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Ensuring food security is a top goal for all nations, yet infected plants can negatively impact agricultural production and the country's economic resources. In the past, farmers have depended on conventional techniques to enhance crop yield. In recent times, there has been a significant decline in crop production due to pest infestations on Chilli crops. The progress of deep learning techniques facilitates the categorization of diverse sorts of images in practical applications. Especially, detecting multi-class Chilli crop pests with good accuracy using deep learning algorithms is consistently a significant challenge. The proposed study concentrated in identifying pests on Chilli leaves using deep learning methods such as YOLOv5 and YOLOv7. To improve classification accuracy, a new and unique dataset called the standard balanced custom 'Chilli pest dataset' is created with 13,414 pest images. This dataset includes three specific pest classes: Black Thrips, Redmites, and White Fly. We analysed the custom Chilli dataset using YOLOv5 and YOLOv7 to evaluate their effectiveness in detecting pests in Chilli crops and obtained novel detection performance metrics. The resultant parameters mean Average Precision (mAP) for all three pest classes is 98.6% for YOLOv5 and 86.1% for YOLOv7. The YOLOv5s detector demonstrates superior performance compared to the YOLOv7 pest classification, with a 12.5% improvement. The YOLOv7 algorithm achieves its best classification accuracy (86.1%) at a lower epoch (110), while the YOLOv5 algorithm achieves its highest classification accuracy (98.6%) at a higher epoch (350). Nevertheless, despite this distinction, the YOLOv5 algorithm is recommended as the superior detector for accurately identifying pests in well-balanced multi-class pest type datasets, in comparison to YOLOv7, VGG-16 (~92.7%), and VGG-19 (~84.24%) deep learning architectures. [ABSTRACT FROM AUTHOR]

Growing mangoes is an important part of life in southern India and a major economic driver for the area. Nevertheless, several leaf diseases often impede mango tree development and production, substantially affecting harvest output and quality. Detecting and identifying mango leaf diseases early can be challenging due to the diverse crop varieties, climatic circumstances, and numerous disease signs. While deep-learning methods have been developed to address this problem, they generally need help to detect illnesses across geographies and crop types. To tackle this difficulty, this research offers a transfer learning model that uses Explainable Artificial Intelligence (XAI) characteristics to identify and categorize leaf diseases. This research proposes MLTNet (Mango Leaf Disease Classification with Transfer Learning, Feature Localization, and Visual Explanations) in this study. Our study utilized a dataset from Southern India comprising 1,275 high-quality images of mango leaves affected by diseases like rust and powdery mildew, augmented to 11,480 images across 14 classes to enhance model training and robustness. This novel model utilizes Explainable Artificial Intelligence (XAI) techniques such that leaf disease detection and categorization may achieve higher levels of accuracy. The research work lies in the development of the MLTNet model, which integrates Explainable Artificial Intelligence (XAI) techniques with the ResNet50 architecture to enhance classification accuracy in mango leaf disease detection. This model uniquely employs advanced data pre-processing methods like Error Level Analysis and incorporates Grad-CAM for feature localization and visual explanations. We compared MLTNet's performance with state-of-the-art models like ResNet-50, VGG-16, and InceptionV3, focusing on accuracy, interpretability, and computational efficiency. MLTNet demonstrated superior performance, achieving a training accuracy of 94.3% and a test accuracy of 86.3%, which notably surpasses other models under similar conditions. This success is attributed to the model's ability to leverage complex features from the augmented dataset and the added interpretability provided by XAI techniques. [ABSTRACT FROM AUTHOR]

Aigialomycin D, a 14-membered benzannulated macrolactone, was synthesized in a simple, efficient and stereoselective approach using inexpensive and commonly accessible starting materials. The main steps in this convergent synthesis are Corey-Fuchs reaction, Yamaguchi esterification and ring closing metathesis (RCM). [ABSTRACT FROM AUTHOR]

The current study sought to determine the influence of strontium addition on the microstructure and machinability of the A357 alloy. Furthermore, the influence of cutting parameters and aging temperatures on the machining performance of the modified alloy was investigated using a statistical technique. The machinability characteristics were investigated by milling experiments with a carbide tool. Experimental trials were carried out using Taguchi's L27 orthogonal array. Process parameters studied were strontium (Sr) percentage, aging temperature, cutting speed, and feed rate. Cutting force, surface roughness, and tool wear were investigated as responses. The microstructure of the specimens revealed that the addition of Sr to the A357 alloy helped to achieve refined grain structure. Furthermore, increasing the Sr concentration from 4 to 8% resulted in the enhanced refined microstructure. ANOVA analysis of responses revealed that Sr%, aging temperature, and feed rate have a significant effect on all the responses considered. However, cutting speed has exhibited the least influence. Further, the increase in Sr% resulted in an increase in cutting force and tool wear. Whereas, a decrease in surface roughness was observed due to increased Sr%. Whereas the increase of aging temperature, cutting temperature, and feed rate has resulted in the increase of response values. The aging temperature has shown significant influence on the variation of cutting force and surface roughness.. [ABSTRACT FROM AUTHOR]

The South Indian mango industry is confronting severe threats due to various leaf diseases, which significantly impact the yield and quality of the crop. Themanagement and prevention of these diseases dependmainly on their early identification and accurate classification. The central objective of this research is to propose and examine the application of Deep Convolutional Neural Networks (CNNs) as a potential solution for the precise detection and categorization of diseases impacting the leaves of South Indian mango trees. Our study collected a rich dataset of leaf images representing different disease classes, including Anthracnose, Powdery Mildew, and Leaf Blight. To maintain image quality and consistency, pre-processing techniques were employed.We then used a customized deepCNNarchitecture to analyze the accuracy of South Indian mango leaf disease detection and classification.This proposed CNN model was trained and evaluated using our collected dataset. The customized deep CNN model demonstrated high performance in experiments, achieving an impressive 93.34% classification accuracy. This result outperformed traditional CNN algorithms, indicating the potential of customized deep CNN as a dependable tool for disease diagnosis. Our proposed model showed superior accuracy and computational efficiency performance compared to other basic CNN models. Our research underscores the practical benefits of customized deep CNNs for automated leaf disease detection and classification in South Indian mango trees. These findings support deep CNN as a valuable tool for real-time interventions and improving crop management practices, thereby mitigating the issues currently facing the South Indian mango industry. [ABSTRACT FROM AUTHOR]

The paper proposes a method for social separating identification based on deep understanding of how to measure the gap between people in order to mitigate the impact of the COVID-19 pandemic. By evaluating with the aid of videos as feedback, the position instrument was developed to make people aware of the importance of keeping a safe distance from one another. The input video outline from the camera has been used as details, along with a free and open source object location system based on YOLOv3. Calculation that was used to determine walker recognition. After that, the input frame outline was modified to elevated perspective for distance estimation in the 2-Dimensional plane. The RED edge and line represent the range between individuals being measured and a part of the rebellious pairing of individuals during the showcase. The proposed strategy is accepted using a pre-recorded feedback frame of people walking around the city on foot. This result demonstrates how the presented methodology can make decisions about social removing estimates for a large number of people in the input picture. As the discovery apparatus was gradually introduced, this developed technique evolved as well. [ABSTRACT FROM AUTHOR]

The aluminum alloy are extensively used in several industrial applications. Stir casting is one of the most frequently accepted methods. In the present investigation, how the microstructure, mechanical and wear mechanics of A357 alloy were impacted by the presence of Sr/Ca was investigated. The outcomes revealed that addition of elements (Sr/Ca) enhance the microstructural features. Uniform dispersal of particulates (Sr/ Ca) in Al357 alloy and also the modified structure of silicon (Si) were observed. Hardness of modified alloy was evaluated by using hardness tester. A result reveals that hardness of modified alloy was improved by increasing in the Sr/Ca content. The wear rate of modified alloy was evaluated by using Pin and Disc wear test rig. Test trials were conducted according to Taguchi technique. L27 array was implemented for evaluation of data. The effect of varying parameters (factors) on wear loss and COF were analyzed using ANOVA (Analysis of Variance) method. ANOVA outcomes shown that, the Sr/Ca content has a better significant impact on wear behavior and COF of the modified alloy. A wear fractography result shows the internal fracture structure of a wornout surface which was studied by SEM analysis. [ABSTRACT FROM AUTHOR]

Voice assistants like Siri, Google Assistant and Alexa are used widely across the globe for home automation. They require the use of unique phrases, also known as hotwords, to wake them up and perform an action like "Hey Alexa!", "Ok, Google!", "Hey, Siri!". These hotword detectors are lightweight real-time engines whose purpose is to detect the hotwords uttered by the user. However, existing engines require thousands of training samples or is closed source seeking a fee. This paper attempts to solve the same, by presenting the design and implementation of a lightweight, easy-to-implement hotword detection engine based on few-shot learning. The engine detects the hotword uttered by the user in real-time with just a few training samples of the hotword. This approach is efficient when compared to existing implementations because the process of adding a new hotword to the existing systems requires enormous amounts of positive and negative training samples, and the model needs to retrain for every hotword, making the existing implementations inefficient in terms of computation and cost. The architecture proposed in this paper has achieved an accuracy of 95.40%. [ABSTRACT FROM AUTHOR]

Terahertz metamaterial absorbers are the latest developments that have tremendous applications in terahertz spectroscopy and terahertz imaging. Especially, the research progressed in the direction of designing multiband absorbers. In this work, a polarization-insensitive metamaterial absorber capable of offering absorption in eight bands is proposed. The unit cell of the absorber consists of two orthogonal elliptical ring resonators (ERRs) that are optimally designed to offer maximum absorption in eight bands. The absorption percentage provided by the structure are 89.46% at 0.63 THz, 99.22% at 1.55 THz, 78.02% at 1.89 THz, 99.27% at 2.33 THz, 99.67% at 2.65 THz, 98.67% at 2.91 THz, 99.64% at 3.22 THz, and 88.02% at 3.42 THz. The absorption characteristics were independent of the polarization angle and thus, the structure is insensitive to variations in polarization angle. This is the first work that reports the design of an octaband terahertz absorber and can find significant use in practical devices. [ABSTRACT FROM AUTHOR]

As one of the major problems is that the time taken for executing the traditional algorithm is larger and that it is very difficult for processing large amount of data. Clusters possess high degree of similarity among each cluster and have low degree of similarity among other clusters. Optimization algorithm for clustering is the art of allocating scarce resources to the best possible effect. The traditional optimization algorithm is not suitable for processing high dimensional data. The main objective of proposed Artificial Bee Colony (ABC) approach is to minimize the execution time and to optimize the best cluster for the various sizes of the dataset. To deal with this, we are normalizing to distributed environment for time efficiency and accuracy. The proposed ABC algorithm simulates the behavior of real bees for solving numerical optimization problems particularly in clustering. The dataset size is varied for the algorithm and is mapped with its appropriate timings. The result is observed for various fitness and probability value which is obtained from the employed and the onlooker phase of ABC algorithm from which the further calibrations of classification error percentage is done. The proposed ABC Algorithm is implemented in Hadoop environment using mapper and reducer programming. An experimental result reveals that the proposed ABC scheme reduces the execution time and classification error for selecting optimal clusters. The results show that the proposed ABC scheme gives a better performance than PSO and DE in terms of time efficiency. [ABSTRACT FROM AUTHOR]

Recent research on shape retrieval render highly acute feature descriptors that are computationally intensive. Hence, a simple approach through a novel tessellated version of the Tetrakis Square tiling scheme for acute feature descriptor aiding supervised shape retrieval is contributed in this paper. The proposed descriptor labeled as Triangulated Second-Order Shape Derivative (TSOSD) performs feature characterization and abstraction by fusing hybrid geometrical concepts with image derivative operators. First, the mechanism tessellates the image into square tiles that are later organized as right-angled triangles. Secondly, the derivatives from the right-angled triangular neighbors interact locally using the trigonometric identities to produce an angle-based feature map. Finally, the feature descriptor is then formulated by local segmentation of the attained feature maps to produce the shape histogram. Experimental results on three standard benchmark databases demonstrate the effectiveness of the proposed approach, particularly rendering a consistent retrieval rate greater than 95% in comparison with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive and devastating disorder. It is characterized by alveolar epithelial cell injury and activation, infiltration of inflammatory cells, initiation of epithelial mesenchymal transition (EMT), aberrant proliferation and activation of fibroblasts, exaggerated deposition of extracellular matrix (ECM) proteins, and finally leading to the destruction of lung parenchyma. MicroRNAs (miRNAs) are endogenous small non-coding RNA molecules that post-transcriptionally regulate gene expression in diverse biological and pathological processes, including cell proliferation, differentiation, apoptosis and metastasis. As a result, miRNAs have emerged as a major area of biomedical research with relevance to pulmonary fibrosis. In this context, the present review discusses specific patterns of dysregulated miRNAs in patients with IPF. Further, we discuss the current understanding of miRNAs involvement in regulating lung inflammation, TGF-β1-mediated EMT and fibroblast differentiation processes, ECM genes expression, and in the progression of lung fibrosis. The possible future directions that might lead to novel therapeutic strategies for the treatment of pulmonary fibrosis are also reviewed. [ABSTRACT FROM AUTHOR]

Jatropha curcas belongs to the Euphorbiaceae family and is found in the coastal areas of tropics. There are few reported cases of its poisoning in pediatric and in adults; we have not come across mass accidental poisoning among the agricultural farmers in literature. However, we found a group of 18 agricultural labourers affected by the poisoning of its seeds, who presented with complaints of vomiting, colicky abdominal pain, giddiness and diarrhea with in few minutes and reported to the casualty in a couple of hours. Illness was self-limiting without any complications during the entire hospital stay and follow up. [ABSTRACT FROM AUTHOR]

[image omitted] Palladium-catalyzed synthetic methodology has been developed for the synthesis of 2-aminobenzenesulfonic acids from benzothiazoles in good to excellent yields using chloramine-B in alkaline (pH 12) acetonitrile/water (1:1) at 80 °C. [ABSTRACT FROM AUTHOR]

Introduction: Chronic otitis media is the most common cause of hearing impairment in the developing countries. Hearing loss can have serious effects on day to day life. Ear discharge in chronic otitis media may continue for months to years with increasing hearing impairment and also life threatening infective complications, which is more common in active and also in inactive disease form as well. Myringoplasty is an operative procedure to close the perforation in tympanic membrane. Aim: To compare the success rate of graft uptake in dry and wet ears and to compare the postoperative hearing improvement in dry and wet ear following myringoplasty. Materials and Methods: The comparative study was carried out on total 30 patients with chronic otitis media with central perforation. Of these 15 patients belong to dry ear group and 15 patients with wet ear group. These selected patients on simple random basis were subjected to clinical, audiological, radiological and laboratory investigations and one day before operation, patients were admitted to the hospital and written informed consent was taken in all cases. All patients underwent underlay technique myringoplasty. Postoperatively all patients were evaluated for graft uptake and hearing improvement by pure tone audiometry at 3rd month follow-up. Results: In our study, the successful graft uptake was seen in 80% in dry ear and 73.3% in wet ear, statistically p-value (²=1.24) is (p>0.05) which is insignificant. Postoperatively hearing gain was (0-5 dB) in 3 patients (20%) with dry ear and 2 patients (13.5%) with wet ear; (6-10 dB), in 4 patients (26.6%) with dry ear and 6 patients (40%) with wet ear; more than 10 dB in 5 patients (33.3%) with dry ear and 3 patients (20%) with wet ear, statistically p-value is ²=1.24 (p >0.05) which is not significant. Conclusion: In this study the success rate of graft uptake and hearing improvement is found almost equal in dry and wet ear by using underlay technique of myringoplaty, and also found statistically insignificant. [ABSTRACT FROM AUTHOR]

Introduction: Meniere's disease is a disorder of the membranous labyrinth of the inner ear manifesting as vertigo, tinnitus, sensory neural hearing loss and aural fullness of known or unknown origin. Some patients may present after years into typical forms. The endocrine disorders, especially hypothyroidism and the factors like inflammation, trauma, otosclerosis, autoimmunity explains the multifactorial causation for the basis of Meniere's disease. Endolymphatic hydrops is the pathologic term named for the defective regulation of endolymph volume with respect to production and absorption. However, hyper production of endolymph cannot be excluded. The cause of Meniers disease is multifactorial so clinical features of repeated attacks of variable duration cannot be explained satisfactorily based on single causation. Aim: To study the incidence of hypothyroidism in Meniere's disease and to assess the success rate of improvement in subjective symptoms after treatment. Materials and Methods: The study was carried out on total 35 out patients who were clinically diagnosed of Meniere's disease and of these 35 patients, 12 patients were found to have hypothyroidism. All patients with hypothyroidism were treated with oral thyroxin supplements and they were evaluated clinically at 3 weeks, 3 months and 6 months interval for the improvement in subjective symptoms on outpatient basis. Results: In our study, the subjective improvement of all symptoms was seen in all cases of Meniere's disease with hypothyroidism after 12 weeks of treatment. Conclusion: The improvement in clinical symptoms after treatment demonstrates an association between Meniere's disease and hypothyroidism which was found to be significant. Hence, clinicians should consider screening patients with Meniere disease for thyroid dysfunction who are not already taking supplements. [ABSTRACT FROM AUTHOR]

A reverse phase HPLC method with UV detection has been developed and validated in order to quantify plumbagin, the bioactive marker of the roots of P. indica and P. zeylanica. A quantitative HPTLC method was also developed using hexane: ethyl acetate (8:2) as the mobile phase. The plumbagin content in the roots were determined using both the methods. P. indica was found to contain significantly higher amount of plumbagin than P. zeylanica. The HPLC and HPTLC methods described here are simple, rapid, accurate and sensitive. [ABSTRACT FROM AUTHOR]

We report the liquid phase epitaxial growth of Zn3As2on InAs (1?1?0) substrates using 100% In solvent. The zinc concentration in the layers was varied by changing the zinc mole fraction in the growth melt from 3 × 10?3to 15 × 10?3, keeping all other growth parameters constant. Layers grown with 3 × 10?3, 5 × 10?3and 8 × 10?3?mole fractions of zinc seem to be polycrystalline, while layers grown with more than 8 × 10?3mole fraction of zinc resulted in highly oriented and single crystalline material. Composition of the epilayers was confirmed by energy dispersive x-ray microanalysis. Scanning electron microscopy was used to determine the thickness of the epilayers and the growth rate was calculated. Furthermore it was found that a linear dependence exists between the growth rate and the zinc mole fraction in the growth melt. Atomic force microscopy results reveal that the smoothness of the epilayers improve when the zinc concentration in the epilayer approaches the stoichiometric value of Zn3As2. Room temperature Hall measurements showed that the grown epilayers were unintentionally doped p-type with an increase in the carrier mobility and a decrease in the carrier concentration with respect to the increase in the zinc mole fraction in the growth melt. [ABSTRACT FROM AUTHOR]

An interesting case of fatal accidental hanging of a habitual burglar is presented in the present case report which poses a controversy in diagnosis whether it is to be included under accidental hanging or in positional asphyxia. This case report stresses on the importance of crime scene visit, police record verification and meticulous autopsy so as to come to an appropriate conclusion regarding the cause of death [ABSTRACT FROM AUTHOR]