Miracle-dz/UniFAR-FTUs huggingface dataset:
The Multiple Influences of Natural Farming Environment on the Cultured Population Behavior of Kuruma Prawn, Penaeus japonicus

Simple Summary To inform the management of shrimp culture strategies, it is important to uncover the interaction of complex factors with shrimp behaviors in the culture environment. This study was carried out with the objective to investigate the behavioral patterns of Penaeus japonicus and the effects of the natural farming environment on its behavior. The results revealed the preference for maintaining silence for P. japonicus in their breeding environment, of which the behavior patterns were prominently influenced by the sex ratio. Light intensity is demonstrated as the most influential factor in the behavior of P. japonicus, followed by population density. In conclusion, this study provides scientific evidence for shrimp farming of significant practical value. Abstract Recent years have witnessed a tremendous development in shrimp farming around the world, which, however, has raised a variety of issues, possibly due to a lack of knowledge of shrimp behavior in farms. This study focused on the relationship between shrimp behavior and the various factors of natural farming environment through situ surveys, as distinguished from the majority of laboratory studies on shrimp behavior. In the survey, the behaviors of kuruma prawn (Penaeus japonicus) were investigated in the groups of swimming in the water, crawling on the sand, resting on the sand, and hiding in the sand, followed by the quantification of the sex ratio, water quality, density, and light intensity. The results showed the average proportions of resting, hiding, crawling, and swimming activities of 69.87%, 20.85%, 8.24%, and 1.04%, respectively, of P. japonicus. The behavior of hiding, resting, and crawling is significantly affected by the sex ratio of the shrimp (p < 0.05). The proportions of hiding behavior exhibited a negative connection with density and a positive connection with light intensity, while the proportions of resting behavior showed the opposite according to both Pearson correlation analysis and multiple linear regression analysis. The light intensity was the only factor that significantly influenced the swimming behavior, in which the probability of the swimming behavior was reduced from 48% to 5% when light intensity varied from 0 to 10 lx, as determined by the generalized linear model. It could be speculated that P. japonicus prefers a tranquil environment. Female shrimp might exhibit less aggression and more adventure compared to male shrimp. The findings suggested light intensity, followed by density, as the most crucial element influencing the behavior of P. japonicus in the culture environment. These findings will contribute to the comprehension of the behavior of P. japonicus and provide a novel perspective for the formulation of its culture management strategy.
A lightweight deep learning approach to mouth segmentation in color images

PurposeMouth segmentation is one of the challenging tasks of development in lip reading applications due to illumination, low chromatic contrast and complex mouth appearance. Recently, deep learning methods effectively solved mouth segmentation problems with state-of-the-art performances. This study presents a modified Mobile DeepLabV3 based technique with a comprehensive evaluation based on mouth datasets.Design/methodology/approachThis paper presents a novel approach to mouth segmentation by Mobile DeepLabV3 technique with integrating decode and auxiliary heads. Extensive data augmentation, online hard example mining (OHEM) and transfer learning have been applied. CelebAMask-HQ and the mouth dataset from 15 healthy subjects in the department of rehabilitation medicine, Ramathibodi hospital, are used in validation for mouth segmentation performance.FindingsExtensive data augmentation, OHEM and transfer learning had been performed in this study. This technique achieved better performance on CelebAMask-HQ than existing segmentation techniques with a mean Jaccard similarity coefficient (JSC), mean classification accuracy and mean Dice similarity coefficient (DSC) of 0.8640, 93.34% and 0.9267, respectively. This technique also achieved better performance on the mouth dataset with a mean JSC, mean classification accuracy and mean DSC of 0.8834, 94.87% and 0.9367, respectively. The proposed technique achieved inference time usage per image of 48.12 ms.Originality/valueThe modified Mobile DeepLabV3 technique was developed with extensive data augmentation, OHEM and transfer learning. This technique gained better mouth segmentation performance than existing techniques. This makes it suitable for implementation in further lip-reading applications.
Regional simulations of greenhouse warming including natural variability

Abstract The perception of the hypothesized greenhouse effect will differ dramatically depending upon the location on the earth at which the effect is analyzed. This is due mainly to two causes: 1) the warming signal depends upon the position on the earth, and 2) the natural variability of the warming has a strong position dependence. To demonstrate these phenomena, simulations were conducted of the surface temperature field with a simple stochastic climate model that has enough geographical resolution to see the geographic dependence. The model was tuned to reproduce the geographical distribution of the present climate, including its natural variability in both the variance and the space-time correlation structure. While such effects have been discussed elsewhere with even more realistic climate models, it is instructive to actually see simulations of time series laid side by side in order to easily compare their differences and similarities. Because of the model's simplicity, the causes of the variation.
LIMITATIONS AND EMPIRICAL EXTENSIONS OF THE k-ε MODEL AS APPLIED TO TURBULENT CONFINED SWIRLING FLOWS

Abstract Shortcomings and recommended corrections to the standard two-equation k-e turbulence model suggested by previous investigators are presented. They are assessed regarding their applicability to turbulent swirling recirculating flow. Recent experimental data on swirling confined flows, obtained with a five-hole pilot probe and a six-orientation hot-wire probe, are used to obtain optimum values of the turbulence parameters Cμ, C2, and σe, for swirling flows. General predictions of moderately and strongly swirling flows with these values are more accurate than predictions with the standard or previous simple extensions of the k-e turbulence model.
Testing for Common Features

This paper introduces a class of statistical tests for the hypothesis that some feature of a data set is common to several variables. A feature is detected in a single series by a hypothesis test where the null is that it is absent, and the alternative is that it is present. Examples are serial correlation, trends, seasonality, heteroskedasticity, ARCH, excess kurtosis and many others. A feature is common to a multivariate data set if a linear combination of the series no longer has the feature. A test for common features can be based on the minimized value of the feature test over all linear combinations of the data. A bound on the distribution for such a test is developed in the paper. For many important cases, an exact asymptotic critical value can be obtained which is simply a test of overidentifying restrictions in an instrumental variable regression.
Conducted Coupling Model and Anti-Interference Filter Design for Digital Control Circuit Under Electrical Fast Transient Noise

Control and protection equipment working in complex electromagnetic environment often suffers from electrical fast transient (EFT) noise, which easily leads to the failure of digital control circuits of the equipment. In order to realize low-cost and effective anti-interference solutions to the failure problem, it is necessary to study the anti-interference quantitative design method of the digital control circuit. In this article, a conducted interference coupling model of the digital control circuit under EFT noise is established. As an important part of the coupling path, the impedance model of the chip is built. A black-box impedance modeling method of voltage regulator chip is proposed based on online measurement. The equivalent impedance model of the digital signal processing (DSP) chip is represented by the high-frequency model of the inductor. The coupling model is validated by comparing the measured results and simulated results of the terminal load port voltage. Then, a measurement and analysis method is presented to obtain the circuit failure threshold. By analyzing EFT test results at different frequencies and amplitudes, the influencing factor of the circuit failure is clarified, and the circuit failure threshold is obtained. Finally, based on the coupling model and the circuit failure threshold, a quantitative design method of an anti-interference filter is developed. The method is validated through filter prototyping for a 5 kHz/+2.0 kV EFT test.
Semantic Segmentation Based on Spatial Pyramid Pooling and Multilayer Feature Fusion

In recent years, significant progress has been made in semantic segmentation methods. Traditional semantic segmentation methods based on convolutional neural network (CNN) are prone to lose spatial information in the feature extraction stage, and pay less attention to global context information, especially, in some lightweight real-time semantic segmentation networks. This is a huge challenge for semantic segmentation tasks. In addition, although some methods have improved this problem to a certain extent, they are often embedded in specific networks and cannot be applied to other network models. Aiming at these problems, a semantic segmentation method based on multilayer feature fusion is proposed. The flexible and lightweight squeeze–excitation module is used to improve the spatial pyramid pooling (SPP) network, and the accuracy of the semantic segmentation method is further improved by extracting network feature information at different levels. To verify the efficiency and commonality of our methodology, we selected ERFNet and Deeplabv3 networks to experiment on Cityscapes and COCO data sets. Experiments show that our best method can improve 3.1% mIoU and 3.2% mAcc on the Cityscapes data set relative to ERFNet, and at the same time, our method can achieve 61.93 FPS on 1024 $\times $ 512 resolution images and the best improvement of 0.9% mIoU 1.4% mAcc was achieved on the Deeplabv3 network. The experimental results show that the improved multilayer feature fusion structure can improve the accuracy of the semantic segmentation network.
Status and prospects of the MiniCLEAN dark matter experiment

The MiniCLEAN dark matter direct detection experiment is a single-phase liquid argon detector, instrumented with photomultiplier tubes to observe scintillation light from a 150 kg fiducial mass. This detector design strategy emphasizes scalability to target masses of order 10 tons or more. The projected light yield is >5 photo-electrons per keV, which allows pulse shape discrimination to separate the electron background from a WIMP-induced nuclear recoil signal. MiniCLEAN is also designed for a liquid neon target, which in the event of a positive signal will provide a unique test of the expected A2 dependence of the WIMP interaction rate. This paper reviews the experimental technique and current status of MiniCLEAN.
The role of sentential prosody in learning voices

The present experiment was designed to investigate the contribution of sentential prosody to the perceptual learning of talker’s voice. Listeners were trained over a three‐day period to learn talker’s voices from three types of utterances: (1) sentence‐length utterances produced with natural sentential prosody, (2) utterances in which individual words were produced in list format and digitally assembled into complete sentences, and (3) words produced in list format and scrambled to remove semantic and syntactic coherence. Equal numbers of male and female listeners learned a set of ten talker’s voices (five male and five female) and learning was evaluated at each training session. On the fourth day of testing, listeners were given a generalization test that consisted of novel sentence‐length utterances produced with natural sentential prosody. Results indicated that learning curves were comparable across conditions, but the ability to generalize to new utterances depended on the utterance type used during .
Development of a laser-Doppler particle sensor for the measurement of velocities in rocket exhausts.

An optical instrument employing a He-Ne laser light source and a Fabry-Perot interferometer frequency (velocity) filter has been developed and successfully applied to the velocity and intensity measurements of backscattered, Doppler-shifted light signals from individual micron-sized particles in gas-particle flows. Distribution data of particle number flux and signal intensity vs velocity can be obtained at a point in a steady flow over an interval as short as 1 sec. An analytical study of the instrument's response characteristics is presented together with a description of the instrument design. Limited information regarding particle sizes is available from an indirect analysis. Results from laboratory cold flows and hybrid and solid-rocket exhaust flows (solid motors up to several thousand pounds thrust) are compared to the predictions of a constant-lag two-phase flow theory. Analysis of a limited number of solid-rocket test results suggests that the entire particulate mass may be approximated as an equivalent lumped mass that is traveling at the velocity of the largest particles in the flow, a velocity that is measured directly by the instrument.
Keeping Learning Open during Covid-19 and beyond through Innovative Learning Design: A Case Study of the University of Namibia

The infusion of technology into Open and Distance Learning (ODL) practices was nothing new at the University of Namibia (UNAM) during the emergence of Covid-19. The Centre for Innovation in Learning and Teaching (CILT) (previously the Centre for Open, Distance and eLearning (CODeL) at UNAM, focuses on leading learning innovations and was tasked with developing innovative approaches during the Covid-19 pandemic’s lockdown period, which remain relevant beyond the pandemic. // This study examined how CILT as an academic support Centre in collaboration with the Commonwealth of Learning (COL) supported efforts of various Higher Education Institutions (HEIs), to keep student learning of all modes of study (face-to-face, online, ODL) open, during the Covid-19 pandemic. This was achieved through the creation and implementation of a Rapid Development Process (RDP) as part of learning design capacity development initiatives and a Creative Commons ascribed resource website, focusing on Learning Design and Digital Assessment resources. The RDP resources were workshopped with various Namibian Universities and an African University. As part of the methodology, the study adopted a single case study research design and used a structured workshop evaluation survey and secondary sources as data collection instruments. // The findings of the study reveal overall satisfaction with the RDP workshop approach and topics covered, as well as its relevance to Continuous Professional Development (CPD) of participants. Most of the participants indicated that they only started to engage in online teaching and online assessment activities because of institutional strategies that were forced by Covid-19. // The study concludes that the RDP workshops were well-structured and had a positive impact as the learning experiences gained can be practiced in participants’ institutional contexts and as part of the new norm in HEIs.
Socio-Demographic, Self-Control, Bullying, Parenting, and Sleep as Proximal Factors Associated with Food Addiction among Adolescents

Adolescence is considered an important period of neurodevelopment. It is a time for the emergence of psychosocial vulnerabilities, including symptoms of depression, eating disorders, and increased engagement in unhealthy eating behaviours. Food addiction (FA) in adolescents is an area of study where there has been substantial growth. However, to date, limited studies have considered what demographic characteristics of adolescents may predispose them to endorse greater symptoms of FA. Studies have found a variety of factors that often cluster with and may influence an adolescent’s eating behaviour such as sleep, level of self-control, and parenting practices, as well as bullying. Therefore, this study investigated a range of socio-demographic, trait, mental health, and lifestyle-related profiles (including self-control, parenting, bullying, and sleep) as proximal factors associated with symptoms of FA, as assessed via the Yale Food Addiction Scale for Children (YFAS-C) in a large sample of Australian adolescents. Following data cleaning, the final analysed sample included 6587 students (age 12.9 years ± 0.39; range 10.9–14.9 years), with 50.05% identifying as male (n = 3297), 48.5% as female (n = 3195), 1.02% prefer not to say (n = 67), and 0.43% as non-binary (n = 28). Self-control was found to be the most significant predictor of total FA symptom score, followed by female gender, sleep quality, and being a victim of bullying. Universal prevention programs should therefore aim to address these factors to help reduce the prevalence or severity of FA symptoms within early adolescent populations.
DETECTION OF SALMONELLA TYPHIMURIUM BACTERIA ON BAKERY PRODUCTS SAMPLES USING BOILING ISOLATION TECHNIQUE

Detection of Salmonella typhimurium ATCC 14028 bacteria in bakery product samples by real-time PCR using boiling isolation technique. The basis of this research is to have an impact on economic value in carrying out the confirmation test for Salmonella typhimurium ATCC 14028, where testing is carried out conventionally will require large costs, so it is necessary to innovate in terms of modifying the testing phase so that it is more effective and efficient. The purpose of this study was to see whether the boiling isolation technique could be used for the detection test for Salmonella typhimurium ATCC 14028 on bacterial product samples. The sample in this study consisted of 15 types of bacterial product samples spiked with Salmonella typhimurium ATCC 14028 cultures that had been cultured into phase 2 working cultures. The method used in this study was qPCR analysis using the SYBR Green method. The results of real-time PCR analysis obtained Ct values in the range 7.55 - 8.91 with an average of 8.28 and a Tm value in the range 85.50 - 86.20 with an average of 85.77 Based on these data it can be concluded that the detection of Salmonella typhimurium bacteria ATCC 14028 with real-time PCR using boiling isolation technique can be applied for testing on bakery product samples.
Promitotic Action of Oenothera biennis on Senescent Human Dermal Fibroblasts

Accumulation of senescent dermal fibroblasts drives skin aging. The reactivation of proliferation is one strategy to modulate cell senescence. Recently, we reported the exact chemical composition of the hydrophilic extract of Oenothera biennis cell cultures (ObHEx) and we showed its skin anti-aging properties. The aim of this work is to assess its biological effect specifically on cell senescence. ObHEx action has been evaluated on normal human dermal fibroblasts subjected to stress-induced premature senescence (SIPS) through an ultra-deep proteomic analysis, leading to the most global senescence-associated proteome so far. Mass spectrometry data show that the treatment with ObHEx re-establishes levels of crucial mitotic proteins, strongly downregulated in senescent cells. To validate our proteomics findings, we proved that ObHEx can, in part, restore the activity of ‘senescence-associated-ß-galactosidase’, the most common hallmark of senescent cells. Furthermore, to assess if the upregulation of mitotic protein levels translates into a cell cycle re-entry, FACS experiments have been carried out, demonstrating a small but significative reactivation of senescent cell proliferation by ObHEx. In conclusion, the deep senescence-associated global proteome profiling published here provides a panel of hundreds of proteins deregulated by SIPS that can be used by the community to further understand senescence and the effect of new potential modulators. Moreover, proteomics analysis pointed to a specific promitotic effect of ObHEx on senescent cells. Thus, we suggest ObHEx as a powerful adjuvant against senescence associated with skin aging.
Identification Of Microplastics Content in Sediment, Water and Digestive Channel of Milkfish (Chanos chanos) in Sidoarjo Pond

Microplastics are a form of new pollution that the Indonesian people are not aware of. The factors for the entry of microplastics into pond waters are water sources, anthropogenic activities, and pond cleanliness. The purpose of this study was to determine the abundance of microplastics, the color and shape of microplastics, and the types of microplastic polymers found in sediment, water, and the digestive tract of milkfish (Chanos chanos) in Sidoarjo ponds. The average abundance of microplastics in sediment samples obtained from semi-intensive ponds is 300 particles per 50 grams, while the sediment samples from traditional ponds are 613 particles per 50 grams. The average abundance of microplastics in the semi-intensive pond water samples was 2.1 particles per 20 liters and in traditional ponds was 2.3 particles per 20 liters. The average abundance of microplastics in the milkfish (Chanos chanos) samples from the Semi-Intensive Pond was as many as 6 particles per tail, while the Traditional Pond had 9.5 particles per tail. In this study, four colors of microplastics were found, namely blue, black, red, and transparent, while the microplastics obtained were fiber, fragment, and filament. The types of microplastic polymers encountered in this study were dominated by polyamide or nylon polymers. In sediment samples from both ponds, we found the presence of Polyamide or Nylon polymers, namely Polystyrene and Polyamide or Nylon, and in the digestive tract of milkfish (Chanos chanos), namely Polyamide or Nylon.
Impact of numerical artifact of the forward model in the inverse solution of density‐dependent flow problem

We investigate the effect of computational error on the inversion of a density‐dependent flow and transport model, using SEAWAT and UCODE‐2005 in an inverse identification of hydraulic conductivity and dispersivity using head and concentration data from a 2‐D laboratory experiment. We investigated inversions using three different solution schemes including variation of number of particles and time step length, in terms of the three aspects: the shape and smoothness of the objective function surface, the consequent impacts to the optimization, and the resulting Pareto analyses. This study demonstrates that the inversion is very sensitive to the choice of the forward model solution scheme. In particular, standard finite difference methods provide the smoothest objective function surface; however, this is obtained at the cost of numerical artifacts that can lead to erroneous warping of the objective function surface. Total variation diminishing (TVD) schemes limit these impacts at the cost of more computation time, while the hybrid method of characteristics (HMOC) approach with increased particle numbers and/or reduced time step gives both smoothed and accurate objective function surface. Use of the most accurate methods (TVD and HMOC) did lead to successful inversion of the two parameters; however, with distinct results for Pareto analyses. These results illuminate the sensitivity of the inversion to a number of aspects of the forward solution of the density‐driven flow problem and reveal that parameter values may result that are erroneous but that counteract numerical errors in the solution.