Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of the quality of medical history taking on diagnostic accuracy

Diagnostic errors are a relevant health-care problem. Although medical history taking is usually the first step in patients’ assessment there are only limited data on the association of its quality and diagnostic accuracy. Accordingly, this prospective randomized simulator-based single-blind trial aimed to investigate the effects of initial cues and history taking skills on diagnostic accuracy. 198 medical students (135 females) were given the task to assess a patient presenting with simulated acute pulmonary embolism. Participants were randomized to six versions of the scenario differing only in the initial cues, i.e., in the reply of the patient to the initial question about the reason for his visit. In three of six versions, initial cues were restricted to thoracic symptoms (chest pain, dyspnoea, or combination of both). In the remaining three versions, initial cues consisted of thoracic and extra-thoracic (leg pain, immobilization) symptoms. The primary outcome was diagnostic accuracy. The number of initial cues was unrelated to diagnostic accuracy. However, the combination of extra-thoracic and thoracic cues resulted in a higher diagnostic accuracy than thoracic cues only (52/96 vs. 35/102, p = 0.006). In multivariate regression, the number of questions asked from the categories “risk factors of pulmonary embolism” (regression coefficient 0.15, p < 0.001) and “dyspnea” (regression coefficient 0.12, p < 0.001) predicted diagnostic accuracy. Moreover, questions relating to “immobilization” (regression coefficient 0.42, p < 0.001), “onset of dyspnea” (regression coefficient 0.23, p = 0.003), and “modifying factors of chest pain” (regression coefficient 0.20, p = 0.04) independently predicted diagnostic accuracy. Interestingly, more systematic history taking was associated with lower diagnostic accuracy (regression coefficient −0.27, p < 0.001). The present trial demonstrates that during history taking cues initially revealed by the patient, kind and category of questions asked during the interview, and the interview’s structural systematics affect diagnostic accuracy.
Managing irrigation jointly with farmers history, present status and future: review of participatory irrigation management in Sri Lanka

Agriculture development has been the main strategy for the socioeconomic development in the country since time immemorial, even though its contribution to GDP has been declining recently. Successive governments of Sri Lanka since independence have invested heavily in the irrigated agriculture sector to address the food security concerns of the country. The continuous investment in irrigation was required to address problems such as spatial and temporal variations in monsoonal rainfall in the country, which has a serious negative impact on food production and livelihoods of people. The need for pursuing irrigation development and management has become more important in the country in the face of rapid population growth and increasing food prices in the world market. In this context, managing irrigation schemes for productivity increase is becoming increasingly important and different irrigation management models have also emerged through attempts made in this direction by countries including Sri Lanka, where irrigation plays a leading role in food production and nation development. Farmers’ active involvement in irrigation management, especially operation and maintenance (O&M) and decision-making as well, has been identified as a key requirement to attain productivity goals and the sustainability of irrigation systems. This paper aims at reviewing participatory irrigation management approaches adopted in medium and major irrigation systems in Sri Lanka with a view to identifying their past and present trends and future directions. The review will contribute to an improved understanding by policymakers, managers of irrigation schemes and farmers of the role of participatory irrigation management, its past and present including institutional structures, responsibilities and performance and the directions it should take to meet future challenges as a dynamic institutional mechanism. As all the medium and major irrigation schemes in the country are jointly managed by farmers and government agencies, the inferences drawn from the review would be important for the agencies and farmers alike to introduce necessary changes in their programs to address future needs and requirements.Length: pp.35-63
Geotechnical Factors in the Dredgeability of Sediments. Report 1. Geotechnical Descriptors for Sediments to be Dredged

Abstract : Existing soil descriptor systems are not universally used, or even understood, by all groups involved in navigation dredging. As part of a 6-year study of this problem, a resource review has been conducted, which includes examination of the published literature and interviews with individuals knowledgeable in this area. The study indicates that the minimum soil identification test data necessary for the design, planning, and execution of dredging projects are as follows: (a) undisturbed soil mass properties, (b) disturbed soil material properties, (c) specific properties such as rheologic properties of a slurry, sedimentation rate in salt water, or bulking factors. Suggestions are made in the report for a standard set of descriptive terms and for a new Dredging Classification System.
Sentiment Analysis Using Hybrid Model of Stacked Auto-Encoder-Based Feature Extraction and Long Short Term Memory-Based Classification Approach

Customer reviews about a brand or product, movie reviews, and social media reviews can be analyzed through sentiment analysis. Sentiment analysis is used to identify the emotional tone of language to comprehend the attitudes, opinions, and feelings represented in online reviews. As for large data, it is a task that can take a lot of time and can be automated as the machine learns through the training and testing of data. Previously, various standard machine learning and deep learning models namely Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), Long Short Term Memory (LSTM), Naïve Bayes (NB), Support Vector Machine (SVM), Gated Recurrent Unit (GRU) have been used. The key issue in our research is that when text is provided to LSTM directly, it cannot adequately extract informative features from the text, leading to less accurate findings. The softmax layer of Stacked Auto-encoder when used directly to categorize the extracted features, is power-constrained and unable to do so accurately. A hybrid of the Stacked Auto-encoder (SAE) and LSTM models was proposed. SAE is used for the extraction of relevant informative features. LSTM was used for further classification of sentiments based on the extracted features. The proposed model is evaluated on an IMDB dataset by splitting it into five different training testing ratios using the following performance evaluation metrics: confusion matrix, classification accuracy, precision, recall, sensitivity, specificity, and F1 score. The hybrid results performed best at a ratio of 90/10 and classified sentiments with an accuracy of 87%. The accuracy of proposed hybrid model is better than that of standard models namely RNN, CNN, LSTM, NB, SVM, and GRU.
Genome-wide characterization of the soybean DOMAIN OF UNKNOWN FUNCTION 679 membrane protein gene family highlights their potential involvement in growth and stress response

The DMP (DUF679 membrane proteins) family is a plant-specific gene family that encodes membrane proteins. The DMP family genes are suggested to be involved in various programmed cell death processes and gamete fusion during double fertilization in Arabidopsis. However, their functional relevance in other crops remains unknown. This study identified 14 genes from the DMP family in soybean (Glycine max) and characterized their physiochemical properties, subcellular location, gene structure, and promoter regions using bioinformatics tools. Additionally, their tissue-specific and stress-responsive expressions were analyzed using publicly available transcriptome data. Phylogenetic analysis of 198 DMPs from monocots and dicots revealed six clades, with clade-I encoding senescence-related AtDMP1/2 orthologues and clade-II including pollen-specific AtDMP8/9 orthologues. The largest clade, clade-III, predominantly included monocot DMPs, while monocot- and dicot-specific DMPs were assembled in clade-IV and clade-VI, respectively. Evolutionary analysis suggests that soybean GmDMPs underwent purifying selection during evolution. Using 68 transcriptome datasets, expression profiling revealed expression in diverse tissues and distinct responses to abiotic and biotic stresses. The genes Glyma.09G237500 and Glyma.18G098300 showed pistil-abundant expression by qPCR, suggesting they could be potential targets for female organ-mediated haploid induction. Furthermore, cis-acting regulatory elements primarily related to stress-, hormone-, and light-induced pathways regulate GmDMPs, which is consistent with their divergent expression and suggests involvement in growth and stress responses. Overall, our study provides a comprehensive report on the soybean GmDMP family and a framework for further biological functional analysis of DMP genes in soybean or other crops.
Medical Image Segmentation Research Status and Development Trends

As one of the important steps in medical image processing, medical image segmentation plays a pivotal role in clinical surgery and is widely used in application scenarios such as preoperative diagnosis, intraoperative navigation, and postoperative evaluation. In this paper, medical image segmentation technology is studied, and a variety of medical image segmentation methods are categorized and compared in an attempt to explore the development law of medical image segmentation technology. Firstly, the medical image segmentation technology is classified and studied according to its different methods, and this paper mainly researches and organizes the deep learning method for medical image segmentation; secondly, the principle, advantages and disadvantages, and applicable scenarios of each model are analyzed; and lastly, the latest progress of the medical image segmentation technology is objectively described. inherent deficiencies and solutions of the existing techniques, and provides a direction for continued improvement in the future. As one of the important steps in medical image processing, medical image segmentation plays a pivotal role in clinical surgery and is widely used in application scenarios such as preoperative diagnosis, intraoperative navigation, and postoperative evaluation. In this paper, we explore the critical role of medical image segmentation in medical image processing, particularly in clinical surgery and its various applications such as preoperative diagnosis, intraoperative navigation, and postoperative evaluation. Our study focuses on categorizing and comparing different medical image segmentation methods, with a special emphasis on deep learning techniques. We delve into the principles, advantages, disadvantages, and suitable scenarios for each model. Additionally, we objectively present the latest progress in medical image segmentation technology, addressing existing deficiencies and proposing potential solutions. This research provides valuable insights to foster continuous advancements in the field.
Dual-Step Acoustic Chirp Signals Detection Using Pervasive Smartphones in Multipath and NLOS Indoor Environments

Indoor localization techniques based on acoustic signals have been a focus of research community in the past decades due to their high accuracy and ubiquity. However, there are still some limitations that need to be overcome, such as multipath and nonline-of-sight (NLOS). To achieve robust and high-precision acoustic ranging for practical applications on most smartphones, we propose a dual-step chirp signal detection algorithm consisting of coarse and fine searches. For robustness, the coarse search extracts the acoustic data segment containing the direct path by monitoring the energy changes based on the time-frequency (TF) analysis methods. For improving the accuracy and stability, adaptive slack and strict thresholds are introduced in cross-correlation function (CCF)-based fine search. Meanwhile, an extremum normalization method is proposed to alleviate the smartphones differences and near–far effects. A thresholds determination experiment and two practical applications are implemented on the proposed algorithm. Threshold determination experimental results show that in multipath and NLOS scenarios, the proposed coarse search can reach a success rate of more than 99.9% and an error rate of less than 0.4%. Furthermore, the proposed fine search offers a ranging accuracy with an average error and root-mean-square-error (RMSE) of less than 0.25 and 0.35 m, respectively. For practical applications, ranging accuracies of 0.17 and 0.14 m at 50%, and 0.59 and 0.54 m at 95% are achieved in two typical indoor environments, which are superior to those achieved by two conventional CCF-based detection algorithms.
A generalization of bivariate splines over polygonal partitions and applications

There has recently been interest in extending various finite element methods to more arbitrary partitions, particularly unstructured partitions of various polygons. Various methods aimed at this task have arisen, but of particular note, in a paper published in 2016, Floater and Lai produced a method for numerical solution of Poisson equations using polygonal splines, which are extensions of bivariate splines. This work first presents a method for numerical solution of partial differential equations which extends the method of Floater and Lai to solve very general second-order elliptic equations, but can also be used to approximate solutions of some mixed hyperbolic and parabolic equations. Next, this work will address a features common to many polygonal finite elements: a lack of global differentiability. This work provides a construction of C local basis functions, particularly over quadrangulations, with some applications to function interpolation and smooth surface construction. The methods used to construct these functions, while computationally difficult, can be extended to higher regularity or to partitions of polygons with more vertices. Index words: bivariate splines, partial differential equation, finite element methods, local basis A Generalization of Bivariate Splines Over Polygonal Partitions and Applications
Aplicación de la dinámica de fluidos computacional a la acción del oleaje sobre estructuras . Application of computational fluid dynamics to wave action on structures

espanolEl diseno de estructuras costeras se fundamenta tradicionalmente en dos pilares: formulaciones semiempiricas y modelado fisico a escala reducida. El modelado numerico, introducido recientemente en este campo, puede ayudar en el proceso de diseno, como una herramienta avanzada complementaria a las tecnicas nombradas. En esta tesis se presenta IHFOAM, un modelo numerico tridimensional nuevo para la simulacion de procesos de transformacion del oleaje y de interaccion con las estructuras. Para ello se detalla el proceso de desarrollo de todos los elementos que permiten la aplicacion en este campo. Las caracteristicas distintivas de este modelo son: generacion y absorcion activas del oleaje funcionando en los contornos, flujo en el interior de medios porosos y mallado dinamico. Asimismo, se pone especial enfasis en la validacion del modelo para un gran numero de procesos fisicos relevantes, asi como en el desarrollo de una metodologia hibrida que permita su aplicacion en el proceso de diseno. Finalmente, se demuestran las capacidades del modelo con una simulacion de una estructura real a escala de prototipo: el dique exterior del Puerto de Laredo (Cantabria). EnglishThe design of coastal structures has been traditionally addressed by means of semiempirical formulations and small-scale physical modelling. Numerical modelling has been recently introduced in this field and can assist in the design process as an advanced tool, complementary to the mentioned techniques. In this thesis, IHFOAM is presented as a new three-dimensional numerical model to simulate wave transformation and wave-structure interaction processes. In order to do so, the full development details for the modules that enable the simulation of these applications are given. The distinctive features of the model are: wave generation and active wave absorption at the boundaries, flow inside porous media and dynamic meshing. Moreover, especial emphasis is given to the validation process, involving a large number of physical processes and, furthermore, in developing a hybrid methodology to design real structures with the model. Finally, IHFOAM capabilities are demonstrated with a simulation of a real structure at prototype scale: the exterior breakwater at the Port of Laredo (Spain)
Highly transparent and conductive ITO/ultra-thin silver/ITO/glass sandwich structure for optical coatings and optoelectronic devices

This study presents an approach for fabricating highly transparent and conductive electrodes on glass substrates by depositing stable, continuous, and ultra-thin silver films between optimized layers of indium tin oxide. Exceptional thermal stability is achieved and annealing at 200°C in vacuum and air enhances the film’s optical and electrical performance. X-ray diffraction analysis confirms improved crystallization, marked with the emergence of a silver (200) peak after annealing in air. The resulting electrodes demonstrate outstanding transparency, conductivity, and thermal stability, making them potential for use in architectural glass coatings and optoelectronic applications, such as photovoltaics and displays.
Prognostic Factors Associated With Sleep Duration: Serum Pro-Oxidant/Antioxidant Balance and Superoxide Dismutase 1 as Oxidative Stress Markers and Anxiety/Depression

Objectives: Sleep is a conserved vital behavior in humans, and insufficient sleep is associated with several disorders. Recent studies have investigated the association of sleep duration, oxidative stress markers, anxiety, and depression. Therefore, we aim to assess the relationship between sleep duration, serum pro-oxidant/antioxidant balance (PAB) and superoxide dismutase 1 (SOD1) levels as markers of oxidative stress, anxiety, and depression. Methods: Participants included in our cross-sectional analysis were recruited as part of the MASHAD study (n = 9,184). Nocturnal sleep duration was identified using a self-reported questionnaire, and serum pro-oxidant/antioxidant balance (PAB) and superoxide dismutase 1 (SOD1) levels were assessed using methods that have been previously reported. Results: Serum PAB, depression, and anxiety scores were found significantly higher in subjects with very short sleep duration. In an adjusted model using MANOVA regression analysis, serum PAB was significantly higher in the subjects with a very short sleep duration (p: 0.016 in depression and p: 0.002 in anxiety). Conclusion: The present cross-sectional study demonstrates a relationship between sleep duration, oxidative balance, and depression/anxiety, especially in anxiety subjects that might predict each other.
Engagement Detection and Its Applications in Learning: A Tutorial and Selective Review

Engagement is critical to satisfaction and performance in a number of domains but is challenging to measure and sustain. Thus, there is considerable interest in developing affective computing technologies to automatically measure and enhance engagement, especially in the wild and at scale. This article provides an accessible introduction to affective computing research on engagement detection and enhancement using educational applications as an application domain. We begin with defining engagement as a multicomponential construct (i.e., a conceptual entity) situated within a context and bounded by time and review how the past six years of research has conceptualized it. Next, we examine traditional and affective computing methods for measuring engagement and discuss their relative strengths and limitations. Then, we move to a review of proactive and reactive approaches to enhancing engagement toward improving the learning experience and outcomes. We underscore key concerns in engagement measurement and enhancement, especially in digitally enhanced learning contexts, and conclude with several open questions and promising opportunities for future work.
Redox Regulation of Microglial Inflammatory Response: Fine Control of NLRP3 Inflammasome through Nrf2 and NOX4

The role of inflammation and immunity in the pathomechanism of neurodegenerative diseases has become increasingly relevant within the past few years. In this context, the NOD-like receptor protein 3 (NLRP3) inflammasome plays a crucial role in the activation of inflammatory responses by promoting the maturation and secretion of pro-inflammatory cytokines such as interleukin-1β and interleukin-18. We hypothesized that the interplay between nuclear factor erythroid 2-related factor 2 (Nrf2) and NADPH oxidase 4 (NOX4) may play a critical role in the activation of the NLRP3 inflammasome and subsequent inflammatory responses. After priming mixed glial cultures with lipopolysaccharide (LPS), cells were stimulated with ATP, showing a significant reduction of IL1-β release in NOX4 and Nrf2 KO mice. Importantly, NOX4 inhibition using GKT136901 also reduced IL-1β release, as in NOX4 KO mixed glial cultures. Moreover, we measured NOX4 and NLRP3 expression in wild-type mixed glial cultures following LPS treatment, observing that both increased after TLR4 activation, while 24 h treatment with tert-butylhydroquinone, a potent Nrf2 inducer, significantly reduced NLRP3 expression. LPS administration resulted in significant cognitive impairment compared to the control group. Indeed, LPS also modified the expression of NLRP3 and NOX4 in mouse hippocampus. However, mice treated with GKT136901 after LPS impairment showed a significantly improved discrimination index and recovered the expression of inflammatory genes to normal levels compared with wild-type animals. Hence, we here validate NOX4 as a key player in NLRP3 inflammasome activation, suggesting NOX4 pharmacological inhibition as a potent therapeutic approach in neurodegenerative diseases.
The Intergenerational Transmission of Automobile Brand Preferences ∗

We document a strong correlation in the brand of automobile chosen by parents and their adult children, using data from the Panel Study of Income Dynamics. This correlation could represent transmission of brand preferences across generations, or it could result from correlation in family characteristics that determine brand choice. We present a variety of empirical specifications that lend support to the former interpretation and to a mechanism that relies at least in part on state dependence. We then discuss implications of intergenerational brand preference transmission for automakers’ product-line strategies and for the strategic pricing of vehicles to different age groups. ∗We thank Evan Herrnstadt, Sarah Johnston, and Katie Lim for excellent research assistance and J.P. Dubé, Matthew Gentzkow, Jesse Shapiro, Gary Solon, Raphael Thomadsen, Clifford Winston, Jeff Wooldrdige, and seminar participants at Arizona, Chicago, Copenhagen Business School, Ford Motor Company, Georgetown, Maryland, Michigan, the NBER, the Quantitative Marketing and Economics Conference, Stanford, UC Berkeley, UC Davis, UC San Diego, Washington University, Wayne State, Western Michigan, and Yale for helpful comments. †Anderson: sta@msu.edu. Kellogg: kelloggr@umich.edu. Langer: alanger@email.arizona. edu. Sallee: sallee@uchicago.edu.