Miracle-dz/UniFAR-FTUs huggingface dataset:
The Juventas GNC Subsystem, Autonomous Landing on the Surface of the Binary Asteroid Dimorphos

Juventas is a deep space 6U XL CubeSat with the objective of performing a scientific and technological demonstration mission, aiming for the characterization of the binary asteroid Didymos, and its small companion Dimorphos. Juventas takes part in the Hera mission as one of the two CubeSats that will be released in the asteroid’s proximities. As experimental payload, Juventas is designed to take higher risks than his mothercraft and get closer to the asteroids, and eventually perform an autonomous landing on Dimorphos. Juventas will be inserted into a special set of orbits, called Self-Stabilized Terminator Orbits (SSTO). The existence of this special type of in which the solar radiation pressure (SRP) perturbation cancels the gravitational pull of the central body. The high latency of the information along with the small distances to the target causes great uncertainties in the predicted CubeSat state. Hence, to prevent the asteroid from abandoning the camera’s field of view, fast onboard attitude corrections must be commanded by the GNC system to achieve continuous imaging of the asteroid. Juventas’ GNC is designed to be able to land on the surface of Dimorphos once the radar observations from the SSTOs have been performed. The landing comprises a controlled touchdown using the autonomous GNC system and a series of uncontrolled bounces until Juventas rests on the asteroid's surface. The highly perturbed dynamics together with the small size of the target make the landing the most challenging operation that Juventas will face. The landing of Juventas on Dimorphos presents a series of difficulties that can only be tackled by the autonomous GNC implementation. Firstly, the insertion maneuver from the SSTO has a high uncertainty due to the long turnaround times. This insertion is corrected by the GNC which allows a flexible solution for the definition of the maneuvers, being flexible to the number of maneuvers and their execution times. Each of the maneuvers employs the estimated instantaneous state estimation of the filter, using data fusion techniques that combine Image Processing of NAVCAM images for line-of-sight estimation and ranging with a laser altimeter. Consequently, braking maneuvers are performed when the estimated SC approaches the surface of Dimorphos. The GNC is also flexible to the moment of execution of the insertion phase, being able to compute the optimal position with respect to the orbital plane to perform the maneuvers. Secondly, Juventas should land at a reduced velocity to guarantee that the CubeSat will not bounce back and escape the gravity field of Dimorphos. This paper will include the consolidated strategies of the GNC designed for the Juventas landing strategy. The focus will be directed towards the modes and functions necessary to operate the CubeSat autonomously.
Comparison of the effectiveness of self-compassion training and integrated mindfulness-cognitive behavioral therapy on parenting stress and experiential avoidance in mothers with slow-paced children

Aim: The present study was conducted with the aim of comparing the effectiveness of self-compassion-based training and integrated mindfulness-cognitive behavioral therapy on parenting stress and experiential avoidance in mothers with slow-paced children. Methods: The current research was an experimental type with a pre-test-post-test design and follow-up with a control group. The statistical population of the current study included all mothers with slow-walking children who lived in Tehran in the winter of 2018 and their children were covered by exceptional educational centers, from which 45 mothers were selected using a targeted sampling method. And randomly, 15 people were placed in two experimental groups and control group. In order to collect data, Abidin's parental stress index questionnaire (1995) and Bund et al.'s acceptance and action questionnaire (2011) were used. Educational sessions based on self-compassion based on the protocol of Gilbert et al. (2014) and integrated mindfulness-cognitive behavioral therapy based on the protocol of Frances, Shavier, Cain, Anticott and Mitos (2020), during eight 90-minute sessions weekly and it was implemented for two months. Analysis of variance with repeated measurements and spss software were used to analyze the data. Results: The results showed that training based on self-compassion and integrated mindfulness-cognitive behavioral therapy on parenting stress (F=90.04, P<0.001) and experiential avoidance (F=48.56, P<0.001) was effective in mothers with slow-paced children, but there was no significant difference between the two interventions. Conclusion: Therefore, it is possible to use self-compassion training and integrated mindfulness-cognitive behavioral therapy to help mothers with slow-paced children.
A Semisupervised GCN Framework for Transfer Diagnosis Crossing Different Machines

When it comes to intelligent diagnosis, the transfer of knowledge and experiences across machines is crucial. By transferring the learned fault features and abstract representations from one machine to another, the intelligent diagnosis algorithm can adapt to new scenarios, thereby enhancing its versatility and robustness. However, for some transfer learning-based diagnosis methods, the accuracy of cross-working condition diagnosis is satisfactory, whereas the accuracy of cross-equipment diagnosis is disappointing. To address this issue, a semisupervised graph convolutional networks framework, named SSGCN, was proposed for transfer diagnosis across different machines. To align feature distributions of different domains and aggregate features within the same category, the multiobjective optimization loss function (MOOLF) was constructed. Taking advantage of graph structure data, a weighting method that defined the edge between nodes using Chebyshev distance, called WECD, was proposed for effective feature propagation and improved learning performance. Furthermore, a pseudo-label was added to unlabeled graph structure data in the target domain to enhance positive migration for transfer diagnosis. Experimental results across different machines demonstrate that the proposed SSGCN framework exhibits superior performance.
University of Birmingham From institutional integration to institutional demise: The disintegration of the International Integrated Reporting Council (IIRC)

This paper presents an in-depth contextual analysis of the rise and recent demise of the International Integrated Reporting Council (IIRC). The IIRC entered its ‘Breakthrough Phase ’ for Integrated Reporting ( < IR > ) in 2013 and progressed to its ‘Momentum Phase ’ in late 2018. The ‘Global Adoption Phase ’ of < IR > was expected to commence in 2021 and conclude in 2026. However, by the middle of 2023, the IIRC ceased to exist as a separate entity and the future adoption of its much vaunted < IR > Framework was fundamentally uncertain. Drawing on a comprehensive examination of documentary evidence and a series of 34 in-depth interviews with key players associated with the IIRC ’ s development, this paper studies how and why the IIRC went so rapidly from being a notable ‘is ’ to a definitive ‘was ’ in less than a decade. Our analysis traces the IIRC ’ s shifting strategic priorities in pursuit of a new corporate reporting norm and illustrates how these priorities underpinned a concerted effort at institutional integration in the corporate reporting field. We show how the nature of this attempted integration eventually led to the IIRC ’ s demise. In seeking to understand the IIRC ’ s strategic choices and actions we pinpoint the interrelated significance of ‘invisibilities and exclusions ’ , ‘the dance of agency ’ , and ‘con-ceptual promiscuity ’ . We conclude that the IIRC ’ s ultimate legacy may not
Enhancing Corn Yield Prediction in Iowa: A Concatenate-Based 2D-CNN-BILSTM Model with Integration of Sentinel-1/2 and SoilGRIDs Data

: Ensuring food security in precision agriculture demands early prediction of corn yield in the USA at international, regional, and local levels. Accurate corn yield estimation can play a crucial role in averting famine by offering insights into food availability during the growing season. To address this, we propose a Concatenate-based 2D-CNN-BILSTM model that integrates Sentinel-1, Sentinel-2, and Soil-GRIDs (global gridded soil information) data for corn yield estimation in Iowa State from 2018 to 2021. This approach utilizes Sentinel-2 features, including spectral bands (Blue, Green, Red, Red Edge 1/2/3, NIR, n-NIR, and SWIR 1/2), and vegetation indices (NDVI, LSWI, DVI, RVI, WDRVI, SAVI, VARIGREEN, and GNDVI), alongside Sentinel 1 features (VV, VH, difference VV, and VH, and RVI), and Soil data (Silt, Clay, Sand, CEC, and pH) as initial inputs. To extract high-level features from this data each month, a dedicated 2D-CNN was designed. This 2D-CNN concatenates high-level features from the previous month with low-level features of the subsequent month, serving as input features for the model. Additionally, to incorporate single-time soil data features, another 2D-CNN was implemented. Finally, high-level features from soil, Sentinel-1, and Sentinel-2 data were concatenated and fed into a BILSTM layer for accurate corn yield prediction. Comparative analysis against random forest (RF), Concatenate-based 2D-CNN, and 2D-CNN models, using metrics like RMSE, MAE, MAPE, and the Index of Agreement, revealed the superiority of our model. It achieved an Index of Agreement of 84.67% with an RMSE of 0.698 t/ha. The Concate-nate-based 2D-CNN model also performed well with an RMSE of 0.799 t/ha and an Index of Agreement of 72.71%. The 2D-CNN model followed closely with an RMSE of 0.834 t/ha and an Index of Agreement of 69.90%. In contrast, the RF model lagged with an RMSE of 1.073 t/ha and an Index of Agreement of 69.60%. Integration of Sentinel 1 – 2 and Soil-GRIDs data with the Concatenate-based 2D-CNN-BILSTM model significantly improved accuracy. Combining soil data with Sentinel 1 – 2 features reduced RMSE by 16 kg and increased the Index of Agreement by 2.59%. This study high-lighted the potential of advanced Machine Learning (ML)/Deep Learning (DL) models in achieving precise and reliable predictions, which could support sustainable agricultural practices and food security initiatives.
Effectiveness of Different Neurocognitive Intervention Approaches on Functionality in Healthy Older Adults: A Systematic Review

Subtle loss of functionality in healthy older adults is considered one of the most important predictors of cognitive decline. Neurocognitive interventions are increasingly being used, from a preventive maintenance approach to functional capacity. This study evaluates the effectiveness of different neurocognitive approaches on the functionality of healthy older adults. In this systematic review (CRD42023473944), an extensive search was conducted for articles published in the last 10 years (2013–2023) in the following databases: Medline, Scopus, and Web of Science. A total of 809 trials were identified, of which 18 were considered to be eligible for inclusion in the review. The data revealed heterogeneity in sample size, measures of functional assessment, neurocognitive interventions used, number of sessions, session duration, and time. Traditional cognitive stimulation is shown to have no significant functional benefit, while other less commonly used neurocognitive interventions, such as those based on everyday cognition, are associated with more significant benefits. Moreover, it is demonstrated that although the Instrumental Activities of Daily Living scale (IADL) is the most used test in similar studies, it is not sensitive enough to detect changes in functionality in healthy elderly individuals, with other tests such as the Timed Instrumental Activities of Daily Living (TIADL) being more advantageous. Therefore, a new guideline is proposed for its use in clinical practice and research, using homogeneous study protocols and neurocognitive interventions that allow for the transfer and generalization of results in daily life.
Research on Drug Classification Using Machine Learning Model

In the recent years, more and more fields around the development and application of artificial intelligence have received new improvements and breakthroughs. Among them, drug design and classification are one of the most popular application areas which can provide the greatest help to the society. According to statistics, the number of newly listed drugs in the world is decreasing year by year. At the same time, the risks and costs of drug development are increasing year by year which illustrates a different trend compared with the newly listed drugs. In this case, the application of artificial intelligence technology provides a new idea and opportunity to solve the problem of drug design and classification with higher accuracy than human work. This paper aims to do a research based on using machine learning models to produce suitable outcomes for patients of different drug types in order to reduce the working pressure of doctors in the hospitals.
Battery-Free Game Boy

Relying on batteries as power sources can often be undesirable due to their contribution to the increased size and weight of electronic devices, as well as the inconvenience associated with charging. Moreover, the batteries themselves and the energy required for recharging are often environmentally unfriendly. However, at-tempting to operate electronic devices without a consistent and stable power source poses significant challenges. This paper explores the design of a deeply interactive device, specifically a Game Boy, to achieve energy self-sufficiency without relying on a battery. The investigation involves examining various methods for power generation, enhancing the device’s energy efficiency, and introducing a novel system called MPatch to address memory loss caused by power outages. Subsequently, the paper presents test results and evaluates the gaming console based on these findings.
Integrating shear wave elastography and estimated glomerular filtration rate to enhance diagnostic strategy for renal fibrosis assessment in chronic kidney disease

Background Assessing renal fibrosis non-invasively in patients with chronic kidney disease (CKD) remains a considerable clinical challenge. This study aimed to investigate the diagnostic efficacy of different approaches that combine shear wave elastography (SWE) and estimated glomerular filtration rate (eGFR) in distinguishing between mild fibrosis and moderate-to-severe fibrosis in CKD patients. Methods In this prospective study, 162 patients underwent renal SWE examinations and renal biopsies. Using SWE, the right renal cortex stiffness was measured, and the corresponding SWE value was recorded. Four diagnostic patterns were used to combine eGFR and SWE value: in isolation, in series, in parallel, and in integration. The receiver operating characteristic (ROC) curve was established, and the area under the ROC curve (AUC) was calculated to quantify diagnostic performance. Sensitivity, specificity, and accuracy were computed. Results The eGFR demonstrated sensitivity of 68.2% and specificity of 83.8%, whereas the SWE value displayed sensitivity of 84.1% and specificity of 62.2%, yielding a similar AUC (78.2% and 77.8%, respectively). Combining in series improved specificity to 97.3%, superior to other diagnostic patterns (all P values <0.01), but compromised sensitivity to 58.0%. When combined in parallel, the sensitivity increased to 94.3%, exceeding any other strategies (all P values <0.05), but the specificity dropped to 48.7%. The integrated strategy, incorporating eGFR with SWE value via the logistic regression algorithm, exhibited an AUC of 85.8%, outperforming all existing approaches (all P values <0.01), with balanced sensitivity, specificity, and accuracy of 86.4%, 74.3%, and 80.9%, respectively. Conclusions Using an integrated strategy to combine eGFR and SWE value could improve diagnostic performance in distinguishing between mild renal fibrosis and moderate-to-severe renal fibrosis in patients with CKD, thereby helping clinicians perform a more accurate clinical diagnosis.
Parents’ Involvement in Young Children’s English Language Education Post Pandemic

The importance of English language has been causing major concerns among parents for many decades now and has prompted parents to find alternatives, apart from formal schooling when it comes to children’s English language acquisition. Parental involvement in children’s education, a phenomenon that has been existing in the Malaysian educational arena has gone multiple transformations prior, during and post the Covid-19 pandemic. Although a vast body of research is available on parental involvement and childr en’s development, in the context of Malaysia very little research has been conducted that exclusively focuses on parental involvement in their children’s education of English Language. In view of this context, this research study was therefore undertaken t o investigate parents’ involvement in teaching English language to their primary school children at home. Apart from gaining insights on whether parents do involve in their children’s English language learning at home, the study also examined the methods parents used and found effective. To undertake this study, a set of questionnaires was designed and distributed via e-mail to the clustered sample. 51 parents from primary schools at Port Klang, Selangor participated in this study. Since the survey was conducted online through Google sheet, the data was processed through Spreadsheet-based data analysis to gather findings. Based on the data analysis, the study found that 70.6% parents were directly involved in the teaching of English language at home. Findings also indicated that the of various methods of teaching children, about 62.5% was through technology mainly consisting online-based learning platforms (32.95%) and online printed materials (29.55%). In terms of effective method, parents strongly pointed that they engaged better through interactive learning platforms when opting to teach English to their young children at home mainly due to certain design elements used such as video, audio and gamification-sort of lesson content that can have an impact on learning, motivation, and educational outcomes
A Neural Network Approximation for a Model of Micromagnetism

: Micromagnetics is a continuum theory describing magnetization patterns inside ferromagnetic media. The dynamics of a ferromagnetic material are governed by the Landau-Lifshitz equation (LL). This equation is highly nonlinear and has a non-convex constraint. In this work, we propose two new algorithms for solving this equation in high dimension, by using deep neural networks. Numerical and comparative tests using TensorFlow illustrate the performance of our algorithms
Insights of effectivity analysis of learning-based approaches towards software defect prediction

Software defect prediction is one of the essential sets of operation towards mitigating issues of risk management in software development known to contribute towards enhancing the quality of software. There is evolution of various methodologies towards resolving this issue while learning-based methodology is witnessed to be the most dominant contributor. The problem identified is that there are yet many unsolved queries associated with practical viability of such learning-based approach adoption in software quality management. Proposed approaches discussed in this paper contributes towards mitigating this challenge by introducing a simplified, compact, and crisp analysis of effectiveness associated with learning-based schemes. The paper presents its major findings of effectivity analysis of machine learning, deep learning, hybrid, and other miscellaneous approaches deployed for fault prediction followed by highlighting research trend. The major findings infer that feature selection, data imbalance, interpretability, and in adequate involvement of context are prime gaps in existing methods. The paper also contributes towards research gap as well as essential learning outcomes of present review work.
Otimização de um Plano de Manutenção de Navios por meio de Algoritmos Evolutivos e Inteligência de Enxame

A otimização da manutenção e operação de equipamentos, sistemas e meios navais tem sido um tópico de estudo há muitos anos, no qual várias técnicas têm sido propostas. Concomitantemente, a busca por algoritmos estocásticos que utilizam computação evolutiva e otimização por enxame de partículas tem sido aplicada com sucesso a problemas de otimização não lineares com grandes espaços de busca de soluções. Este artigo aborda um problema de otimização para os planos de manutenção e operação dos ativos navais da Marinha do Brasil, no qual a computação evolutiva é usada para resolver um problema de otimização com um grande espaço de busca de soluções. Isso envolve a construção de duas a seis embarcações de guerra por mais de meio século. As restrições e parâmetros utilizados não foram encontrados na literatura. Os resultados do modelo evolutivo e da combinação de novos operadores genéticos são inovadores e comprovam que o modelo proposto resulta em planos de manutenção e operação melhores e viáveis em comparação com técnicas previamente utilizadas.
Association between Perfluoroalkyl Substances in Follicular Fluid and Polycystic Ovary Syndrome in Infertile Women

In recent years, perfluoroalkyl substances (PFASs), a family of fluorinated organic com pounds, have garnered much attention due to their reproductive and developmental toxicity in humans. Polycystic ovary syndrome (PCOS) is a prevalent endocrine disease that affects women of reproductive age and is a significant contributor to female infertility. A previous study suggested that PFASs play a possible role in PCOS. We conducted a clinical study investigating the relationship between PCOS and PFAS in follicular fluid. A total of 73 infertile patients with PCOS and 218 controls were recruited from the International Peace Maternity and Child Health Hospital, affiliated with the Shanghai Jiao Tong University School of Medicine. The concentrations of 12 PFASs in follicular fluid samples and sex hormones in serum were measured. Correlation analysis and multiple linear regression revealed a positive relationship between perfluorooctanoic acid (PFOA) and testosterone (T) concentrations. The adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for each PFAS were estimated using multivariable logistic regression and quantile-based g-computation (QGC). The PFOA concentrations in follicular fluid were correlated with increased odds of PCOS (second vs. first quartile: OR = 3.65, 95% CI: 1.47–9.05, p = 0.005; third vs. first quartile: OR = 2.91, 95% CI: 1.17–7.26, p = 0.022; fourth vs. first quartile: OR = 3.13, 95% CI: 1.21–8.09, p = 0.019; P for trend = 0.032). This association was confirmed with QGC. Mediation analysis suggested that the mediation effect of T in association with PFOA and PCOS was not statistically significant. Our study suggests that PFOA may be a risk factor for PCOS.
Journal of Quantitative Spectroscopy & Radiative Transfer

Propene (C 3 H 6 ) has been measured in the planetary atmospheres of Earth and Titan, and may be detected in the giant planets by infrared spectroscopy. Propene infrared absorption cross sections have been determined in the 2680–3220 cm − 1 and 450–1250 cm − 1 regions by high-resolution Fourier transform spectroscopy with H 2 and N 2 , respectively, as broadening gases. Samples of propene were held at 202, 232, 265, 297 K.