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The use of automated feedback (AF) has been increasing in English writing courses for emergency remote education (ERE) due to the escalating COVID-19 pandemic crisis in Vietnam. The ERE English writing courses for in-service bachelor programs demanded an effective, fast, free, and user-friendly AF tool that does not require a login ID, which can help students to develop English writing skills and increase their motivation and self-learning ability. The main objectives of Vietnamese in-service students were to improve their English writing skills and update teaching methodologies that integrate technology. Although many studies emphasized the impact of AF tools in normal teaching contexts, relatively less research was conducted the use of AF tools among in-service students. This study examined the contribution of the Marking Mate AF to the improvement of the English writing skills of in-service learners in ERE writing classes. The participants were 82 in-service students in a Vietnamese public university. The dataset included English writing pre and posttest results collected during the first semester of academic year 2020-2021. The mean scores (standard deviation) of these tests were used to benchmark the writing performance of students. A self-report survey was also conducted to investigate the attitude of students toward Marking Mate AF. The results demonstrated that the posttest score (8.739; SD = 0.8495) was higher than the pretest score (7.439; SD = 0.976). Students expressed favorable opinions toward the usefulness of Marking Mate. Based on the findings, the study discussed the pedagogical implications regarding the incorporation of AF in face-to-face and online classes.

In case of a hypothetical severe accident in a Sodium-cooled Fast Reactor (SFR), coolability of the debris bed in the post-accident phase plays a vital role in mitigating the accident and ensuring the structural integrity of the reactor vessel. Few numerical studies are reported in literature, in which the boiling heat transfer in debris bed is expressed as equivalent heat conduction using similarity law between heat conduction and two-phase heat transfer. However, these studies assumed steady state mass conservation for the boiling zone and neglected the gravity force. Hence, a detailed study has been carried out for various particle sizes and porosities of SFR debris to investigate the influence of above considerations. The effect of gravity on debris bed coolability is studied using steady state model of Lipinski, which showed that gravity has a non-negligible effect, for particle size of 0.3 mm and porosity of 0.5. However, the gravitation force was found to have a negligible effect in dryout heat flux estimation for the bottom cooled configuration. A transient numerical model is developed for simulating the boiling phenomena in debris beds and validated with the published experimental results. The assumption of steady state mass conservation is verified by carrying out transient analysis, which indicated early prediction of the dryout inception. For time dependent heat generation case, the unsteady mass conservation predicted higher DHF compared to constant heat generation.

Abstract This paper presents a comprehensive study on the implementation and analysis of PID controllers in an automated voltage regulator (AVR) system. A novel tuning technique, Virtual Time response-based iterative gain evaluation and re-design (V-Tiger), is introduced to iteratively adjust PID gains for optimal control performance. The study begins with the development of a mathematical model for the AVR system and initialization of PID gains using the Pessen Integral Rule. Virtual time-response analysis is then conducted to evaluate system performance, followed by iterative gain adjustments using Particle Swarm Optimization (PSO) within the V-Tiger framework. MATLAB simulations are employed to implement various controllers, including the V-Tiger PID controller, and their performance is compared in terms of transient response, stability, and control signal generation. Robustness analysis is conducted to assess the system's stability under uncertainties, and worst-case gain analysis is performed to quantify robustness. The transient response of the AVR with the proposed PID controller is compared with other heuristic controllers such as the Flower Pollination Algorithm, Teaching–Learning-based Optimization, Pessen Integral Rule, and Zeigler-Nichols methods. By measuring the peak closed-loop gain of the AVR with the controller and adding uncertainty to the AVR's field exciter and amplifier, the robustness of proposed controller is determined. Plotting the performance degradation curves yields robust stability margins and the accompanying maximum uncertainty that the AVR can withstand without compromising its stability or performance. Based on the degradation curves, robust stability margin of the V-Tiger PID controller is estimated at 3.5. The worst-case peak gains are also estimated using the performance degradation curves. Future research directions include exploring novel optimization techniques for further enhancing control performance in various industrial applications.

Little millet is an important small millet grown mostly in India. The availability of genetic variability is a critical requirement for crop improvement. Principal Component Analysis (PCA) and correlation study was undertaken in a set of 28 little millet genotypes to estimate genetic diversity, association pattern among seven quantitative traits and to select suitable genotypes for crop improvement. In cluster analysis, the genotypes were classified into three distinct clusters, each with one or two subgroups. Cluster I had the major genotypes with comparable ancestry, followed by Cluster II. The total variance was split into seven major principal components, with the top two PCs with eigenvalues greater than one accounting for 82.57% of the overall variation. PC1, which explained a larger part of the variance (58.05%), was strongly influenced by days to 50% flowering, days to maturity, number of productive tillers per plant, 1000-grain weight, and grain yield. PC2 was primarily influenced by plant height and fodder yield. PCA and association analysis revealed a significant positive association between grain yield and the number of productive tillers per plant, 1000-grain weight, plant height. These traits would be useful for direct selection for little millet crop improvement. In both cluster analysis and PCA, the genotypes DHLM 14-5, IIMR LM-8004, NDL LM1, TNPSu 242, VS 33, WV 168, DPLN 1 and VS 38 displayed diversity, implying that these lines may be used in crossing programme to generate further genetic variability to select suitable transgressive segregants.

The objective of the present investigation was to evaluate 135 sunflower germplasm accessions through Principal Component Analysis and Hierarchical cluster analysis to assess the extent of genetic diversity. The principal components with eigen values more than one i.e., PC1 and PC2 describes 38.3% and 21.24% respectively with a cumulative effect of 59.62% of the total variation. The biplot analysis revealed positive association of seed yield per plant for 100 seed weight, head diameter, days to 50% flowering and it was negatively associated with oil content. By using Wards method of clustering, 135 sunflower accessions were aggregated in 12 clusters based on different traits in which cluster VIII includes 30 accessions and is the largest one. The accessions in cluster IV, I and VI had maximum values for 100 seed weight, seed yield per plant and oil content, respectively. The germplasm accessions 213R, 121R, RHA-GPR-110, CMS-107B, IB-11-3, SCG-107, 132R, NGM-15 and OPH-123 were identified to be potential accessions and these can be used for improvement of yield and its attributing traits in sunflower.

Heart failure, a common clinical syndrome caused by functional and structural abnormalities of the heart, affects 64 million people worldwide. Long-term mechanical circulatory support can offer lifesaving treatment for end-stage systolic heart failure patients. However, this treatment is not without complications. This review covers the major complications associated with implantable mechanical circulatory support devices, including strokes, pump thrombosis and gastrointestinal bleeding. These complications were assessed in patients implanted with the following devices: Novacor, HeartMate XVE, CardioWest, Jarvik 2000, HeartMate II, EVAHEART, Incor, VentrAssist, HVAD and HeartMate 3. Complication rates vary among devices and remain despite the introduction of more advanced technology, highlighting the importance of device design and flow patterns. Beyond clinical implications, the cost of complications was explored, highlighting the difference in costs and the need for equitable healthcare, especially with the expected rise in the use of mechanical circulatory support. Future directions include continued improvement through advancements in design and technology to reduce blood stagnation and mitigate high levels of shear stress. Ultimately, these alterations can reduce complications and enhance cost-effectiveness, enhancing both the survival and quality of life for patients receiving mechanical circulatory support.

One major factor in the rising incidence of traffic accidents is the driver’s drowsiness. Innovations in computer vision technology have made it possible to construct smart cams that can recognize driver fatigue. By alerting drivers, this technology successfully lowers the total number of accidents caused by weariness. This study proposes a DrowsyDetectNet that utilizes a shallow Convolutional Neural Network (CNN) architecture to identify driver drowsiness. The 68-point face landmark identification approach is used to identify faces and extract eye areas. The proposed system employs a shallow CNN architecture with fewer layers and parameters to detect driver drowsiness with limited training data. Feature extraction focuses on relevant visual cues for drowsiness detection, such as eyelid closure. The transfer learning models, such as VGG19, ResNet50, MobileNetV2, and InceptionV3, are also used to identify driver drowsiness. Two datasets, Dataset-1 and Dataset-2, were utilized to assess this study. On two datasets, the proposed DrowsyDetectNet produced an accuracy of 99.23% and 99.14%, respectively. The proposed DrowsyDetctNet framework achieved better accuracy when compared with state-of-the-art models and pre-trained models.

In the domain of printed circuit board (PCB) defect detection and classification, the availability of diverse and comprehensive datasets is fundamental for developing effective detection models. However, existing datasets often lack comprehensive labeling and focus on specific defect types, limiting their applicability to real-world scenarios. To address this gap, we introduce a new dataset named ‘dataset for Mixed Defect Detection in PCB’ (MDD_PCB), which includes intentionally induced mixed PCB defects to provide a more realistic representation of practical scenarios. We evaluate the MDD_PCB dataset using YOLO models and implement it successfully for real-time inference on Jetson Nano, achieving enhanced detection capabilities. Our optimized YOLOv5n model trained on the MDD_PCB dataset achieves impressive metrics (accuracy 93%, precision 95%, recall 96%, mAP 95%, F1-score 94%) with a detection speed of 120.69 frames per second (FPS). Real-time deployment on the Jetson Nano demonstrates practical usability with a detection speed of 30 frames per second (FPS). These results underscore the significance of the diverse dataset proposed, which contributes to robust detection solutions and advances in PCB defect detection methodologies.

Amniotic Fluid Embolism (AFE) is a rare obstetric emergency with a potentially fatal outcome, occurring in approximately 1.9 to 6.1 cases per 100,000 deliveries, with variations observed among different countries. Obstetric management continues to be the most common preceding event associated with AFE, making it the primary contributing factor. However, traumatic AFE cases are being typically reported as isolated case reports in literature. While previous case reports on the traumatic basis of AFE have primarily associated AFE with blunt-force abdominal trauma resulting from car accidents and evident gross injuries, this article presents a unique case of AFE following minor blunt trauma from a self-fall from a height of 2.5 ft, where no macroscopic lesions were observed. The autopsy lung histopathology exhibited alveolar spaces filled with serous fluid and lymphocytes, as well as emboli composed of fatty material in blood vessels. Some blood vessels contained keratinous emboli. Severe pulmonary oedema and embolized vessels were consistently observed throughout the lung sections. We hypothesise that a simple fall from a height of 2.5 feet onto a firm surface, leading to transmitted blunt force to the abdomen, may trigger AFE by disrupting the flow dynamics of the foeto-placento-maternal circulation. Despite the absence of gross injuries, the underlying pressure transmission is emphasized as a significant factor in AFE initiation due to the disruption of the microvasculature. This case report highlights the importance of recognizing the possibility of AFE even in cases of seemingly minor trauma without visible external signs.

The relentless global population growth and the ever-increasing food demand pose formidable challenges to the agricultural sector. Farmers grapple with the ongoing challenge of wildlife-induced crop damage and human loss, which not only impedes food production but also exacerbates supply and demand imbalances. However, the rise of TinyML enables Edge AI as a promising avenue for implementing resource-efficient deep learning techniques on low-end edge devices. In this paper, we introduce an innovative solution that harnesses the power of Edge AI using tinyML-based deep learning algorithms in conjunction with the Internet of Things (IoT) for animal intrusion detection and deterrence system. The proposed system is developed to create remotely managed defense system tailored to safeguard vast agricultural expanses. It integrates a laser detection system and an AI-CAM with light weight deep learning algorithms for animal intrusion detection and classification. This system also ensures efficient animal deterrence and real-time monitoring for farmers, enabling them to assess the situation with the assistance of an intelligent rover build using IoT. This work emphasizes on proposing a light-weight deep learning model named EvoNet for animal classification. Results reveal that the proposed model achieves the highest accuracy at 96.7%, surpassing other models presented in this paper. However, for edge devices where compact file sizes are crucial, the model also offers comparable accuracy with file sizes as low as 1.63MB with the help of pruning and quantization techniques. This conceptualized solution has the potential to revolutionize agricultural wildlife management, ushering in a new era of crop protection and economic resilience.

Advancements in network science have facilitated the study of brain communication networks. Existing techniques for identifying event-related brain functional networks (BFNs) often result in fully connected networks. However, determining the optimal and most significant network representation for event-related BFNs is crucial for understanding complex brain networks. The presence of both false and genuine connections in the fully connected network requires network thresholding to eliminate false connections. However, a generalized framework for thresholding in network neuroscience is currently lacking. To address this, we propose four novel methods that leverage network properties, energy, and efficiency to select a generalized threshold level. This threshold serves as the basis for identifying the optimal and most significant event-related BFN. We validate our methods on an openly available emotion dataset and demonstrate their effectiveness in identifying multiple events. Our proposed approach can serve as a versatile thresholding technique to represent the fully connected network as an event-related BFN.

The most versatile resource for storing energy is one that can rapidly charge or discharge while supporting the use of renewable energy. As renewable energy sources advance rapidly, batteries play a pivotal role in this progress. When integrating battery energy storage into a renewable energy system, it’s crucial to address the issue of battery degradation while implementing operational strategies. Furthermore, since solar irradiation varies due to changing cloud conditions, it can impact how batteries charge and discharge. This study focuses on investigating battery degradation and lifetime. Experimental work is being conducted with lead acid batteries connected to a solar photovoltaics system. The paper provides a detailed investigation of commonly used methods for predicting battery lifespan. It also analyzes aspects such as the effects of depth of discharge (DoD) and battery charge/discharge on temperature changes due to degradation. Using the coarse average approach, global battery aging, weighted Ah aging method, and RFC method, this paper estimates the DoD, temperature, life cycle loss (%), and lifespan and evaluates the extent of battery degradation. The battery lifespan is estimated using this method to be 8.42, 8.72, 8.33, and 8.93 years, respectively.