Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative Skull Stripping Techniques on Pediatric Magnetic Resonance Imaging

Skull stripping (SS) is a well-known preprocessing step commonly adopted in Magnetic Resonance (MR) scans to eliminate non-brain tissues. While it is well-established for mid-adults, there is limited experimentation with the pediatric population. The continuous development of the pediatric brain presents challenges for existing SS techniques. This paper aims to compare different SS techniques for a pediatric population quantitatively and qualitatively. Due to the absence of manual brain segmentation (ground truth), the quantitative analysis focuses on pairwise Dice Similarity Coefficient (DSC) comparisons of SS techniques, while qualitative analysis was performed by visual inspection. The work also intends to detect potential outliers by thresholding DSC and visualizing SS results. We performed experiments with five techniques, Freesurfer (FS) versions 6.0. 0, 7.1. 1, 7.4. 1, FSL-BET, and SynthStrip, using 279 T1-weighted MR scans from pediatric subjects. We observed the highest agreement between FSL-BET and SynthStrip, confirmed by the visual inspection. The different FS versions showed inconsistent and poor agreement with other techniques. Furthermore, we noticed complementary information for different SS techniques. For instance, when FSL-BET failed to remove the scalp, SynthStrip performed well in removing non-brain tissues and vice-versa. This suggests the potential benefits of combining these techniques into a consensus approach, such as ensembles in machine learning. In conclusion, the quantitative and qualitative analyses of different SS techniques for the pediatric population highlight the superiority of FSL-BET and SynthStrip and allow the identification of prospective outliers. The future exploration of ensemble techniques and tailored technique adaptations could lead to specialized SS techniques and more accurate brain analyses in pediatric subjects.
Layer Height Estimation Using Laser Line Scanners for Closed-Loop Directed Energy Deposition

Enhancing the geometric accuracy of additive manufacturing (AM) processes remains a vital challenge, particularly in the context of directed energy deposition (DED). This paper investigates the use of a laser line scanner for real-time layer height estimation during the DED process. The proposed system offers a method for 3D reconstruction and optimized layer height estimation for a multi-axis DED system using computer vision, enabling adaptive control and quality assurance in DED manufacturing. Experimental results demonstrate the effectiveness of the laser line scanner in achieving higher geometric accuracy in printed objects. The findings contribute to closed-loop control methodologies in AM, paving the way for a broader adoption of DED technology.
Research Center of Radioisotopes at University of Osaka Prefecture Radiocarbon Dates I

The radiocarbon dating laboratory has been in operation since 1984 at the Radiation Center of Osaka Prefecture (OR), predecessor of the Research Center of Radioisotopes, University of Osaka Prefecture. We use liquid scintillation counting (LSC), following sample conversion to methanol through combustion and LiAlH4 reduction. This method was developed by Yamada et al. (Yamada, Higashimura and Shidei 1966; Yamada and Kobashigawa 1986). In cooperation with Yamada, we somewhat modified their procedure: 1) sample charcoal is burned at 700° in the presence of CuO needles and Sulfix grains to remove sulfur and halogen compounds produced during the combustion; 2) the combustion is carried out by using N2-O2 mixed gas of minimized O2 content and stopped when a small amount of the charcoal still remains unchanged, because precise investigation of methanol preparation revealed that O2 gas stimulates byproduct formation during LiAlH4 reduction (Shibata et al. 1985; Shibata et al. 1993). Usually, methanol is prepared directly from sample charcoal in a reaction apparatus (“direct method”). When the sample quantity is insufficient, generated CO2 is isolated as CaCO3 and diluted with inactive commercial CaCO3 if necessary (< 40 g of CaCO3 yield). Then CaCO3 is hydrolyzed with HCl to CO2 for methanol preparation in the usual way (“separate method”). We use standard oxalic acid SRM 4990C (HOxII) for determination of modern 14C (Stuiver 1983). The acid is oxidized to CO2 using the wet method of Valastro, Land and Valera (1977) followed by methanol preparation in the same manner as for unknown samples.
An Account of Systematic Review of English for Medical Purposes (EMP) Vocabulary Memorization

A high volume of global medical information exchanges has escalated due to the recent pandemic that has hit the world. In addressing international demands of sorts, it is imperative that students improve their English language proficiency, especially for medical purposes. Sufficient vocabulary knowledge is vital for language skills to be employed effectively. However, in China, limited attention has been given to vocabulary learning over the years, especially English for Medical Purposes (EMP) vocabulary since it is very large, complex and filled with jargon. Research shows that students perceive the difficulty of EMP to lie in the learning of vocabulary itself. Faced with problems and challenges in the process of EMP vocabulary learning through memorization technique, theoretical research is urgently required to comprehensively reveal the research status of EMP vocabulary memorization. Therefore, this study adopts the systematic literature review method to evaluate related research on EMP vocabulary memorization in China and abroad from 2011 to 2021. The main characteristics of EMP vocabulary memorization research, the factors that affect EMP vocabulary memorization, and the most popular memory strategies are elaborated in detail in order to integrate the existing fragmented knowledge and demonstrate the development of EMP vocabulary memorization technique(s).
Géothermie : retour d'expérience de Soultz-sous-Forêts

The ongoing research programme at Soultz-sous-Forets aims to the definition of the required conditions for heat mining in the tectonic situation of graben area. From 1994 to present, several important progresses were done: - mastering of costs and techniques for deep drilling operations in granite; - demonstration of the possibilities to develop huge permeable volumes at the periphery of the wells using hydraulic stimulation then to initiate circulations between wells through large surfaces and lengths (450 m). The hydraulic tests will continue in 1997 in order to evaluate the stability of the results within the present situation (depth between 3 000 m and 3 500 m - temperature around 160 °C). If they are successful, it is planned to continue, with the support of industries, the same kind of experiments but in deeper (around 5 000 m) and hotter (200 °C) rocks.
DTI-based Corpus Callosum parcellation using the Tensorial Morphological Gradient and Self-Organizing Maps

The Corpus Callosum (CC) is a major white matter structure that plays a crucial role in the communication between the cerebral hemispheres and is associated with many subjects’ characteristics and brain diseases. The subdivision of the CC into smaller regions, called parcellation, is important for a detailed analysis of the structure. However, the lack of visible landmarks makes the CC parcellation a challenging task. In this study, we introduce a novel automatic data-driven approach based on Diffusion Tensor Imaging. It uses the Tensorial Morphological Gradient (TMG) and Self-Organizing Maps for CC parcellation into five regions. Qualitative and quantitative comparisons were performed between the proposed method and three other methods in the literature, using a dataset composed of images from five hundred subjects. Results demonstrate that our method provides distinct CC regions and exhibits similarities with one of the compared methods, which is also data-driven. Moreover, the proposed methodology is the only of the compared methods to consider the complete tensorial information, provided by the TMG map.
SMILENets: Audio Representation Learning via Neural Knowledge Distillation of Traditional Audio-Feature Extractors

Considerable work has been invested over the years into crafting features for different modalities, to facilitate the modelling of information. Said work – and the knowledge it is based on – has thus far been unused as a prior in Deep Neural Networks (DNNs) being trained to learn representations of different modalities. The representations that DNNs have been extracting thus-far are only based on the data at hand, which may either not necessarily be the best representations possible, or depend on a pre-training the DNNs using data with a combined length of thousands to tens of thousands of hours. In this paper, we introduce SMILENets, DNNs that are trained as students to distil the feature knowledge of audio features such as the ComParE challenge features as extracted by the teacher openSMILE toolkit. We explore SMILENets in terms of their ability to extract said audio features for different datasets under in- and out-of training distribution conditions and in terms of their utility as neural feature extractors for neural networks that would model datasets under the same validation conditions. We show that SMILENets learn to distil, and thus, extract features accurately for in-distribution conditions, while exhibiting close results for out-of-distributions scenarios where the SMILENet has been trained on a representative training dataset.
Analysis of Emotions in Speech Acts for Chatbots: An Overview and a Model Proposal

A chatbot is a machine with conversational capabilities that tries to resemble a person. In the 90s the A.L. I.C. E. chatbot was created, showing significant advances over its predecessors. Since then, different progress has been made until, thanks to the advancement of technology, the development of current improved models has been achieved. However, now special attention is being paid to chatbots having affective recognition capabilities to enrich the user ex-perience, which is an understudied area. This paper overviews state-of-the-art works on recognition of speaker's emotions and intentions and proposes to design a speech acts-based model of a chatbot that can interpret human emotions in text and give a coherent response in content and the expressed feelings. A set of techniques will be included in the design to recognize both the user's emotions and her intentions when expressing herself.
Forecasting and Visualization using Dash for Improved Stock Analysis

Since stock market is a dynamic, transforming, and sophisticated investment system, one of the important financial considerations is to make a long-term investment. In this perspective, financial market analysis plays an important role in determining a new strategy to outline and visualize the stock market forecasts to maximize the profit from investments. Stock investment gives best returns when compared to other investment approaches. Despite their volatility, share prices and other statistical elements can be visualized to carefully select the stock to invest. The proposed concept includes the Dash (Dynamic Adaptive Streaming Hypertext) module in python language to create dynamic charts of financial data analysis, which is strongly supported by the in-built finance Python library package. To forecast the future stock values, this research study employs a machine learning system.
Time–space variability of paralic strata deposited in a high accommodation, high sediment supply setting: example from the Cretaceous of Utah

Abstract A previously unstudied section of the John Henry Member (Upper Cretaceous, Straight Cliffs Formation) preserves four stacked regressive–transgressive cycles of paralic strata from the Kaiparowits Plateau in south-central Utah. Meso-scale (10–100 s m thick) shoreface, wave-dominated delta and estuarine depositional environments stack vertically and show the complexity of paralic facies in a single location through time. Correlations with nearby exposures show the palaeogeographical variability updip and along-strike over c. 6.5 myr. Such variability highlights the importance of high accommodation settings in preserving transgressive deposits, including landwards-stepping barrier island and lagoon systems. The Buck Hollow section is expanded two to three times compared with correlative successions only 15–40 km away. Tectonics, eustasy and climate contributed to relative shifts in base level, but these regional controls do not explain the dramatic local thickening observed. Local controls on accommodation were quantified through decompaction analysis. The results showed that the expanded thickness of the John Henry Member in Buck Hollow can be explained by differences in decompaction (c. 9%), local erosion by fluvial incision (c. 5%), early compaction (c. 30%) and local structures such as faults (c. 100–150%). This outcrop-based study illustrates facies variability within a thick paralic succession and investigates accommodation controls on the preservation of these strata with the goal of improving predictive models for analogous deposits. Supplementary material: 40Ar/39Ar age data methods and sample location available at https://doi.org/10.6084/m9.figshare. c.3277436. v1
Revisioning Financial Education: Shortcomings and Opportunities for Transformation

Early, comprehensive and contextually relevant instruction in financial literacy for youth has been found to positively influence financial behaviours as well as psychosocial outcomes such as stress management and feelings of self-efficacy. Yet, financial literacy resources and secondary school curricula have several key limitations, such as piecemeal delivery, which hinder their potential to promote positive and lasting habits. With a focus on the Canadian context, we first assess the potential benefits and limitations of financial literacy education. We then present and assess a newly released financial education curriculum package from Western Canada, analyzing its proposed delivery framework and possibilities for implementation elsewhere.
GPS constraints on broad scale extension in the Ethiopian Highlands and Main Ethiopian Rift

Measurements from GPS sites spanning the Ethiopian Highlands, Main Ethiopian Rift, and Somali Platform in Ethiopia and Eritrea show that present‐day finite strain rates throughout NE Africa can be approximated at the continent scale by opening on the MER. Most sites in the Ethiopian Highlands are consistent with the motion of the Nubian plate at the level of 1 mm/yr with 95% confidence. However, sites at least as far as 60 km west of the rift show higher velocities relative to the stable Nubian frame of 1–2 mm/yr, requiring a combination of localized and distributed deformation to accommodate the African extensional domain. Off‐rift velocities are consistent with ongoing strain related to either high gravitational potential energy or intrusive magmatism away from midrift magmatic segments either on the western rift margin or within the Ethiopian Highlands, especially when combined with likely rheological differences between the Ethiopian Rift and Highlands. Velocities from the Somali Platform are less well determined with uncertainties and residuals from a Somali frame definition at the level of 2–3 mm/yr but without spatially correlated residuals.
Survey of Machine Learning-Based Predictions of Photovoltaic Power Outputs

Solar Photovoltaic (PV) energy has been deployed at exponential rates since the last decade to produce power from renewable and green resources as a contribution to the causes of climate change and global warming. PV power generation is subject to very dynamic changes due to its dependence on the environment and the geography, often fluctuating erratically. The variability of PV power generation poses electric grid system stability, reliability, and planning downturns. Hence, grid operations could operate at higher levels of performance and efficiencies through adequate grid scheduling that primarily relies on accurate prediction of photovoltaic power output. Due to the paramount importance of the topic, the research community investigated several forecasting strategies that rely on numerical methods, probabilistic methods, physical models and machine learning-based (ML-based) techniques. The present paper presents a comparative review of the literature targeting ML-based algorithms for short-term PV power output prediction. A complementary case study exposes a much-needed homogeneous comparison of the state-of-the-art ML-based forecasters, trained on a dataset from the University of Liège. Model structural enhancement for the state-of-the-art is proposed and evaluated in the light of literature findings.
APPROACH OF THE SYSTEMIC DESIGN IN MATERIAL AND INTANGIBLE CULTURE OF ESTRADA REAL: TERRITORIAL SERRO CASE

This study aimed to apply the Systemic Design methodology in the context of the Estrada Real (Royal Road), specifically in Serro Territory, in order to generate a new economic and development model in this location, by creating relationships between their production systems. The first step of the methodology involved the understanding the territory in all its amplitude, i.e., the history of the place, its physical and climatic aspects, its natural resources, its activities (social, cultural and productive), the way and the pace of life community, as well as its infrastructure. The second phase encompassed the systematization and analysis of existing artisanal production systems in Serro Territory. For such a field survey was conducted in which the artisans were interviewed and the production sites were visited. On this occasion, we investigated all output and input of their activities. In the third step of the methodology were designed the matter and energy flows of the nine production systems in question, from the analysis of the "negative points" that emerged in the previous step, as well as the characteristics and potential of the material and immaterial local resources. This process allowed the emergence of several new activities and new products. In the fourth and last step, the current system (existing) and the systemic system (proposed) were compared. Such confrontation demonstrated that, with the approach of Systemic Design the same territory would come to get, on average, an increase of 530% of products and 820% of activity, resulting therefore in a huge business volume. As envisioned results are highlighted: i) in the economic field: expansion of activities; increase in jobs; increase income generation in the community; ii) in the environmental sphere: sustainable management of natural resources; iii) in the cultural field: appreciation of culture; enhancement of local know-how; iv) in the social sector: improving quality of life; keeps its inhabitants in their territory
Advancing Audio Phylogeny: A Neural Network Approach for Transformation Detection

In this study we propose a novel approach to audio phylogeny, i.e. the detection of relationships and transformations within a set of near-duplicate audio items, by leveraging a deep neural network for efficiency and extensibility. Unlike existing methods, our approach detects transformations between nodes in one step, and the transformation set can be expanded by retraining the neural network without excessive computational costs. We evaluated our method against the state of the art using a self-created and publicly released dataset, observing a superior performance in reconstructing phylogenetic trees and heightened transformation detection accuracy. Moreover, the ability to detect a wide range of transformations and to extend the transformation set make the approach suitable for various applications.
Hybrid metaheuristics for integrated dispatching and conflict-free vehicle routing of automated guided vehicles in flexible manufacturing systems

Automated guided vehicles (AGVs) are pervasively used for transportation in flexible manufacturing systems (FMSs), in which production scheduling, vehicle dispatching, and conflict-free vehicle routing (CVR) are three pivot sub-problems. Even though these sub-problems are interrelated, they are often solved sequentially due to their complexity. By combining the particle swarm optimization (PSO), genetic algorithm (GA), and A* algorithm, this paper proposed a metaheuristic (HPSO-GA) that can solve these three sub-problems simultaneously. A hybridization method, specially designed for FJSPCVR, can let PSO compensate for GA’s weakness and vice versa by using their complementary strengths. A modified A* algorithm based on time windows is embedded in the HPSO-GA to find the shortest route on the time domain and resolve conflicts along the path. The proposed HPSO-GA is implemented on a new benchmark with a more complex roadmap and larger manufacturing job scale compared to a former benchmark used in flexible job shop problems. Simulation results indicate that HPSO-GA is applicable for solving integrated scheduling problems in FMSs from an engineering perspective.
SDNet: An Extremely Efﬁcient Portrait Matting Model via Self-Distillation

Most existing portrait matting models either require expensive auxiliary information or try to decompose the task into sub-tasks that are usually resource-hungry. These challenges limit its application on low-power computing devices. In addition, mobile networks tend to be less powerful than those cumbersome ones in feature representation mining. In this paper, we propose an extremely efﬁcient por-trait matting model via self-distillation (SDNet), that aims to provide a solution to performing accurate and effective portrait matting with limited computing resources. Our SD-Net contains only 2M parameters, 2.2% of parameters of MGM, and 1.5% of that of Matteformer. We introduce the training pipeline of self-distillation that can improve our lightweight baseline model without any parameter addition, network modiﬁcation, or over-parameterized teacher models which need well-pretraining. Extensive experiments demonstrate the effectiveness of our self-distillation method and the lightweight SDNet network. Our SDNet outperforms the state-of-the-art (SOTA) lightweight approaches on both synthetic and real-world images.
Adaptive Graph Modeling With Self-Training for Heterogeneous Cross-Scene Hyperspectral Image Classification

The small-sample-size problem of hyperspectral image (HSI) classification has recently gained considerable attention. Cross-scene HSI classification has emerged as an effective solution to this problem. In real-world applications, different HSI scenes are often captured by diverse sensors, resulting in variations between scenes. Graph modeling, as a method to represent relationships, leverages semantic information to establish connections between scenes, thereby facilitating transfer learning by aligning their features. However, in scenarios with only a few labeled target samples, the resulting graph is typically sparse and can only capture weak cross-scene relationships. Studies have shown that a dense and fault-tolerant graph is beneficial for transfer learning in small-sample-size cases. Consequently, we propose a novel heterogeneous transfer learning approach called adaptive graph modeling with self-training (AGM-ST). Unlike conventional graph modeling methods that employ predefined graph weights, adaptive graph modeling (AGM) employs a learnable network to generate graph weights based on the similarities of spectral–spatial features. Additionally, an adaptive cutoff threshold is trained to eliminate weak relationships between samples that may be potentially incorrect. Subsequently, a cross-scene graph loss is designed based on the generated graph to align the feature spaces of the source and target scenes. Furthermore, the unlabeled samples from the target scene are gradually updated with pseudo labels using the self-training (ST) technique, which enhances semantic information and improves graph modeling. Experimental evaluations conducted on three cross-scene HSI datasets have demonstrated the effectiveness of the proposed AGM-ST approach.