Research Based Learning: Added Value in Students' Science Critical Thinking Skills

Many new learning models in the 21st century have emerged in improving students' academic skills, one of which is research-based learning (RBL). This pedagogic and constructivist model connects research and learning in improving students' critical thinking skills (CTS). The results of the study show that studies in empowering students' CTS using RBL are still limited. Therefore, exploration and deeper measurement of CTS with the RBL model were carried out through this study. The purpose of this study was to improve students' CTS using the RBL learning model. The results of the ANCOVA test showed that there was an effect of the RBL model in improving students' CTS. Descriptive data also shows that the average value of CTS is 72.70 using RBL, while students who take part in learning using conventional models show an average value of critical thinking skills of 58.30. Thus, RBL can be recommended in increasing the CTS of elementary school students in science learning.

Uncertainty-guided learning with scaled prediction errors in the basal ganglia

To accurately predict rewards associated with states or actions, the variability of observations has to be taken into account. In particular, when the observations are noisy, the individual rewards should have less influence on tracking of average reward, and the estimate of the mean reward should be updated to a smaller extent after each observation. However, it is not known how the magnitude of the observation noise might be tracked and used to control prediction updates in the brain reward system. Here, we introduce a new model that uses simple, tractable learning rules that track the mean and standard deviation of reward, and leverages prediction errors scaled by uncertainty as the central feedback signal. We provide a normative analysis, comparing the performance of the new model with that of conventional models in a value tracking task. We find that the new model has an advantage over conventional models when tested across various levels of observation noise. Further, we propose a possible biological implementation of the model in the basal ganglia circuit. The scaled prediction error feedback signal is consistent with experimental findings concerning dopamine prediction error scaling relative to reward magnitude, and the update rules are found to be consistent with many features of striatal plasticity. Our results span across the levels of implementation, algorithm, and computation, and might have important implications for understanding the dopaminergic prediction error signal and its relation to adaptive and effective learning.

Qualitative Interviews Exploring the Views of Healthcare Professionals Working in a Mental Health Trust in England on Pharmacists as Future Approved Clinicians

BackgroundThe Mental Health Act 1983 was amended in 2007 introducing the role of the Approved Clinician (AC) which could be assumed by individuals from several professional groups. Although the role of mental health pharmacists have undergone significant transformation over the past few decades, pharmacists remain ineligible to train and practise as an AC. There is a paucity of research on non-medical ACs and there are currently no known studies exploring the potential of mental health pharmacists to be considered for the role of AC in future.AimThis qualitative research explored the views and attitudes of a range of healthcare professionals towards the role of the mental health pharmacist, and whether they could and/or should be enabled, via a legislative change, to become ACs in the future.MethodRecruitment based on systematic purposive sampling principles took place at one mental health trust in England. Six pharmacists, five medical ACs and two experienced mental health nurses participated in digitally audio-recorded semi-structured interviews between June-November 2020. The recordings were transcribed verbatim before being inductively coded and thematically analysed.ResultsNotwithstanding the wide recognition among participants of several key skills possessed by mental health pharmacists, various obstacles were also identified to their becoming ACs in future, including prevalent conventional models of pharmacy services delivery restricting adequate patient access, as well as insufficient training opportunities to acquire advanced clinical skills particularly in diagnosis and assessment. Participants also highlighted wider concerns with current uptake of the non-medical AC role which could influence the success of pharmacists’ involvement, including legislative restrictions and a lack of perceived training support.ConclusionChanges to the skill mix within multidisciplinary mental health teams as well as to the training of staff may be required to equip pharmacists with essential skills to be able to transition towards the AC role. Further research is required to gain a better understanding of the challenges facing the clinical development and enhanced utilisation of highly specialised mental health pharmacists across services.

Smart Heart Disease Prediction System with IoT and Fog Computing Sectors Enabled by Cascaded Deep Learning Model

Chronic illnesses like chronic respiratory disease, cancer, heart disease, and diabetes are threats to humans around the world. Among them, heart disease with disparate features or symptoms complicates diagnosis. Because of the emergence of smart wearable gadgets, fog computing and “Internet of Things” (IoT) solutions have become necessary for diagnosis. The proposed model integrates Edge-Fog-Cloud computing for the accurate and fast delivery of outcomes. The hardware components collect data from different patients. The heart feature extraction from signals is done to get significant features. Furthermore, the feature extraction of other attributes is also gathered. All these features are gathered and subjected to the diagnostic system using an Optimized Cascaded Convolution Neural Network (CCNN). Here, the hyperparameters of CCNN are optimized by the Galactic Swarm Optimization (GSO). Through the performance analysis, the precision of the suggested GSO-CCNN is 3.7%, 3.7%, 3.6%, 7.6%, 67.9%, 48.4%, 33%, 10.9%, and 7.6% more advanced than PSO-CCNN, GWO-CCNN, WOA-CCNN, DHOA-CCNN, DNN, RNN, LSTM, CNN, and CCNN, respectively. Thus, the comparative analysis of the suggested system ensures its efficiency over the conventional models.

STHarDNet: Swin Transformer with HarDNet for MRI Segmentation

In magnetic resonance imaging (MRI) segmentation, conventional approaches utilize U-Net models with encoder–decoder structures, segmentation models using vision transformers, or models that combine a vision transformer with an encoder–decoder model structure. However, conventional models have large sizes and slow computation speed and, in vision transformer models, the computation amount sharply increases with the image size. To overcome these problems, this paper proposes a model that combines Swin transformer blocks and a lightweight U-Net type model that has an HarDNet blocks-based encoder–decoder structure. To maintain the features of the hierarchical transformer and shifted-windows approach of the Swin transformer model, the Swin transformer is used in the first skip connection layer of the encoder instead of in the encoder–decoder bottleneck. The proposed model, called STHarDNet, was evaluated by separating the anatomical tracings of lesions after stroke (ATLAS) dataset, which comprises 229 T1-weighted MRI images, into training and validation datasets. It achieved Dice, IoU, precision, and recall values of 0.5547, 0.4185, 0.6764, and 0.5286, respectively, which are better than those of the state-of-the-art models U-Net, SegNet, PSPNet, FCHarDNet, TransHarDNet, Swin Transformer, Swin UNet, X-Net, and D-UNet. Thus, STHarDNet improves the accuracy and speed of MRI image-based stroke diagnosis.

Beyond Welfare

Invoking a broad catalog of applicable Bahá’í principles, this paper presents the conceptual and theoretical underpinnings of a Bahá’í approach to economic growth and disparity and then maps these concepts onto an applied framework of economic rights and responsibilities. The framework that emerges thus both conceptualizes the underlying virtues that govern economic prosperity in a Bahá’í model and shows how these principles might lead to normative prescriptions for economic rights and responsibilities. The paper concludes that the Bahá’í principles dealing with economic prosperity expand the theory and practice of economic justice and give rise to individual and institutional rights and responsibilities that go beyond the imperatives of conventional models of welfare.

THE IMPACT OF EMERGING INFORMATION COMMUNICATION TECHNOLOGY IN THE PORT: INCREASING UTILITIES RENDERING TO SHARED VERDICTS

Today, transportation quantities on roadways have increased to the point where alternate forms of transportation are required. Sea Routes Transportation (SRT) is one option that has the potential to assist relieve traffic congestion on roadways. Most SRT systems employ vessels in which cargo is rolled on and off by a ramp with relatively limited capacity, often less than 500 TEU, however with increased cargo flow, it is unclear if such alternatives will be economical. The dilemma for ports participating in SRT to face this new massive change is to make suitable expenditures and analysis software. Transition is not just a current trend, as well as a structured process. That modification could not be avoided by excluding ports. A transformation project has begun in order to modify their operating structure as well as the services they provide. Artificial intelligence and data-driven services expand the landscape of services far beyond conventional models now in use. The purpose of this study is to examine and assess the new potential for Telecommunications/Information and Communication Technology (ICT) companies at port facilities. These prospects are a first step in transforming ports for the long term. The research’s important component is the application of technology science approaches in the area of strategy and planning connected to the construction of components systems (e.g., interrelations among ships and materiel) with managing equipment selections. KEYWORDS: Information and Communication Technology (ICT), Port, Sea Routes Transportation (SRT)

Domain-Specific On-Device Object Detection Method

Object detection is a significant activity in computer vision, and various approaches have been proposed to detect varied objects using deep neural networks (DNNs). However, because DNNs are computation-intensive, it is difficult to apply them to resource-constrained devices. Here, we propose an on-device object detection method using domain-specific models. In the proposed method, we define object of interest (OOI) groups that contain objects with a high frequency of appearance in specific domains. Compared with the existing DNN model, the layers of the domain-specific models are shallower and narrower, reducing the number of trainable parameters; thus, speeding up the object detection. To ensure a lightweight network design, we combine various network structures to obtain the best-performing lightweight detection model. The experimental results reveal that the size of the proposed lightweight model is 21.7 MB, which is 91.35% and 36.98% smaller than those of YOLOv3-SPP and Tiny-YOLO, respectively. The f-measure achieved on the MS COCO 2017 dataset were 18.3%, 11.9% and 20.3% higher than those of YOLOv3-SPP, Tiny-YOLO and YOLO-Nano, respectively. The results demonstrated that the lightweight model achieved higher efficiency and better performance on non-GPU devices, such as mobile devices and embedded boards, than conventional models.

Penerapan Model React Terhadap Pemahaman Konsep Siswa Kelas XI SMA

This study aims to determine differences in conceptual understanding of students who are taught using the Relating, Experiencing, Applying, Cooperating, Transfering (REACT) model with students who are taught using conventional models and to describe the improvement in conceptual understanding of students in class XI SMA Kemala Bhayangkari on Acid Basa material. The form of research used was a quasi experimental design with a nonequivalent control group design research design. The population in this study were all students of class XI MIA SMA Kemala Bhayangkari Kubu Raya for the academic year 2019/2020, totaling 62 students. The sample selection technique was carried out by simple random sampling. The data collection technique used was a test of students' conceptual understanding. Data analysis used Shapiro-Wilk test, Levene statistic, U-Mann Whitney and N-Gain calculation. The results of data analysis using the U-Mann Whitney test obtained an asymp.sig (2-tailed) value of 0.208, which means that there are differences in conceptual understanding between students taught using the REACT model and students taught using conventional models.. Based on the N-Gain calculation, it is known that the REACT model can improve students' conceptual understanding by 0.42 in the moderate category.

Deep Learning based Handwriting Recognition with Adversarial Feature Deformation and Regularization

Due to the complex and irregular shapes of handwritten text, it is challenging to spot and recognize the handwritten words. In low-resource scripts, retrieval of words is a difficult and laborious task. The need for increasing the number of samples and introducing variations in the extended training datasets occur with the use of deep learning and neural network models. All possible variations and occurrences cannot be covered in an efficient manner with the use of the existing preprocessing strategies and theories. A scalable and elastic methodology for wrapping the extracted features is presented with the introduction of an adversarial feature deformation and regularization module in this paper. In the original deep learning framework, this module is introduced between the intermediate layers while training in an alternative manner. When compared to the conventional models, highly informative features are learnt in an efficient manner with the help of this setup. Extensive word datasets are used for testing the proposed model, which is built on popular frameworks available for word recognition and spotting, while enhancing them with the proposed module. While varying the training data size, the results are recorded and compared with the conventional models. Improvement in the mAP scores, word-error rate and low data regime is observed from the results of comparison.