Miracle-dz/UniFAR-FTUs huggingface dataset:
High resolution micro-CT imaging in mice stroke models: from 3D detailed infarct characterization to automatic area segmentation

Characterization of brain infarct lesion in rodent models of stroke is crucial to assess the outcome of stroke therapies and to study the disease pathophysiology. However, so far it has been mostly performed by: 1) histological methods, a time-consuming process that lead to significant flaws and tissue distortion; or 2) via MRI imaging, which is faster, yielding 3D information but at high costs. High-resolution micro-CT imaging became, in the last decade, a simple, fast, and cheaper solution, allowing 3D analysis of samples. Here, we describe that high-resolution micro-CT, either using iodine/OsO4 as contrast agents, can be successfully applied for fine quantification and localization of lesion size and edema volume in preclinical stroke models. We successfully correlated this new approach with the standard histological method TTC. In transient MCAO mouse stroke model, we were able to identify/quantify large lesioned areas (segmented in core and penumbra), up to degenerated finer striatal myelinated fibers in a transient ischemic attack (TIA) mouse model, at different timepoints post-ischemia, through manual and automatic segmentation approaches (deep learning). Furthermore, 3D reconstructions of the whole brain allow for brain atlas co-registration of the specific affected brain areas. Hence, the presented methodology, through iodine/OsO4 micro-CT imaging, constitutes a valuable advance in tools for precise and detailed assessment of stroke outcomes in preclinical animal studies.
Weighted (cid:2) p ( 0 < p ≤ 1 ) minimization with non-uniform weights for sparse recovery under partial support information

In this paper, we study the recovery conditions and recovery guarantees of the weighted (cid:2) p minimization method when multiple different weights are allowed. It shows that the sufﬁcient recovery condition of the weighted (cid:2) p minimization method with non-uniform weights is weaker than that of the weighted (cid:2) p minimization method with single weight when p ∈ ( 0 , 0 . 8 ] . And compared with the weighted (cid:2) p minimization method with single weight, the weighted (cid:2) p minimization method with non-uniform weights provides a better upper limit for the recovery error in the noisy case. We demonstrate our results with numerical experiments on synthetic signals. Moreover, we apply the results to the case of the recovery of sparse signals in terms of redundant dictionary.
Micro and small enterprises in Thailand : definitions and contributions

Reviews the current classification of enterprises in Thailand and describes how it can be improved. Briefly describes the informal sector. Briefly discusses the contribution of the informal sector and small enterprises to economic development. Includes in an annex the text of ILO Recommendation concerning General Conditions to Stimulate Job Creation in Small and Medium- sized Enterprises, 1998 (No. 189).
Ferrostatin-1 and 3-Methyladenine Ameliorate Ferroptosis in OVA-Induced Asthma Model and in IL-13-Challenged BEAS-2B Cells

Ferroptosis was reported to be involved in the occurrence and development of asthma. However, the potential mechanism underlying the role of ferroptosis in asthma remains unclear. In this study, we established the mouse asthma model following the ovalbumin (OVA) method in C57BL/6 mice and the cell model with IL-13 induction in bronchial epithelial cells (BEAS-2B cells). Treatment of ferrostatin-1 (Ferr-1) and 3-methyladenine (3-MA) decreased iron deposition in IL-13-induced BEAS-2B cells and lung tissues of asthma mice, opposite to that in bronchoalveolar lavage fluid (BALF). Meanwhile, excessive lipid peroxidation asthma model in vivo and in vitro was alleviated by Ferr-1 or 3-MA treatment. In addition, Ferr-1 and 3-MA inhibited the expression of LC-3 in these cells and lung tissues of mice. Moreover, Ferr-1 and 3-MA also suppressed the production of inflammatory cytokines (IL-1β, IL-6, and TNF-α) and oxidative stress factors (ROS and MDA), while promoting the level of SOD, in vivo and in vitro. Furthermore, application of Ferr-1 exhibited a greater inhibitory effect on iron release and lipid peroxidation in IL-13-induced BEAS-2B cells and asthma mice than 3-MA, accompanied with a weaker effect on ferritinophagy than 3-MA. Collectively, Ferr-1 and 3-MA ameliorated asthma in vivo and in vitro through inhibiting ferroptosis, providing a new strategy for the clinical treatment of asthma.
Magnetic Field Imaging for Non-Destructive 3D Package Fault Isolation

While microelectronic packages are becoming more and more advanced, the need for non-destructive Electrical Fault Isolation (EFI) becomes ever more critical for the entire product life-cycle ranging from the chip development yield enhancements to failures on product returns. In the beginning of product development, short failures are often the main issue while opens and cracks become the reliability problems after the product reaches the marketplace. In this paper we present Magnetic Field Imaging (MFI) as the one technique that can find all static defects: shorts, leakages and opens in a true non-destructive way.
Single‐Ion Magnetostriction in Gd2O3–CeO2 Solid Solutions

Ceria (CeO2) and its solid solutions with Gd2O3 are technologically important and environmentally friendly materials with numerous interesting properties and important applications. Nevertheless, the magnetic properties of ceria are even today not fully understood, and magnetoelastic coupling in pure or doped ceria remains essentially unexplored. This has been so, in part, due to the difficulty of measuring very small magnetostrictive strains in weakly paramagnetic materials. During the last decade, however, technical advances have enabled sensitive and accurate measurements of sample deformation in high magnetic fields. Here, forced magnetostriction (MS) in Gd2O3‐CeO2 solid solution ceramics (Ce1−xGdxO2−x/2, 0 ≤ x ≤ 1) at room temperature is characterized. In a pulsed magnetic field μ0H ≤ 60 Tesla, longitudinal MS strain is observed to depend on the square of the field amplitude and to increase linearly with Gd3+ concentration but is not sensitive to the lattice symmetry of the ceramics. The theory attributes the origin of the observed strain to the single‐ion MS response of Gd3+ to the crystal field via mixing of ground and excited electronic states and covalent hybridization with oxygen ligands. Contributions of charge‐compensating oxygen vacancies and/or Van Vleck paramagnetism to the observed magnetoelastic coupling are determined to be negligible.
Image Information Mining: Perspectives seen by DLR

Based on our experience with existing image information mining (IIM) systems and their principal building blocks, we propose new implementation technologies for some of their typical functionalities. These new technologies are supposed to be more efficient than existing solutions and offer new performance perspectives. Motivated by potential new applications of TerraSAR-X data, we deal with feature extraction in transform domains, Kolmogorov complexity, hierarchical clustering, and Dirichlet modeling.
Deep Learning-Based Microbubble Localization for Ultrasound Localization Microscopy

Ultrasound localization microscopy (ULM) is an emerging vascular imaging technique that overcomes the resolution-penetration compromise of ultrasound imaging. Accurate and robust microbubble (MB) localization is essential for successful ULM. In this study, we present a deep learning (DL)- based localization technique that uses both Field-II simulation and in vivo chicken embryo chorioallantoic membrane (CAM) data for training. Both radiofrequency (RF) and in-phase quadrature (IQ) data were tested in this study. The simulation experiment shows that the proposed DL-based localization was able to reduce both missing MB localization rate and MB localization error. In general, RF data showed better performance than IQ. For the in vivo CAM study with high MB concentration, DL-based localization was able to reduce the vessel MB saturation time by more than 50% as compared to conventional localization. Additionally, we propose a DL-based framework for real-time visualization of the high-resolution microvasculature. The findings of the paper support the use of DL for more robust and faster MB localization, especially under high MB concentrations. The results indicate that further improvement could be achieved by incorporating temporal information of the MB data.
‘It is like a hammer and toothbrush; you need it for the rest of your life’ – Five Norwegian teachers’ experiences of working at a ‘thinking school’

This article investigates how a group of Norwegian teachers experience working at a ‘thinking school’ (i.e. a school that cooperates with UK-based consultancy Thinking Matters). The context is curriculum development in Norway, where schools and teachers are given increasing freedom in terms of what methods to apply and what content to teach while being expected to reflect and develop collectively as professional communities. In this setting, it is interesting to see how teachers utilise the tools and models offered by an external actor and how they negotiate between these and their own pedagogical ideas as well as the demands put on them by the curriculum. We approached this study by firstly analysing two documents published by Thinking Matters, and secondly by asking how the teachers experience the effects of the thinking school approach on pupils’ learning and themselves as professional teachers. We interviewed five teachers who emphasised several positive aspects of working within the thinking school approach. However, in this article, we also identify some tensions resulting from the adoption of a whole-school approach that emphasises the methods that teachers should use and that pupils should master as a way of achieving the overarching aims of the curriculum.
Quality Management in Engineering

The application of quality management in engineering can be achieved through imposing a systems engineering approach based on a defined engineering policy with traditional engineering standards and procedures. Quality assurance of horizontal cross business unit engineering activities or functions can best be achieved through a formal quality system accreditation, such as AS 3901. Internal quality assurance of engineering activities can then be benchmarked against internal engineering standards and procedures and monitored using quality audits. The quality of engineering activities in the company can be compared to world best practice benchmarks by identifying those core business activities that require direct engineering support. Performance against key performance indicators for these core activities can then be assessed to measure the performance of engineering activities. Identifying customer needs and satisfying those needs is the prime performance indicator. Examples of the application of systems engineering and the AS 3901 quality system are given when discussing configuration management and risk management.
Comparing the Impact of Energy Model Results on Life Cycle Energy: Focus on High Performance Residential Building

Studies have shown that design-phase energy models, such as those used in Leadership in Energy and Environmental Design (LEED) calculations, have significant error rates, sometimes as much as 50%. These same energy model results are also being utilized in the field of life cycle assessment (LCA), where energy model results are being input for electricity and energy inventory calculations for the entire life cycle of the building. Error rates in energy modeling results have been well documented; however, research is lacking on the effect of this uncertainty within LCA, specifically life cycle energy. This research analyzes energy modeling results in terms of building life cycle energy use and metered energy data with a case study of a Solar Decathlon House. The life cycle assessment results indicated that the impact of energy model results is dependent on the impact category. Life cycle energy, however, appeared to be dependent on the electricity, resulting in an average error rate of about 44%. The sensitivity study analyzed these discrepancies and produced results that reduced the life cycle energy error rate to 26%.
Design of an automated on-car brake lathe

An on-car brake lathe resurfaces disk-brake rotors by rotating them "in place" and making a light cut along the surface of the disk. The primary goal of this thesis is to develop an automated cutting head for an on-car brake lathe. The new cutting head must produce a surface finish that matches or exceeds that of the current (manual) cutting head. Pro-Cut International, a leading manufacturer of on-car brake lathes, developed functional requirements and cost constraints for the automated cutting head. Technical challenges include design and fabrication of low-cost precision linear bearings and actuators with dynamic stiffness sufficient to suppress chatter. During this thesis, two prototype cutting heads were designed, manufactured, and tested. The first prototype employed modular linear bearings, and produced unacceptable surface finish due to chatter. Cutting-tip vibration measurements combined with modal testing showed that chatter was caused by low structural stiffness of the modular bearing rails. A second prototype employing a unitary ground bearing system produced an acceptable surface finish of 70 1-inch at 0.015" depth of cut per side. The key components of this design can be extruded and sliced to form the assembly, thereby meeting cost constraints.
Demographic, military, and health comorbidity variables by mild TBI and PTSD status in the LIMBIC-CENC cohort

ABSTRACT Objective To describe associations of demographic, military, and health comorbidity variables between mild traumatic brain injury (mTBI) history and posttraumatic stress disorder (PTSD) status in a sample of Former and current military personnel. Setting Participants recruited and tested at seven VA sites and one military training facility in the LIMBIC-CENC prospective longitudinal study (PLS), which examines the long-term mental health, neurologic, and cognitive outcomes among previously combat-deployed U.S. Service Members and Veterans (SM/Vs). Participants A total of 1,540 SM/Vs with a history of combat exposure. Data were collected between 1/1/2015 through 3/31/2019. Design Cross-sectional analysis using data collected at enrollment into the longitudinal study cohort examining demographic, military, and health comorbidity variables across PTSD and mTBI subgroups. Main Measures PTSD Checklist for DSM-5 (PCL-5), mTBI diagnostic status, Patient Health Questionnaire 9-item (PHQ-9), Pittsburgh Sleep Quality Index (PSQI), AUDIT-C, and other self-reported demographic, military, and health comorbidity variables. Results Ten years following an index date of mTBI exposure or mid-point of military deployment, combat-exposed SM/Vs with both mTBI history and PTSD had the highest rates of depression symptoms, pain, and sleep apnea risk relative to SM/Vs without both of these conditions. SM/Vs with PTSD, irrespective of mTBI history, had high rates of obesity, sleep problems, and pain. Conclusion The long-term symptom reporting and health comorbidities among SM/Vs with mTBI history and PTSD suggest that ongoing monitoring and intervention is critical for addressing symptoms and improving quality of life.
Analysis of soil fertility and optimal nitrogen application of brown earth (luvisols) in China

The excessive application of nitrogen (N) has caused a series of environmental problems. Brown earth (BE) is one of the main soil types in China, and the specific soil‐forming conditions make it high risk of nutrient leaching. Determining the soil fertility of BE and optimal N application rates for main crops’ production is important to reduce nitrogen leaching. Based on China's second national soil survey, the basic fertility level of BE was defined, and the effects of four N management methods [one‐time base fertilizer (BF), topdressing (TF), combined organic fertilizer (OF) and controlled‐release fertilizer (CF)] on maize and wheat yield and N efficiency were analysed. The study found that except for lower phosphorus content (level 4), the soil fertility of BE was at a moderate level (level 3). Compared with no N application, these four N management methods significantly increased the maize and wheat yield and showed the trend of OF > CF > TF > BF; TF, CF and OF significantly increased the N efficiency comparison with BF. The optimal N application rates for wheat and maize were 129–174 kg ha−1 and 170–235 kg ha−1, respectively, when considering the win–win of yield and N efficiency. N fertilizer reduction in BE needs to be further strengthened, and the specific N application rates to achieve profit maximization should be adjusted depending on environmental factors.
Simulation and experimental analysis of tip response of tapping mode atomic force microscope

In this study, the vertical deflection responses of tapping mode atomic force microscope (TM-AFM) micro-cantilever tip are obtained by simulation and experiment. The results show that, under the blocking of the sample on one side, the steady-state response of the tip is still a sinusoidal form almost symmetrical about the equilibrium position. Furthermore, from the perspective of energy dissipation of the micro-cantilever system, the phases of two surfaces with different properties are simulated under different background dissipation. The result shows that eliminating partial background dissipation can increase the phase contrast between the two surfaces. These results are of significance for understanding the tip response and phase optimization in TM-AFM.
Gene and sample selection for cancer identification.

Gene-expression data gathered with microarrays play an important role in detection, classification, and understanding of many diseases including cancer. However, the numbers of samples gathered in experiments still remain in hundreds compared to the thousands of genes whose expressions are measured. One way to handle this problem is to identify relevant genes that contribute to the disease and thereafter inferring the underlying mechanisms of their functions. This thesis focuses on identification of relevant genes, which is hindered due to several reasons. For example, relevant genes could be correlated with other genes that are biologically relevant but redundant for the classification of disease. While ranking the genes according to their relevance, it is important to consider the quality of samples as microarray samples are highly heterogeneous and multimodal in nature. This further raises an issue of stability of a gene selection method because a gene selection method should be repeatable and reproducible, giving high confidence for selected genes. For multiclass classification, sample distribution of various classes may play important role in gene selection. By considering these aspects into gene selection criteria, this research has evolved in multiple ways by introducing several novel gene ranking algorithms. To demonstrate the effectiveness of proposed approaches, the techniques are tested on cancer samples taken from various tumors such as colon, breast, lung, leukemia etc. Chapter 1 introduces the problem of gene and sample selection and a literature review on earlier work. The major contributions of the thesis are detailed in Chapters 2-5. Chapter 6 concludes the thesis with a summary and potential future work. In Chapter 2, we propose an improved support vector machine recursive feature elimination (SVM-RFE) technique for gene ranking by incorporating minimum redundancy maximum relevancy (MRMR) filter criteria. SVM weights are directly related to the importance of genes in sample classification, which has been demonstrated in the SVM-RFE algorithm. However, SVM-RFE does not penalize for redundant genes adequately. Therefore, we propose to combine SVM weights and the MRMR criterion
Rearchitecting In-Memory Object Stores for Low Latency

Low latency is increasingly critical for modern workloads, to the extent that compute functions are explicitly scheduled to be co-located with their in-memory object stores for faster access. However, the traditional object store architecture mandates that clients interact with the server via inter-process communication (IPC). This poses a significant performance bottleneck for low-latency workloads. Meanwhile, in many important emerging AI workloads, such as parallel tree search and reinforcement learning, all the worker processes accessing the object store belong to a single user. We design Lightning, an in-memory object store rearchitected for modern, low-latency workloads in a single-user, multi-process setting. Lightning departs from the traditional design by adopting a shared memory model, enabling clients to directly access the object store without IPC boundary. Instead, client isolation is achieved by a novel integration of Intel Memory Protect Keys (MPK) hardware, transaction logging, and formal verification. Our evaluations show that Lightning outperforms state-of-the-art in-memory object stores by up to 9.0x on five standard NoSQL workloads and up to 4.5x in scaling up a Python tree search program. Lightning improves the throughput of a popular reinforcement learning framework that uses an in-memory object store for data sharing by up to 40%.
Enabling streamlined life cycle assessment : materials-classification derived structured underspecification

As environmental footprint considerations for companies gain greater importance, the need for quantitative impact assessment tools such as life cycle assessment (LCA) has become a higher priority. Currently, the cost and time burden associated with LCA has prevented it from becoming more prevalent. While several streamlining approaches have been suggested, questions regarding the effectiveness and efficiency of the streamlined results are still of concern. The streamlining method of probabilistic underspecification has shown initial success in its ability to reduce LCA efforts while simultaneously increasing certainty in the final impact assessment. Probabilistic underspecification streamlines LCA by prioritizing targets of more refined data collection and by implementing the use of underspecified surrogate data within LCI analysis. This thesis concentrates on further developing and improving the streamlining methodology of probabilistic underspecification through refinement of the materials classification systems for polymers and minerals and through additional case study analysis. The classification system allows for a better understanding of the relationship between the degree of materials specificity and the uncertainty in the resulting impact values. Additionally, the resulting polymer and mineral classifications were combined with existing materials classifications to conduct an alkaline battery case study in order to test the effectiveness of the streamlining method. The material classifications created through this research provide a logical and practical approach to underspecification while maintaining consistent and reasonable levels of uncertainty. Furthermore, the case study analysis showed that the streamlining methodology significantly lowered LCA burden by systematically reducing the number of product components requiring full specification. This research provides further evidence that probabilistic underspecification may provide a promising LCA streamlining method among a set of such strategies that can significantly reduce LCA efforts while maintaining the accuracy of the overall impact assessment.