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Dynamic nuclear architecture and chromatin organizations are the key features of the mid‐prophase I in mammalian meiosis. The chromatin undergoes major changes, including meiosis‐specific spatiotemporal arrangements and remodeling, the establishment of chromatin loop–axis structure, pairing, and crossing over between homologous chromosomes, any deficiencies in these events may induce genome instability, subsequently leading to failure to produce gametes and infertility. Despite the significance of chromatin structure, little is known about the location of chromatin marks and the necessity of their balance during meiosis prophase I. Here, we show a thorough cytological study of the surface‐spread meiotic chromosomes of mouse spermatocytes for H3K9,14,18,23,27,36, H4K12,16 acetylation, and H3K4,9,27,36 methylation. Active acetylation and methylation marks on H3 and H4, such as H3K9ac, H3K14ac, H3K18ac, H3K36ac, H3K56ac, H4K12ac, H4K16ac, and H3K36me3 exhibited pan‐nuclear localization away from heterochromatin. In comparison, repressive marks like H3K9me3 and H3K27me3 are localized to heterochromatin. Further, taking advantage of the delivery of small‐molecule chemical inhibitors methotrexate (heterochromatin enhancer), heterochromatin inhibitor, anacardic acid (histone acetyltransferase inhibitor), trichostatin A (histone deacetylase inhibitor), IOX1 (JmjC demethylases inhibitor), and AZ505 (methyltransferase inhibitor) in seminiferous tubules through the rete testis route, revealed that alteration in histone modifications enhanced the centromere mislocalization, chromosome breakage, altered meiotic recombination and reduced sperm count. Specifically, IOX1 and AZ505 treatment shows severe meiotic phenotypes, including altering chromosome axis length and chromatin loop size via transcriptional regulation of meiosis‐specific genes. Our findings highlight the importance of balanced chromatin modifications in meiotic prophase I chromosome organization and instability. [ABSTRACT FROM AUTHOR]

Optical projection tomography (OPT) is a threedimensional mesoscopic imaging modality that can use absorption or fluorescence contrast, and is widely applied to fixed and live samples in the mm-cm scale. For fluorescence OPT, we present OPT implemented for accessibility and low cost, an open-source research-grade implementation of modular OPT hardware and software that has been designed to be widely accessible by using low-cost components, including light-emitting diode (LED) excitation and cooled complementary metal-oxidesemiconductor (CMOS) cameras. Both the hardware and software are modular and flexible in their implementation, enabling rapid switching between sample size scales and supporting compressive sensing to reconstruct images from undersampled sparse OPT data, e.g. to facilitate rapid imaging with low photobleaching/phototoxicity. We also explore a simple implementation of focal scanning OPT to achieve higher resolution, which entails the use of a fan-beam geometry reconstruction method to account for variation in magnification. This article is part of the Theo Murphy meeting issue 'Open, reproducible hardware for microscopy'. [ABSTRACT FROM AUTHOR]

Vehicular ad-hoc network (VANET) is widely applied in transportation applications to ensure safety and security during road transportation. In VANET, all the information's are interchanged among vehicles with the available infrastructure on the roadside to form an ad-hoc network. In VANET, due to its communication in an open medium achieving the major security goals like integrity, authenticity, anonymity and traceability are the major concern. Since, there are numerous vehicles in the network generation and verification of digital signature requires high storage along with and verification time. To overcome all these issues, in this paper an Efficient group signature based certificate less verification scheme (EGSCVS) has been proposed to provide lightweight end to end authentication in the network. The proposed EGSCVS employs certificate less aggregation signature mechanism which is based on Elliptical curve cryptography to optimize the storage and reduce the overhead in terms of communication and computation during authentication process. Moreover, the proposed protocol utilizes the Random oracle model (ROM) to provide the formal security analysis. The implementation of the proposed protocol has been implemented using NS3 simulator with realistic simulation parameters. The simulation result shows that the proposed protocol improves the transmission overhead by 53.16%, computational cost by 48.75%, communication cost by 42.81%, signing delay by 55.17%, verification delay by 28.38% and aggregate verification delay by 45.35%. [ABSTRACT FROM AUTHOR]

The supercontinuum spectrum is generated through a wide range of wavelengths by sending a short and strong pulse to the nonlinear medium and sputtering at the output and is used in optical measurements, spectroscopy, biological imaging optical coherence photography, etc. Integrated photonics is an idea to realize low-cost and microscale communication, sensing, and fast computing methods. In addition, the miniaturization and integration of photonic structures make possible new designs and applications that are inaccessible in their large volumes. Lithium niobate is one of the most widely used and attractive materials in the field of photonics due to its extraordinary electro-optical, acoustic-optical, nonlinear optics, wide transparency window, and relatively high refractive index. In this work, a lithium niobate waveguide is designed by choosing basic solitons as the input pulse and considering various effects such as high-order scattering, self-phase modulation, second harmonic generation, Raman effect, self-downward effect, etc. The proposed waveguide has super-sustainable production. This supercontinuum spectrum is designed in the waveguide, for the 10th order bright soliton at a distance of 35 mm, 4 times the initial width, for the 20th order bright soliton at 9 mm, 4 times the initial width, and for the 30th order bright soliton at 4.5 mm, 5 times the initial pulse width in the frequency domain are created. Such dimensions are suitable for use in photonic integrated circuits. [ABSTRACT FROM AUTHOR]

Superalloys go through cyclic oxidation which refers to the progression of reputed exposure of materials to alternating oxidizing and non-oxidizing environments at high temperatures. The oxidation behavior of cobalt-based superalloy coatings depends on several variables, including the environment, surface roughness, manufacturing conditions, & coating composition. Because of their higher temperatures and protective oxide layer, coatings with a greater cobalt concentration are more oxidation-resistant. The behavior of oxidation is influenced by surface roughness, where rough surfaces offer greater surface area to oxidation while smooth surfaces decrease interaction with the environment. The microstructure and porosity of the coating are further impacted by processing variables used in thermal spraying, including temperature, particles velocity, and spray distance. Adding reactive components to the coating composition, such as silicon and aluminum, and applying post-treatments like nitriding or sealing are two ways to increase oxidation resistance. A greater temperature and particle speed can lead to a denser with less porous covering, improving the oxidation process resistance. The two typically used heat spraying techniques are high-velocity oxy-fuel (HVOF) and plasma spraying. To increase the resistance to oxidation of thermal spray cobalt-based superalloy coating, some strategies have been devised, such as inclusion of reactive materials, such as aluminum and silicon, which is the coating composition. These elements may establish an oxide layer protecting the coating’s appearance, preventing further oxidation. [ABSTRACT FROM AUTHOR]

An efficient green method is developed as a one-pot synthesis of pyrrolo[1,2-c]imidazole-1-one derivatives under microwave conditions via two components in a substoichiometric quantity of water. The conformational and structural studies are conducted through DFT optimization and single crystal X-ray diffraction. The molecular packing is mediated through hydrogen bonding and C–H⋯π interactions. The Hirshfeld surface analysis highlights significant H–H interactions in all compounds, with compound 2a exhibiting the highest percentage. HOMO and LUMO studies reveal compound 2a as more stable and less reactive. The molecular electrostatic potential provides the charged proportionality along with the molecular surface. The drug-likeness properties suggest that compounds 2b, 2c, 2d, 2e, 2f, 2g, and 2i possess favorable properties for potential pharmaceutical applications. [ABSTRACT FROM AUTHOR]

The numerical model for AlGaN/GaN High Electron Mobility Transistors (HEMTs) device of Zero-Dimensional Freestanding Nanowire Field-Effect Rectifiers (NW-FER) is developed by utilizing the parabolic approximation approach. This model investigates factors including surface potential, electric field, drain current, transconductance, and noise analysis to evaluate the device's performance. The Cappy model is introduced to analyze the noise performance for the device with gate length effect on transconductance. The proposed device exhibits a high drain current value of 0.823 (A/mm), and a high transconductance of 0.2965 (S/mm) is achieved in NW-FER-based HEMTs. This analysis demonstrates that the proposed device and model are well applicable for low-noise and High-Frequency applications. A good level of agreement is found when numerical model results are compared to Technology Computer-Aided Design simulation software. [ABSTRACT FROM AUTHOR]

Pistachios are a tremendous source of fiber, protein, antioxidants, healthy fats, and other minerals like thiamine and vitamin B6. They may help people lose weight, lower cholesterol, and blood sugar levels, lead to better gut, eye, and blood vessel health. The two main varieties farmed and exported in Turkey are kirmizi and siirt pistachios. Understanding how to detect the type of pistachio is essential as it plays an important role in trade. In this study, it is aimed to classify these two types of pistachios and analyze the performance of the various small-scale machine learning algorithms. 2148 sample images for these two kinds of pistachios were considered for this study which includes 1232 of Kirmizi type and 916 of Siirt type. In order to evaluate the model fairly, stratified random sampling is applied on the dataset. For feature extraction, we used deep neural network-based embeddings to acquire the vector representation of images. The classification of pistachio species is then performed using a variety of small-scale machine learning algorithms29,31 that have been trained using these feature vectors. As a result of this study, the success rate obtained from Logistic Regression through features extracted from the penultimate layer of Painters network is 97.20%. The performance of the models was evaluated through Class Accuracy, Precision, Recall, F1 Score, and values of Area under the curve (AUC). The outcomes show that the method suggested in this study may quickly and precisely identify different varieties of pistachios while also meeting agricultural production needs. [ABSTRACT FROM AUTHOR]

In this paper, a multilayer perceptron neural network has been used to represent temperature measurement during rotary drilling of five types of rock samples. To forecast the temperature at various thermocouple depths, the experimentally collected data was standardized. Indicators of model performance was also obtained in order to assess the correctness of the model. One hidden layer and one output layer were employed with MLPANN, which has ten input parameters (bit diameter (DD), Spindle Speed (SS), Penetration Rate (PR), thrust, and torque) and rock properties. Levenberg Marquardt learning algorithm with transfer function of logsig is the most optimal neuron number of 10-16-1 was successfully forecasting the temperature with a correlation of 0.9936 and 0.9941 for training and testing algorithm during drilling after analysis based on the trial-anderror approach to identify the optimum algorithm. Ten input parameters, a logsig sigmoid transfer function, and the trainlm algorithm in this study provide good prediction ability with tolerable accuracy. [ABSTRACT FROM AUTHOR]

Medical image fusion plays a crucial role in accurate medical diagnostics by combining images from various modalities. To address this need, we propose an AI model for efficient medical image fusion using multiple modalities. Our approach utilizes a Siamese convolutional neural network to construct a weight map based on pixel movement information extracted from multimodality medical images. We leverage medical picture pyramids to incorporate multiscale techniques, enhancing reliability beyond human visual intuition. Additionally, we dynamically adjust the fusion mode based on local comparisons of deconstructed coefficients. Evaluation metrics including F1-score, recall, accuracy, and precision are computed to assess performance, yielding impressive results: an F1-score of 0.8551 and a mutual information (MI) value of 2.8059. Experimental results demonstrate the superiority of our method, achieving a remarkable 99.61% accuracy in targeted experiments. Moreover, the Structural Similarity Index (SSIM) of our approach is 0.8551. Compared to state-of-the-art approaches, our model excels in medical picture classification, providing accurate diagnosis through high-quality fused images. This research advances medical image fusion techniques, offering a robust solution for precise medical diagnostics across various modalities. [ABSTRACT FROM AUTHOR]

The scope of the work is to develop in-organic–organic hybrid epoxy nanocomposites using a combination of DGEBA (CY 230) epoxy resin and areca fibre (Af) mixture in the ratio of 70:30, respectively. The mixture was reinforced with varying weight percentages (1 and 2 wt%) of surface-modified nanozinc oxide (M-nZnO) and cured with triethylene-tetra-amine (HY951) at room temperature. 3-aminopropyltriethoxysilane was used as a coupling agent to introduce amine groups on the surface of ZnO and its structure was confirmed by FT-IR. The thermal stability and mechanical properties of in-organic–organic hybrid epoxy nanocomposites having different weight percentages of M-nZnO are investigated by thermogravimetric analysis. Their surface morphology of in-organic–organic hybrid epoxy nanocomposites was analysed by scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The introduction of M-nZnO improved the mechanical and thermal properties of the in-organic–organic hybrid epoxy nanocomposites. The mechanical characteristics and thermal stability analyses yielded the best results with a 2 wt% addition of M-nZnO nanoparticles. The study's findings clearly suggest that 2 wt% M-nZnO with Af/DGEBA epoxy resin can be used in a wide range of high-performance applications. Interestingly, the Af/DGEBA/M-nZnO epoxy nanocomposites displayed an excellent antibacterial action against high concentrations of Gram-negative bacteria like Escherichia coli, Klebsiella pneumoniae and Pseudomonas aeruginosa. [ABSTRACT FROM AUTHOR]

Providing adequate green cover in the form of trees, parks and gardens is a nature-based solution which every city must adopt to address the urbanization and its impacts, as it helps to reduce the temperature, maintain the ecology and biodiversity, provide recreational facilities for the city inhabitants and many more. Accurate mapping and monitoring of Urban Green Spaces (UGS) through satellite images is essential to check whether a city has adequate green cover or not. Studies on UGS have mostly used Landsat and Sentinel-2A satellite images for extracting the green covered areas in cities. Though it is advantageous to use these freely available images, however the accuracy of extraction is a major concern as the images are of >10m spatial resolution. Distinguishing the various green cover types, such as trees, shrubs, cropland, etc. is not an easy task in low and medium resolution satellite images. To overcome these drawbacks, one has to use high (1-10m) and very-high resolution (<1m) images for extracting the UGS accurately, however, purchasing such 'cm' resolution satellite images is a costly affair. In order to balance between the cost and resolution, the best and viable option is to use the Google earth (GE) imagery and the same was attempted in the present study by proposing a simple threshold-based approach to extract the UGS and its types from GE imagery. To illustrate the proposed method, 15 sites from the city of Vellore, India were taken and the latest cloud free GE imagery was then downloaded, projected and carefully checked for its range of pixel values in different types of UGS classes. The threshold values of each UGS type were then used to reclassify the original GE image and finally the accuracy assessment was performed on the reclassified UGS map. The results were promising as the overall accuracy (OA) was > 90% at 12 sites and only 3 out of 15 sites exhibits OA in the range of 75-86%. In order to assess the performance of the proposed approach at other locations, 5 cities across the globe [2 from India (Bangalore and Hyderabad) and 3 from abroad (Barcelona, Kuala Lumpur, New York)] were taken and the results revealed that the proposed method performs well in extracting the UGS and its types as the OA was found to be above 90% at all the 5 cities. The proposed approach can be used by civic authorities not only to check whether the city has adequate green cover or not but also can monitor whether UGS has increased or decreased over the years using the historical GE images. [ABSTRACT FROM AUTHOR]

Introduction: Laparoscopic cholecystectomy is considered the gold standard for benign gallbladder disease due to its minimal invasiveness, reduced bleeding, and rapid recovery. Enhanced Recovery After Surgery (ERAS) protocols, recognised for lowering surgical stress and complications, are increasingly adopted for their postoperative benefits. Aim: To assess and compare postoperative outcomes in laparoscopic cholecystectomy patients undergoing ERAS versus conventional approaches. Materials and Methods: This prospective interventional study was conducted at the Surgery Department of Mahatama Gandhi Medical College and Research Institute, Puducherry, India from January 2021 to June 2022. All patients above 18 years of age undergoing laparoscopic cholecystectomy with American Soceity of Anaesthesiologists (ASA) I and II were included. A total of 90 subjects, 45 subjects in the Group A (ERAS protocol) and 45 subjects in the Group B (Conventional approach), were included based on computer-generated random numbers with concealment of allocation. Key parameters, including length of hospital stay, morbidity, postoperative pain, and protocol compliance, were evaluated between both groups. Continuous variables were presented as means with standard deviations and analysed using unpaired t-tests. Categorical variables were expressed as percentages and compared using chisquare tests. Results: The present study found that the majority of residents were male 108 (72%), belonged to the Hindu religion (134, 89.33%), were unmarried (150, 83.33%), and resided in the hostel (118, 78.67%) during their residency tenure. The prevalence of depression among clinical branch residents was 52 (50.0%), and in other than clinical branch residents, it was 17 (36.96%) (p=0.633). The prevalence of anxiety among clinical branch residents was 65 (62.5%), and in other than clinical branch residents, it was 18 (39.14%) (p=0.002). QoL was better in other than clinical branch residents compared to clinical branch residents in physical health, social, and environmental domains (p<0.04). Conclusion: The study concluded that clinical branch residents were experiencing significantly higher levels of anxiety. Overall, the QoL of residents in other than clinical branches was better than their counterparts in clinical branches. Results: The mean age of the study population in ERAS and conventional was 41.3±7.9 years and 41.6±9.6 years, respectively. Similarly, 17 male participants were from the ERAS group and 15 were from the conventional group, whereas among female participants 28 were from the ERAS group and 30 were from the conventional group. The ERAS group demonstrated significant advantages: shorter hospital stays (91.2% vs. 73.4%, p=0.0274), lower Grade 1 morbidity (p=0.0213), and reduced postoperative pain (p=0.0001). Conclusion: The ERAS group exhibited notable benefits, including a shorter hospital stay, reduced morbidity, and lower postoperative pain. These findings suggest the potential for enhanced recovery outcomes with ERAS protocol implementation in laparoscopic cholecystectomy patients. [ABSTRACT FROM AUTHOR]

Underground structures constitute a vital component in infrastructure development. When subjected to dynamic events, these underground structures will result in catastrophic failures. Hence proper understanding of soil-underground structure interaction is essential when designing in seismic-prone areas. Though analytical models estimate the performance of underground structures under dynamic loading conditions, detailed experimental studies provide more insights into the parameters influencing the underground structure's performance during dynamic conditions. Due to the limitations in model development, time, and cost, 1-g shaking table experiments were preferred by most of the researchers compared to centrifuge and field scale experiments. This paper presents a detailed literature review of experimental studies on tunnel models in 1-g shaking table experiments and critical observations on tunnel-soil interaction under dynamic loading conditions. Discussions on scaling laws, instrumentation scheme, material selection, and other influencing parameters involved in soil tunnel interaction studies in 1-g shaking table testing conditions are presented. Although the 1-g experimental testing conditions have limitations in simulating equivalent ground and testing conditions as compared to centrifuge studies, this paper highlights the possible steps to simulate the tunnel model interaction studies in a better way to minimize the limitations for upcoming future research works. [ABSTRACT FROM AUTHOR]

Introduction: Serum amylase and lipase elevations are classically related to pancreatic diseases. Hyperamylasemia is noticed to various common non-pancreatic clinical conditions, however hyperlipasemia is considered the specific marker of pancreatic diseases. Methodology and Results: This paper set forth 3 cases of Diabetic Keto Acidosis that presented with abdominal pain which are initially misapprehended to be Acute Pancreatitis due to markedly raised serum amylase and lipase levels. Conclusion: Markedly raised amylase and lipase levels are also noticed in non-pancreatic diseases. Optimal clinical judgement plays an essential role in diagnosis. [ABSTRACT FROM AUTHOR]

Facial Emotion detection (FER) is primarily used to assess human emotion to meet the demands of several real-time applications, including emotion detection, computer–human interfaces, biometrics, forensics, and human–robot collaboration. However, several current techniques fall short of providing accurate predictions with a low error rate. This study focuses on modeling an effective FER with unrestricted videos using a hybrid SegNet and ConvNet model. SegNet is used to segment the regions of facial expression, and ConvNet is used to analyze facial features and to make predictions about emotions like surprise, sadness, and happiness, among others. The suggested hybridized approach uses a neural network model to classify face characteristics depending on their location. The proposed model aims to recognize facial emotions with a quicker convergence rate and improved prediction accuracy. This work takes into account the internet-accessible datasets from the FER2013, Kaggle, and GitHub databases to execute execution. To accomplish generalization and improve the quality of prediction, the model acts as a multi-modal application. With the available datasets throughout the testing procedure, the suggested model provides 95% prediction accuracy. Additionally, the suggested hybridized model is used to calculate the system's importance. The experimental results show that, in comparison to previous techniques, the expected model provides superior prediction results and produces better trade-offs. Other related statistical measures are also assessed and contrasted while the simulation is being run in the MATLAB 2020a environment. [ABSTRACT FROM AUTHOR]

Reaching the maximum number of customers at the right time to increase sales and profitability of the business is the primary goal of Electronic Commerce (E-Commerce). However, owing to the low-influenced product, the profitability of e-commerce has been drastically affected in recent years. Therefore, this work proposes an Empirical Probability assigned Satty's method integrated Multicriteria Decision-Making model (EP-Satty-MCDM)-based business decision-making model for improving the sales of low-influenced products by advertising with celebrities. Primarily, the authenticated user securely downloaded the encrypted data using Armstrong number private key generated-Trident Curve-Cryptography (Arm-TCC) in the web application. After that, the data is cleansed and the attributes of review and behavior data are extracted. Then, by utilizing the Interval-valued Atanassov intuitionistic fuzzy-based Mann-Whitney U test (IAF-MWU), the correlation between the review and behavior data is evaluated. The correlated features under each user are mapped under the product, and semantic ontology is constructed, where the data is again mapped with the product's subsections. Afterward, the domains are extracted. Thereafter, to identify whether the product is high-influenced or low-influenced, the obtained ratings from ontology and extracted domains are inputted into the Boosting Regression Tree-Recurrent Neural Network (BRT-RNN). Then, for the decision-making, the positively forecasted celebrities with their garment and low-influenced products are given as the input to EP-Satty-MCDM. The experimental outcomes exhibited that the proposed technique withstands maximum accuracy when contrasted with the existing methodologies. [ABSTRACT FROM AUTHOR]

The central nervous system can develop complex and deadly neoplastic growths called brain tumors. Despite being relatively uncommon in comparison to other cancers, brain tumors pose particular challenges because of their delicate anatomical placement and interactions with critical brain regions. The data are taken from TCIA (The Cancer Image Archive) and Kaggle Datasets. Images are first pre-processed using amplified median filter techniques. The pre-processed images are then segmented using the Grabcut method. Feature extraction is extracted using the Shape, ABCD rule, and GLCM are the features were retrieved. The MRI images are then classified into several classes using the Bi-directional Encoder Representations from Transformers-Bidirectional Long Short Term Memory (BERT-Bi-LSTM) model. Kaggle and TICA datasets are used to simulate the proposed approach, and the results are evaluated in terms of F1-score, recall, precision and accuracy. The proposed model shows improved brain tumour identification and classification. To evaluate the expected technique's efficacy, a thorough comparison of the current techniques with preceding methods is made. The trial results showed that an efficient hybrid bert model for brain tumor classification suggested strategy provided precision of 98.65%, F1-score of 98.25%, recall of 99.25%, and accuracy of 99.75%. [ABSTRACT FROM AUTHOR]

The Second Target Station project at Oak Ridge National Laboratory will develop a cold neutron source to meet growing experimental needs. This paper describes calculations of the residual dose rates associated with the monolith shield plug and the beamline bunker, two key conventional operations and radiation safety features. While neutron production is active, the instrument hall outside the bunker must be generally accessible with dose rates of less than 0.25 mrem/h. When neutron production is halted, the bunker must be accessible for hands-on maintenance operations. These two requirements form the cause for the assessments reported herein of residual dose rates caused by the monolith shield plug and residual dose rates in the bunker. The monolith shield plug was shown to not produce significant dose rates inside the bunker after a 20-year lifetime, and the residual dose rates inside the bunker for the case of an operating beamline were shown to reasonably allow for hands-on maintenance. These calculations are based on preliminary design models of the relevant systems. Additionally, an example showing the significance of considering neutron supermirror physics in transport calculations that track nuclide production and destruction rates to produce gamma sources for residual dose rate calculations is included. The example shows that if neutron supermirror physics is not considered, dose rate fields may be significantly underpredicted. [ABSTRACT FROM AUTHOR]

In Geometric function theory, the Ma-Minda class of starlike functions has a unique place as it unifies various subclasses of starlike functions. There has been an vivid interplay between special functions and their geometric properties, like starlikeness. In this article, we establish certain special function's radius of Ma-Minda starlikness. As an application, we obtain conditions on parameters for these special functions to be in the Ma-Minda class. Further, we focus on certain convolution properties for the Ma-Minda class that are not done so far, and study their applications in radius problem. Finally, we prove a variational problem of Goluzin, namely, the region of variability for the Ma-Minda class. Our results simplify and generalize the already-known ones. [ABSTRACT FROM AUTHOR]

Background This study was conducted to ascertain whether the modified Selvester QRS scoring system would be able to predict the clearance of ST segment elevation in patients undergoing streptokinase-based thrombolytic treatment for acute myocardial infarction. Methods This study was conducted in a hospital setting and involved 61 patients aged between 30 and 80 years who were diagnosed with a new onset acute ST elevation myocardial infarction receiving thrombolytic therapy with streptokinase. Based on 37 criteria and a modified Selvester scoring method, the QRS score was determined, yielding a total of 29 points. A QRS score greater than four was regarded as non-resolution. Additionally, patients were divided into non-resolution groups (STR < 50% of STE1) and resolution groups (STR = 50% of STE1). The SPSS 24 program was used to carry out the statistical analysis. Results Patients with diabetes exhibited high QRS scores, but there was no relationship between them and the ST resolution. There was a substantial association between the QRS score, ST resolution and cardiac failure. The QRS score was higher and the incidence of ST nonresolution was higher in patients with heart failure. In this investigation, there was no significance for patients with dyslipidemia or MI. The resolution group's QRS score was lower than that of the non-resolution group. A noteworthy inverse relationship was observed between the STR and QRS scores. Additionally, there was a negative connection between QRS score and ejection percentage. Conclusion When patients with STEMI are receiving thrombolytic therapy, the QRS score accurately predicts the resolution of the ST segment. Patients are more likely to experience nonresolution of the ST segment if their QRS score is greater than 4. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are a distributed collection of sensor nodes which are distributed geographically in the deployed environment to sense the natural phenomena. The sensed data are transmitted by the nodes using multi-hop communication until it reaches to the base station. Due to its resource-constraint nature of device and its communication in open and unfriendly environment, providing energy optimization along with the secured communication is a major challenge. In this work, a trust-aware neuro-fuzzy-based clustering along with sparrow search optimization algorithm (NF-SSOA) is proposed to provide energy-efficient trust-aware cluster-based secured data transmission in WSNs. The proposed protocol performs effective clustering of the nodes by employing neuro-fuzzy clustering algorithm, and routing is performed by sparrow search optimization algorithm. ECC-based digital signature algorithm is used in the proposed system to provide an efficient lightweight key generation, encryption, decryption, signature generation, and verification and to ensure hop-to-hop authentication of the nodes in WSNs. Moreover, the proposed protocol employs pseudo-random identity generation for performing anonymous authentication during data transmission in the network. The proposed protocol is implemented by using NS3 simulator. The simulation results prove that the proposed protocol improves energy consumption analysis, throughput, network delay, network lifetime, and packet delivery ratio when it is compared with other existing protocols. Moreover, the proposed protocol shows significant potential for resistance to various security and improves the quality of services in the network. [ABSTRACT FROM AUTHOR]

A new fifteen-level stepped DC to AC hybrid converter is proposed for Solar Photovoltaic (SPV) applications. A boost chopper circuit is designed and interfaced with the fifteen-level hybrid converters specific to Electric Vehicles' Brushless DC Motor (BLDC) drive systems. In chopper units, the output of solar panels is regulated and stepped up to obtain the nominal output voltage. In the stepped DC-link hybrid converter configuration, fifteen-level DC-link voltage is achieved by the series-operated DC-link modules with reduced electrical energy compression. From the comprehensive structure, it is anecdotal that the proposed topology has achieved minimum switching and power loss. Elimination of end passive components highlights the merits of the proposed hybrid systems. The reduction of controlled power semiconductor switches and gate-firing circuits has made the system more reliable than other hybrid converters. From the extensive analysis, the experimental setup has reported that 7% reduction in harmonics and a 54% reduction in controlled power switches than the existing fifteen-level converter topologies. Mitigation of power quality issues in the voltage profile of a fifteen-level multilevel hybrid converter is achieved through the implementation of dsPIC digital-controller-based gate triggering circuits. [ABSTRACT FROM AUTHOR]

Early identification of Cervical Cancer (CC) helps in reducing the death rate of CC patients. A Pap smear/Pap test, which is also a widely used method, is used to detect CC in its early stages. The automatic detection and categorization of Pap smear images is a difficult task. So, it is important to design and implement a high-efficiency with low-cost screening system. Therefore, this paper compares the efficiency of two deep learning-based cervical cancer screening strategies. The first technique taken for comparison is the Mutation based Atom Search Optimization (MASO) optimized DenseNet 121 architecture. The MASO technique is used to adjust hyperparameters such as learning rate value, batch sizes, and the number of neurons in the dense layer in the DenseNet 121 architecture. The second technique taken is the Sooty Tern optimized CNN-based Long Short-Term Memory (ST-CNN-LSTM) classifier to identify the different types of cervical cancer stages. The areas affected by the cancer are identified using Kernel-Weighted Fuzzy Local Information C-Means clustering (KWFLICM) model. This model removes the nucleus and cytoplasm from the background area of the cell. Normal, mild dysplastic, severe dysplastic, and carcinoma are among the CC classifications offered by these two classifiers. The efficiency of these classifiers is compared in two datasets (Hervel and SIPaKMeD) via different performance metrics such as specificity, precisions, F-score, accuracy, confusion matrix, sensitivity, and recall. The ST-CNN-LSTM offers improved performance in terms of accuracy (99.80%), sensitivity (98.83%), and specificity (99%) when compared to other classifiers. [ABSTRACT FROM AUTHOR]

High-tilt angle ferroelectric liquid crystal material in surface stabilised geometry (SSFLC) is investigated for the smectic layer reorientation and its molecular dynamics by electro-optical and dielectric spectroscopy techniques. Smectic layer reorientation (5 $^o$ o to 45 $^o$ o ) is obtained by controlled cooling rate and applying electric field in the vicinity of transition temperature from chiral nematic (${N^*}$ N ∗ ) to chiral smectic C ($Sm{C^*}$ Sm C ∗ ) phase. After slow cooling down to room temperature, the stripe domain textures along the rubbing are observed with quasi-bistable states. Dielectric spectroscopy has revealed the unusual increment in the dielectric permittivity. A dielectric relaxation process at lower frequency than Goldstone mode with the increase in DC bias up to 2 $V$ V is observed in the sample cooled without any bias. This increment is found to be originated by the heliconical structure within the stripes which is seen in the optical textures recorded at the time of dielectric scan. However, this increment is not observed in the sample cooled under DC bias. Thus, there exist different phenomena in the high tilt angle SSFLC sample cooled in different conditions of DC bias; stripe domains, smectic layers reorientation, heliconical stricture and twisted structure within stripes which are significant to explore deeply in high-tilt angle ferroelectric liquid crystals. [ABSTRACT FROM AUTHOR]

Porous carbons have attracted substantial interest within the realm of energy storage applications. However, their traditional production methods often involve the use of elevated temperatures. In this study, we introduce a simple technique to transform titanium silicon carbide (Ti3SiC2) MAX phases into porous carbons, known as carbide-derived carbons (CDCs), at room temperature by selective etching of the metal atoms (Ti and Si). We investigate how temperature affects the activation of CDCs so formed with potassium hydroxide to enhance their electrochemical properties. Our results unveil the remarkable potential of CDCs activated at 700 °C, demonstrating superior electrochemical performance with a specific capacitance of 198 F g−1 at a scan rate of 20 mV s−1 in a three-electrode configuration. The symmetric supercapacitor based on CDC-700 maintains a respectable specific capacitance of 98 F g−1 at 1 A g−1 and an energy density of 13.7 Wh kg−1 at a power density of 1 kW kg−1. This cost-effective approach offers a pathway for large-scale CDC production, with excellent specific supercapacitor characteristics, promising advancements in energy storage technology. [ABSTRACT FROM AUTHOR]

Microaneurysms (MAs) are the early indications of diabetic retinopathy (DR), which may result in total visual loss. MAs detection is an exciting work due to its small, darkish color and subtle nature. The automatic detection and categorization of MAs in retinal fundus images using a multi‐scale approach based on Deep Neural Networks and Neighborhood Analysis is proposed in this research article. MAs segmentation, classification, and preprocessing comprise the three steps of the proposed technique. To extract the multi‐scale MA features, the auto‐weight dilated convolutional unit (AD) is specifically used for convolutional feature maps. To fuse convolutional feature maps in encoding layers, the AD unit used a learnable set of parameters. An efficient architecture for feature extraction during the encoding step is incorporated into the AD unit. We integrated the AD unit with the benefits of the U‐Net network for deep and shallow features. Additionally, in order to improve the suggested model and produce the final derivation, we developed a novel optimization approach. After that, neighborhood analysis is performed to name the Micro‐aneurysm because the lesion is actually a collection of independent little images rather than the entire image. The classification accuracy of the proposed method for the three different data sets such as MESSIDOR, online retinal fundus image database for glaucoma analysis (ORIGA), and RIM‐ONE‐R1, is 99.28%, 98.95%, and 98.76% respectively. The results show a good performance of the proposed model against the other analyzed procedure. [ABSTRACT FROM AUTHOR]

Introduction: Snakebites are a significant cause of morbidity and mortality in India despite availability of anti-snake venom and the absence of a large number of highly venomous snakes. This may be attributed to treatment seeking behaviour of the population. The study aims to find out common clinical features, outcome, and delay in arrival time to the hospital in snakebite cases. Materials and Methods: This is a cross-sectional retrospective record-based study among the patients presented with a history of snakebites to the Basaveshwara Medical College Hospital and Research Centre, Chitradurga, conducted from 1 January 2019 to 31 January 2021. Results: Out of the 96 patients, the majority of snakebite victims were male and of an age group of 21-30 years. The most common site for snakebite was the lower limb, and about 57.2% of patients had visible fang marks. Among snake species identified, the most common was krait bites, followed by cobra bites. Neurotoxic envenomation manifestation and severe presentation were found in almost half (53.1%) of the cases. Almost half of the patients took 1-4 hours to arrive at the health facility. Surprisingly, only 35% cases arrived to the health care facility within an hour of snakebite. Most of the patients (82%) recovered with the treatment with very minimal fatality/mortality. Conclusion and Recommendations: Although half of the patients presented with severe forms, only 35% of the patients arrived within an hour to the health care facility. This indicates the need for an information dissemination system to avoid severe disease as well as to prevent mortality. [ABSTRACT FROM AUTHOR]

The present paper reviewed the role of factors influencing the wear properties of iron alloy‐based coating material. In brief, these factors are categorized into four major types such as substrate properties, high‐velocity oxy‐fuel spraying parameters, heat treatment processes and wear testing parameters. Substrate properties include roughness, thermal resistance, and adhesive strength between substrate and coating materials. High velocity oxy fuel parameters include spray distance, oxygen flow rate, fuel flow rate, angle of spray, spray density, coating speed, thickness, number of coating layers etc. Erosion wear testing parameters include impact speed, impingement angle, erodent discharge, slurry concentration etc. Furthermore, erosion wear testing parameters are sub‐categorized into three types which are particulate‐based parameters such as size, shape and hardness of particle, impact‐based parameters such as angle of impact, impact velocity, and composition‐based parameters such as microstructure, physical and mechanical properties of materials. The annealing process speeds up the development of crystal phases. Therefore, to get the best coating materials, a detailed investigation and optimization of all the factors must be performed. [ABSTRACT FROM AUTHOR]

The main advantage of Vehicular Adhoc Networks being an inherent part of Intelligent Transportation Systems is to ensure the safety and comfort of commuters on the roads. In VANETs, due to the wide open nature of wireless communications, data transmission has been subjected to variety of security and privacy threats which causes disruptions in the network. Hence data has to be authenticated prior to communication. To address this problem enhanced elliptic curve cryptography based certificate-less signature aggregation scheme has been proposed to ensure full privacy preservation and to provide efficient security to the VANETs. Through formal and informal security analysis the proposed scheme has been tested and proven to be secure under suppositions made using Diffie-Hellman assumptions by using Random Oracle Model. The proposed scheme is implemented using MIRACL C + + library and by using Veins Integrated Simulation Framework. The proposed scheme has been analyzed and compared with the existing standards which prove that the proposed scheme is efficient in terms of computation cost and communication costs. The major novelty of the proposed scheme is the use of aggregation and point addition which does not cause delay in verification and also reduces overhead in the road-side units by employing elliptic curve cryptography based distributed data authentication scheme. Hence, the traditional problems of certificate management and key escrow problem is solved by the proposed scheme in an efficient manner. This makes the proposed scheme to be much suitable for high volatile and resource-constrained vanet environments. [ABSTRACT FROM AUTHOR]

Underwater Wireless Sensor Networks (UWSNs) are the type of WSNs that transmit the data through water medium and monitor the oceanic conditions, water contents, under-sea habitations, underwater beings and military objects. Unlike air medium, water channel creates stronger communication barriers. In addition, the malicious data injection and other network attacks create security problems during data communication. Protecting the vulnerable UWSN channel is not an easy task under critical water conditions. Many research works proposed in the literature used cryptography principles and intelligent intrusion detection systems to secure the network activities from malicious nodes. However, the need for Machine Learning (ML) and Deep Learning (DL) associated Medium Access Control (MAC) principles is expected for handling the barriers in uncertain UWSN. In this regard, this article proposes a new Intrusion detection system with Integrated Secure MAC principles and Long Short-Term Memory (LSTM) architectures for organizing real-time neighbor monitoring tasks. The proposed system implements Generative Adversarial Network (GAN) driven UWSN channel assessment models and Secure LSTM-MAC principles to protect the data communication. In this regard, the proposed model creates the Intrusion Detection System (IDS) using trained distributed agents. These agents run in each legitimate sensor node contain novel LSTM-MAC engine, intrusion dataset, rule-based monitoring techniques, Secure Hashing Algorithm-3 (SHA-3), Two Fish algorithm and packet filtering tools. The proposed LSTM and agent-based model drives adaptive MAC channel operations to avoid malicious traffics in to legitimate nodes. In addition, this work implements neighbor-based packet monitoring, signal jamming and alert messaging procedures to build reliable security services against different types of attacks. The experiments and the observations reveal the performance of proposed techniques is proved to be 5% to 10% higher than existing techniques in various aspects measured with different metrics. [ABSTRACT FROM AUTHOR]