Similar Tests Research Articles

A graph theory based similarity metric enables comparison of subpopulation psychometric networks.

Network psychometrics leverages pairwise Markov random fields to depict conditional dependencies among a set of psychological variables as undirected edge-weighted graphs. Researchers often intend to compare such psychometric networks across subpopulations, and recent methodological advances provide invariance tests of differences in subpopulation networks. What remains missing, though, is an analogue to an effect size measure that quantifies differences in psychometric networks. We address this gap by complementing recent advances for investigating whether psychometric networks differ with an intuitive similarity measure quantifying the extent to which networks differ. To this end, we build on graph-theoretic approaches and propose a similarity measure based on the Frobenius norm of differences in psychometric networks' weighted adjacency matrices. To assess this measure's utility for quantifying differences between psychometric networks, we study how it captures differences in subpopulation network models implied by both latent variable models and Gaussian graphical models. We show that a wide array of network differences translates intuitively into the proposed measure, while the same does not hold true for customary correlation-based comparisons. In a simulation study on finite-sample behavior, we show that the proposed measure yields trustworthy results when population networks differ and sample sizes are sufficiently large, but fails to identify exact similarity when population networks are the same. From these results, we derive a strong recommendation to only use the measure as a complement to a significant test for network similarity. We illustrate potential insights from quantifying psychometric network similarities through cross-country comparisons of human values networks. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Experimental Study on 3D RC Frame Structures with VE Energy Dissipation Braces Using STM32-Matlab-OpenSees Hybrid Test Platform

This study aims to reveal the control mechanism of viscoelastic (VE) energy dissipation braces with strong mechanical nonlinear characteristics on the seismic response of reinforced concrete (RC) frame structures. Firstly, a new STM32-Matlab-OpenSees joint hybrid test platform is developed by introducing OpenSees, which is convenient for structural modeling and fast for structural calculation. The overall architecture and operation of the hybrid test platform are comprehensively analyzed. Then, a series of simulated seismic hybrid tests on three-dimensional (3D) RC frame structures with VE energy dissipation braces under different seismic waves are successfully carried out. Lastly, the hybrid test results of this structure system at different working conditions are compared and analyzed comprehensively. The research results show that the developed platform breaks the boundary limitation that OpenSees relies on the framework software OpenFresco and can only be connected to specific control systems in the hybrid test. Thus, it greatly expands the application scope of flexible configuration controller and actuator categories for the hybrid test platform, and it also provides a good reference value for the establishment and implementation of similar hybrid test platforms. Under the action of various seismic waves, VE energy dissipation braces has good universality for the shock absorption of RC frame structures. The damping control effect and energy dissipation efficiency of VE energy dissipation braces are significantly different under various seismic waves. In the design of RC frame structures with VE energy dispersion braces, it is very necessary to conduct the seismic analysis of the structures under multiple ground motions with wide frequency domain. The additional position of VE energy dissipation braces should be an important analysis indicators in the optimization design of the structures.

Wildlife diversity and community structure in northern deciduous forest of Bangladesh

Monitoring wildlife communities in protected areas is pivotal to successful conservation efforts. This study employed a direct observations-based approach to examine the diversity and community structure of wildlife in four protected deciduous national parks (Ramsagar, Birganj, Singra, and Nawabganj) in the northern Dinajpur district of Bangladesh. This study assessed the wildlife assemblage structures by measuring α diversity and β diversity. This research recorded a total of 159 wildlife species under 29 orders. Singra National Park displayed the highest species richness (N=73) and was found to be more diverse (H=3.36 ± 0.16) and even in distribution (J=0.9 ± 0.01). Analysis of Similarity test showed significant differences across all study sites (R=0.5216; p=0.0001). Whittaker Plot ranked Dendrocygna javanica as dominating, species making the community uneven. We found significant differences in species richness among seasons (F2, 9=17.8, p=0.0001). For example, winter and rainy seasons were significantly richer over summer. This study identified profound human intrusions, which could potentially impact wildlife communities in the study area. Our findings underscore the conservation efforts to safeguard the threatened species in the study area. J. Bangladesh Acad. Sci. 47(2); 249-265: December 2023

Behaviour of aluminium/steel hybrid RSW joints under high cycle fatigue loading

The lightweight construction of automotive car bodies is the more important to reduce the fuel consumption and costs. High-strength steels and aluminium alloys are suitable for achieving these aims. Recent car bodies contain both materials, therefore necessary to make reliable joints between them. The resistance spot welding (RSW) can be used for joining of car bodies and it is applicable for aluminium/steel hybrid joints, too. High cycle fatigue (HCF) test results can be rarely found in the literature while HCF loading basically determines the lifetime of hybrid joints. 5754-H22, 6082-T6, and DP600 base materials were used for similar and hybrid RSW joints and HCF tests were performed. Number of cycles to failure values, failure modes, furthermore brittle intermetallic compound (IMC) layers were studied and analysed. In both aluminium/steel hybrid joints, the HCF test results showed better endurance limit like concerning aluminium/aluminium similar joints, but worse than steel/steel joints. For 5754-H22 alloy the endurance limit values are 648 N, 939 N, and 1285.5 N, for similar aluminium, hybrid, and similar steel joints, respectively. For 6082-T6 alloy these values are 513 N, 625.5 N, and 1285.5 N, respectively. In case of similar joints only base material fracture happens, but hybrid joint specimens show different failure modes. Base material fracture and shearing after partial base material fracture were typical failure modes in case of 5754-H22/DP600 and 6082-T6/DP600 hybrid joints, respectively. The full and partial plugging as a failure modes appeared for hybrid joints, too. The IMC layer characteristics showed opposite results in cases of hybrid joints, both the layer thicknesses of the shared and plugged joints and the thickness differences between the inner and outer parts of the joints were different.

Investigation of in-situ lithium dendrite growth in the garnet type electrolyte Li7-xLa3Zr2-xTaxO12 (LLZTO) under an applied DC voltage

A translucent Al-containing garnet type electrolyte (Al-LLZTO) with high lithium-ion conductivity was made by pressure filtration followed by sintering to study in-situ lithium dendrite growth. A transparent tube furnace was built for in-situ dendrite growth studies above room temperature. In-situ lithium dendrite growth in a garnet-type electrolyte under an applied DC voltage was investigated at three different temperatures (room temperature, 100 °C, and 200 °C (above the melting point of lithium)). Critical current density (CCD) was estimated based on a previous model developed for sodium dendrites (which are actually filaments) in Na-β”-alumina. The CCD defined here is different than most of the reported work in lithium and lithium-ion batteries. When the temperature is higher than room temperature but lower than the melting point of lithium, lithium dendrites grow rapidly. However, when the temperature is above the melting point of lithium, no lithium dendrites were observed under similar testing conditions. This is because the CCD for liquid lithium dendrites to form is much higher than that for solid lithium dendrites to form. Approximate estimates of the CCD below and above the melting point of lithium are also given. This investigation provides significant insights into dendrite (filament) growth above room temperature, and possible prevention of lithium dendrites in working solid-state lithium-ion batteries, and the efficiency improvement of solid-state batteries.

Disparities in access to COVID-19 testing in Los Angeles County

COVID-19 has disparately impacted low-income persons and racial and ethnic minorities–primarily Black and Hispanic populations. Our objective is to quantify disparities in access to COVID-19 testing and identify barriers to testing during the winter 2020–2021 surge in COVID-19 infections in Los Angeles County. An online survey was administered between December 2020 and January 2021 through which respondents were asked about their use of COVID testing and the barriers to testing they experienced. Our sample of 1,984 was reweighted to match the demographics of Los Angeles County. Despite similar testing rates to White residents, Hispanic residents were more likely to report testing positive. Persons with an annual income of $20,000 or less were less likely to receive a test than those with an income of $100,000 or more. Barriers to testing were more prevalent among racial/ethnic minorities and low-income persons. White respondents and high-income persons were more likely to report the ability to take time off work to await test results. Rates of testing were not commensurate with the rates of infection across racial/ethnic groups, which may be explained by higher rates of reported barriers to testing among Black and Hispanic residents. These findings may inform policies that address structural barriers to testing that disproportionately impact racial/ethnic minorities and low-income populations.

Analysis of Mental Ray Rendering Techniques in 3D Animation Film Analisis Teknik Rendering Mental Ray Pada Film Animasi 3D

Rendering is the final stage of the modeling or animation process. It is during this rendering that the polygons that make up a model will be computed on the computer to form an image. Along with the times, there are many rendering provider applications for 3D animation, one of which is Mental Ray. The formulation of the research problem is How long does it take to render a 3D animated film using mental ray and analyze the size of the rendered using mental ray? The development model in this study uses the Multimedia Development Life Cycle (MDLC) development which is the Luther version of the development method. Data collection by taking two finished or ready-to-rendering 3D films. The results show that the average length of time needed to render of The Little Human animation is 8.3 minutes, while the animated film Go Alex is 8.8 minutes. While the average size of the 12 rendered scenes of The Little Human animation is 17.3 mb and the animated film Go Alex is 17.8 Mb. Computer specifications will affect the performance of the rendering engine itself. This test only uses one type of computer, so to really test the performance of the rendering engine, a similar test is needed using several different types of computers and sepsis. When using the Autodesk Maya 2019 application, it is recommended to use incrementalSave to find out every step of the changes made, because it is to remember the steps to be taken if you forget and anticipate unexpected things such as blackouts.

N2 injection to enhance gas drainage in low-permeability coal seam: A field test and the application of deep learning algorithms

N2 injection to enhance coal seam gas drainage is an effective technology for coalbed methane recovery. Nevertheless, there is a limited body of research addressing the application of ECBM technology for gas management in low permeability coal seams within underground coal mines, and the effect is uncertain. Additionally, numerical simulators have been challenged to accurately predict methane emissions during gas injection and replacement. In this study, firstly, an underground field trials of N2 injection replacement in a coal mine with low-permeability coal seams in China were conducted, and a comparative analysis of “N2 injection to enhance gas drainage” and “conventional drainage” technique was performed. The result show that injecting pressurized N2 into a low-permeability coal seam could increase the pressure gradient to promote the directional flow of free gas from the coal seam cracks to the gas-production boreholes. After N2 injection, the mixed flow rate and CH4 flow rate significantly increased. Additionally, the gas content in the coal seam in the experimental area significantly decreased. Secondly, the lag-effect was discussed. Upon discontinuation of N2 injection, the gas-producing mixed flow initially increased slightly, then gradually decreased, and eventually settled at a high level. Despite the attenuation of N2 injection volume, the CH4 flow rate continued to increase during intermittent injection and remained high after gas injection discontinuation. Finally, three deep learning algorithms (BP, LSTM, and TCN) were employed to predict the dynamic change of gas drainage in coal seams displaced by N2 injection, based on the results of the field test. The results indicated that deep learning algorithms exhibited superior prediction performance. The TCN model demonstrated the lowest approximation error rate, displaying optimal performance in predicting the CH4 flow rate during nitrogen injection for enhanced gas drainage. Additionally, it exhibited good applicability to other similar field test results.

Use of a humanoid robot for auditory psychophysical testing.

Tasks in psychophysical tests can at times be repetitive and cause individuals to lose engagement during the test. To facilitate engagement, we propose the use of a humanoid NAO robot, named Sam, as an alternative interface for conducting psychophysical tests. Specifically, we aim to evaluate the performance of Sam as an auditory testing interface, given its potential limitations and technical differences, in comparison to the current laptop interface. We examine the results and durations of two voice perception tests, voice cue sensitivity and voice gender categorisation, obtained from both the conventionally used laptop interface and Sam. Both tests investigate the perception and use of two speaker-specific voice cues, fundamental frequency (F0) and vocal tract length (VTL), important for characterising voice gender. Responses are logged on the laptop using a connected mouse, and on Sam using the tactile sensors. Comparison of test results from both interfaces shows functional similarity between the interfaces and replicates findings from previous studies with similar tests. Comparison of test durations shows longer testing times with Sam, primarily due to longer processing times in comparison to the laptop, as well as other design limitations due to the implementation of the test on the robot. Despite the inherent constraints of the NAO robot, such as in sound quality, relatively long processing and testing times, and different methods of response logging, the NAO interface appears to facilitate collecting similar data to the current laptop interface, confirming its potential as an alternative psychophysical test interface for auditory perception tests.

A Modified KLEIN Encryption-based Knight Tour for Image Encryption

The security considerations should be balanced with the specific use case and potential risks associated with using lightweight encryption. The security offered by lighter encryption algorithms could not be as high as that offered by heavier encryption techniques. In this paper, a Modified KLEIN Algorithm is proposed for image encryption based on the Knight Tour movement in Chessboard. The required key generation is represented by inputting an image as a key image and then applying a specific operation based on knight tour movement to produce a key scheduled in the proposed encryption algorithm. The movement of Knight Tour applied in modifying the proposed algorithm for increasing security. The experimental results explain the efficiency of a modified algorithm when comparing the histogram of the input image with the encrypted image also the correlation is tested before and after encryption in three directions horizontal, vertical, and diagonal which explains there are very low values of them in all directions. The similarity test also explains there are high differences between the plain and encrypted images. The chessboard movement might be useful when used with another encryption algorithm which increases the confidentiality of transferring data. The contribution of this work is the use of an image as a key for encryption with a specific planning method which helps in key management.

Adaptive shifts in plant traits associated with nitrogen removal driven by phytoremediation strategies in subtropical river restoration

Phytoremediation, which is commonly carried out through hydroponics and substrate-based strategies, is essential for the effectiveness of nature-based engineered solutions aimed at addressing excess nitrogen in aquatic ecosystems. However, the performance and mechanisms of plants involving nitrogen removal between different strategies need to be deeply understood. Here, this study employed in-situ cultivation coupled with static nitrogen tracing experiments to elucidate the influence of both strategies on plant traits associated with nitrogen removal. The results indicated that removal efficiencies in plants with substrate-based strategies for ammonium nitrogen and nitrate nitrogen were 30.51–71.11 % and 16.82–99.95 %, respectively, which were significantly higher than those with hydroponics strategies (25.98–58.18 % and 7.29–79.19 %, respectively). Similarly, the plant nitrogen uptake rates in the substrate-based strategy also generally showed higher levels compared to hydroponics strategies (P < 0.05). Meanwhile, the microorganisms-mediated nitrous oxide emission rates in the substrate-based strategy during summer (unamended: 0.00–0.58 μg/g/d; potential: 3.35–7.65 μg/g/d) were obviously lower than those in the hydroponics strategy (unamended: 2.23–11.70 μg/g/d; potential: 9.72–43.09 μg/g/d) (P < 0.05). Notably, analysis of similarity tests indicated that the influences of strategy on the above parameters generally surpass the effects attributable to interspecies plant differences, particularly during summer (R > 0, P < 0.05). Based on statistical and metagenomic analyses, this study revealed that these differences were driven by the stabilizing influence of substrate-based strategy on plant roots and enhancing synergistic interplay among biochemical factors within plant-root systems. Even so, phytoremediation strategies did not significantly alter the characteristics of plants with regards to their tendency towards ammonium nitrogen uptake (up to 87.68 %) and dissimilatory nitrate reduction to ammonium as primary biological pathway for nitrogen transformation which accounted for 53.66–96.47 % nitrate removal. In summary, this study suggested that the substrate-based strategy should be a more effective strategy for enhancing the nitrogen removal ability of plants in subtropical river restoration practices.

Research on reasonable layout parameters of working faces based on the concept of harmonic extraction to reduce the damage of coal seam waiting for upward mining

Based on the idea of harmonic extraction, the problem of reducing the damage of the coal seam waiting for upward mining. Using the theoretical analysis method, a schematic diagram of coordinated mining along the dip direction of coal seam is established, and the calculation method of reasonable layout parameters of coal seam working face early mining is given. Based on the upward mining problem of the No.1 coal seam in the fifth panel of Zaoquan Coal Mine, the influence parameters of the No.2 coal seam, No.6 coal seam and No.7 coal seam mining on the No.1 coal seam were determined by similar simulation test. Then, the layout parameters of working face were determined. The research method of numerical calculation was used to evaluate the degree and uniformity of movement and deformation of the No.1 coal seam, combining five indicators: subsidence, horizontal movement, inclined deformation, curvature deformation, and horizontal deformation. The results indicate that when the working face is arranged using the layout parameters provided in this article, it can promote the further subsidence of the No.1 coal seam at the position of the remaining coal pillar. The movement and deformation indicators of the No.1 coal seam all reach the most uniform degree of the geological mining conditions, which can effectively offset the uneven deformation problem of the No.1 coal seam caused by the influence of the remaining coal pillars, making multiple mining operations a favorable condition for upward mining and achieving the goal of reducing the damage of the No.1 coal seam.

Research on the Mechanism and Evolution Law of Delayed Water Inrush Caused by Fault Activation with Mining

Confined water inrush caused by fault activation is the main form of water disaster in deep mining. With theoretical analysis and similar simulation tests, the mechanism and evolution law of delayed water inrush caused by fault activation are revealed. At the theoretical level, the expansion and extension of the internal microstructure in the fault zone under the action of the mining stress field and seepage field are the essential causes of fault activation. Overlying strata movement and surrounding rock creep failure are the basic reasons for delayed water inrush caused by fault activation, and delayed time caused by surrounding rock creep failure is much longer than that of overlying strata movement. A similar simulation test was carried out with self-development solid–liquid coupling with similar simulation materials; the results show that delayed water inrush caused by fault activation with mining includes three stages. Micro-activation stage: Water inrush weakness point is formed because of the expansion and extension of the micro-fissure and structure at the bottom of the fault zone. Macro-activation stage: With the change in the stress of the waterproof coal pillar and surrounding rock, the micro-fissures and structures in the stress relief area and tension area of the fault zone expand and extend sharply; meanwhile, water intrudes into the interlayer stratification of the floor in the stress relief area, forming a strong laminar flow phenomenon, and cracks in the floor form and expand; finally, water-conducting channels in the fault zone and floor are formed. Water inrush stage: The waterproof coal pillar and water-resisting layer fail and are destroyed, and the first confined water inrush point is located at the junction of the waterproof coal pillar and gob floor.

Research on Subsidence Prediction Method of Water-Conducting Fracture Zone of Overlying Strata in Coal Mine Based on Grey Theory Model

The development height and settlement prediction of water-conducting fracture zones caused by coal seam mining play an important role in the stability of overburden aquifers and the safety of roadways. Based on the engineering geological data of the J60 borehole in the Daliuta Coal Mine and the mining conditions of the 2−2 coal seam, China, this study established a similar material test model of mining overburden. The deformation characteristics of overlying strata in the mining process of coal seam were studied by using distributed optical fiber sensing technology, and the development height of water flowing fractured zone was determined. According to the equidistant sampling characteristics of Brillouin optical time domain reflection technology and the principle of the grey theory model, the settlement prediction model of the water-conducting fracture zone was established. By analyzing and comparing the prediction accuracy of the GM (1, 1) model, grey progressive model, and metabolic model, the optimal method for settlement prediction of the water-conducting fracture zone was discussed. The results show that, for the metabolic model, with the increase in the number of test sets and the decrease in the number of prediction sets, the mean square error ratio c and the small error probability p of the prediction accuracy evaluation parameters display a downward trend. The accuracy is related to the sudden change in the settlement of the water-conducting fracture zone caused by the breaking of the key stratum of the overlying rock. The optimal time of test sets selected for the best settlement prediction model is 7~8, and that of prediction sets selected is 5~6. For the GM (1, 1) model and the grey progressive model, the prediction accuracy of mining overburden subsidence is grade 4, which is not suitable for settlement prediction of water-flowing fractured zones.

A comparison of the original ADAS‐cognitive subscale and a novel extended version in mild cognitive impairment and Alzheimer’s disease.

AbstractBackgroundThe Original Alzheimer’s disease Assessment Scale‐cognitive subscale (oADAS) is the gold standard for assessing cognition in Alzheimer’s disease (AD) medication clinical trials. One criticism is that this measure is not sensitive to pre‐dementia syndromes such as mild cognitive impairment (MCI), possibly due to ceiling effects for many of the subtests in the original ADAS‐cog (oADAS). In this study, we examined group differences between individuals with MCI, AD, and normal controls (NC) on the oADAS and a novel extended version of the ADAS (eADAS). We hypothesized the eADAS would be more sensitive than the oADAS at detecting differences between the groups after controlling for age, education, and sex.MethodThe oADAS was administered using standardized methods. The eADAS included short‐delay recall and recognition trials for the first wordlist, a short‐delay true‐false word recognition trial for the second word‐list, a letter‐digit timed psychomotor test, supermarket fluency, spontaneous clock drawing, three additional items to the object naming subtest, three additional figures to copy, and a similarities test. MCI and possible AD were diagnosed using established diagnostic criteria. The NC group was significantly younger and more educated than the MCI and AD groups. A 3×2 ANCOVA model with group as the between subject factor and ADAS type as the within subject factor was used. Total error scores from the oADAS and eADAS were converted to z‐scores.ResultThere was no main effect of ADAS type or ADAS type X education or ADAS type X group interactions. There were ADAS type x age and ADAS type x gender interactions (p’s = 0.01). Pairwise comparisons demonstrated that the NC group had lower total error scores compared to the MCI and AD groups and the MCI group had lower total error scores compared to the AD group for both the oADAS and eADAS (p’s&lt;0.001).ConclusionThe results of the current study indicate that oADAS and eADAS are sensitive to group differences between MCI and AD, which was contrary to our hypothesis. We suggest more rigorous diagnostic criteria for MCI in the present study may explain some of the conflicting findings in the literature.

Longitudinal Trajectories of Normal versus Abnormal Cognitive Aging in the SIU Longitudinal Cognitive Aging Study as measured by the original Alzheimer’s disease Assessment Scale and a novel extended version

AbstractBackgroundThe original Alzheimer’s disease Assessment Scale‐cognitive subscale (oADAS) is a common measure of cognition used in Alzheimer’s disease (AD) clinical trials. The oADAS may not be sensitive to pre‐dementia syndromes such as mild cognitive impairment (MCI), possibly due to ceiling effects for many of the subtests. However, little is known about the trajectories of cognitive aging on the oADAS in cognitively‐normal individuals who decline over time. We used Linear Mixed Effects Modeling to examine trajectories of cognitive aging on the oADAS, a novel extended version (eADAS), and the MMSE. We hypothesized that, compared to non‐decliners, individuals who declined from normal to MCI and/or AD (decliners) would demonstrate increased error scores on the ADAS and lower scores on the MMSE over time.MethodsWe included data from 608 older adults (72% female, mean age = 69.0 years) who were enrolled in SIU Longitudinal Cognitive Aging Study (LCAS). Total years of educational attainment ranged from 8‐26 years. The vast majority of the participants were White/Not‐Hispanic. There were 50 decliners and 558 non‐decliners included in the present analysis. The eADAS included short‐delay recall/recognition trials for the first word list, a short‐delay true‐false word recognition trial for the second word list, a letter‐digit d psychomotor test, supermarket fluency, spontaneous clock drawing, three additional items to the object naming subtest, three additional figure copies, and a similarities test. All analyses were completed after controlling for gender, education, age at first visit, time between visits, and number of visits.ResultsThere was no significant difference between the groups for age at first visit. Greater age at first visit and lower education were associated with increased error scores for both ADAS versions and MMSE in the decliner group (p’s &lt;0.001). There was no significant effect of the number of days between visits. The mean coefficients for the change in error score over time between visits for the oADAS, eADAS, and MMSE was 2.84, 10.30, and ‐1.09, respectively.ConclusionOur results indicate that the oADAS and eADAS are useful measures for examining trajectories of cognitive decline in cognitively‐normal individuals at increased risk for AD.

Assessment of the Production Process Organization in the Metal Industry - Case Study

Abstract The article provides a detailed case study illustrating the practical application of BOST surveys while focusing on the third principle of Toyota. This particular principle underscores the importance of achieving an even distribution in production to mitigate fluctuations and ensure the smooth functioning of the production process. The article delves into a comprehensive analysis of how this principle is implemented in real-world scenarios, exploring its effects on production efficiency and elucidating the advantages gained from the elimination of downtime and the prevention of excessive inventory buildup. Moreover, the article offers a discussion on examples of applications across various industries, shedding light on practical strategies for implementing the third Toyota principle. The goal is to not only optimize production processes but also enhance flexibility and adaptability to evolving demand patterns. The article concludes by examining the pivotal role of this principle within the broader context of operational excellence and the development of more efficient production systems. The research in focus centers around a company operating in the metal industry. Through the utilization of BOST questionnaire surveys, production workers within the company expressed their perspectives, revealing which factors they deemed most crucial. The analysis of research results takes various forms, including tables, histograms, radar charts, and several statistical tools. The overarching objective of this analysis is to identify and present the most critical factors by constructing significance sequences based on the obtained results. The findings obtained for small and medium-sized enterprises align with results from similar tests conducted in different business settings, indicating a level of consistency and applicability across various organizational contexts.

Practice effects on cognitive testing and amyloid status

AbstractBackgroundPractice effects are improvements in cognitive test scores due to repeated exposure to the same or similar test materials. Reduced practice effects have been reported in older adults with impaired cognition (e.g., Mild Cognitive Impairment, Alzheimer’s disease). However, to our knowledge, practice effects have not been examined in those who are classified as being amyloid positive or negative. Such information could be useful for screening individuals for clinical trials in these conditions.MethodOne hundred fifty‐five older adults with either intact (n = 63) or impaired (n = 92) cognition completed a brief battery of neuropsychological tests twice across one week and an amyloid positron emission tomography (PET) scan. Based on FDA approved prescribing and visual interpretation information, the amyloid PET scan was classified as positive (n = 95) or negative (n = 60). Practice effects across one week were quantified using regression‐based change scores from an independent sample.ResultOn a composite of seven neuropsychological test scores, those who were classified as amyloid positive showed significantly smaller practice effects than those classified as amyloid negative. Similar results were seen on practice effects for 6 of the 7 individuals tests, with smaller practice effects being seen in the amyloid positive participants.ConclusionPractice effects across one week were related to amyloid status in this sample of older adults. As such, these short‐term changes in scores on cognitive tests may be useful for enriching samples for future clinical trials in Alzheimer’s disease.

Federated BrainAge estimation from MRI: a proof of concept

AbstractBackgroundThe gap between chronological and biological brain‐age, represented by MRI features, associates with dementia risk (Wang et. al., He et. al.). Age‐scores are predicted, however, using deep learning models, which require large amounts of MRI data. This poses a challenge, as patient brain MRI data is highly privacy‐sensitive and legal barriers often impede the sharing or transferring of data. Federated learning provides a potential solution by enabling training a global model on distributed data, allowing biobanks to keep data local while creating a shared model. In this study, we assess whether a federated set‐up is feasible for brain‐age estimation.MethodWe set up a federated learning framework across three biobanks (Fig. 2) to train a BrainAge model (Wang et. al). Data of healthy individuals from the ADNI dataset was randomly split across the three biobanks (N = 221, 219, 217). MRI images were pre‐processed into gray matter density maps (Good et. al.). The model was iteratively trained using federated averaging (McMahan et. al.) on data at two biobanks (70/30 training/validation split), and tested at the remaining biobank. We trained the model for ten rounds, and optimized for the number of epochs, learning rate, and learning decay hyperparameters. These results were compared to a centrally trained model, where the training samples were pooled together. Additionally, we applied the trained model to individuals with Mild Cognitive Impairment and Alzheimer’s Disease and performed a survival analysis. We assessed performance using Mean Absolute Error (MAE).ResultFederated learning (MAE = 4.02) achieves similar test performance as central learning (MAE = 3.94) (Table 1). The model yielded an MAE of 4.89 for MCI and 5.45 for AD patients; this increase may be explained by their neurodegeneration. We also see a statistically different survival curve for developing AD in individuals with a brain‐age‐gap (Fig. 1), displaying an ‘older’ profile for their chronological age. This might indicate higher risk of developing AD, in line with previous results (Wang et. al.).ConclusionResults show federated learning has the potential to address the challenge of data accessibility for machine learning in complex and data hungry research, including dementia.

Effect of Small Concaves Surface on the Acoustical Characteristics of Chicken Feather Panel

This study aims to determine the impact of small concave surfaces on the acoustic characteristics of chicken feather panels. Based on the feather-glue ratio reference, the researchers applied a mixture of 70% feather and 30% PVAc for the production of the specimens. In the first stage, the absorption coefficient of the specimen on a small concave surface (CS) was measured using an impedance tube type B&amp;K 4602 with two microphones and a set of computer FFT analyzers. The measurement results were then compared with the absorption coefficient of similar test objects with flat surfaces (FS). The acoustic characteristics of the two types of surfaces (FS and CS) in the specimen panel showed the maximum absorption coefficient (α) value of 0.52 and 0.63 for FS and CS, respectively. These results indicate that specimens with a small concave surface (CS) have a relatively higher absorption coefficient than those with a flat surface (FS). However, further studies are needed to identify other characteristics of these acoustic panels, including their resistance to environmental influences.