Miracle-dz/UniFAR-FTUs huggingface dataset:
The Value of Marine Protected Areas: Through the Eyes of Community Members

Values and beliefs play a vital role in how people make choices, judgments, and undertake actions related to marine resource use. This information is vital for marine protected area (MPA) managers since values and beliefs influence behavior of the community members, which in turn affect the management operation of the MPA. Gender roles can influence the community’s values and beliefs. MPAs affect women and men differently, and thus MPA implementation and management need to take this into account. This study was conducted to examine the values and beliefs on MPAs of community members in San Joaquin, Central Philippines. Results show that women and men have different perspectives on their marine resources and differ on their knowledge and perceptions regarding MPAs. A greater percentage of women respondents than men had complete awareness of, and some formal knowledge about, MPAs. Similarly, a higher percentage of women respondents than men had a “sustainability mindset” that acknowledged the need to manage marine resources sustainably for future generations. Statistical analyses showed a significant difference between the responses of women and men respondents. This study supports the findings of other studies that strategies need to integrate gender perspectives in implementing resource management initiatives such as the establishment and management of MPAs.
Acute Respiratory Distress Syndrome due to Acute Mesenteric Ischemia

Introduction: An important and critical cause of acute respiratory failure is respiratory distress syndrome (ARDS). It can be precipitated by several clinical disorders, including pneumonia, sepsis, aspiration of gastric contents, and major trauma. We describe a case of a patient who developed ARDS due to acute mesenteric ischemia. Case Report: TL, an 89-years-old, Caucasian female, visited the emergency room after experiencing domestic maxillo-facial trauma. During the first days of hospitalization the patient presented with fever, generalized abdominal pain and nausea and diarrhea, with mild tenderness on deep palpation. She was subjected to an abdominal computed tomography scan with contrast which detected acute mesenteric ischemia (AMI) and showed a rare anatomical variation of the celiac trunk: the common hepatic artery arose from the superior mesenteric artery and not from the celiac trunk. She was treated by angiography and revascularization. In the following days, the patient experienced severe hypoxemia and acute lung failure. The chest xray showed bilateral ground glass infiltrates. Thus, oxygen supplementation was administered until noninvasive ventilation was required. Nevertheless, the progression of ARDS in the subsequent days led to clinical worsening and the patient’s transfer to the intensive care unit. Conclusion: AMI is a vascular emergency with high mortality. This case report describes how an ischemia/reperfusion injury can cause an ARDS due to a systemic inflammatory response. Prompt diagnosis and management of AMI are essential for bettering the prognosis and decreasing the risks of mortality.
The predictive capacity of polygenic risk scores for disease risk is only moderately influenced by imputation panels tailored to the target population

Polygenic risk scores (PRS) predict individuals genetic risk of developing complex diseases. They summarize the effect of many genetic variants discovered in genome-wide association studies (GWASs). However, to date, large GWASs exist primarily for the European population and the quality of PRS prediction declines when applied to target sets of other ethnicities. A key step in using a PRS is imputation, which is the inference of un-typed SNPs using a set of fully-sequenced individuals, called the imputation panel. The SNP genotypes called by the imputation process depend on the ethnic composition of the imputation panel. Several studies have shown that imputing genotypes using a panel that contains individuals of the same ethnicity as the genotyped individuals improves imputation accuracy. However, until now, there has been no systematic investigation into the influence of the ethnic composition of imputation panels on the accuracy of PRS predictions when applied to ethnic groups that differ from the population used in the GWAS. In this study we estimated the effect of imputation of the target set on prediction accuracy of PRS when the discovery (GWAS) and the target sets come from different ethnic groups. We analyzed twelve binary phenotypes and three populations from the UK Biobank (Europeans, South-Asians, and Africans). We generated imputation panels from several ethnic groups, imputed the target set using each panel, and generated PRS to compute individuals risk scores. Then, we assessed the prediction accuracy obtained from each imputation panel. Our analysis indicates that using an imputation panel matched to the ethnic group of the target population yields only a marginal improvement and only under specific conditions. Hence, while a target-matched imputation panel can potentially improve prediction accuracy of European PRSs in non-EUR populations, the improvement is limited.
P2P Schema-Mapping over Network-bound XML Data

The rise in availability of web-based data sources has led to new challenges in data integration systems for obtaining decentralized, wide-scale sharing of data preserving semantics. In this paper we present a framework for integrating heterogeneous XML data sources distributed over a large-scale, highly dynamic network of autonomous nodes.We propose a query reformulation algorithm to combine and query distributed XML databases through a decentralized point-to-point mediation process among the different data sources by using P2P schema mappings.More precisely, our integration model is based on path-to-path mappings, using the XPath language. We demonstrate the utility and scalability of our ideas and algorithms with a detailed set of experiments. Finally, we present our experience implementing the above cited query reformulation algorithm as a Web service within the GDIS system, a service-based grid architecture.We have evaluated GDIS on several real world schemas with promising results.
Bayesian calibration of fluvial flood models for risk analysis

Flood risk analysis is now fundamental to flood management decision making. It relies on the use of computer models to estimate flood depths for given hydrological conditions. The correct calculation of risks associated with different management options requires that the uncertainty in the computer model output is carefully estimated. There are several sources of uncertainty in flood models, including structural uncertainties in the model representation of reality, uncertainty in model parameters, and observation errors. We refer to the first of these as “model inadequacy”. The work described in this thesis concerns the calibration of computer models to describe fluvial flooding, taking into account model inadequacy and paying particular attention to the requirements of risk analysis calculations. A methodology which has had some success in other application areas is Bayesian model calibration, using Gaussian process representation both for the error arising from model inadequacy, and to emulate the computer model output. The effectiveness of this methodology is demonstrated for steady state flood models, both of a series of laboratory experiments, and of a historical flood using a satellite image of flood outline for calibration. Extension of the methodology to calibration of dynamic models using gauged data is not straightforward, but is achieved for flood models by means of an emulator, which replaces the computationally expensive hydrodynamic model with a time-dependent transfer function. This permits calibrated prediction of floods using historical gauged data, both in the existing channel and after modelling potential modifications to the channel. It is shown that calibration without inclusion of a model inadequacy function cannot match measured data. Finally, application of the methodology is demonstrated in the context of a calculation of probability of inundation in the channel, both with and without modification.
Design of an Unordered Gripping System Based on 3D Vision

For the current mainstream 2D vision-guided robotic arm gripping system in industrial production, the vision part is seriously affected by the lighting environment, camera imaging distance, and other factors. It cannot provide more comprehensive 6DOF position information on the target workpiece and complete the gripping of mutually scattered and stacked workpieces. This paper designs a disordered grasping system based on the 3D vision to solve the above problems. In addition, the traditional Euclidean clustering algorithm and fast point feature histogram algorithm are improved to enhance the segmentation and feature recognition efficiency of the point cloud. The accuracy of the system is 94.3 % without stacking and 88.2 % with stacking, which is 5.2 % higher than the average accuracy of the system before the algorithm improvement and more than 2s higher than the time. The accuracy and speed of the system meet the requirements of general industrial production for a robotic arm grasping system, which has certain practical application value.
Enhanced synaptic characteristics of H x WO3-based neuromorphic devices, achieved by current pulse control, for artificial neural networks

Artificial synapses capable of mimicking the fundamental functionalities of biological synapses are critical to the building of efficient neuromorphic systems. We have developed a H x WO3-based artificial synapse that replicates such synaptic functionalities via an all-solid-state redox transistor mechanism. The subject synaptic-H x WO3 transistor, which operates by current pulse control, exhibits excellent synaptic properties including good linearity, low update variation and conductance modulation characteristics. We investigated the performance of the device under various operating conditions, and the impact of the characteristics of the device on artificial neural network computing. Although the subject synaptic-H x WO3 transistor showed an insufficient recognition accuracy of 66% for a handwritten digit recognition task with voltage pulse control, it achieved an excellent accuracy of 88% with current pulse control, which is approaching the 93% accuracy of an ideal synaptic device. This result suggests that the performance of any redox-transistor-type artificial synapse can be dramatically improved by current pulse control, which in turn paves the way for further exploration and the evolution of advanced neuromorphic systems, with the potential to revolutionize the artificial intelligence domain. It further marks a significant stride towards the realization of high-performance, low-power consumption computing devices.
Attention-based Image Captioning Assisted Visual Question Answering

Visual question answering has received increasing attention as a multimodal task whose goal is to be able to answer natural language-related questions by reasoning about a given picture. The SOTA model in the current mainstream visual question answering usually uses language bias in the training data to predict the question, resulting in the VQA model often lacking an image foundation. Based on the attention mechanism, this paper designs a method to use image description to assist the training of a visual question-answering model. The model is problem-oriented and encodes images and image descriptions based on a collaborative attention mechanism, which enhances the feature representation of the model and the ability to learn image information, making the model more robust and generalizable. The experimental results show that the accuracy of this method on the dataset VQA v2 has increased by 3.89% compared with the baseline, which proves that the method is effective.
Educational Software System For Teaching STEM To Visually Impaired People

‘Education for All’ is a farfetched dream for all Developing Countries. ‘Inclusive Education’ is still farther. Visually challenged individuals are still far from main steam Education. Their overall education is limited to humanity subjects only. They are not able to take up Science and Mathematics as a subject beyond middle school because of the lack of concepts, study material & sufficient teaching aid. The existing solution in Braille is a partial solution to this problem. Our research and resulting solutions enable them to develop the concepts & understanding of science & mathematics which help them study STEM subjects(Science, Technology, Engineering, Mathematics) beyond Middle School too. In the proposed solution, we have developed a Machine Learning-based Software –Hardware Integrated system that helps them understand middle level & higher concepts of Mathematics & Science. It is an integrated system that has a software application connected with hardware comprising features like ‘Concepts and Explanation’, ‘Study Material’, ‘and Practice and Test Yourself’. The whole application can be accessed using the TalkBack accessibility feature of the application. If a blind individual has to trace a Mathematical shape such as say ‘sinusoidal graph’, then the person will draw a graph in real-time on the digital screen attached to the hardware (along with real-time voice assistance for tracing the shape/graph). When the sketching of the shape is completed, the coordinates of the figure traced on the digital screen are mapped and those particular pins/buttons are raised on the hardware device. Thus, the blind individual can touch and understand the shape of the hardware board. Moreover, the figure sketched is further checked for its accuracy and the result is read aloud by the voice assistant embedded in the application which is based on Machine Learning Algorithms.
Detection of Oriented Scene Text in Natural Images

Recently, in the field of computer vision, people are paying more attention to extracting and understanding text information in natural scenes, which are widely used in real life, such as image OCR, image retrieval, and multi-language translation. Text detection plays a vital role in the process of text information extraction and understanding. Diverse text patterns and highly cluttered backgrounds are the main challenges of precise text detection. This paper studies oriented scene text detection, the horizontal scene text detector, and CTPN network architecture, which is improved and extended for oriental scene text detection. The main proposed improvements include: 1. The pre-processing method and parameter setting in the post-processing are improved to make the annotation more accurate; 2. The anchor mechanism is improved by setting a series of different anchor widths depending on the anchor heights instead of the fixed width. The improved model was trained with the training set of ICDAR2015 and part of ICDAR2017 MLT. Then it was tested on the ICDAR online platform with test sets of ICDAR2013 and ICDAR2015. Finally, it was tested with some images outside the datasets. The results show that the improved model can detect text with a tilt angle more precisely.
Effectiveness of ReCOVery APP to improve the quality of life of Long COVID patients: a 6-month follow-up randomized clinical trial.

The main objective of this study is to analyse the clinical efficacy of medium-term telerehabilitation in the recovery of patients with Long COVID using ReCOVery APP, administered in the Primary Health Care (PHC) setting. The second objective is to identify significant patterns associated with an improvement in their quality of life predicted by other study variables. To this end, a randomised clinical trial was conducted with two parallel groups of a total of 100 patients with Long COVID. The control group continued with their usual treatment (TAU), established by their primary care physician. The intervention group, in addition to continuing with their TAU, attended three sessions based on motivational methodology and used ReCOVery APP for six months. The main variable was quality of life. The results of this study concluded that ReCOVery APP was not significantly more effective in improving the quality of life of patients with Long COVID. There was low adherence of participants. However, linear regression analyses revealed significant patterns of improvement in overall quality of life and mental health predicted by time of use of the APP and the personal construct of self-efficacy. In addition, all participants significantly improved their physical and mental health over the duration of the intervention. In conclusion, meaningful use of the ReCOVery APP may contribute to improving the quality of life of patients with Long COVID, but strategies to improve adherence need to be encouraged.
Divided we fall: Children's friendships and peer victimization

This study examined the contributions of peer acceptance and friendship (i.e., number and quality) on children being relationally or overtly peer victimized. Fifth-and sixth-grade children (N= 207, 92 boys) provided self-and peer-reports of victimization, friendship, and peer acceptance. General peer acceptance and high-quality friendships uniquely predicted both overt and relational victimization. Fall victimization did not predict spring friendship quality; however, fall friendship quality did predict spring victimization levels. For children with lower peer acceptance, high-quality friendships appeared to play an especially important role in determining the targets of peer abuse. Approaches that include friendship quality as well as interactive models of relationship dimensions may be informative for understanding victimization as well as children's general adjustment.
An Image Feature Matching Algorithm with Clustering Constraints

Aiming at the problem that the traditional ORB feature matching algorithm has many mismatches and poor robustness, a feature matching optimization algorithm with clustering constraints is proposed. Firstly, the K-means++ algorithm is used to assign classification labels to the feature points obtained in the current image, and the clustering region is redivided. Secondly, the corresponding clustering region is found in the next picture, and the feature points in the region are also assigned classification labels. Then, the classification labels of feature points in the two images were compared during matching, and the inconsistent matching point pairs were removed. Finally, the RANSAC algorithm is used to optimize the matching results, and the results are verified. The results show that the proposed algorithm can effectively eliminate the false matching points, and the running speed is greatly improved.
Gait recognition with covariate separation based on adversarial learning

In response to the poor recognition accuracy caused by viewpoint changes and wearing changes in gait recognition problems, a viewpoint classifier and a wearing classifier are proposed to be connected after the feature extractor. The parameters of the feature extractor are updated using the gradient reversal mechanism so that its function is opposite to that of the two classifiers, thereby separating the viewpoint information, wearing information at the feature extraction level, and reducing the impact of these two most common factors on gait recognition. Comparative experiments on the CASIA-B dataset show that under BG and CL conditions, our model has improved by an average of 2.1% and 1.9% in cross-viewpoint accuracy compared to the baseline model. Our model is more robust under complex walking conditions.
Nuevos índices de competencia independientes de la distancia para predecir el crecimiento en diámetro y altura de Pinus cooperi Blanco

espanolSe evaluo la relacion de 11 indices de competencia independientes de la distancia, que incorporan el tamano de los arboles, la densidad de la parcela, con modelos de crecimiento en diametro y altura de arboles individuales, en un bosque irregular y mezclado (coniferas y latifoliadas) en Pinus cooperi Blanco en Durango, Mexico. El modelo cuadratico fue el que mejor predijo el crecimiento en diametro y el crecimiento en altura en funcion de indices de competencia. Los indices de competencia de diametro cuadratico, el de longitud de copa, el de area basal, numero de arboles y el de area basal total, presentaron relaciones positivas y significativas (entre 0.681** y 0.691**) con el crecimiento en diametro y altura al utilizar el modelo cuadratico. La inclusion del indice de sitio contribuyo significativamente para mejorar la prediccion del crecimiento en diametro y el crecimiento en altura. EnglishThe relationship between eleven competition indexes independent on distance, including tree size, density of plot, with the diameter growth and height growth of individual trees, was evaluated for a mixed uneven-aged forest of Pinus cooperi Blanco in Durango, Mexico. The cuadratic model best predicted diameter growth and height growth as a function of competition indexes. Cuadratic diameter competition index, crown longitude, tree number basal area and total basal area, showed positive and significative relationships (from 0.681** to 0.691**) with growth in diameter and height growth when the cuadratic model was used. The inclusion of site index improved significantly the height growth and diameter growth prediction.
Effect of lifetime exposure to depression on brain structure and function in the UK Biobank

Background: Depression is a common mental health condition in the general population. Whereas various neuroimaging studies have reported altered activity and structural impairments in depression, several recent meta-analyses only found small or inconsistent effects of depression. Different operational inclusion criteria and processing methodologies may contribute to these discrepancies. We addressed these issues by systematically testing for the effects of different criteria of lifetime exposure to depression on functional and structural neuroimaging measures in the UK biobank cohort. Methods: We included 36,359 participants (20,794 lifetime depression and 15,565 healthy individuals) from the UK biobank. Six increasingly conservative groups of lifetime depression were defined based on the six available depression criteria from meeting only one to more restrictive meeting all six criteria. We tested the effect of these definitions on voxel-wise measures of local functional activity, global connectivity and gray matter volume. Results: Across all depression definitions, individuals with lifetime depression displayed regionally consistent decreases in local functional activity alterations in sensorimotor cortex but not in global connectivity and gray matter volume alterations. More restrictive definitions and antidepressant usage resulted in most pronounced alterations. Conclusion: Lifetime exposure to depression is associated with robust functional changes with more restrictive criteria revealing more pronounced alterations. Different inclusion criteria of depression may strongly contribute to the substantial variation of imaging findings reported in the literature.