Miracle-dz/UniFAR-FTUs huggingface dataset:
Winner of the Student Award in the Hospital Intern Category, 10th World Biomaterials Congress, May 17–22, 2016, Montreal QC, Canada: Peptide biomaterials raising adaptive immune responses in wound healing contexts

Biomaterials used in the context of tissue engineering or wound repair are commonly designed to be “nonimmunogenic. ” However, previously it has been observed that self-assembled peptide nanofiber materials are noninflammatory despite their immunogenicity, suggesting that they may be appropriate for use in wound-healing contexts. To test this hypothesis, mice were immunized with epitope-containing peptide self-assemblies until they maintained high antibody titers against the material, then gels of the same peptide assemblies were applied within full-thickness dermal wounds. In three different murine dermal-wounding models with different baseline healing rates, even significantly immunogenic peptide assemblies did not delay healing. Conversely, adjuvanted peptide assemblies, while raising similar antibody titers to unadjuvanted assemblies, did delay wound healing. Analysis of the healing wounds indicated that compared to adjuvanted peptide assemblies, the unadjuvanted assemblies exhibited a progression of the dominant T-cell subset from CD4+ to CD8+ cells in the wound, and CD4+ cell populations displayed a more Th2-slanted response. These findings illustrate an example of a significant antibiomaterial adaptive immune response that does not adversely affect wound healing despite ongoing antibody production. This material would thus be considered “immunologically compatible” in this specific context rather than “nonimmunogenic,” a designation that is expected to apply to a range of other protein- and peptide-based biomaterials in wound-healing and tissue-engineering applications. © 2016 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 104A: 1853–1862, 2016.
Child's Rights and the Challenges of Educating the Girl-Child: Assessing the Contributions of UNICEF in Nigeria

In 2003, Nigeria domesticated the twin international instruments on child rights, the United Nations Convention on the Rights of the Child and the African Charter on the Rights and Welfare of the Child. The resultant legislation from Nigeria’s domestication efforts is the Child’s Rights Act (CRA) of 2003. Despite the provisions of this legislation detailing the elaborate atlas of rights to be enjoyed by the Nigerian child, their fortune especially that of the girl-child, has not got better. At the base of the contentious forces militating against the rights of the girl-child are the cultural and religious norms that are intrinsically embedded in the dominant patriarchal system prevalent in Nigeria, especially in northern Nigeria. These forces are intricately united in marginalizing and precluding the girl-child from accessing education. Using the lens of radical feminism in combination with human-rights based approach, this paper interrogates the challenges faced by the girl-child in accessing education and the interventionist role played by UNICEF to salvage the situation. The paper finds that although the interventionist program of UNICEF, that is, the Nigeria girls’ education project (NGEP), contributed in re-enrolling over one million out-of-school girls back to school, a lot needs to be done to salvage the girl-child from the doldrums of structural alienation that deprives her of access to education.
Effect of arbitrary matter-geometry coupling on thermodynamics in f(R) theories of gravity

In this paper, the thermodynamics of the Friedmann–Lemaître–Robertson–Walker universe have been explored in f(R) theories of gravity with arbitrary matter-geometry coupling. The equivalence between the modified Friedmann equations with any spatial curvature and the first law of thermodynamics is confirmed, where the assumption of the entropy plays a crucial role. Then laws of thermodynamics in our considering case are obtained. They can reduce to the ones given in Einstein’s general theory of relativity under certain conditions. Moreover, a particular model is investigated through the obtained generalized second law of thermodynamics with observational results of cosmographic parameters.
Association between Dietary Macronutrient Intake and Symptoms in Uninvestigated Dyspepsia: Evidence from a Population-Based, Cross-Sectional Study

(1) Background: Limited evidence from laboratory-based studies suggests that specific dietary macronutrients, particularly fat, can induce dyspeptic symptoms. Through a population-based study, we investigated the relationship between dietary macronutrients and dyspeptic symptoms and sought to determine macronutrient intake thresholds to predict or prevent dyspepsia and reduce symptoms in patients with dyspepsia. (2) Methods: A total of 4763 Iranian people were enrolled in this population-based, cross-sectional study. Uninvestigated dyspepsia (UD) and its symptoms, including postprandial fullness, early satiation, and epigastric pain, were evaluated using a modified Persian version of the Rome III criteria. The dietary intakes of participants were evaluated using a validated food–frequency questionnaire. Receiver operating characteristic (ROC) curve analysis was used to calculate threshold intakes of dietary macronutrients to prevent UD in the general population. The analysis was then repeated in those with UD to calculate intake thresholds for reducing UD symptoms. (3) Results: Early satiation occurred in 6.3% (n = 302), postprandial fullness in 8.0% (n = 384) and epigastric pain in 7.8% (n = 371) of participants. The prevalence of UD was 15.2%. Compared with individuals without UD, those with UD had a lower intake of carbohydrates (48.2% vs. 49.1%) and a higher intake of fats (38.3% vs. 37.4%), while protein and energy intakes did not differ. Higher dietary fat and protein intakes were associated with a higher prevalence of postprandial fullness and epigastric pain, respectively. Macronutrient intakes to predict UD in the general population were <49% of energy from carbohydrates, >14.7% from protein, and >37.7% from fats. Carbohydrate, protein, and fat intakes to prevent symptoms among those with UD were calculated to be >48.2%, <14.6%, and <38.6%, respectively. (4) Conclusion: Higher carbohydrate intake and lower fat or protein intakes were associated with a lower likelihood of UD. Prospective studies carefully manipulating dietary macronutrient composition are warranted to investigate the value of dietary changes to improve symptoms in people with UD.
Intelligent Underwater Stereo Camera Design for Fish Metric Estimation Using Reliable Object Matching

The resolution of the computed depth maps of fish in an underwater environment limits the 3D fish metric estimation. This paper addresses this problem using object-based matching for underwater fish tracking and depth computing using convolutional neural networks (CNNs). First, for each frame in a stereo video, a joint object classification and semantic segmentation CNN is used to segment fish objects from the background. Next, the fish objects in these images are cropped and matched for the subpixel disparity computation using the video interpolation CNN. The calculated disparities and depth values estimate the fish metrics, including the length, height, and weight. Next, we tracked the fish across frames of the input stereo video to compute the metrics of the fish frame by frame. Finally, the fish median metrics are calculated for noise reduction caused by the fish motions. Hereafter, the fish with incorrect measurement by the stereo camera is cleaned before generating the final fish metric distributions, which are relevant inputs for learning decision models to manage a fish farm. We also constructed underwater stereo video datasets with actual fish metrics measured by humans to verify the effectiveness of our approach. Experimental results show a 5% error rate in our fish length estimation.
Implementation of Service-Learning as a Strategy to Foster Intercultural Coexistence in the Local Community: A Case Study

Service-learning (SL) is a participatory teaching–learning methodology through which students learn certain content while also meeting a number of real social needs in their environment. The implementation of SL in different areas and educational stages has been extensively described. However, its potential as a community and intercultural development strategy at the local level has not been widely studied. Through a documentary analysis, the present work sought to understand the characteristics of socio-educational interventions, which, based on the service-learning methodology, aim at improving coexistence in local communities with a high degree of cultural diversity. A total of 18 projects were included in a community programme implemented in the municipality of Elche (Spain) between 2010 and 2016. They all focused or included the promotion of intercultural coexistence among their objectives. The study design was quantitative, with a descriptive and explanatory, univariate and bivariate analysis using the IBM Statistical Package for Social Sciences 26. The results showed that service-learning can contribute to the improvement of intercultural coexistence. Moreover, a number of SL basic, pedagogical and organisational components are enhanced when integrated into broader community development processes.
Early Prediction of Coffee Yield in the Central Highlands of Vietnam Using a Statistical Approach and Satellite Remote Sensing Vegetation Biophysical Variables

Given the present climate change context, accurate and timely coffee yield prediction is critical to all farmers who work in the coffee industry worldwide. The aim of this study is to develop and assess a coffee yield forecasting method at the regional scale in Dak Lak province in the central highlands of Vietnam using the Crop Growth Monitoring System Statistical Tool (CGMSstatTool—CST) software and vegetation biophysical variables (NDVI, LAI, and FAPAR) derived from satellite remote sensing (SPOT-VEGETATION and PROBA-V). There has been no research to date applying this approach to this specific crop, which is the main contribution of this study. The findings of this research reveal that the elaboration of multiple linear regression models based on a combination of information from satellite-derived vegetation biophysical variables (LAI, NDVI, and FAPAR) corresponding to the first six months of the years 2000–2019 resulted in coffee yield forecast models presenting satisfactory accuracy (Adj.R2 = 64 to 69%, RMSEp = 0.155 to 0.158 ton/ha and MAPE = 3.9 to 4.7%). These results demonstrate that the CST may efficiently predict coffee yields on a regional scale by using only satellite-derived vegetation biophysical variables. This study findings are likely to aid local governments and decision makers in precisely forecasting coffee production early and promptly, as well as in recommending relevant local agricultural policies.
Multiple Embeddings Contrastive Pretraining for Remote Sensing Image Classification

This letter focuses on remote sensing image interpretation and aims to promote the use of contrastive self-supervised learning (SSL) in varied applications of remote sensing image classification. The proposed method is a contrastive self-supervised pretraining framework that encourages the network to learn image representations by comparing image embeddings extracted by different encoders and predictors. Experiments were carried out on a variety of remote sensing image datasets to determine the efficacy of the proposed method for classification tasks. Results show that the proposed framework exploits the capabilities of encoders and outperforms the supervised learning method in terms of classification accuracy. Besides, it takes a few pretraining epochs to find a suboptimal initialization of network weights, and the pretrained encoders use a little training data to get outstanding classification results, which shows the time and data efficiency of the proposed framework. Code is available at https://github.com/yinxu98/MECo.
Group Imagery Rescripting on Childhood Memories Delivered via Telehealth: A Preliminary Study

Background Imagery Rescripting (ImR) has proved to be effective in the treatment of different mental disorders as an integral part of broader clinical protocols or as a standalone technique. ImR has also been successfully incorporated as part of group Schema Therapy treatment; however, to the best of our knowledge, it has never been assessed as a standalone technique in a group setting. Aim In this study, we focused on ImR delivered via telehealth in groups and we aimed to assess whether group ImR is effective in responding to basic emotional needs, in changing participants’ affective state, and in reducing dysfunctional beliefs. We also wanted to assess whether memory realism is associated with a greater effectiveness of the technique. Methods A total of 52 participants were presented with 3 ImR sessions on childhood memories related to the current dysfunctional belief that elicited more suffering. Results The technique was effective in facilitating the retrieval of a memory in almost the entire sample (in the range of 92.3–100%). Overall, memory realism values (level of vividness, ability to immerse, and participants’ distance from the images) were high in all three sessions. Almost all participants were reported having their needs met during ImR (89.7%). Importantly, need satisfaction was associated with the ability to immerse in the image. In addition, the intensity of the dysfunctional belief decreased significantly from pre-test to Session 3. The technique also changed the affective state, reducing arousal. Importantly, we also observed a general reduction in shame levels from the first to the third session. Conclusion A telehealth delivered ImR group intervention on childhood memories provides cognitive and emotional improvement. Along with the ability to satisfy the patient’s basic emotional needs, the technique seems to be effective in modifying maladaptive beliefs encapsulated in memory.
Layer-By-Layer Self-Assembled Dip Coating for Antifouling Functionalized Finishing of Cotton Textile

The fouling of surfaces such as textiles is a major health challenge, and there is a continuous effort to develop materials and processes to overcome it. In consideration of this, this study regards the development of antifouling functional nanoencapsulated finishing for the cotton textile fabric by employing a layer-by-layer dip coating technique. Antifouling textile finishing was formulated by inducing the nanoencapsulation of the antifouling functional group inside the hydrophobic polymeric shell. Cotton fabric was taken as a substrate to incorporate antibacterial functionality by alternatively fabricating multilayers of antifouling polymeric formulation (APF) and polyelectrolyte solution. The surface morphology of nanoencapsulated finished textile fabric was characterized through scanning electron microscopy to confirm the uniform distribution of nanoparticles on the cotton textile fabric. Optical profilometry and atomic force microscopy studies indicated increased surface roughness in the coated textile substrate as compared to the uncoated textile. The surface thickness of the fabricated textile increased with the number of deposited bilayers on the textile substrate. Surface hydrophobicity increased with number of coating bilayers with θ values of x for single layer, up to y for 20 bilayers. The antibacterial activity of the uncoated and layer-by-layer coated finished textile was also evaluated. It was significant and exhibited a significant zone of inhibition against microbial strains Gram-positive S. aureus and Gram-negative E. coli. The bilayer coating exhibited water repellency, hydrophobicity, and antibacterial activity. Thus, the fabricated textile could be highly useful for many industrial and biomedical applications.
Thermal-Inertial SLAM for the Environments With Challenging Illumination

In recent years, longwave infrared (LWIR) cameras have become potential in visual simultaneous localization and mapping (SLAM) research since the delivered thermal images can provide information beyond the visible spectrum and are robust to environment illumination. However, due to modality differences, SLAM methods designed for visible cameras cannot be directly applied to thermal data. In this paper, we propose a thermal-inertial SLAM method for all-day autonomous systems. To overcome the challenge of the thermal data association, the proposed method represents several improvements, including singular-value-decomposition-based (SVD-based) image processing and ThermalRAFT tracking methods. Based on the characteristics of the thermal images, the SVD-based image processing method can exploit the fixed noise pattern of thermal images and enhance the image quality to improve the performance of subsequent steps, including thermal feature extraction and loop detection. To achieve real-time and robust feature tracking, we develop ThermalRAFT, an efficient optical flow network with iterative optimization. Moreover, the system introduces a bag-of-words-based loop detection method to maintain global consistency in long-term operation. The experimental results demonstrate that the proposed method can provide competitive performance in indoor and outdoor environments and is robust under challenging illumination conditions.
Poster Abstract: Feasibility on Detecting Door Slamming towards Monitoring Early Signs of Domestic Violence

By using low-cost microcontrollers and TinyML, we investigate the feasibility of detecting potential early warning signs of domestic violence and other anti-social behaviors within the home. We created a machine learning model to determine if a door was closed aggressively by analyzing audio data and feeding this into a convolutional neural network to classify the sample. Under test conditions, with no background noise, an accuracy of 88.89% was achieved, declining to 87.50% when assorted background noises were mixed in at a relative volume of 0.5 times that of the sample. The model is then deployed on an Arduino Nano BLE 33 Sense attached to the door, and only begins sampling once an acceleration greater than a predefined threshold acceleration is detected. The predictions made by the model can then be sent via BLE to another device, such as a smartphone of Raspberry Pi.
How can the positive Indian Ocean Dipole events co‐occur with La Niña?

Positive/negative Indian Ocean Dipole (IOD) tends to co‐occur with El Niño/La Niña. Five positive IODs, however, are observed under La Niña condition in 1961, 1966, 1967, 2007, and 2011. This study uses the monthly ocean temperature and atmospheric reanalysis datasets from ECMWF to investigate how these positive IOD events occurred. It is found that the five positive IODs are consistently accompanied by a moderately strong positive subsurface IOD (Sub‐IOD) event that initiated earlier. The Sub‐IOD‐related oceanic dynamical processes including oceanic Kelvin and Rossby waves help to promote the development of the positive IODs. More specifically, the equatorial easterly anomalies in the Tropical Indian Ocean (TIO) that are crucial for the development of the positive Sub‐IODs and IODs in 1961/2011 are triggered by an anticyclonic curl over South Indian Ocean that is induced by a Southern Hemispheric Indian Ocean Subtropical Dipole mode. The crucial equatorial easterly anomalies in 1967 are triggered by a thermocline warming in the southwestern TIO induced by westward downwelling oceanic Rossby waves, while in 1966/2007, the equatorial easterly anomalies are connected with the anomalous Walker circulation resulting from the certain sea surface temperature anomaly pattern over the equatorial Pacific. On the other hand, the remote effects of La Niña on TIO are relatively weaker, being overwhelmed by the above triggering processes.
Study of Implementation of CNN on Low-power Platform for Smart Traffic Optimization

Accompanied with the rise of smart city and the development of IoT (Internet of Things), people are looking forward to monitoring and regulating the traffic in a smarter way. Since the deep neural network has shown its great value in vehicle detection area, people may wonder what kind of impact would be brought by the combination of IoT and deep learning techniques. In this work, an exploration of implementation of CNN (convolutional neural network) on low-power platform for smart traffic optimization has been conducted. During the research, a new optimization approach, which aims at S-CNN (Sparse Convolutional Neural Network) optimization from architecture level, has been proposed; and outstanding performance has been obtained when compared to mainstream deep learning frameworks, such as Tensorflow. In the experiments, the new proposed S-CNN optimization approach is as twice fast as Tensorflow on 94% sparse model and becomes 5 times faster on 98% sparse model. Besides, the author also verified the feasibility of real-time CNN implementation on ARM platform and Jetson TX1 embedded system, which reveals the shortage of computational resource on ARM platform and the potential of Jetson series to become the low-power platform for CNN implementation.
MODEL-DRIVEN PERFORMANCE PREDICTION OF DISTRIBUTED SIMULATION SYSTEMS

ABSTRACT Performance models offer a convenient tool to assess design alternatives and predict the execution time of distributed simulation (DS) systems at design time, before system implementation. Currently, perfor-mance models are to be manually developed and the related extra effort often becomes the limiting factor for their cost- and time-effective use. In this paper, we aim to reduce this extra effort with the introduction of a model-driven method for the automated building of performance models whose evaluation provides a prediction about of the execution time of a distributed simulation system. As such, the method contributes to bring software performance engineering techniques into the distributed simulation system lifecycle. In particular, we show how the SysML-based specification of the system to be simulated and the design documents of the DS system can be used to derive the topology and the parameters of a performance model specified according to the Extended Queueing Network formalism. We present an example appli-cation of the proposed method and a preliminary validation.
The Effect of Earnings Opacity on Cost of Equity with Earnings Persistence as Moderating Variable

The purpose of this study was to determine the effect of earnings opacity as measured by earnings aggressiveness and earnings smoothing on the cost of equity with earnings persistence as a moderating variable. This research focuses on manufacturing companies listed on the Indonesia Stock Exchange in 2017-2021. The population used in this study amounted to 16 manufacturing companies using purposive sampling method based on certain criteria. This research uses associative technique with quantitative methodology. The technique used in collecting data is by using library research derived from the company's annual financial statements. The hypothesis test used is the Multiple Linear Regression Test and Moderated Regression Analysis using the SPSS 25 program. The results of this study show a significant positive effect of earnings aggressiveness on the cost of equity, earnings smoothing has a positive effect on the cost of equity but is not significant, earnings persistence is able to weaken the relationship between earnings aggressiveness and the cost of equity, and earnings persistence is not able to moderate the relationship between earnings smoothing and the cost of equity.