Miracle-dz/UniFAR-FTUs huggingface dataset:
Initial evaluation of the care and rehabilitation success of Cape Cormorants Phalacrocorax capensis rescued from Robben and Jutten islands, South Africa, in January 2021

The population of the endangered Cape Cormorant Phalacrocorax capensis more than halved over the last three decades (BirdLife International 2018a). In January 2021, nearly 2 000 Cape Cormorant chicks were found abandoned, suffering from dehydration and heat stress, at two important nesting sites. The chicks were rescued and rehabilitated by the Southern African Foundation for the Conservation of Coastal Birds (SANCCOB, Cape Town, South Africa). About half (53.7%) of the cormorant chicks were successfully rehabilitated and released back into the breeding colonies. This study found a direct link between the initial body mass of cormorant chicks admitted to the rehabilitation centre and their probability of surviving during rehabilitation, with birds that were initially heavier having a greater probability of eventual release. Most cormorant chicks that died (80.7%) did so within the first 5 days of admission. This rescue required SANCCOB to care for and rehabilitate the largest number of Cape Cormorant chicks that has ever been admitted to its rehabilitation centre at one time, making it the first rescue of its kind. Despite the presumably limited positive impact on overall population numbers of Cape Cormorants, the rescue campaign improved SANCCOB’s preparedness to respond successfully to future disaster events and to deal with different species, both locally and globally.
Research on credibility evaluation method of infrared small target detection network based on YOLOv5

At present, the credibility evaluation system of infrared small target detection network is not perfect. In this paper, we propose a set of credibility evaluation methods for infrared small target detection networks, including generalization evaluation and robustness evaluation. The generalization evaluation uses the original dataset to test the trained YOLOv5 network, and then obtains test indexes such as accuracy, precision, recall and so on. Robustness evaluation process the dataset with noise, which includes Gaussian noise, salt and pepper noise and random occlusion, test the trained network with the dataset after noise processing, obtain the robustness test index, and evaluate the anti-interference ability of the model.
Can Graph Neural Networks Go Deeper Without Over-Smoothing? Yes, With a Randomized Path Exploration!

Graph Neural Networks (GNNs) have emerged as one of the most powerful approaches for learning on graph-structured data, even though they are mostly restricted to being shallow in nature. This is because node features tend to become indistinguishable when multiple layers are stacked together. This phenomenon is known as over-smoothing. This paper identifies two core properties of the aggregation approaches that may act as primary causes for over-smoothing. These properties are namely recursiveness and aggregation from higher to lower-order neighborhoods. Thus, we attempt to address the over-smoothing issue by proposing a novel aggregation strategy that is orthogonal to the other existing approaches. In essence, the proposed aggregation strategy combines features from lower to higher-order neighborhoods in a non-recursive way by employing a randomized path exploration approach. The efficacy of our aggregation method is verified through an extensive comparative study on the benchmark datasets w.r. t. the state-of-the-art techniques on semi-supervised and fully-supervised learning tasks.
Effects of supplemental mixed bile acids on growth performance, body composition, digestive enzyme activities, skin color, and flesh quality of juvenile large yellow croaker (Larimichthys crocea) in soybean oil based diet

Now the replacement of fish oil (FO) with vegetable oils (VOs) has been broadly applied in aquatic feed, but studies reported that there were negative effects on growth, skin color, flavor and muscle texture. A 10-week feeding trial was conducted to investigate the effects of dietary mixed bile acids (BA) on growth performance, body composition, digestive enzyme activities, skin color and flesh quality of juvenile large yellow croaker (initial weight, 13.10 ± 0.18 g). Four iso-nitrogenous and iso-lipidic experimental diets were formulated and designated as soybean oil (SO), SO supplemented with 300 (BA300), 600 (BA600), and 1200 (BA1200) mg/kg bile acids. Two hundred and forty fish were randomly allocated into 12 floating net cages (1 m × 1 m × 1.5 m) that were located at marine fishing rafts. Each diet was randomly distributed in triplicate (three replicates per treatment, 20 fish per replicate). Fish were hand-fed to apparent satiation twice daily (05:30 and 17:30) for 10 weeks. Results showed that specific growth rate presented quadratic pattern with supplemental bile acids level, peaking at BA600 group. Meanwhile, feed conversion rate of cultured fish was significantly improved in BA600 group (P < 0.05). The redness (a*) of dorsal and lateral line skin and the yellowness (b*) of abdominal skin showed significantly quadratic pattern with the increase of supplemental BA level (P < 0.05), peaking at BA600 group. In terms of dorsal muscle texture, springiness showed a decreasing trend in significantly linear pattern with the increase of supplemental BA level, bottoming at BA600 group. However, cohesiveness and gumminess were significantly linear increased with the increase of supplemental BA level (P < 0.05). No significant differences were observed in lipase, amylase, and trypsin activities (P > 0.05). Total proportion of muscle n-3LCPUFA showed a linearly increasing trend with the increase of supplemental BA level. Muscle TG content was linearly increased with the increase of supplemental BA level (P < 0.05), and significant differences occurred as compared to fish fed diets with SO when supplemental BA level were reached 600 mg/kg and 1200 mg/kg (P < 0.05). The gene expression of acyl-CoA oxidase (aco) was significantly decreased in fish fed diets supplemented with 600 mg/kg bile acids compared with 300 mg/kg other than those fed diets with SO. In conclusion, these results suggested that mixed bile acids supplemented to SO-based diets could improve feed conversion ratio, skin color and flesh quality to some extent.
Event-Triggered Adaptive Backstepping Dynamic Surface Control for Strict-feedback Fractional-Order Nonlinear Systems*

This paper addresses an event-triggered adaptive dynamic surface control (DSC) problem for a class of strictfeedback uncertain fractional-order nonlinear systems (FONS) with external disturbances. An adaptive backstepping DSC approach combined with a serial-parallel estimation (SPE) model is introduced by designing a compensation signal. Specifically, the predicted errors of the system states and the estimated states are utilized for the control and adaptive laws design. A novel eventtriggered scheme with relative-threshold is introduced to reduce the triggering number. It is shown that with the proposed scheme, the semiglobally uniformly ultimate stability of the uncertain FONS is guaranteed, and all the signals of the closed-loop system are bounded. An example for Chua-Hartley’s system are shown to demonstrate the effectiveness of the proposed method.
Smartphone app-based intervention for reducing stress, depression, and anxiety in caregivers of people with dementia in Vietnam: Study protocol for a pilot randomized controlled trial

Objectives This study aims to: (1) identify the information required by family caregivers of people with dementia to be targeted within our dementia family caregiver intervention and (2) test the feasibility of the intervention and methodology to underpin a fully powered randomized controlled trial. Methods The study setting will be the Department of Geriatrics at Gia Dinh People's Hospital in Ho Chi Minh City. Inclusion criteria will be the family caregivers of people with dementia living in the community, who attend the Department and use smartphones. In phase 1, we will identify the intervention content with family caregivers of people with dementia through 20 in-depth interviews to determine what information and skills they need. In phase 2, a pilot randomized control trial design will be conducted, with 60 family caregivers of people with dementia being assigned to the intervention or control group by the block randomization method with a ratio of 1:1. The intervention will include weekly, online, psycho-educational, group sessions hosted on the Zalo app. The participants will complete questionnaires at baseline, immediately postintervention, and 3-month postintervention. The feasibility of the intervention and methodology will be assessed, including the rates of recruitment, retention, completion of assessments, and acceptability of the intervention. Results The required information and skills in phase 1 may include dealing with worrying behavior changes in people with dementia, emotional support, and seeking support sources. The rates of recruitment, retention, completion of assessments, and acceptability of the intervention will be obtained in phase 2. The scores of symptoms of stress, depression, and anxiety in the intervention group may be lower than those in the control group at postintervention and 3-month postintervention. Conclusion The study will provide a foundation for a fully powered clinical trial for the smartphone app-based intervention to reduce stress, depression, and anxiety among family caregivers of people with dementia in Vietnam.
Diet and physical activity interventions in Black and Latina women with breast cancer: A scoping review

Background A growing number of lifestyle interventions are being developed to promote weight loss and adoption of a healthful lifestyles among breast cancer survivors; yet Black and Latina women remain underrepresented. Purpose We performed a scoping review of the available peer-reviewed literature to describe and compare the content, design, methods, and primary outcomes of current diet and/or physical activity (PA) interventions after a breast cancer diagnosis among Black and Latina women. Methods We queried PubMed, EMBASE, CINAHL, MEDLINE, and Clinicaltrials.gov up to October 1, 2022, to identify all randomized controlled trials of diet and/or PA after diagnosis of breast cancer with a majority (>50%) of Black or Latina participants. Results Twenty-two randomized controlled trials were included in this review (five efficacy, twelve pilot, five on-going). Nine trials were among Latinas (two diet, four PA, and three diet/PA), six among Blacks (one PA and five diet/PA) and seven included both populations (five PA and two diet/PA), all of which examined different endpoints. Two of the five efficacy studies achieved their a priori outcome (one diet trial improved short term dietary intake; one PA trial achieved clinically significant improvements in metabolic syndrome score), both in Latinas. Eight pilot trials intervened on both diet and PA and three of them found favorable behavioral changes. Three (two for Latinas and one for Blacks) out of the nine diet and PA trials and three (all for Latinas) efficacy trials incorporated a culturally focused approach (i.e., traditional foods, music, Spanish content, bicultural health coaches, spirituality). Overall, four trials, including one efficacy trial, had one-year follow-up data, with three finding sustained behavior change. Electronic/mobile components were incorporated in five trials and one involved informal care givers. Most of the trials were geographically limited to the Northeast USA (n=8, NY, NC, DC, NJ) and Texas (n=4). Conclusions Most of the trials we identified were pilot or feasibility studies and of short duration, demonstrating the need for large randomized controlled efficacy lifestyle interventions among Black and Latina breast cancer survivors. Culturally tailored programing was limited but is an important component to incorporate in future trials in these populations.
Study of Influence of Extraction Method on the Recovery Bioactive Compounds from Peel Avocado

The avocado peel is a waste material from consumption avocado (Persea americana Mill.) with big biotechnology potential. The purpose of the present work was to study the influence of six extraction methods, maceration (M), maceration plus β-cyclodextrin (MβC), solid-state fermentation (SSF), sonication with water or ethanol, wet grinding (WG), wet grinding plus maceration (WGM), on the recovery of bioactive compounds from the avocado peel such as total phenols, epicatechin and chlorogenic acid. The results showed that the extraction method has a significant effect on the content of total phenols, the WGM method obtaining the highest value of total phenols (2143.1 mg GAE/100 g dry weight). Moreover, the results indicated that the extraction method had a significant effect on chlorogenic acid and epicatechin recovery, the WGM method obtaining the highest amount of epicatechin and chlorogenic acid, 181.7 and 244.3 mg/100 g dry matter, respectively. Additionally, the characterization of WGM extract was realized by UPLC-ESI-MS/MS and GC-MS. Thus, the WGM method allowed for obtaining good yields of recovery of phenolic compounds using an accessible technology and a more environment-friendly solvent.
Online Data-Driven Model Predictive Control in Variable Noise Environment Based on Neural Network and Gaussian Process Regression

This paper investigates model predictive control in variable noise environment. A modeling method based on neural network and online Gaussian process regression is proposed, and the corresponding model predictive control algorithm is designed. Compared with the common data-driven model predictive control, this paper uses neural network to replace the traditional mathematical model, which reduces the workload of measuring parameters in modeling. In addition, Gaussian process regression is used for online learning to improve the performance of agents under noise interference. A simulation experiment is designed to demonstrate the effectiveness of the method. Finally, the experiments on a real quadrotor are conducted and the results show that the proposed algorithm remains feasible in the real world.
Perioperative Complications after Hip and Knee Revision Arthroplasty in the over 80 Years Old Population: A Retrospective Observational Case–Control Study

Background: The number of joint revision arthroplasties has increased in the elderly population, which is burdened by several perioperative risks. Methods: Patients who underwent hip and knee revision arthroplasty were retrospectively included, and they were divided into two groups by age: <80 years old (Group 1) and ≥80 years old (Group 2). The primary outcome was to compare perioperative complication rates. The secondary outcome was to compare the 30-day, 90-day, and 1-year readmission rates. Results: In total, 74 patients in Group 1 and 75 patients in Group 2 were included. Postoperative anemia affected 13 patients in Group 1 (17.6%) and 25 in Group 2 (33.3%, p 0.027); blood units were transfused in 20 (26.7%) and 11 (14.9%, p 0.076) patients, respectively. In Group 1, two (2.7%) patients reported wound infection. In Group 2, eight (10.7%) patients presented hematomas, and two (2.7%) patients reported dislocations. No significant differences in the two groups were observed for 30-day (p 0.208), 90-day (p 0.273), or 1-year readmission rates (p 0.784). Conclusion: The revision arthroplasty procedure in patients over 80 years old is not associated with a higher risk of perioperative complications, or higher readmission rate compared with younger patients undergoing hip and knee revision surgery.
Recent Insights into Sample Pretreatment Methods for Mycotoxins in Different Food Matrices: A Critical Review on Novel Materials

Abstract Mycotoxins pollution is a global concern, and can pose a serious threat to human health. People and livestock eating contaminated food will encounter acute and chronic poisoning symptoms, such as carcinogenicity, acute hepatitis, and a weakened immune system. In order to prevent or reduce the exposure of human beings and livestock to mycotoxins, it is necessary to screen mycotoxins in different foods efficiently, sensitively, and selectively. Proper sample preparation is very important for the separation, purification, and enrichment of mycotoxins from complex matrices. This review provides a comprehensive summary of mycotoxins pretreatment methods since 2017, including traditionally used methods, solid-phase extraction (SPE)-based methods, liquid-liquid extraction (LLE)-based methods, matrix solid phase dispersion (MSPD), QuEChERS, and so on. The novel materials and cutting-edge technologies are systematically and comprehensively summarized. Moreover, we discuss and compare the pros and cons of different pretreatment methods and suggest a prospect.
Generalized Formulation and Application of Characteristic Boundary Conditions

Extended kinds of numerical boundary conditions for simulating subsonic inflow, non-reflecting outflow and solid wall are formulated on the basis of characteristics and applied to computational aeroacoustics (CAA) with high-order and high-resolution numerical algorithms. A new formalism of characteristic boundary conditions for Navier-Stokes computations in the generalized coordinates is presented by using the flux-Jacobian and transformation matrices between the conservative and characteristic variables, which were already derived in the references to analyze Euler equations through the eigenvalue-eigenvector modes in an arbitrary direction. Navier-Stokes equations of entirely conservative form in the generalized coordinates are solved on the boundaries and any kind of extrapolation or simplification of the equations is not included in this formalism. Especially, new additional terms in the flux derivatives are devised to concrete the conservation in the generalized coordinates. The boundary conditions presented in this paper are applied to actual computation of 2-D cylinder flow on non-uniform and non-rectangular grid meshes, which are clustered on the cylinder surface and downstream region. Far-field acoustic pressures due to Karman vortex streets are measured and its Strouhal number is evaluated correctly. The feasibility of. using the generalized characteristic boundary conditions is investigated for unsteady CAA.
Feature Extraction and Feature Combination for Basic Taste Sensation Recognition Based on Facial Electromyography

Taste sensation recognition is valuable for developing virtual taste and the early diagnosis of taste system disorders. Electromyography (EMG) can characterize the information of taste sensation and have the advantages of a high signal-to-noise ratio and easy acquisition. This paper presents feature extraction and feature combination methods for taste sensation knowledge based on facial EMG. The time domain features, frequency domain features, time-frequency domain features, and entropy features are extracted as individual feature sets after data pre-processing. Combinations of different features are performed to acquire multivariate feature representations. Machine learning methods are used to predict taste sensation categories. The combination of the frequency domain, time-frequency domain, and entropy features as feature set and Random Forest method as classifier obtain the highest recognition accuracy. The results provide a reference for feature extraction and selection for basic taste sensation recognition.
A Lightweight Method for Fast Deep Learning Inference in Medical Image Classification Based on Fourier Transform and Signal Processing

Medical image classification often relies on Convolutional Neural Network (CNN) for its powerful ability to obtain accurate predictions. However, considering novel diseases such as COVID variants and complications, the medical and clinical field desires diagnosis that is both fast and accurate. This paper proposes a lightweight method that conducts deep learning-based classification in the Fourier domain without convolution operation and reduces the computational cost. The paper focuses specifically on pneumonia, which is a lung infection and a typical COVID complication. To achieve a decent accuracy that is comparable to the CNN performance, signal processing techniques, namely Fourier transform is utilized to extract features from the frequency domain. The proposed method uses Discrete Cosine Transform (DCT) to find the frequency domain values as well as other useful parameters. As part of the methodology, a fundamental Artificial Neural Network (ANN) is built to perform the classification task. In the meanwhile, two pre-trained CNN architectures, ResNet50V2 and VGG19, are implemented under the same environment as standards for comparison. With the same hyperparameters and training epochs, the ANN obtained a validation accuracy that is 2.35% lower than the CNNs but 15 times faster in training. The experimental result demonstrates the advantage of the proposed method in inference speed and model size, indicating that the overall objective is attained. The findings also open the possibility of generalizing such an approach for other medical diagnosis in the future.
Mid-Term Follow-Up Study of Children Undergoing Autologous Skin Transplantation for Burns

Deep partial and full-thickness burns require surgical treatment with autologous skin grafts after necrectomy, which is the generally accepted way to achieve permanent wound coverage. This study sought to examine the grafted and donor areas of children who underwent autologous skin transplantation, using two assessment scales to determine the severity of the scarring and the cosmetic outcome during long-term follow-up. At the Surgical Unit of the Department of Paediatrics of the University of Pécs, between 1 January 2015 and 31 December 2019, children who had been admitted consecutively and received autologous skin transplantation were analyzed. Twenty patients met the inclusion criteria in this retrospective cohort study. The authors assessed the results using the Patient and Observer Scar Assessment Scale (POSAS) and the Vancouver Scar Scale (VSS). There was a significant difference in how parents and examiners perceived the children’s scars. In the evaluation of the observer scale, the most critical variables for the area of skin grafted were relief and thickness. Besides color, relief was the worst clinical characteristic on the patient scale. However, when medical professionals evaluated the donor site, significantly better results were obtained compared to the transplanted area (average observer scale score: 1.4 and 2.35, p = 0.001; VSS: 0.85 vs. 2.60, p < 0.001), yet it was similar to the graft site in the parents’ opinion (Patient Scale: 2.95 and 4.45, p = 0.181).
Analysis of the future trends of typical mountain glacier movements along the Sichuan-Tibet Railway based on ConvGRU network

ABSTRACT The anomalous movements of glaciers cause disasters, such as debris flows and landslides. It is very important to assess the glacier movements and their future trends. Glacier velocity refers to movement process. The current research aims to analyse past and current spatiotemporal changes in glacier velocity. No study has used neural network model to conduct a spatiotemporal prediction for glacier velocity. Therefore, this paper selected typical mountain glaciers G2 and G5 along the Sichuan-Tibet Railway as research objects and constructed the Convolutional Gate Recurrent Unit (ConvGRU) spatiotemporal prediction model based on 1988–2018 Landsat data to predict velocities in 2019–2028, and analysed the future trends of G2 and G5. The evaluation indexes met the model requirements to a large extent, quantitatively showing that the model has high accuracy and can successfully capture the fluctuation changes in time series data of glacier velocity. The mean deviations of G2 and G5 were 0.09 and −0.47 m/yr, respectively, reflecting the high reliability of the model applied to extraction of glacier velocity. The velocities of G2 and G5 showed a slow downtrend with fluctuations; that is, they will not cause damage to the construction and operation of the Sichuan-Tibet Railway in the short term.