Miracle-dz/UniFAR-FTUs huggingface dataset:
Simplified Evaluation of Salt Affected Soils Using 1:5 Soil–Water Extract

ABSTRACT Because of the global increase in salt-affected soils, rapid and cost-effective remediations and easily measurable soil salinity evaluations are required. Soil salinity, through EC, and soil sodicity through exchangeable sodium percentage values, were predicted using a 1:5 soil-water extract. To determine the differences of soil salinity evaluations between arid regions, 48 soil samples (Inceptisols and Entisols) from China and 120 soil samples (Aridisol) from Sudan were used in this study. Positive correlations between the EC of a soil saturation extract and EC, exchangeable sodium percentage, and sodium adsorption ratio of a 1:5 soil-water extract were observed in all the soil samples. A simplified evaluation of salt-affected soils was possible using these relational expressions. Although the correlations differed by region, each correlation was strong. This was due to the differences in soil clay content and mineral composition between China and Sudan, but it also affected the salt content and composition in the soils. In addition, a strong correlation of soil salinity evaluations between saturated extract method and 1:5 soil–water extract method was observed even in the same area even when the salt content varied. Since accumulated salts were affected by the salt composition in irrigation water used in each region, soil salinity, and soil sodicity could be evaluate via simplified method based on the irrigation area. This results can be useful for farmland management in arid regions.
In defense of play: children’s play preferences and thoughts about play prohibition

ABSTRACT In this qualitative study, a story-based interview method was applied to examine children's play-related preferences and their thoughts about the reasons for and feelings about play prohibition and ways of defending play as a phenomenon. The study group comprised 197 children, who were enrolled in preschools in a city in the southwestern part of Turkey. A story game was designed to use during the interviews. The thematic content analyzing method was used. The results showed that most of the children chose physical play and socio-dramatic play types. Their playmate choices were friends, family members, and toys. They preferred to play outside and at home. Their views about reasons for play prohibition mostly arose from themselves like ‘making a noise' and ‘making a mess’. Sadness and badness were the most common feelings about the prohibition of play. The majority of children chose persuasion as a way of defending play.
Rice Bran Hydrolysates Minimize Freeze-Denaturation of Washed Fish Mince during Extended Freeze-Thaw Cycles

ABSTRACT Rice bran hydrolysate (RBH) was produced from hexane-defatted rice bran using subcritical alkaline water followed by enzymatic hydrolysis (Protease G6 at 2% for 6 hr). The RBH was added to Pacific whiting washed mince. The physicochemical and rheological properties of the mince with and without RBH or commercial cryoprotectants (sucrose-sorbitol (S/S)) were evaluated after 0, 7, and 14 freeze-thaw (FT) cycles. The protein solubility and total sulfhydryl group content of all samples decreased as the number of FT cycles increased, while surface hydrophobicity increased. Samples prepared with 2% RBH mixed with 4% S/S (RBH+S/S) and 2% RBH exhibited higher total sulfhydryl group content and protein solubility than 8% S/S and a control. Samples prepared with RBH+S/S and 2% RBH exhibited lower surface hydrophobicity than control but slightly higher than 8% S/S sample. The elastic modulus (G′) of samples prepared with RBH and S/S was higher than that of the control. After gelation was completed, the sample with RBH+S/S showed the highest G′. RBH showed a significant ability to maintain the quality of frozen fish proteins, especially when blended with S/S. This study suggests RBH can effectively protect fish myofibrillar proteins from denaturation during frozen storage and further contribute to reducing sugar/sorbitol.
Mining for gold and finding only nuggets: attempting a rapid systematic review, on trust in higher education IBL classrooms

ABSTRACT It is well-documented throughout the available literature that trust, or a leap of faith, plays a key role in facilitating success in student learning. In this study, we attempted a rapid systematic review of relevant databases looking for studies that addressed developing and maintaining trust among students, between students and their professors, within higher education institutions, paying particular attention to inquiry-based learning practices where taking intellectual risks is key. Our efforts revealed that there are currently no empirical studies that link risk and trust in early engagement in higher education IBL (i.e., IBL-HE) classrooms. Though the ‘gold mine’ remained out of reach, our secondary review uncovered several important nuggets which confirmed our belief that this is an important area of future inquiry and allowed us to identify barriers to trust and note student fears. Further research is required to examine the factors that promote risk-taking and trust development and the relationship with early engagement in IBL-HE courses.
Saroglitazar for the Treatment of NASH: The Peroxisome Proliferator‐Activated Receptor Story Goes On!

Despite intensive research, no drug has to date been approved for the management of NAFLD, and many clinical trials have failed, including some recent large phase 3 trials. This has in part to do with the complex disease pathophysiology, which is only partially unravelled(1). Furthermore, the disease is embedded in a set of metabolic drivers, creating an environment of metabolic and inflammatory stress.
The More Insufficient, the More Avoidance? Cognitive and Affective Factors that Relates to Information Behaviours in Acute Risks

This study examines the relationship between cognitive and affective factors and people's information-seeking and -avoiding behaviours in acute risks with a 1,946-sample online survey conducted in February 2020, during the outbreak of the COVID-19 pandemic in mainland China. Multiple linear regression analysis showed that perceived information insufficiency correlates negatively with information-seeking behaviour and there was an inverted U-shaped relationship between information insufficiency and avoidance behaviour. As for the risk-related cognitive factors, information seeking increases as perceived severity of risks rises, while information avoiding increases as perceived susceptibility rises. Perceived response efficacy positively correlates with information-seeking and negatively with information-avoidance behaviours. Preliminary results also indicated that different affective factors relate to information-seeking and avoidance behaviours differently.
Emotional Well-Being and Traditional Cypriot Easter Games: A Qualitative Analysis

The aim of the current study is to examine the effect of the Traditional Easter Games of Cyprus on the emotional well-being of the participants. Data were collected using a qualitative analysis. It consisted of interviews from 51 participants aged 32–93 years old, and observations were made from audiovisual material of the Traditional Cypriot Easter Games being played by a sample of 20 children aged 6–14 years old and 43 adults aged 18–65 years old. Demographic data were collected by using interviews and analyzed using IBM SPSS program. The observations of the audiovisual material focused on the emotions of the participants and were grouped into prevailing and secondary emotions according to frequency and duration. The results indicate that games produce emotions such as joy, excitement, and euphoria. Emotions such as embarrassment, frustration, and anger were also observed occasionally, specifically in situations of competitiveness and defeat. In addition, the differences and similarities between adults and children were recorded. The findings of the present study extend previous work by demonstrating the positive impact of the traditional games on children’s and adult’s emotional well-being.
Rehabilitation Needs of Head and Neck Cancer Patients and Stakeholders: Case Study

Background The incidents of Head and Neck Cancer (HNC) are rising worldwide, suggesting that this type of cancer is becoming more common. The foreseen growth of incidents signifies that future rehabilitation services will have to meet the needs of a wider population. Objective The aim of this paper is to explore the needs of patients, caregivers and healthcare professionals during HNC rehabilitation. Methods This paper reports the empirical findings from a case study that was conducted in a cancer rehabilitation center in Copenhagen to elicit the needs of HNC cancer patients, informal caregivers and healthcare professionals. Results Four areas of needs during the rehabilitation process were identified: service delivery, emotional, social and physical needs. Service delivery needs and emotional needs have been identified as the most prevalent. Conclusions Stakeholders’ needs during the rehabilitation process were found to be interrelated. All stakeholders faced service delivery challenges in the form of provision and distribution of information, including responsibilities allocation between municipalities, hospitals and rehabilitation services. Emotional and social needs have been reported by HNC patients and informal caregivers, underlining the importance of inclusion of all actors in the design of future healthcare interventions. Connected Health (CH) solutions could be valuable in provision and distribution of information.
Comparative Chloroplast Genomics and Phylogenetic Analysis of Zygophyllum (Zygophyllaceae) of China

The genus Zygophyllum comprises over 150 species within the plant family Zygophyllaceae. These species predominantly grow in arid and semiarid areas, and about 20 occur in northwestern China. In this study, we sampled 24 individuals of Zygophyllum representing 15 species and sequenced their complete chloroplast (cp) genomes. For comparison, we also sequenced cp genomes of two species of Peganum from China representing the closely allied family, Nitrariaceae. The 24 cp genomes of Zygophyllum were smaller and ranged in size from 104,221 to 106,286 bp, each containing a large single-copy (LSC) region (79,245–80,439 bp), a small single-copy (SSC) region (16,285–17,146 bp), and a pair of inverted repeat (IR) regions (3,792–4,466 bp). These cp genomes contained 111–112 genes each, including 74–75 protein-coding genes (PCGs), four ribosomal RNA genes, and 33 transfer RNA genes, and all cp genomes showed similar gene order, content, and structure. The cp genomes of Zygophyllum appeared to lose some genes such as ndh genes and rRNA genes, of which four rRNA genes were in the SSC region, not in the IR regions. However, the SC and IR regions had greater similarity within Zygophyllum than between the genus and Peganum. We detected nine highly variable intergenic spacers: matK-trnQ, psaC-rps15, psbZ-trnG, rps7-trnL, rps15-trnN, trnE-trnT, trnL-rpl32, trnQ-psbK, and trnS-trnG. Additionally, we identified 156 simple sequence repeat (cpSSR) markers shared among the genomes of the 24 Zygophyllum samples and seven cpSSRs that were unique to the species of Zygophyllum. These markers may be useful in future studies on genetic diversity and relationships of Zygophyllum and closely related taxa. Using the sequenced cp genomes, we reconstructed a phylogeny that strongly supported the division of Chinese Zygophyllum into herbaceous and shrubby clades. We utilized our phylogenetic results along with prior morphological studies to address several remaining taxonomic questions within Zygophyllum. Specifically, we found that Zygophyllum kaschgaricum is included within Zygophyllum xanthoxylon supporting the present treatment of the former genus Sarcozygium as a subgenus within Zygophyllum. Our results provide a foundation for future research on the genetic resources of Zygophyllum.
In Search of Harmful Stress

Human body produces different physiological stress reaction when you hit a toe to a doorstep than when you panic at a job interview. The impact for body’s homeostasis varies depending on the reaction type and some reactions are harmful to our health. Currently, stress estimation is focused on binary identification between stress and non-stress stages. More detailed separation of stress reaction types is needed for detecting harmful stress. In this study, the Extreme Gradient Boosting algorithm was used to classify a baseline condition and physiological and psychosocial stress, based on psychophysiological signals monitored using a wrist sensor device. Classification was robust in separating the two stress states from baseline and from each other. The results provide support for novel approaches utilizing fine-grained estimation of stress type from wearable sensor data.
Improving Efficient Neural Architecture Search Using Out-net

∗ Over the past years, there are many achievements in neural networks architecture design. The artificial neural architecture search (NAS) becomes a new way to find good architecture. Architecture searching with parameters sharing proposed by Google greatly decrease training time. However, it brings other problems like over-fitting and unfair performance evaluation introduced by parameters sharing. To solve these problems, we propose a mechanism that using out-net to help training parameters, and select the best model from several candidate models produced by the controller. Experiments show that our method has a better performance when searching a small network, which got 77.3% accuracy on cifar100 with a lower latency.
Fundamental contemplation on the adequacy of the analysis of brain waves: case of EEG

In recent years, the use of biological signals to understand the operations of software engineers has emerged, although with a limited understanding of its successful application. This paper provides primary evidence that biological signals obtained by electroencephalography (EEG) may provide valuable information from the perspective of software engineers, who then decode the adequacy, consistency, and efficiency of their work. The experimentation with 20 different professional male software engineers has been completed. Two natural situations have been investigated: pair programming and programming with (without) music. The early findings show the methodology’s effectiveness.
An Ensemble of ConvTransformer Networks for the Sussex-Huawei Locomotion-Transportation (SHL) Recognition Challenge

The task of the SHL recognition challenge 2021 is to recognize eight modes of locomotion-transportation based on radio sequential data collected by smartphones. These data includes GPS reception, GPS location, WiFi reception and GSM cell tower scans. In this challenge, our team (Transformers) presents a recognition scheme. First, a deep model (ConvTransformer) composed of convolutional and Transformer subnets is proposed. The convolutional subnet captures local features, and the Transformer subnet constructs long-term dependencies. Then, the ConvTransformer network is used to recognize different locomotion and transportation modes through location data. Finally, since the still and subway categories are easily confused, our team uses another ConvTransformer network to further classify them through cells and location data. Through cross validation techniques on the training dataset, our proposed scheme achieved a F1 score of 0.6838 on the validation dataset.
Towards Explainability of non-Convolutional Neural Networks

Artificial intelligence has risen in popularity in research and applications in the past years. Explainability is a topic that has been proven to work on human interpretable data like images or sentences, but the research is narrow whenever such data is missing. Creating trust through explainability on raw data processing neural networks of any kind will become necessary in the future as networks are evolving further towards artificial general intelligence. This research is focused to visualize parts of the hidden layers instead of focusing explainability on the input data and is independent of the neural network’s size. We create a model that represents the neural network in a way that neurons that are activating on similar features are grouped together in structures. This model will be analyzed in a machine-learning equivalent process to identify parts of the network being responsible for a decision. In a further step we use the model to test the processing of raw sensor-data versus an approach of heatmapped explainability with a convolutional neural network. Relevant data points in the input are visualized by a common heatmap approach while the hidden layers are analyzed in this research and should point to structures that have a comparable function in the network. For example, if the heatmap highlights peaks of values, the model will be highlighted in the area that is observed as the activation of the neural network on peaks. We will provide the research of artificial general intelligence with a solution for explainability which is necessary for advanced research and the operation of such applications in complex or dangerous scenarios.
CTC-Based Learning of Chroma Features for Score–Audio Music Retrieval

This paper deals with a score–audio music retrieval task where the aim is to find relevant audio recordings of Western classical music, given a short monophonic musical theme in symbolic notation as a query. Strategies for comparing score and audio data are often based on a common mid-level representation, such as chroma features, which capture melodic and harmonic properties. Recent studies demonstrated the effectiveness of neural networks that learn task-specific mid-level representations. Usually, such supervised learning approaches require score–audio pairs where the score's individual note events are aligned to the corresponding time positions of the audio excerpt. However, in practice, it is tedious to generate such strongly aligned training pairs. As one contribution, we show how to apply the Connectionist Temporal Classification (CTC) loss in the training procedure, which only uses weakly aligned training pairs. In such a pair, only the time positions of the beginning and end of a theme occurrence are annotated in an audio recording, rather than requiring local alignment annotations. We evaluate the resulting features in our theme retrieval scenario and show that they improve the state of the art for this task. As a main result, we demonstrate that with the CTC-based training procedure using weakly annotated data, we can achieve results almost as good as with strongly annotated data. Furthermore, we assess our chroma features in depth by inspecting their temporal smoothness or granularity as an important property and by analyzing the impact of different degrees of musical complexity on the features.
Raman spectroscopy in black phosphorus

In this article, we review the current status of Raman spectroscopy in orthorhombic black phosphorus (BP) (for simplicity, henceforth, referred to as BP). BP is a layered semiconductor crystal that recently regained interest because it can be exfoliated down to the single layer, thus exhibiting 2-D properties. First, we briefly review the crystalline structure and the phonon dispersion relations in BP. Then, the symmetries of the Raman-active modes are discussed in the light of group theory, and the scattering configurations for observing the different phonon modes are presented. Polarized Raman spectroscopy results are discussed and reveal unusual angular dependence features, which can be ascribed to the linear dichroism and to the complex nature of the electron–phonon matrix elements. Edge phonon effects originated from rearrangements of the atomic terminations are also discussed. Subsequently, Raman modes that emerge from interlayer interaction and that are only visible in the few-layer regime are presented and discussed. Finally, we outline new perspectives to BP Raman spectroscopy in directions that remain partially or totally unexplored and that can provide potentially important outcomes to problems such as defects, oxidation, doping, strain, stacking order and other BP-like 2-D materials. Copyright © 2017 John Wiley & Sons, Ltd.
Deep Neural Network Model based on One-Stage Detector for Identifying Fundus Lesions

Diabetic Retinopathy is a major cause of vision loss caused by retina lesions, including hard and soft exudates, microaneurysms, and hemorrhages. The development of a computational tool capable of detecting these lesions can assist in the early diagnosis of the most severe forms of the lesions and assist in the screening process and definition of the best treatment form. However, the detection of tiny objects of very different sizes and shapes makes the detection process more complicated. This paper proposes a computational model based on pre-trained convolutional neural networks capable of detecting fundus lesions to promote medical diagnosis support. We trained, adjusted, and evaluated the model using the DDR diabetic retinopathy dataset and implemented it based on a YOLOv4 architecture and Darknet framework, achieving an mAP of 7.26% and a mloU of 11.64%. The experimental results show that the proposed model presented results superior to those obtained in related works found in the literature.
Detection and Classification of Cardiovascular Disease from Phonocardiogram using Deep Learning Models

Cardiovascular disease (CVD) is one of the prime reason for death in India and across the globe. Rural areas of India suffer from shortage of cardiologist and medical facilities. Hence there is a need for the development of an efficient, automated heart disease detection system that can analyse the phonocardiogram to detect the disease. The paper proposes deep learning architectures for anomaly detection from heart sounds. The work classifies the unsegmented phonocardiograms into five classes, four cardiovascular diseases and normal(N). The detected pathological conditions are mitral valve prolapse (MVP), mitral stenosis (MS), mitral regurgitation (MR) and aortic stenosis (AS). Features are extracted using Mel Frequency Cepstral Coefficient (MFCCs) and learning and classification are performed using deep learning methods such as Convolutional Neural Network (CNN), Long Short Term Memory (LSTM) and a combination of 1DCNN and LSTM. A total of 1960 phonocardiogram (PCG) segments are used to develop the models with 392 segments in each class. We have achieved an accuracy of 99.1%, 98.2%, 99.4% for CNN, LSTM and 1DCNN-LSTM respectively.
A Review on Sentiment Analysis in Different Language

Due to advancements in web technology, an immense amount of data is present on the internet. Besides examining data present on the internet, users also feedback thus generating more useful data. This led to the expansion in the availability of data in various languages because people use different languages to communicate with each other. This data present in different forms like social media, blogs, reviews, or tweets. To recover useful information it is necessary to examine the data. Sentiment analysis is a technique that analyzes the data present in the form of text and determines the opinion from the text. Sentiment analysis is performed for different types of text. It is the need of the hour to perform sentiment analysis on different languages because of the increasing amount of data over the internet, which leads researchers to analyze the data and get useful information from it. Researchers have proposed various models for sentiment analysis of the text. These models use Machine learning, Lexicon-based, or a combination of both approaches for analysis of the text. This paper aims to provide a systematic review of different languages which are used for sentiment analysis in research work and analyzes various approaches used for sentiment analysis. Also, discuss various evaluation parameter which is used by them for the performance evaluation of their modal.