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Numerous reviews are available in the literature on PV inverter topologies. These reviews have intensively investigated the available PV inverter topologies from their modulation techniques, control strategies, cost, and performance aspects. However, their compliance with industrial standards has not been investigated in detail so far in the literature. There are various standards such as North American standards (UL1741, IEEE1547, and CSA 22.2) and Australian and European safety standards and grid codes, which include IEC 62109 and VDE. These standards provide detailed guidelines and expectations to be fulfilled by a PV inverter topology. Adherence to these standards is essential and crucial for the successful operation of PV inverters, be it a standalone or grid-tied mode of operation. This paper investigates different PV inverter topologies from the aspect of their adherence to different standards. Both standalone and grid-tied mode of operation-linked conditions have been checked for different topologies. This investigation will help power engineers in selecting suitable PV inverter topology for their specific applications.

Dental impression tray is frequently used in dentistry to record patient’s oral structure for clinical oral diagnosis and treatment planning. Manual procedure of taking impressions is costly, time-consuming, and additionally, no research has been done to select dental impression tray from dental arch images using computer vision in real-life scenarios. In this spirit, an intelligent model is proposed based on computer vision and machine learning to select appropriate dental impression trays from maxillary arch images. A dataset of 52 patients’ maxillary arch images that matches one from 4 sizes of Kurten’s impression tray have been acquired. Various sets features such as, colors, textures, and shapes of the images were extracted to better characterize the maxillary arch images. Considering the importance of the features in describing the maxillary arch object and to improve the classification performance, a method based on multi-feature with ensemble classifier is proposed. Besides, the performance of a deep-learning based multilayer perceptron neural network is also investigated. The proposed multi-feature with ensemble classifier attained 92.31% precision, 91.75% recall, 91.75% accuracy, respectively, on the dataset. This clearly establishes the feasibility of this study. An illustration of a real-life application of the proposed model is also provided.

This work addresses a critical issue: the deterioration of concrete structures due to fine-grained cracks, which compromises their strength and longevity. To tackle this problem, experts have turned to computer vision (CV) based automated strategies, incorporating object detection and image segmentation techniques. Recent efforts have integrated complex techniques such as deep convolutional neural networks (DCNNs) and transformers for this task. However, these techniques encounter challenges in localizing fine-grained cracks. This paper presents a self-supervised 'you only look once' (SS-YOLO) approach that utilizes a YOLOv8 model. The novel methodology amalgamates different attention approaches and pseudo-labeling techniques, effectively addressing challenges in fine-grained crack detection and segmentation in concrete structures. It utilizes convolution block attention (CBAM) and Gaussian adaptive weight distribution multi-head self-attention (GAWD-MHSA) modules to accurately identify and segment fine-grained cracks in concrete buildings. Additionally, the assimilation of curriculum learning-based self-supervised pseudo-labeling (CL-SSPL) enhances the model's ability when applied to limited-size data. The efficacy and viability of the proposed approach are demonstrated through experimentation, results, and ablation analysis. Experimental results indicate a mean average precision (mAP) of at least 90.01%, an F1 score of 87%, and an intersection over union threshold greater than 85%. It is evident from the results that the proposed method yielded at least 2.62% and 4.40% improvement in mAP and F1 values, respectively, when tested on three diverse datasets. Moreover, the inference time taken per image is 2 ms less than that of the compared methods.

Fused Deposition Modeling (FDM) is an additive manufacturing (AM) technique based on the principle of forced extrusion. It is the most commonly used 3D printing processes, subjected to its ease of utilization. With the increase in product customizations, the use of 3D printing technique for manufacturing of the end-use product is on the rise. Therefore, the strength and other mechanical properties of the 3D-printed finished component are of great importance. These mechanical properties of an FDM-produced part are greatly affected by the selection of different values for printing parameters. Due to operational simplicity and low cost, FDM is widely researched, and a number of scholars have examined the effects of varying the values of parameters on the mechanical properties of the FDM-printed specimens. Where tensile strength of the 3D-printed parts is the mostly studied property among all mechanical properties. However, the effect of changing values of parameters on the tensile strength in relation to build time is least researched. The objective of this research is not only to examine the influence of printing parameters such as layer thickness, print angle, and infill density on the tensile strength of the 3D-printed components and optimize them but also to achieve the desired strength in a faster and timely manner. In this study, tensile test specimens were printed and tested according to ISO-527–2 standards. Analysis of variance (ANOVA) is also performed to check the significance of print parameters. The results suggested that an increase in layer thickness has an inverse impact on the tensile strength, whereas an increase in print angle and infill density has a direct impact on the tensile properties of the FDM produced specimens. Furthermore, the print time is reduced with an increase in layer thickness and a decrease in infill density, as both lead to fewer passes required to print the part. However, print time has variable relationship with the print angle, with the least value at a 90° print angle and the maximum value at a 15° print angle.

Teachers who are aware of their students’ strengths and weakness can tailor their teaching methodologies to meet the challenging students efficiently for better results. This helps them to identify any potential learning challenges at an early stage leading to improved academic performance and success ratio. This also fosters a learning environment where students feel motivated and valued to excel in their respective fields. This study offers a robust adaptive feedback system tailored for Learning Management System leveraging instance level explorations, helping teachers to find the specific instance affecting the learner’s learning outcome. The proposed system can also be utilized by the institutions where the outcome-based education system has been adopted. The study includes Stacking, Capsule Network, SVM, Random Forest, Decision Tree, and KNN for experiments. Stacking achieved the highest accuracy of 76.70% while SVM demonstrated the highest precision of 0.78 showing the effectiveness of ensemble learning techniques. The primary objective of this endeavor is to elevate automated assessment to provide precise and meaningful feedback, enhancing the educational experience for tertiary students through the integration of technology and pedagogical concepts. The learning feedback has been made available via a user-friendly webserver at: https://khan-learning-feedback.streamlit.app/ .

Binary NiO-Fe 2 O 3 , NiO-CdO nanocomposites, and ternary NiO-Fe 2 O 3 -CdO nanocomposite are synthesized using facile co-precipitation method, and their photocatalytic and antibacterial properties are studied. The as-obtained products are characterized using different analytical techniques. The microstructural parameters were calculated using X-ray diffraction data. UV–vis spectra technique was used to calculate the bandgap and listed 3.1, 2.7, and 2.5 eV for NiO-Fe 2 O 3 , NiO-CdO, and NiO-Fe 2 O 3 -CdO nanocomposite, respectively. The photocatalytic activity of as-obtained products was tested under visible light against methylene blue (MB) dye. The NiO-Fe 2 O 3 -CdO nanocomposite has shown higher degradation efficiency as compared to binary nanocomposites and revealed improve electron–hole separation efficiency. The photocatalytic performance of NiO-Fe 2 O 3 -CdO nanocomposite was also tested for other synthetic dyes such as rhodamine-B (RhB), methyl orange (MO), and cresol red (CR). The antibacterial performance of grown products was tested against E. coli bacteria. The ternary NiO-Fe 2 O 3 -CdO nanocomposite has shown higher antibacterial activity than binary NiO-Fe 2 O 3 and NiO-CdO nanocomposites.

Mango sudden death (MSD) or quick decline (QD) is the most destructive disease found in mango orchards of Pakistan and is characterized by collapse of the vascular system by Ceratocystis fimbriata and Lasiodiplodia theobromae. Cultural practices, chemicals, and biological control are the most valuable tools for the management of MSD, but the role of micronutrient deficiencies has remained an area that is heavily ignored by the farming community. To study the impact of micronutrients, four mango orchards were selected at different locations where different combinations of micronutrients, i.e., Zinc (Zn), Boran (B), and Copper (Cu) in the form of Zinc sulphate (ZnSO4), Borax/Boric acid (H3BO3), and Copper Sulphate (CuSO4), were applied both foliar and in drench along with the recommended doses of Nitrogen: Phosphorous: Potassium (NPK), and Farmyard manure (FYM), respectively. The quantities of micronutrients were determined from the soil and leaves before and after application of the treatments. The impact of micronutrients was measured in terms of reduction in disease severity and increase in fruit yield. The results revealed that the application of all three micronutrients both in soil drench and in foliar form significantly decreased the disease severity at three locations and increased the yield in all four mango orchards. Application of ZnSO4 (0.8%), +H3BO3 (0.8%), +CuSO4 (0.5%) and as soil drench ZnSO4 (400 g) + Borax (200 g) + CuSO4 200 g plant−1 proved to be the best treatments, with an average of 12.88 and 14.03% reduction in disease severity and with an average yield of 128 and 119 kg, respectively. The application of micronutrients would be a promising solution in an integrated disease management program used to tackle MSD.

Scientific evidence suggests that quercetin (QUR) has anxiolytic-like effects in experimental animals. However, the mechanism of action responsible for its anxiolytic-like effects is yet to be discovered. The goal of this research is to assess QUR’s anxiolytic effects in mouse models to explicate the possible mechanism of action. After acute intraperitoneal (i.p.) treatment with QUR at a dose of 50 mg/kg (i.p.), behavioral models of open-field, hole board, swing box, and light–dark tests were performed. QUR was combined with a GABAergic agonist (diazepam) and/or antagonist (flumazenil) group. Furthermore, in silico analysis was also conducted to observe the interaction of QUR and GABA (α5), GABA (β1), and GABA (β2) receptors. In the experimental animal model, QUR had an anxiolytic-like effect. QUR, when combined with diazepam (2 mg/kg, i.p.), drastically potentiated an anxiolytic effect of diazepam. QUR is a more highly competitive ligand for the benzodiazepine recognition site that can displace flumazenil (2.5 mg/kg, i.p.). In all the test models, QUR acted similar to diazepam, with enhanced effects of the standard anxiolytic drug, which were reversed by pre-treatment with flumazenil. QUR showed the best interaction with the GABA (α5) receptor compared to the GABA (β1) and GABA (β2) receptors. In conclusion, QUR may exert an anxiolytic-like effect on mice, probably through the GABA-receptor-interacting pathway.

Palatal defects are rehabilitated by fabricating maxillofacial prostheses called obturators. The treatment incorporates taking deviously unpredictable impressions to facsimile the palatal defects into plaster casts for obturator fabrication in the dental laboratory. The casts are then digitally stored using expensive hardware to prevent physical damage or data loss and, when required, future obturators are digitally designed, and 3D printed. Our objective was to construct and validate an economic in-house smartphone-integrated stereophotogrammetry (SPINS) 3D scanner and to evaluate its accuracy in designing prosthetics using open source/free (OS/F) digital pipeline. Palatal defect models were scanned using SPINS and its accuracy was compared against the standard laser scanner for virtual area and volumetric parameters. SPINS derived 3D models were then used to design obturators by using (OS/F) software. The resultant obturators were virtually compared against standard medical software designs. There were no significant differences in any of the virtual parameters when evaluating the accuracy of both SPINS, as well as OS/F derived obturators. However, limitations in the design process resulted in minimal dissimilarities. With further improvements, SPINS based prosthetic rehabilitation could create a viable, low cost method for rural and developing health services to embrace maxillofacial record keeping and digitised prosthetic rehabilitation.

Titanium, stainless steel, and CoCrMo alloys are the most widely used biomaterials for orthopedic applications. The most common causes of orthopedic implant failure after implantation are infections, inflammatory response, least corrosion resistance, mismatch in elastic modulus, stress shielding, and excessive wear. To address the problems associated with implant materials, different modifications related to design, materials, and surface have been developed. Among the different methods, coating is an effective method to improve the performance of implant materials. In this article, a comprehensive review of recent studies has been carried out to summarize the impact of coating materials on metallic implants. The antibacterial characteristics, biodegradability, biocompatibility, corrosion behavior, and mechanical properties for performance evaluation are briefly summarized. Different effective coating techniques, coating materials, and additives have been summarized. The results are useful to produce the coating with optimized properties.