Miracle-dz/UniFAR-FTUs huggingface dataset:
Local understandings of community resilience in earthquake prone Nepal

Abstract Local understandings of community resilience in earthquake prone Nepal Hanna A. Ruszczyk The concept of resilience is receiving increasing attention amongst academics, policy makers and practitioners. International frameworks have been developed; position papers drafted; and donor projects formulated with the aim of building community resilience. However this is being undertaken without a clear understanding of what community resilience is, if and how it can be enhanced, and how to measure the effectiveness of interventions with notable political and financial implications. This thesis sets out to address this knowledge gap, by focusing on local understandings of community resilience to earthquakes in urban Nepal. Underpinned by theoretical engagement with everyday geographies, resilience research and disaster risk reduction, this research set out to address: 1. How is the concept of resilience understood by community members at the local level; and what, in local terms, are the characteristics or components of resilience. 2. How do local, academic and practitioner understandings of resilience vary. 3. Can resilience be enhanced or supported through external intervention? And if so, what form might these interventions take? Drawing on interviews with a range of stakeholders, the findings highlight the importance of embedding earthquake risk reduction interventions in the everyday lived experience of urban communities as well as the importance of governance structures. However, how communities are defined in urban settings is complex, based on migration trends resulting in extended family networks between the rural and urban settings being viewed as important by the two urban wards. Understandings of resilience varied significantly between the academics, practitioner and local communities. While the case study communities were found to have a high level of resilience to everyday risks, building resilience to infrequent high magnitude events such as earthquakes requires local structures as well as support from local (ward and municipal level) and central government. This presents particular challenges due to fragmented governance arrangements in this post conflict state.
Domain Adaptation for Convolutional Neural Networks-Based Remote Sensing Scene Classification

Remote sensing (RS) scene classification plays an important role in the field of earth observation. With the rapid development of the RS techniques, a large number of RS scene images are available. As manually labeling large-scale RS scene images is both labor and time consuming, when a new unlabeled data set is obtained, how to use the existing labeled data sets to classify the new unlabeled images is an important research direction. Different RS scene image data sets may be taken from different type of sensors, and the images may vary from imaging modalities, spatial resolutions, and image scales, so the distribution discrepancy exists among different image data sets. As a result, simply applying convolutional neural networks (CNN) trained on source domain cannot accurately classify the images on target domain. Domain adaptation (DA) can be helpful to solve this problem. In this letter, we design a subspace alignment (SA) and CNN-based framework to solve the DA problem in RS scene image classification. A new SA layer is proposed and added into CNN models for DA, which could align the source and target domains in some feature subspace. Fine-tuning the modified CNN model with the added SA layer makes the CNN model adapt to the aligned feature subspace and helps to relieve the domain distribution discrepancy. The experiments conducted on two public data sets show that adding the SA layer into CNN improves the scene classification on the target domain.
Instructional design : a systematic approach for reflective practice

Design Activities. Note to Readers and Instructors. How Can Instructional Design Improve My Teaching? Using this Text. Acknowledgments. I. MAKING CONNECTIONS.1. Connecting Learning and Teaching. What is Learning? What is Instruction? Learning Principles List. What is Effective Teaching? Your Mission Statement. 2. Connecting Planning and Designing. Design a Lesson. How Teachers Plan. Critique an ID Model. II. UNDERSTANDING YOUR CLASSROOM. 3. Learning About Content, Learners, and Context. Thinking About Content. Learner Profile. Learning Context. Pre-lesson Thinking. III. DEVELOPING LESSONS. 4. Determining Learning Outcomes. Learning Topics and Learning Outcomes. Lesson Search and Evaluation. Lesson Plan Draft. 5. Exploring Assessment Options. Views on Assessment. Matching Learning Outcomes with Assessment Tools. Develop a Rubric. Assessment Overview. 6. Exploring Teaching Options. View of Teaching. Instructional Events. Teaching Options for Lessons. 7. Exploring Technology Options. Wrap-Up: Completing Your Lessons. Revised Lesson Plans. IV DEVELOPING UNITS. 8. Designing and Teaching Units. Unit Intent Statement. Pre-unit Thinking. Unit Overview. Wrap-Up: Completing Your Unit. Unit Outline and Checklist. Unit Evaluation. V. GROWING AS A TEACHER. 9. Developing Curriculum. Content Area Standards Using an Integrated Curriculum. School Themes. 10. Reflecting on Your Learning. Evaluate Your Learning. Re-evaluate Your View of Teaching. Re-evaluate Your Planning Process. Choose a National Board Certification Area. Improve Your Technology Skills. Develop a Professional Development Plan. Glossary. Index.
Compressed Sensing based Radial Mode Analysis of the Broadband Sound Field in a Low-Speed Fan Test Rig

A new inverse method for the radial mode analysis of the broadband sound fields in flow ducts is presented combining a Compressed Sensing approach and the eigenvalue decomposition of the sound pressure cross-spectral matrix. The Compressed Sensing method allows the accurate analysis of dominant modes by exploiting the block-structure of the radial mode spectrum and using sparse sensor arrays. The eigenvalue decomposition of the cross-spectral matrix is able to separate incoherent sound field constituents, which are sparse with respect to the in-duct modal spectrum and therefore are suitable for the Compressed Sensing approach. The application of the new method is demonstrated using measured data from a laboratory fan test-rig and the method is validated by comparison with results from a reference method using a full sensor array of 138 sensors. The method's potential to reduce the required number of sensors is evaluated by application of a reduced array with 34 sensors, by which the sound field is subsampled at high frequencies. The separation of incoherent sound field constituents by the eigenvalue decomposition is investigated in detail and its limitations are identified regarding the use of sparse arrays.
Data-driven Prior Elicitation for Bayes Factors in Cox Regression for Nine Subfields in Biomedicine

Biomedical research often utilizes Cox regression for the analysis of time-to-event data. The pervasive use of frequentist inference for these analyses implicates that the evidence for or against the presence (or absence) of an effect cannot be directly compared and that researchers must adhere to a predefined sampling plan. As an alternative, the use of Bayes factors improves upon these limitations, which is especially important for costly and time-consuming biomedical studies. However, Bayes factors involve their own difficulty of specifying priors for the parameters of the statistical model. In this article, we develop data-driven priors for Cox regression tailored to nine subfields in biomedicine. To this end, we extracted hazard ratios and associated x% confidence intervals from the abstracts of large corpora of already existing studies within the nine biomedical subfields. We used these extracted data to inform priors for the nine subfields. All of our suggested priors are Normal distributions with means of 0 and standard deviations closely scattered around 1. We propose that researchers use these priors as reasonable starting points for their analyses.
Rainfall-Runoff modeling of river Kosi using SCS-CN method and ANN

A rainfall-runoff model is a mathematical model describing the rainfall - runoff relations of a catchment area, drainage basin or watershed. In other words, the model calculates the conversion of rainfall into runoff. There can be used many methods to calculate the runoff among which the methods used in the current report are • SCS-CN Method • Artificial Neural Network (ANN) In rainfall-runoff modeling SCS-CN uses the soil information, rainfall, storm duration, soil texture, type & amount of vegetation cover and conservation practices are considered while a new dimension has been added to the modeling approach through the adoption of the ANN technique as these models possess desirable attributes of universal approximation, and the ability to learn from examples. The performance comparison of both the models is made with coefficient of determination (R2) which is coming to be 0.82 in case of SCS-CN method and 0.89 in case of ANN. Further a comparison is made of both the models i.e. ANN and SCS-CN for the runoff of river Kosi in year 2009.
Metric learning by simultaneously learning linear transformation matrix and weight matrix for person re-identification

Mahalanobis metric learning is one of the most popular methods for person re-identification. Most existing metric learning methods regularly formulate the person re-identification as an unconstrained optimisation problem and the constraints on the Mahalanobis matrix are seldom imposed. In addition, weights are often used to model the relationships between different variables but they often suffer from boundedness caused by their hand-designed feature. Taking the above two disadvantages into consideration, the authors propose a new metric learning method for person re-identification, which formulates the metric learning problem as a constrained optimisation problem by imposing a constraint on the linear transformation matrix. Furthermore, they treat the weights as unknown variables and introduce a weight learning method instead of designing weight intuitively. Finally, they evaluate the proposed method on two challenging person re-identification databases and show that it performs favourably against the state-of-the-art approaches.
Point-of-care testing compared to gold-standard laboratory methods in the measurement of serum lipids.

BACKGROUND Cardiovascular disease is the leading cause of mortality worldwide, with dyslipidaemia being one of the major risk factors. Point-of-care testing (POCT) allows for the rapid measurement of serum lipids. The aim of this study was to assess the accuracy of serum lipid measurement by the Fujifilm™ NX700 POCT compared to a gold-standard clinical laboratory method (Medpace, Leuven, Belgium). METHODS This was a prospective, observational study conducted at the Lipid Clinic at Charlotte Maxeke Johannesburg Academic Hospital from July to September 2022. Participants were known to have a lipid disorder, most commonly, familial hypercholesterolaemia. Samples sent for lipid measurement by standard laboratory methods were simultaneously measured by the Fujifilm™ NX700 POCT. RESULTS Lipograms evaluating total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C) and calculated low-density lipoprotein cholesterol (LDL-C) were obtained from 115 participants. No statistically significant difference was noted between the parameters tested on the different platforms. The Fujifilm™ NX700 POCT correctly identified > 91% of serum lipid results as normal or abnormal, as defined by NCEP-ATP III criteria, and exhibited good sensitivity and specificity for each parameter. Lin's concordance correlation coefficient demonstrated a strong correlation for all parameters; TC (ρc = 0.9861), HDL-C (ρc = 0.95919), LDL-C (ρc = 0.98134) and TG (ρc = 0.92775). Bland-Altman plots identified low bias and a good level of agreement between the two test methods. CONCLUSION The Fujifilm™ NX700 POCT compared favourably with gold-standard laboratory methods in the determination of serum lipid measurements, allowing for rapid screening at the primary healthcare level.
Integration of peripheral blood‐ and tissue‐based biomarkers of response to immune checkpoint blockade in urothelial carcinoma

As predictive biomarkers of response to immune checkpoint inhibitors (ICIs) remain a major unmet clinical need in patients with urothelial carcinoma (UC), we sought to identify tissue‐based immune biomarkers of clinical benefit to ICIs using multiplex immunofluorescence and to integrate these findings with previously identified peripheral blood biomarkers of response. Fifty‐five pretreatment and 12 paired on‐treatment UC specimens were identified from patients treated with nivolumab with or without ipilimumab. Whole tissue sections were stained with a 12‐plex mIF panel, including CD8, PD‐1/CD279, PD‐L1/CD274, CD68, CD3, CD4, FoxP3, TCF1/7, Ki67, LAG‐3, MHC‐II/HLA‐DR, and pancytokeratin+SOX10 to identify over three million cells. Immune tissue densities were compared to progression‐free survival (PFS) and best overall response (BOR) by RECIST version 1.1. Correlation coefficients were calculated between tissue‐based and circulating immune populations. The frequency of intratumoral CD3+LAG‐3+ cells was higher in responders compared to nonresponders (p = 0.0001). LAG‐3+ cellular aggregates were associated with response, including CD3+LAG‐3+ in proximity to CD3+ (p = 0.01). Exploratory multivariate modeling showed an association between intratumoral CD3+LAG‐3+ cells and improved PFS independent of prognostic clinical factors (log HR −7.0; 95% confidence interval [CI] −12.7 to −1.4), as well as established biomarkers predictive of ICI response (log HR −5.0; 95% CI −9.8 to −0.2). Intratumoral LAG‐3+ immune cell populations warrant further study as a predictive biomarker of clinical benefit to ICIs. Differences in LAG‐3+ lymphocyte populations across the intratumoral and peripheral compartments may provide complementary information that could inform the future development of multimodal composite biomarkers of ICI response. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
Decreased plasma cartilage acidic protein 1 in COVID‐19

Cartilage acidic protein‐1 (CRTAC1) is produced by several cell types, including Type 2 alveolar epithelial (T2AE) cells that are targeted by SARS‐CoV2. Plasma CRTAC1 is known based on proteomic surveys to be low in patients with severe COVID‐19. Using an ELISA, we found that patients treated for COVID‐19 in an ICU almost uniformly had plasma concentrations of CRTAC1 below those of healthy controls. Magnitude of decrease in CRTAC1 distinguished COVID‐19 from other causes of acute respiratory decompensation and correlated with established metrics of COVID‐19 severity. CRTAC1 concentrations below those of controls were found in some patients a year after hospitalization with COVID‐19, long COVID after less severe COVID‐19, or chronic obstructive pulmonary disease. Decreases in CRTAC1 in severe COVID‐19 correlated (r = 0.37, p = 0.0001) with decreases in CFP (properdin), which interacts with CRTAC1. Thus, decreases of CRTAC1 associated with severe COVID‐19 may result from loss of production by T2AE cells or co‐depletion with CFP. Determination of significance of and reasons behind decreased CRTAC1 concentration in a subset of patients with long COVID will require analysis of roles of preexisting lung disease, impact of prior acute COVID‐19, age, and other confounding variables in a larger number of patients.
40Ar-39Ar ages of dolerite dykes from Sri Lanka

: Two 40 Ar- 39 Ar ages were obtained for dolerites from Sri Lanka which had . been dated by the K-Ar method as 143-152 Ma. 40 Ar- 39 Ar ages, which are more reliable than the K-Ar ages, are 160-170 Ma. These ages are important for constraining the scenario of break-up of Gondwana in the early Jurassic period.
Acute appendicitis in pregnancy - do we treat correctly, or do we delay unnecessarily?

OBJECTIVES Acute appendicitis is the most common non-gynaecological indication for surgical intervention during pregnancy. The aim of this study was to compare perioperative and postoperative results of surgical treatment of acute appendicitis in the early and late stage of pregnancy. MATERIAL AND METHODS This is a retrospective study focused on the evaluation of perioperative and postoperative results of appendectomy in pregnancy. The study included all pregnant patients who underwent laparoscopic or open appendectomy at the University Hospital Ostrava during the observed 10-year period (January 2012-December 2021). The patients were divided into two subgroups according to the stage of pregnancy in relation to the expected viability of the foetus (the viability limit was defined as the 23rd week of pregnancy). RESULTS In the monitored 10-year period, a total of 25 pregnant patients underwent appendectomy. Comparing the two subgroups of patients, there were no statistically significant differences in any of the admission parameters. Laparoscopy was performed in 100% of the patients in the lower stage of pregnancy (< 23 g.w.) and in 61% of the subgroup of patients with more advanced pregnancy (> 23 g.w.); this difference was statistically significant (p = 0.039). Differences in subgroups regarding duration of surgery, risk of revision and 30-day postoperative morbidity were not statistically significant. In the subgroup of patients < 23 g.w. , uncomplicated forms of appendicitis predominated (66%), whereas in the subgroup > 23 g.w. , complicated forms predominated (69%); this difference was statistically significant (p = 0.026). When comparing the two subgroups of patients, there was a statistically significant difference in the length of hospitalization (p = 0.006). The mortality rate of the group was zero. CONCLUSIONS The results of the study confirm the fact that advanced pregnancy may be related to complicated forms of appendicitis. Therefore, early appendectomy is still the method of choice. In accordance with the Society of American Gastrointestinal and Endoscopic Surgeons (SAGES) recommendations, laparoscopic approach is preferred in pregnant patients, even in advanced pregnancy.
The arginine methyltransferase PRMT5 promotes mucosal defense in the intestine

Arginine methyltransferase PRMT5 promotes select colonic goblet cell-associated gene programs and facilitates host defense against enteric pathogens. PRMT5 is a type II arginine methyltransferase abundantly expressed in the colonic epithelium. It is up-regulated in inflammatory bowel disease and colorectal cancer. However, its role in mucosal defense against enteric infection has not been studied. Here, we report that Prmt5 in the murine colon is up-regulated in response to Citrobacter rodentium infection. Pathogen clearance in mice with haploinsufficient expression of Prmt5 is significantly delayed compared with wildtype littermate controls. Transcriptomic analyses further reveal that PRMT5 regulates the expression of canonical crypt goblet cell genes involved in mucus production, assembly, and anti-microbial responses via methyltransferase activity–dependent and –independent mechanisms. Together, these findings uncover PRMT5 as a novel regulator of mucosal defense and a potential therapeutic target for treating intestinal diseases.
A 97-Channel Read-Out ASIC for an Electrophysiological Mapping Catheter With an Optical Link

Electrophysiological (EP) mapping catheters are medical equipment, which are widely used to diagnose and treat atrial fibrillation. The electrophysiology signals are sensed by the catheter's electrodes, for which a large electrode count becomes more and more essential because of the demand for a higher local resolution. A drawback of the large electrode count is the effort to pass through and to integrate the wires inside the catheter shaft. To overcome with this issue, this article describes the realization of an EP ASIC, which is placed close to the 97 electrodes and to perform an in-tip digitization. Thanks to an integrated optical link, only a single fiber is required to connect the catheter tip to an externally located electro-optical unit and thus shrinking the shaft volume to a minimum. The fiber is used to guide light from the electro-optical unit to the catheter tip and illuminate a blue LED, which is located close to the EP ASIC and acts as a photovoltaic cell. The EP ASIC is designed to use the LED as power source and a data transceiver while performing signal conditioning and digitization of the EP signals at the same time. The EP signals are captured with the ASIC's multi-channel read-out circuit consisting of 97 fully differential preamplifiers and additional filter stages. A switch network sequentially selects one single channel for further amplification and digitization of the EP signal. The read-out circuit is designed to process signals in the range of 500 μVpp to 20 mVpp with a bandwidth of 5 Hz to 100 Hz.
Human Locomotion Databases: A Systematic Review

The analysis of human locomotion is highly dependent on the quantity and quality of available data to obtain reliable evidence, due to the great variability of gait characteristics between subjects. Researchers usually have to make significant efforts to generate well-structured and trustworthy datasets. This situation is aggravated when patients are involved, due to experimental, privacy, and safety constraints. The availability of public datasets can facilitate this process. In this work, we systematically review the scientific and technical literature to identify the human locomotion databases publicly available nowadays. Within the 93 datasets identified, we observed that the most basic motor skills, e.g., flat or sloped walking, are well covered, whereas many other daily-life motor skills are poorly represented. The most common sensors used to record gait are optical motion capture systems, followed by RGB cameras and inertial sensors. We observed a lack of consistency in the data formats and limited sample size in most reviewed datasets. These issues hinder researchers from systematically standing on previous research results and represent a major barrier to using Artificial Intelligence and Big Data algorithms. With this work, we aim to provide the scientific community with a comprehensive, critical, and efficient guide to human locomotion datasets across different application domains.
Parametric subharmonic instability in a narrow-band wave spectrum

Parametric subharmonic instability arising in a narrow-band wave spectrum is investigated. Using a statistical equation that describes weakly nonlinear interactions in a random wave field, we perform analytical and numerical stability analyses for a modulating wave train. The analytically obtained growth rate $\unicode[STIX]{x1D706}=(-\unicode[STIX]{x1D707}+\sqrt{\unicode[STIX]{x1D707}^{2}+4CE_{B}})/2$ agrees favourably with the results from direct numerical experiments, where $\unicode[STIX]{x1D707}$ is the half-value width of the background wave frequency spectrum, $E_{B}$ is the background wave energy density, and $C$ is a constant. This expression has two asymptotic limits: $\unicode[STIX]{x1D706}\sim \sqrt{CE_{B}}$ for $\unicode[STIX]{x1D707}\ll \sqrt{CE_{B}}$ and $\unicode[STIX]{x1D706}\sim CE_{B}/\unicode[STIX]{x1D707}$ for $\unicode[STIX]{x1D707}\gg \sqrt{CE_{B}}$ . In the terms of weak turbulence, these two growth rates correspond to the ones occurring in the dynamic and kinetic time scales. In this way, our formulation successfully unifies the two conventional types of parametric subharmonic instability and offers a new criterion to determine the applicability of the classical kinetic equation in three-wave systems.