Is lumbar facet joint tropism developmental or secondary to degeneration? An international, large-scale multicenter study by the AOSpine Asia Pacific Research Collaboration Consortium

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Comparison of the traditional outdoor and recirculation indoor rearing systems on survival rate and growth performance of common carp (Cyprinus carpio) larvae during early development

The larvae of common carp (Cyprinus carpio L.) were studied for 6 weeks in an indoor recirculatory system (RAS) and an outdoor earthen pond to compare their effects on growth performance and survival rate. Larvae reared in outdoor earthen ponds achieved significantly higher total length, weight, weight gain, and length increments than indoor groups (P?0.05). However, the indoor recirculatory system had the highest survival rate (83±1.9%) than outdoor earthen ponds (42±3.6%) (P?0.05). The results of the present study revealed that the raising system has a significant impact on the survival and growth performance of the larvae in common carp, and the best growth performance was in the outdoor earthen ponds, while the better survival rate was recorded in the indoor recirculatory system. Therefore, to minimize mortality and maximize survival rate, it is suggested that the larvae were released after hatching into closed and controlled rearing systems before being reared into earthen ponds after starting the exogenous feeding

Critical values of facet joint angulation and tropism in the development of lumbar degenerative spondylolisthesis: an international, large-scale multicenter study by the AOSpine Asia Pacific Research Collaboration Consortium

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Hydrographic, seasonal diversity, distribution and abundance of phytoplankton in coastal waters off Cochin - south-eastern Arabian Sea

319-326Phytoplanktons are the key primary producers in the ocean; their growth mainly depends on various physico-chemical parameters. In this study, a total of 73 species of phytoplanktons from 48 genera were identified from Cochin coastal waters. Diatoms dominated the phytoplankton community followed by dinoflagellates. Asterionellopsis glacialis, Chaetoceros decipiens and Chaetoceros curvisetum were the major diatoms noticed during the study period. The results revealed relatively high species diversity in the pre-monsoon season when compared to monsoon and post-monsoon seasons. The hierarchical multidimensional scaling of phytoplankton communities using PRIMER 6 revealed maximum similarity between the species from stations 1 and 2 during monsoon season. The canonical correspondence analysis was done using PAST version 2.17c. The results showed that post-monsoon stations were characterized by high dissolved oxygen, low temperature, phosphate, ammonia and silicate. Phytoplanktons, such as Rhizosolenia sp., Thalasionema sp., Navicula sp. were found to be strongly linked to these parameters. A bloom of Skeletonema costatum was also observed during the study period

Exploring the Perceptions of mHealth Interventions for the Prevention of Common Mental Disorders in University Students in Singapore: Qualitative Study.

BACKGROUND: Mental health interventions delivered through mobile health (mHealth) technologies can increase the access to mental health services, especially among university students. The development of mHealth intervention is complex and needs to be context sensitive. There is currently limited evidence on the perceptions, needs, and barriers related to these interventions in the Southeast Asian context. OBJECTIVE: This qualitative study aimed to explore the perception of university students and mental health supporters in Singapore about mental health services, campaigns, and mHealth interventions with a focus on conversational agent interventions for the prevention of common mental disorders such as anxiety and depression. METHODS: We conducted 6 web-based focus group discussions with 30 university students and one-to-one web-based interviews with 11 mental health supporters consisting of faculty members tasked with student pastoral care, a mental health first aider, counselors, psychologists, a clinical psychologist, and a psychiatrist. The qualitative analysis followed a reflexive thematic analysis framework. RESULTS: The following 6 main themes were identified: a healthy lifestyle as students, access to mental health services, the role of mental health promotion campaigns, preferred mHealth engagement features, factors that influence the adoption of mHealth interventions, and cultural relevance of mHealth interventions. The interpretation of our findings shows that students were reluctant to use mental health services because of the fear of stigma and a possible lack of confidentiality. CONCLUSIONS: Study participants viewed mHealth interventions for mental health as part of a blended intervention. They also felt that future mental health mHealth interventions should be more personalized and capable of managing adverse events such as suicidal ideation

A technique for representing multiple-output binary functions with applications to verfication and simulation

This paper presents a technique for representing multiple output binary and word-level functions in GF(N) ($N=p^m$, p a prime number and m a nonzero positive integer) based on decision diagrams (DD). The presented DD is canonical and can be made minimal with respect to a given variable order. The DD has been tested on benchmarks including integer multiplier circuits and the results show that it can produce better node compression (more than an order of magnitude in some cases) compared to shared BDDs. The benchmark results also reflect the effect of varying the input and output field sizes on the number of nodes. Methods of graph-based representation of characteristic and encoded characteristic functions in GF(N) are also presented. Performance of the proposed representations has been studied in terms of average path lengths and the actual evaluation times with 50,000 randomly generated patterns on many benchmark circuits. All these results reflect that the proposed technique can out perform existing techniques

Investigation of the superelastic behavior of a Ti-16Zr-13Nb-2Sn sputtered film by nanoindentation

A new Ti-16Zr-13Nb-2Sn superelastic film incorporating beta-stabilizing and highly biocompatible elements was elaborated by magnetron sputtering. The morphological, crystallographic and microstructural characteristics of the obtained films were studied by scanning electron microscopy, atomic force microscopy, X-ray diffraction, and transmission electron microscopy. Superelastic response of the film was investigated at local scale by nano-indentation using both spherical and Berkovich indenters. The sputter-deposited film revealed nanograined beta microstructure with preferential growth orientation along [110] direction and excellent superelastic recovery at room temperature. Special attention was paid to the indenter geometry influencing reliable evaluation of the superelastic nature of the film. Evolution of the deformation mechanisms during nanoindentation at increasing depths was rationalized by the calculated representative strain beneath the indenting tips and is discussed in this work

Enhanced controllable triplet proximity effect in superconducting spin-orbit coupled spin valves with modified superconductor/ferromagnet interfaces

In a superconductor/ferromagnet hybrid, a magnetically controlled singlet-to-triplet Cooper pair conversion can modulate the superconducting critical temperature. In these triplet superconducting spin valves, such control usually requires inhomogeneous magnetism. However, in the presence of spin-orbit coupling from an interfacial heavy-metal layer, the singlet/triplet conversion rate and thus the critical temperature, can be controlled via the magnetization direction of a single homogeneous ferromagnet. Here, we report significantly enhanced controllable pair conversion to a triplet state in a Nb/Pt/Co/Pt superconducting spin valve in which Pt/Co/Pt is homogeneously magnetized and proximity-coupled to a superconducting layer of Nb. The Co/Pt interface furthest away from Nb is modified by a sub-nanometer-thick layer of Cu or Au. We argue that the enhancement is most likely associated from an improvement of the Co/Pt interface due to the insertion of Cu and Au layers. Additionally, the higher normalized orbital moments in Au measured using X-ray magnetic circular dichroism shows that increasing spin-orbit coupling enhances the triplet proximity effect - an observation supported by our theoretical calculations. Our results provide a pathway to enhancing triplet pair creation by interface engineering for device development in superspintronics.Comment: 11 pages, 4 figure

Comprehensive Evaluations of Student Performance Estimation via Machine Learning

Success in student learning is the primary aim of the educational system. Artificial intelligence utilizes data and machine learning to achieve excellence in student learning. In this paper, we exploit several machine learning techniques to estimate early student performance. Two main simulations are used for the evaluation. The first simulation used the Traditional Machine Learning Classifiers (TMLCs) applied to the House dataset, and they are Gaussian Naïve Bayes (GNB), Support Vector Machine (SVM), Decision Tree (DT), Multi-Layer Perceptron (MLP), Random Forest (RF), Linear Discriminant Analysis (LDA), and Quadratic Discriminant Analysis (QDA). The best results were achieved with the MLP classifier with a division of 80% training and 20% testing, with an accuracy of 88.89%. The fusion of these seven classifiers was also applied and the highest result was equal to the MLP. Moreover, in the second simulation, the Convolutional Neural Network (CNN) was utilized and evaluated on five main datasets, namely, House, Western Ontario University (WOU), Experience Application Programming Interface (XAPI), University of California-Irvine (UCI), and Analytics Vidhya (AV). The UCI dataset was subdivided into three datasets, namely, UCI-Math, UCI-Por, and UCI-Fused. Moreover, the AV dataset has three targets which are Math, Reading, and Writing. The best accuracy results were achieved at 97.5%, 99.55%, 98.57%, 99.28%, 99.40%, 99.67%, 92.93%, 96.99%, and 96.84% for the House, WOU, XAPI, UCI-Math, UCI-Por, UCI-Fused, AV-Math, AV-Reading, and AV-Writing datasets, respectively, under the same protocol of evaluation. The system demonstrates that the proposed CNN-based method surpasses all seven conventional methods and other state-of-the-art-work

Intelligent Face Recognition: Comprehensive Feature Extraction Methods for Holistic Face Analysis and Modalities

Face recognition technology utilizes unique facial features to analyze and compare individuals for identification and verification purposes. This technology is crucial for several reasons, such as improving security and authentication, effectively verifying identities, providing personalized user experiences, and automating various operations, including attendance monitoring, access management, and law enforcement activities. In this paper, comprehensive evaluations are conducted using different face detection and modality segmentation methods, feature extraction methods, and classifiers to improve system performance. As for face detection, four methods are proposed: OpenCV’s Haar Cascade classifier, Dlib’s HOG + SVM frontal face detector, Dlib’s CNN face detector, and Mediapipe’s face detector. Additionally, two types of feature extraction techniques are proposed: hand-crafted features (traditional methods: global local features) and deep learning features. Three global features were extracted, Scale-Invariant Feature Transform (SIFT), Speeded Robust Features (SURF), and Global Image Structure (GIST). Likewise, the following local feature methods are utilized: Local Binary Pattern (LBP), Weber local descriptor (WLD), and Histogram of Oriented Gradients (HOG). On the other hand, the deep learning-based features fall into two categories: convolutional neural networks (CNNs), including VGG16, VGG19, and VGG-Face, and Siamese neural networks (SNNs), which generate face embeddings. For classification, three methods are employed: Support Vector Machine (SVM), a one-class SVM variant, and Multilayer Perceptron (MLP). The system is evaluated on three datasets: in-house, Labelled Faces in the Wild (LFW), and the Pins dataset (sourced from Pinterest) providing comprehensive benchmark comparisons for facial recognition research. The best performance accuracy for the proposed ten-feature extraction methods applied to the in-house database in the context of the facial recognition task achieved 99.8% accuracy by using the VGG16 model combined with the SVM classifier