content of the shoot and root biomass, particularly K, P, and S (p = 0.0001). This study demonstrates that biochar application at 2–6% is an effective means to increase chickpea yield and reduce water stress. Given small differences in performance within this application range, 2% application is recommended. The study establishes valorization of cellulose rich food waste in the form of biochar as a potential method for positive soil management and increased agricultural productivity in arid environments.

Retention time prediction in hydrophilic interaction liquid chromatography with graph neural network and transfer learning

The combination of retention time (RT), accurate mass and tandem mass spectra can improve the structural annotation in untargeted metabolomics. However, the incorporation of RT for metabolite identification has received less attention because of the limitation of available RT data, especially for hydrophilic interaction liquid chromatography (HILIC). Here, the Graph Neural Network-based Transfer Learning (GNN-TL) is proposed to train a model for HILIC RTs prediction. The graph neural network was pre-trained using an in silico HILIC RT dataset (pseudo-labeling dataset) with ∼306 K molecules. Then, the weights of dense layers in the pre-trained GNN (pre-GNN) model were fine-tuned by transfer learning using a small number of experimental HILIC RTs from the target chromatographic system. The GNN-TL outperformed the methods in Retip, including the Random Forest (RF), Bayesian-regularized neural network (BRNN), XGBoost, light gradient-boosting machine (LightGBM), and Keras. It achieved the lowest mean absolute error (MAE) of 38.6 s on the test set and 33.4 s on an additional test set. It has the best ability to generalize with a small performance difference between training, test, and additional test sets. Furthermore, the predicted RTs can filter out nearly 60% false positive candidates on average, which is valuable for the identification of compounds complementary to mass spectrometry.

Syngas Production in a 1.5 kWth Biomass Chemical Looping Gasification Unit Using Fe and Mn Ores as the Oxygen Carrier.

Biomass chemical looping gasification (BCLG) uses lattice oxygen from an oxygen carrier instead of gaseous oxygen for high-quality syngas production without CO2 emissions. In this work, the effect of the main operating variables, such as oxygen/biomass ratio (λ), gasification temperature, and steam/biomass ratio (S/B), was investigated using two low-cost materials: a Fe ore and a Mn ore. Oxygen fed to the air reactor for oxidation was used as an effective method for controlling the amount of lattice oxygen used for syngas production. The main variable that affected the process performance and the syngas quality was λ, while the fuel reactor temperature and the S/B ratio had a minor effect. Small performance differences found between the ores can be attributed to different degrees of CH4 and light hydrocarbons reforming in the process. The CO2 content in the syngas was high (40 −43%) under autothermal conditions because the gasification reactions required the heat to be generated by combustion. CH4 contents of around 10% were found in syngas, coming from the unburned or unreformed volatiles. Syngas yields around 0.60 Nm3/kg of dry biomass were found for both ores. Additionally, high biomass conversions (Xb > 94%) and carbon conversion efficiencies (ηcc > 95%) were obtained in all cases, showing the capability of the process of avoiding CO2 emissions to the atmosphere. No agglomeration was found in the bed during the BCLG process, although attrition rates were high, leading to lifetimes of 160 and 300 h for the manganese and iron ores, respectively. Migration of Fe or Mn to the external part of the particle, generating a metal concentrated shell, was observed. Its detachment was responsible for the decrease in the oxygen transport capacity (ROC) of the material with the operating time and the reduced lifetime. The results obtained here allowed the iron ore to be considered as an oxygen carrier suitable for the BCLG process.

Effect of Gas Diffusion Layer Thickness on the Performance of Anion Exchange Membrane Fuel Cells

Gas diffusion layers (GDLs) play a critical role in anion exchange membrane fuel cell (AEMFC) water management. In this work, the effect of GDL thickness on the cell performance of the AEMFC was experimentally investigated. Three GDLs with different thicknesses of 120, 260, and 310 µm (denoted as GDL-120, GDL-260, and GDL-310, respectively) were prepared and tested in a single H2/O2 AEMFC. The experimental results showed that the GDL-260 employed in both anode and cathode electrodes exhibited the best cell performance. There was a small difference in cell performance for GDL-260 and GDL-310, while water flooding was observed in the case of using GDL-120 operated at current densities greater than 1100 mA cm−2. In addition, it was found that the GDL thickness had more sensitivity to the AEMFC performance as used in the anode electrode rather than in the cathode electrode, indicating that water removal at the anode was more challenging than water supply at the cathode. The strategy of water management in the anode should be different from that in the cathode. These findings can provide a further understanding of the role of GDLs in the water management of AEMFCs.

Multi-scale topology optimization for stiffness and de-homogenization using implicit geometry modeling

In this article, we demonstrate the state-of-the-art of multi-scale topology optimization for 3D structural design. Many structures designed for additive manufacturing consist of a solid shell surrounding repeated microstructures, so-called infill material. We demonstrate the performance of different types of infill microstructures, such as isotropic truss or plate lattice structures and show that the best results can be obtained using spatially varying and oriented orthotropic microstructures. Furthermore, we demonstrate how implicit geometry modeling using nTop platform can help to interpret these multi-scale designs as single-scale manufacturable designs (de-homogenization). More importantly, we demonstrate the small difference in performance between these multi-scale and single-scale designs through extensive numerical testing. The presented method is at least 3 orders of magnitude more efficient compared to standard density-based topology optimization, allowing for high-resolution 3D structures to be obtained on a standard workstation PC.

Cognitive and behavioral profiles of pediatric surgical candidates with frontal and temporal lobe epilepsy

BackgroundWe aimed to prospectively analyze memory and executive and social cognitive functioning in patients with drug-resistant frontal lobe epilepsy (FLE) and temporal lobe epilepsy (TLE) with focal lesions and isolate the impact of intellectual ability on specific deficits. MethodsA neuropsychological evaluation was performed in 23 children with FLE, 22 children with TLE, and 36 healthy pediatric controls (HCs). Patients in the epilepsy groups had a range of lesions, including low-grade epilepsy-associated tumors (LEAT), focal cortical dysplasia (FCD) type II, and mesial temporal sclerosis (MS). ResultsThere were no significant differences between children with FLE and TLE regarding memory, executive, or social cognitive functioning. General Ability Index (GAI) was a predictor of memory, executive function, and social cognition scores and was influenced by age at onset, duration of epilepsy, and number of antiepileptic drugs (AEDs) prescribed at the time of assessment. Working Memory Index scores of patients with TLE, which measure verbal mnesic processing, were significantly lower than those of HCs and patients with TLE. The greatest differences in both clinical groups compared to HCs were recorded in cognitive executive functions, and patients with FLE had lower scores in this domain. Regarding behavioral executive functions, patients with TLE presented impaired emotional control and impulse inhibition and patients with FLE exhibited decreased flexibility. ConclusionConsistent with previous research, our findings provide further detailed evidence of small differences in cognitive performance among children with FLE and TLE. These differences emerge on analysis of the factors with which deficits are associated.