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In rainfed and dryland agricultural areas with smallholder farms (less than 2 ha), crop diversity is high due to farmers' decisions and local climatic conditions, leading to a complex spatial–temporal distribution of crops. Monitoring and mapping crops is crucial for food security and implementing agricultural support programs. This study aims to map crop types across Senegal using Sentinel-2 satellite imagery and the limited ground reference data available, which has been increasing recently. The study compares conventional supervised classification algorithms to unsupervised classification algorithms using high-resolution satellite imagery. Crop type classification for 2020 in Senegal employed supervised machine learning algorithms, including Classification and Regression Trees (CART), Random Forest (RF), and Support Vector Machine (SVM) on the Google Earth Engine (GEE) cloud platform, and the unsupervised Iso-clustering classification algorithm with Spectral Matching Techniques (SMTs). Due to limited ground data, supervised classifiers achieved 45-55% accuracy, whereas the unsupervised semi-automatic approach achieved over 75% accuracy. The study indicates that supervised classifiers' performance depends on ground data quantity, while SMT shows good performance even with limited ground data. This SMT approach is valuable for classifying crop types in dryland areas with smallholder farms and diverse cropping patterns.

Efficient monitoring of crop water requirements is crucial for assessing the impacts of major irrigation projects, such as the Kaleshwaram lift irrigation project, both before and after their implementation. These projects can significantly change agricultural practices and water usage patterns, necessitating thorough evaluations to ensure sustainable water management and agricultural resilience. The main aim of this study is to evaluate and compare crop water needs during the winter (rabi) seasons of 2018–2019 and 2022–2023 across the command area of the project. This is achieved by mapping major crops and their respective length of growing periods across the study area using sentinel-2 satellite data and ground data, and quantifying crop water requirements using reference evapotranspiration and FAO crop coefficients. Results reveal a significant shift towards rice cultivation, with an over 80% increase in the winter season of 2022–2023 compared to 2018–2019, indicating substantial escalations in crop water requirements. These findings provide valuable insights into agricultural transformations induced by large-scale irrigation interventions, emphasizing the need for sustainable water management practices to ensure agricultural resilience and resource conservation in similar contexts.

Background and Objectives: Therapeutic plasma exchange (TPE) is commonly used as a treatment of certain autoimmune neurological diseases, and its main target is to remove pathogenical toxins or autoantibodies. Removed plasma with toxins and autoantibodies is replaced by crystalloids, colloids, and/or normal saline. It is more effective and cost-effective than immunoglobulins. The aim of this study was to know the effectiveness of TPE in autoimmune neurological diseases. Methods: This is a prospective study involving 376 autoimmune neurological cases, and the duration of this study was 5 years. All autoimmune neurological patients irrespective of age with complaints of weakness in limbs, respiratory distress, and acute myasthenia gravis (MG) crisis cases were included in the study. The Modified Rankin Score and MG Composite Score were used to analyze the severity of the disease, and the number of TPE procedures or cycles was planned. Results: The total number of cases included was 376 (322 newly registered cases and 54 old and relapse cases), and the total number of TPE procedures done was 1491 between the years 2017 and 2021. Male patients were 138 and female patients 238. The most number of cases were of MG, followed by Guillain–Barre syndrome. The mean number of TPE procedures done was 6.1 cycles in myasthenia crisis patients. There was a significant improvement in the patients after the treatment. The Modified Rankin Score and MG Composite Score were 1/6 and 3/50, respectively (P = 0.0321 and P = 0.0298, respectively) after completion of the TPE. Conclusion: TPE is the most effective method in cases of neurological autoimmune diseases. Most of the cases show improvement immediately after the first cycle, and more than half of the cases will be able to walk or do their routine activities after 2 or 3 cycles. It is a safe and cost-effective treatment modality with minimal side effects or complications.

In the present study, efficacy of minimally-invasive serum Raman spectroscopy (SRS) in stratification of congenital heart diseases was explored. Blood was collected from 62 subjects [42 congenital heart defect (CHD) patients (19 with atrial septal defect, 13 with ventricular septal defect and 10 with tetralogy of fallot) and 20 controls], and serum separated. Raman spectra of sera were recorded, pre-processed and subjected to spectral and multivariate analyses. Multivariate curve resolution-alternating least squares (MCR-ALS) analyses indicated alterations in lipid and protein levels between the study groups. Principal Component Analysis (PCA) and Principal Component based Linear Discriminant Analysis (PC-LDA), cross-validated with Leave-one-out cross validation (LOOCV), were employed to study stratification between the different groups. CHD could be classified from controls with 76 % efficiency. The different CHD subtypes could be distinguished with efficiencies as high as ∼90 %. To the best of our knowledge, differentiation between controls and CHDs as well as the stratification between controls and CHDs subtypes was for the first time successfully accomplished by serum-based Raman spectroscopy.

BLDC motors are extensively used in various industries, including CNC machine tools, industrial robots, and electric vehicles. Because of their compact size, high efficiency, high torque-to-power ratios, and low maintenance requirements due to their brushless operation, BLDC motors are the backbone of many industrial automation systems. However, they pose significant challenges when it comes to speed control. In this study, the speed of BLDC motors is controlled by PI-based speed controllers. In the described method, the BLDC motor is commutated using Hall Effect sensors. A proposed approach uses PI to regulate the speed of BLDC motors in an open-loop PWM method. The speed-controlled BLDC motor is analysed using MATLAB/simulink. The hardware of the proposed system is also implemented.

This study underscores the critical importance of accurate crop yield information for national food security and export considerations, with a specific focus on wheat yield estimation at the Gram Panchayat (GP) level in Bareilly district, Uttar Pradesh, using technologies such as machine learning algorithms (ML), the Decision Support System for Agrotechnology Transfer (DSSAT) crop model and semi-physical models (SPMs). The research integrates Sentinel-2 time-series data and ground data to generate comprehensive crop type maps. These maps offer insights into spatial variations in crop extent, growth stages and the leaf area index (LAI), serving as essential components for precise yield assessment. The classification of crops employed spectral matching techniques (SMTs) on Sentinel-2 time-series data, complemented by field surveys and ground data on crop management. The strategic identification of crop-cutting experiment (CCE) locations, based on a combination of crop type maps, soil data and weather parameters, further enhanced the precision of the study. A systematic comparison of three major crop yield estimation models revealed distinctive gaps in each approach. Machine learning models exhibit effectiveness in homogenous areas with similar cultivars, while the accuracy of a semi-physical model depends upon the resolution of the utilized data. The DSSAT model is effective in predicting yields at specific locations but faces difficulties when trying to extend these predictions to cover a larger study area. This research provides valuable insights for policymakers by providing near-real-time, high-resolution crop yield estimates at the local level, facilitating informed decision making in attaining food security.

Recommendation systems play an important role in creating personalized content for consumers, improving their overall experiences across several applications. Providing the user with accurate recommendations based on their interests is the recommender system’s primary goal. Collaborative filtering-based recommendations with the help of matrix factorization techniques is very useful in practical uses. Owing to the expanding size of the dataset and as the complexity increases, there arises an issue in delivering accurate recommendations to the users. The efficient functioning of the recommendation system undergoes the scalability challenge in controlling large and varying datasets. This paper introduces an innovative approach by integrating matrix factorization techniques and community detection methods where the scalability in recommendation systems will be addressed. The steps involved in the proposed approach are: 1) The rating matrix is modeled as a bipartite network. 2) Communities are generated from the network. 3) Extract the rating matrices that belong to the communities and apply MF to these matrices in parallel. 4) Merge the predicted rating matrices belonging to the communities and evaluate root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE). In our paper different matrix factorization approaches like basic MF, NMF, SVD++, and FANMF are taken along with the Louvain community detection method for dividing the communities. The experimental analysis is performed on five different diverse datasets to enhance the quality of the recommendation. To determine the method’s efficiency, the evaluation metrics RMSE, MSE, and MAE are used, and the time required to evaluate the computation is also computed. It is observed in the results that almost 95% of our results are proven effective by getting lower RMSE, MSE, and MAE values. Thus, the main aim of the user will be satisfied in getting accurate recommendations based on the user experiences.

Exploring the significance of popular node’s impact in complex networks yields numerous advantages, such as improving network resilience and accelerating information dissemination. While conventional centrality measures accurately quantify individual node importance, they may inadvertently overlook certain properties of influential nodes. The quest for new centrality metrics has garnered substantial research due to their theoretical relevance and practical applicability in real-world network scenarios. The existing research has predominantly focused on designing centrality metrics based on the local and/or global topological characteristics of nodes. Nevertheless, these metrics do not consider the nodes located in the intermediary zones between the inner and outer regions of a network, resulting in reduced effectiveness when applied to large-scale network scenarios. To address these challenges, we have introduced a novel convex framework to formulate the Convex Mixed Centrality (COMC) measure. This metric aims to overcome the limitations of traditional centrality metrics by incorporating insights from both local and global network dynamics, thus enhancing its ability to identify influential nodes across various network regions. To prove the efficacy of our proposed measure, we utilize the Susceptible-Infected-Recovered (SIR) and Independent Cascade (IC) models, alongside the Kendall tau metric. Extensive simulation experiments conducted on various real-world datasets demonstrate that the COMC measure outperforms conventional centrality indices in terms of spreading efficiency, all while maintaining comparable computational complexity.

In complex networks, node impact refers to an individual node’s significance or influence within the structure. The evaluation of the impact of the nodes in information transmission, prevention of pandemics, and resilience applications of the infrastructure is studied. Centrality measures are crucial for understanding the impact of particular nodes in the network structure. Most centrality measures, such as degree centrality, betweenness centrality, and eigenvector centrality, provide influential node information based on network aspects such as connection patterns, paths for communication, and influence propagation dynamics. However, these centrality measures could capture local and global information by balancing time complexity and spreading efficiency. This paper proposes an Isolating-Betweenness Centrality (ISBC) for quantifying node impact by incorporating the properties Betweenness Centrality and Isolating Centrality. The proposed measure evaluates a node’s impact by considering local and global structural influence. We verify the SIR and IC epidemic models to evaluate ISBC’s performance compared with conventional and recent centrality measures on real-world datasets. Furthermore, we show that the proposed measure exhibits improved spreading efficiency over recent and conventional measures with moderate time complexity.

Introduction: Cryoprecipitate is a slushy product derived from Fresh Frozen Plasma (FFP) through centrifugation at specific temperatures and speeds. It is rich in factor VIII or antihaemorrhagic factor, fibrinogen, and minor amounts of other clotting factors. Cryoprecipitate is primarily used to correct hypofibrinogenaemia or replenish depleted fibrinogen levels. Aim: To examine the appropriate clinical utilisation of cryoprecipitate (cryo) by various departments in the Institute and evaluate the improvement in patients through laboratory tests. Materials and Methods: A single group quasi-experimental study was conducted in the Department of Immunohaematology and Transfusion Medicine (IHBT) at Nizam’s Institute of Medical Sciences (NIMS), Hyderabad, Telangana, India. The duration of the study was over a period of six years, from May 2016 to April 2022. A total of 486 cases where cryoprecipitate was requested were included in the present study. The results were analysed using Prothrombin Time (PT) and International Normalised Ratio (INR). Percentages and means were used to assess various parameters in this study. All statistical analyses were performed using Statistical Package for Social Sciences (SPSS) version 25.0. Results: The mean age of the study participants was 52.7 years. A total of 486 cases were included over a period of six years, with 3375 cryoprecipitate transfusions performed. The youngest patient was a 9-year-old male, the oldest patient was an 83-year-old male. Most of the cryoprecipitate was appropriately utilised by the Medical Oncology Department (995 cases, 995 units, 20-25 mL per unit-29.48%), while the Cardiology Department had the lowest utilisation (12 cases, 12 units-0.35%). Utilisation by other departments ranged between 45 and 564 cases or units (1.33% to 16.71% per department). The majority of cryoprecipitate units were issued for multiple myeloma and acute leukaemias (new cases and relapse)-28.38% (958 units), while the least were issued for minor surgery and medical cases (29 cases or 29 units-0.85%). Conclusion: Cryoprecipitate should be used appropriately to achieve desired results and avoid unnecessary transmission. Clinicians, residents, and surgeons should adhere to comprehensive regulatory guidelines for the appropriate usage of cryoprecipitate or any other blood products, as outlined by their hospital transfusion committee.

The detection and ranking of influential nodes in complex networks are crucial for various practical applications such as identifying potential drug targets in protein-to-protein interaction networks, critical devices in communication networks, key people in social networks, and transportation hubs in logistics networks. The knowledge of influential spreaders in complex networks is extremely useful for controlling the spread of information. Centrality measures are known for effectively quantifying the influential nodes information in large-scale complex networks. Researchers have proposed different centrality measures in the literature, including Degree, Betweenness, Closeness, and Clustering coefficient centralities. However, these measures have certain limitations when implemented over large-scale complex networks. Most of these measures can be classified as global and local structural approaches. The global structure based algorithms are too complex to evaluate key nodes, particularly in large-scale networks, whereas the local measures overlook the essential global network information. To address these challenges, an extended clustering coefficient local global centrality (ECLGC) is proposed, which combines the local and global structural information to measure the node’s influence in large-scale networks. The effectiveness and computational efficiency of the proposed measure are compared with existing centrality measures on real-world network datasets. Susceptible-Infected-Recovered (SIR) model is utilized to evaluate the performance of the ECLGC to capture the high-information dissemination compared to conventional measures. Further, we demonstrate that the proposed measure outperforms the conventional measures in terms of spreading efficiency.

Many real-world problems can be modelled in the form of complex networks. Social networks such as research collaboration networks and facebook, biological neural networks such as human brains, biomedical networks such as drug-target interactions and protein-protein interactions, technological networks such as telephone networks, transportation networks and power grids are a few examples of complex networks. Any complex system with entities and interactions existing between the entities can be modelled as a graph mathematically, with nodes representing entities and edges reflecting interactions. In numerous real-world circumstances, interactions are not confined to pair of entities. Majority of these intricate systems inherently possess hypergraph structures, characterized by interactions that extend beyond pairwise connections. Existing studies often transform complex interactions at a higher level into pairwise interactions and subsequently analyze them. This conversion frequently leads to both the loss of information and the inability to reconstruct the original hypergraph from the transformed network with pairwise interactions. One of the most essential tasks that can be performed on these graphs is Link Prediction (LP), which is the task of predicting future edges (links) in a graph. LP in graphs is well investigated. This article presents a novel methodology for predicting links in hypergraphs. Unlike conventional approaches that transform hypergraphs into graphs with pairwise interactions, the proposed method directly leverages the inherent structure of hypergraphs in predicting future interaction between a pair of nodes. This is motivated by the fact that hypergraphs enable the depiction of intricate higher-order relationships through hyperlinks, enhancing their representation. Their capacity to capture complex structural patterns improves predictive capabilities. Node neighborhoods within hypergraphs offer a comprehensive framework for LP, where hyperlinks simplify interactions between nodes across cliques. We propose a novel method of Link Prediction in Hypergraphs (LPH) to predict interactions within hypergraphs, maintaining their original structure without conversion to graphs, thus preserving information integrity. The proposed approach LPH extends local similarity measures like Common Neighbors, Jaccard Coefficient, Adamic Adar, and Resource Allocation, along with a global measure, Katz index, to hypergraphs. LPH’s effectiveness is assessed on six benchmark hyper-networks, employing evaluation metrics such as Area under ROC curve, Precision, and F1-score. The proposed measures of LP on hypergraphs resulted in an average enhancement of 10% in terms of Area under ROC curve compared to contemporary as well as conventional measures. Additionally, there is an average improvement of 70% in precision and around 50% in F1-score. This methodology presents a promising avenue for predicting pairwise interactions within hypergraphs while retaining their inherent structural complexity as well as information integrity.

Ranking influential nodes within complex networks offers invaluable insights into a wide array of phenomena ranging from disease management to information dissemination and optimal routing in real-time networking applications. Centrality measures, which quantify the importance of nodes based on network properties and relationships of nodes within the network, are instrumental in achieving this task. These measures are typically classified into local and global centralities. Global measures consider the overall structure and connectivity patterns. However, they often suffer from high computational complexity in large-scale networks. On the other hand, local measures focus on the immediate neighborhood of each node, potentially overlooking global information. To address these challenges, we propose a novel metric called Isolating Clustering Centrality (ISCL), which leverages a convex combination approach. By introducing a convex tuning parameter, ISCL enhances the applicability and adaptability of centrality measures across a wide range of real-world network applications. In this study, we assess the efficacy of the proposed measure using real-world network datasets and simulate the spreading process using susceptible-infected-removed (SIR) and independent cascade (IC) models. Our extensive results demonstrate that ISCL significantly improves spreading efficiency compared to conventional and recent centrality measures, while also maintaining better computational efficiency in large-scale complex networks.

The study addresses the limitations of traditional centrality measures in complex networks, especially in disease-spreading situations, due to their inability to fully grasp the intricate connection between a node’s functional importance and structural attributes. To tackle this issue, the research introduces an innovative framework that employs machine learning techniques to evaluate the significance of nodes in transmission scenarios. This framework incorporates various centrality measures like degree, clustering coefficient, Katz, local relative change in average clustering coefficient, average Katz, and average degree (LRACC, LRAK, and LRAD) to create a feature vector for each node. These methods capture diverse topological structures of nodes and incorporate the infection rate, a critical factor in understanding propagation scenarios. To establish accurate labels for node significance, propagation tests are simulated using epidemic models (SIR and Independent Cascade models). Machine learning methods are employed to capture the complex relationship between a node’s true spreadability and infection rate. The performance of the machine learning model is compared to traditional centrality methods in two scenarios. In the first scenario, training and testing data are sourced from the same network, highlighting the superior accuracy of the machine learning approach. In the second scenario, training data from one network and testing data from another are used, where LRACC, LRAK, and LRAD outperform the machine learning methods.

The present work describes the optimization of reinforcement parameters for hardness, thermal conductivity, and coefficient of thermal expansion while developing LM6 alloy/soda-lime glass particulate composite through Taguchi-based Grey Relational Analysis (GRA). Soda-lime glass particle weight % (1.5, 3.0 and 4.5 %), particle size (100, 150 and 300 μm) and pre-heat temperature (260, 380 and 500oC) are varied accordingly to explore the effect of reinforcement parameters on LM6 alloy/soda-lime glass composite properties. Composites are developed through stir casting based on the L9 Taguchi orthogonal array approach. The properties such as hardness, thermal conductivity and coefficient of thermal expansion of developed composites are assessed. Signal to Noise Ratios (S/N ratios) are calculated and used for the optimization of parameters. GRA is employed for multi-response optimization to find the levels of parameters that affect the desirable properties of the composite. Thus, the reinforcement parameters are optimized for attaining the combined objectives of higher hardness, higher thermal conductivity and lower coefficient of thermal expansion values considered in this investigation. The analysis shows that 4.5 wt %, particle size of 200 μm and pre-heat temperature of 380oC are optimal parameter levels. A confirmation test is carried out with the optimal parameter levels and the GRG value of 0.7778 is obtained. The GRG with the initial parameter settings is 0.4711, and the improvement of GRG is found to be 65.1 %. ANOVA is performed on GRG to find out significant parameters and the contribution of each parameter is identified. The wt.% of soda-lime glass is the most significant parameter and its contribution is 92.6 %.

Introduction: Retinopathy Of Prematurity (ROP), is the major preventable blindness in infants. A study was conducted to find the risk factors of ROP in premature infants with gestational age (GA) < 36 weeks. Methods: It was a descriptive study conducted in Kakatiya Medical College, Hanumakonda. Babies with major congenital, chromosomal anomalies were excluded. One hour before screening, a drop of Tropicamide was instilled for every 10 – 15 minutes for 4 times followed by a drop of Phenylephrine just before examination. ROP screening was carried out by a senior ophthalmologist in NICU. First, the anterior segment was examined. This was followed by sequential examination of all clock hours of the peripheral retina and findings were recorded. Association between variables was analysed by using Chi-Square test. P < 0.05 was considered to be statistically significant. Results: ROP was detected in 31.4%; statistically there was no significant difference between gender. Apnoea, hyperbilirubinemia, anaemia, respiratory distress were found to be the risk factors for ROP; statistically there was significant difference, respectively. Conclusion: Preventing prematurity is the best way to reduce ROP. Proper coordination between obstetrician, neonatologist and ophthalmologist can help to reduce the incidence.

Introduction: The prevalence of co-existing cataract and glaucoma is increasing in the adult population. Combined surgeries have become more popular. However, there is a conflict over which technique provides the best Intraocular Pressure (IOP) control with good postoperative outcomes. Aim: To compare the efficacy of single-site versus two-site phacotrabeculectomy with mitomycin-C in patients with Primary Open-angle Glaucoma (POAG) and cataract. Materials and Methods: A prospective cohort study was conducted in the Department of Ophthalmology, S.V. Medical College, Tirupati, Andhra Pradesh, India, over a period of one year from January 2019 to January 2020. A total of 50 cases of POAG coexisting with cataract were analysed in the present study. Twentyfive cases were included in each group (Group-1 and Group-2). Phacoemulsification and trabeculectomy were both performed through a superior scleral tunnel in the single-site approach. The two-site method combines a superior trabeculectomy with a temporal clear corneal phacoemulsification. A concentration of 0.2 mg/mL of MMC was applied in both groups for three minutes. Patients were followed-up for three months after surgery to evaluate Intraocular Pressure (IOP), the need for antiglaucoma medication, and postoperative best-corrected Visual Acuity (VA). Comparative analysis was done using the Student’s t-test, and a p-value

Crop yield estimation has gained importance due to its vital significance for policymakers and decision-makers in enacting schemes, ensuring food security, and assessing crop insurance losses due to biotic and abiotic stress. This research focused on rice yield estimation at the field level in the Karimnagar district of Telangana during 2021 and 2022 by employing the leaf area index (LAI) as the primary criterion for integrating remote sensing technology and crop simulation models. Using Sentinel-2 satellite data, the rice crop was mapped with the help of ground data and machine learning algorithms, attaining an accuracy of 93.04%. Crop management data for the DSSAT tool were collected during the field visits; the model results revealed a 0.80 correlation between observed and predicted yields. Due to its strong correlation with LAI (0.82), the normalized difference vegetation index (NDVI) was selected as the critical element for integration with the model. A spatial LAI map was generated using the linear equation developed between the NDVI and LAI. The relationship between LAI and yield was used to create a spatial yield map. The study’s findings show that assimilating remote sensing data with crop models enhances the precision of rice yield prediction for insurance companies and policy- and decision-makers.

Today, crop suggestions and necessary guidance have become a regular need for a farmer. Farmers generally depend on their local agriculture officers regarding this, and it may be difficult to obtain the right guidance at the right time. Nowadays, crop datasets are available on different websites in the agriculture sector, and they play a crucial role in suggesting suitable crops. So, a decision support system that analyzes the crop dataset using machine learning techniques can assist farmers in making better choices regarding crop selections. The main objective of this research is to provide quick guidance to farmers with more accurate and effective crop recommendations by utilizing machine learning methods, global positioning system coordinates, and crop cloud data. Here, the recommendation can be more personalized, which enables the farmers to predict crops in their specific geographical context, taking into account factors like climate, soil composition, water availability, and local conditions. In this regard, an existing historical crop dataset that contains the state, district, year, area-wise production rate, crop name, and season was collected for 246,091 sample records from the Dataworld website, which holds data on 37 different crops from different areas of India. Also, for better analysis, a dataset was collected from the agriculture offices of the Rayagada, Koraput, and Gajapati districts in Odisha state, India. Both of these datasets were combined and stored using a Firebase cloud service. Thirteen different machine learning algorithms have been applied to the dataset to identify dependencies within the data. To facilitate this process, an Android application was developed using Android Studio (Electric Eel | 2023.1.1) Emulator (Version 32.1.14), Software Development Kit (SDK, Android SDK 33), and Tools. A model has been proposed that implements the SMOTE (Synthetic Minority Oversampling Technique) to balance the dataset, and then it allows for the implementation of 13 different classifiers, such as logistic regression, decision tree (DT), K-Nearest Neighbor (KNN), SVC (Support Vector Classifier), random forest (RF), Gradient Boost (GB), Bagged Tree, extreme gradient boosting (XGB classifier), Ada Boost Classifier, Cat Boost, HGB (Histogram-based Gradient Boosting), SGDC (Stochastic Gradient Descent), and MNB (Multinomial Naive Bayes) on the cloud dataset. It is observed that the performance of the SGDC method is 1.00 in accuracy, precision, recall, F1-score, and ROC AUC (Receiver Operating Characteristics–Area Under the Curve) and is 0.91 in sensitivity and 0.54 in specificity after applying the SMOTE. Overall, SGDC has a better performance compared to all other classifiers implemented in the predictions.

A cropping system practice is the sequential cultivation of crops in different crop seasons of a year. Cropping system practices determine the land productivity and sustainability of agriculture in regions and, therefore, information on cropping systems of different regions in the form of maps and statistics form critical inputs in crop planning for optimal use of resources. Although satellite-based crop mapping is widely practiced, deriving cropping systems maps using satellites is less reported. Here, we developed moderate-resolution maps of the major cropping systems of South Asia for the year 2014–2015 using multi-temporal satellite data together with a spectral matching technique (SMT) developed with an extensive set of field observation data supplemented with expert-identified crops in high-resolution satellite images. We identified and mapped 27 major cropping systems of South Asia at 250 m spatial resolution. The rice-wheat cropping system is the dominant system, followed by millet-wheat and soybean-wheat. The map showing the cropping system practices of regions opens up many use cases related to the agriculture performance of the regions. Comparison of such maps of different time periods offers insights on sensitive regions and analysis of such maps in conjunction with resources maps such as climate, soil, etc., enables optimization of resources vis-à-vis enhancing land productivity. Thus, the current study offers new opportunities to revisit the cropping system practices and redesign the same to meet the challenges of food security and climate resilient agriculture.

Shrimp farming and exporting is the main income source for the southern coastal districts of the Mekong Delta. Monitoring these shrimp ponds is helpful in identifying losses incurred due to natural calamities like floods, sources of water pollution by chemicals used in shrimp farming, and changes in the area of cultivation with an increase in demand for shrimp production. Satellite imagery, which is consistent with good spatial resolution and helpful in providing frequent information with temporal imagery, is a better solution for monitoring these shrimp ponds remotely for a larger spatial extent. The shrimp ponds of Cai Doi Vam township, Ca Mau Province, Viet Nam, were mapped using DMC-3 (TripleSat) and Jilin-1 high-resolution satellite imagery for the years 2019 and 2022. The 3 m spatial resolution shrimp pond extent product showed an overall accuracy of 87.5%, with a producer’s accuracy of 90.91% (errors of omission = 11.09%) and a user’s accuracy of 90.91% (errors of commission = 11.09%) for the shrimp pond class. It was noted that 66 ha of shrimp ponds in 2019 were observed to be dry in 2022, and 39 ha of other ponds had been converted into shrimp ponds in 2022. The continuous monitoring of shrimp ponds helps achieve sustainable aquaculture and acts as crucial input for the decision makers for any interventions.

Agriculture consumes the largest share of freshwater globally; therefore, distinguishing between rainfed and irrigated croplands is essential for agricultural water management and food security. In this study, a framework incorporating the Budyko model was used to differentiate between rainfed and irrigated cropland areas in Africa for eight remote sensing landcover products and a high-confidence cropland map (HCCM). The HCCM was generated for calibration and validation of the crop partitioning framework as an alternative to individual cropland masks which exhibit high disagreement. The accuracy of the framework in partitioning the HCCM was evaluated using an independent validation dataset, yielding an overall accuracy rate of 73 %. The findings of this study indicate that out of the total area covered by the HCCM (2.36 million km2), about 461,000 km2 (19 %) is irrigated cropland. The partitioning framework was applied on eight landcover products, and the extent of irrigated areas varied between 19 % and 30 % of the total cropland area. The framework demonstrated high precision and specificity scores, indicating its effectiveness in correctly identifying irrigated areas while minimizing the misclassification of rainfed areas as irrigated. This study provides an enhanced understanding of rainfed and irrigation patterns across Africa, supporting efforts towards achieving sustainable and resilient agricultural systems. Consequently, the approach outlined expands on the suite of remote sensing landcover products that can be used for agricultural water studies in Africa by enabling the extraction of irrigated and rainfed cropland data from landcover products that do not have disaggregated cropland classes.

Abstract Background RNA sequencing (RNA-Seq) is a technique that utilises the capabilities of next-generation sequencing to study a cellular transcriptome i.e., to determine the amount of RNA at a given time for a given biological sample. The advancement of RNA-Seq technology has resulted in a large volume of gene expression data for analysis. Results Our computational model (built on top of TabNet) is first pretrained on an unlabelled dataset of multiple types of adenomas and adenocarcinomas and later fine-tuned on the labelled dataset, showing promising results in the context of the estimation of the vital status of colorectal cancer patients. We achieve a final cross-validated (ROC-AUC) Score of 0.88 by using multiple modalities of data. Conclusion The results of this study demonstrate that self-supervised learning methods pretrained on a vast corpus of unlabelled data outperform traditional supervised learning methods such as XGBoost, Neural Networks, and Decision Trees that have been prevalent in the tabular domain. The results of this study are further boosted by the inclusion of multiple modalities of data pertaining to the patients in question. We find that genes such as RBM3, GSPT1, MAD2L1, and others important to the computation model’s prediction task obtained through model interpretability corroborate with pathological evidence in current literature.

Link prediction plays a crucial role in identifying future connections within complex networks, facilitating the analysis of network evolution across various domains such as biological networks, social networks, recommender systems, and more. Researchers have proposed various centrality measures, such as degree, clustering coefficient, betweenness, and closeness centralities, to compute similarity scores for predicting links in these networks. These centrality measures leverage both the local and global information of nodes within the network. In this study, we present a novel approach to link prediction using similarity score by utilizing average centrality measures based on local and global centralities, namely Similarity based on Average Degree (SACD), Similarity based on Average Betweenness (SACB), Similarity based on Average Closeness (SACC), and Similarity based on Average Clustering Coefficient (SACCC). Our approach involved determining centrality scores for each node, calculating the average centrality for the entire graph, and deriving similarity scores through common neighbors. We then applied centrality scores to these common neighbors and identified nodes with above average centrality. To evaluate our approach, we compared proposed measures with existing local similarity-based link prediction measures, including common neighbors, the Jaccard coefficient, Adamic–Adar, resource allocation, preferential attachment, as well as recent measures like common neighbor and the Centrality-based Parameterized Algorithm (CCPA), and keyword network link prediction (KNLP). We conducted experiments on four real-world datasets. The proposed similarity scores based on average centralities demonstrate significant improvements. We observed an average enhancement of 24% in terms of Area Under the Receiver Operating Characteristic (AUROC) compared to existing local similarity measures, and a 31% improvement over recent measures. Furthermore, we witnessed an average improvement of 49% and 51% in the Area Under Precision-Recall (AUPR) compared to existing and recent measures. Our comprehensive experiments highlight the superior performance of the proposed method.

Dryland crops are highly prone to various stresses such as water stress (drought) and heat stress. The identification of stress-prone regions is crucial for effective and efficient implementation of appropriate solutions, such as stress-tolerant crop varieties. This study was conducted in Rajasthan state located in North-western India. Rajasthan is predominantly a rainfed pearl millet ecosystem (>50 % during monsoon season) in India. The pearl millet productivity in Western Rajasthan is the lowest in India with a significant decline in its cultivated area. The present study tried to analyse the pearl millet cropping systems in various ecologies and identified the stress-prone areas by analysing the stress pattern from 2011 to 2020 which helps in targeting the stress tolerant cultivars. The spatial distribution of pearl millet areas was mapped using Sentinel-2 time-series data and spectral matching techniques. The mapped pearl millet areas were well correlated with district-level statistics obtained from secondary sources. Application of geospatial techniques for monitoring changes in pearl millet cropped area proves to be a cost-effective, and reliable approach. It also helps in assessing the cultivated area changes as well as the quantification of yield losses caused by abiotic stresses such as drought and heat. Agricultural research institutes, progressive farmers and line departments from the government can use these findings for better targeting and introduction of climate SMART pearl millet technologies in the state. Introduction of resilient technologies minimize the production risks faced by small and marginal farmer thereby reduces the crop income negative deviations. Scaling-up of such technologies not only protects farmer's livelihoods but also enhances the food and nutritional security in the state.

In 2014, the State of Telangana in southern India began repairing and restoring more than 46,000 irrigation water tanks (artificial reservoirs) under the Mission Kakatiya project with an investment in excess of USD 2 billion. In this study, we attempted to map the temporal changes that have occurred in cropland areas and water bodies as a result of the project, using remote sensing imagery and applying land use/land cover (LULC) mapping algorithms. We used 16-day time series data from Landsat 8 to study the spatial distribution of changes in water bodies and cropland areas over the 2013–18 period. Ground survey information was used to assess the pixel-based accuracy of the Landsat-derived data. The areas served by these tanks were identified on the basis of training data and Random Forest algorithms using Google Earth Engine. Our spatial analysis revealed a substantial increase in cropped area under irrigation and expansion of water bodies over the study period. We observed a 20% increase in total tank area in 2017–18 and total cropland and irrigated area expansion of the order of 0.6 M ha and 0.2 M ha, respectively. A comparison of ground survey data and four LULC classes derived from Landsat temporal imagery showed an overall accuracy of 87%, significantly correlated with national agriculture statistics. Periodic monitoring based on remote sensing has proved to be an effective method of capturing LULC changes resulting from the Mission Kakatiya interventions. Higher-resolution satellite data can further improve the accuracy of estimates.

Summary: Bacteria that are resistant to antibiotics present an increasing burden on healthcare. To address this emerging crisis, novel rapid antibiotic susceptibility testing (AST) methods are eagerly needed. Here, we present an optical AST technique that can determine the bacterial viability within 1 h down to a resolution of single bacteria. The method is based on measuring intensity fluctuations of a reflected laser focused on a bacterium in reflective microwells. Using numerical simulations, we show that both refraction and absorption of light by the bacterium contribute to the observed signal. By administering antibiotics that kill the bacteria, we show that the variance of the detected fluctuations vanishes within 1 h, indicating the potential of this technique for rapid sensing of bacterial antibiotic susceptibility. We envisage the use of this method for massively parallelizable AST tests and fast detection of drug-resistant pathogens.

In this paper, an attempt has been made to employ deep learning technique to predict the phase of brain tumors. The deep learning methods help the practitioners to correlate patients’ status in hand with similar subjects and assess as well as predict the future anomalies due to brain tumor. This certainly emerges as a handy tool for remote medication, especially citing the current pandemic needs. On the other hand, the digitization of the images followed by intelligent analysis can provide a first impression in a quick which usually consumes lot manual computations and cross verifications. Deep learning has been the most successful tool to handle supervised classification while dealing with complex patterns. A fully automated deep learning method can be developed with the existing toolboxes in the simulation environment. The purpose of the study is to apply this machine learning technique to classifying images of brains with different types of tumors: meningioma, glioma, and pituitary. The simulation is carried in Matlab environment and analysis is carried out using standard metrics. The confusion matrix shows a maximum of 95% and minimum of 89% accuracy. The results are compared with the Convolutional Neural Network (CNN) and Artificial Neural Network models (ANN).

Abstract Human monkeypox (MPOX) which recently hit the headlines is a rare, emerging zoonotic disease, only next to smallpox yet never attended adequately to halt the epidemic outbreak threat. MPOX is caused by Orthopox virus, which is a double-stranded, linear DNA virus, transmitted from infected animals, commonly rodents to humans. Monkeypox is endemic to the tropical jungles in Central-West Africa; occasional cases reported in other nations could be due to people traveling from endemic regions of MPOX. Transmission may occur via direct contact with human body secretions, cutaneous or mucosal lesions in the mouth or throat or respiratory droplets, and contaminated objects. Typical MPOX symptoms are fever, lymphadenopathy, skin rashes, intense headache, muscle, back pain, etc. Lesions can range from a few to numerous and may be filled with clear or yellowish fluid that later dries up or crusts, eventually falling off. MPOX is often considered as infrequent and self-limiting; nonetheless, the latest sporadic reports call for urgent vigilance, precautionary preparedness, and immediate response. Paucity of the data available about MPOX virus diversity and incomplete information on validated management protocols instigate a sense of impending danger and loom large as a global health emergency. MPOX is a completely preventable infection, and this article will cater to the need for creating general awareness and developing cutting-edge surveillance measures to curtail the spread of the disease. Genomic investigations of new cases of MPOX must be undertaken to check for mutations which can lead to higher human susceptibility. Local health stakeholders and clinicians should emphasize early identification and give out appropriate treatment as per the existing protocol

Aims: The aim of this study was to compare the immediate and long-term outcomes after high spermatic vessel ligation (HSVL) and low spermatic vessel ligation (LSVL) in a high undescended testis (UDT) model in rats. Materials and Methods: A prospective randomized controlled study was conducted on 24 male Wistar rats. The rats were randomly divided into three groups. Group A underwent a sham laparotomy and acted as the control. Group B underwent HSVL of both testicular vessels. Group C underwent LSVL of both testicular vessels. Each group was again subdivided into two subgroups. One sub-group underwent blood collection and testicular biopsy of both testes 24 h after the procedure to demonstrate immediate changes. Other subgroups underwent blood sample collection and testicular biopsy of both testes on day 50 following the procedure for hormonal changes and long-term changes. Results: All the testes in HSVL showed atrophy (100%) in the long term, whereas LSVL showed atrophy in 12.5% of testes, even though both groups showed adequate neovascularization. Testes in HSVL showed poor bleeding on incision at both 24 h and day 50. On histology, 75% of testes in HSVL showed complete necrosis, and 50% in LSVL showed partial necrosis at 24 h. On day 50, all the testes in HSVL (100%) showed complete necrosis with dystrophic calcification, whereas all the testes in LSVL showed normal histology with good maturation of seminiferous tubules. There was no significant difference in testosterone levels between both groups. Conclusions: Both immediate and long-term changes following LSVL showed an increase in blood flow to the testis after ligation through collaterals and reverses early ischemic changes to the testis. Given the higher testicular atrophic rate after HSVL, LSVL or at least low ligation can be preferred for the management of high intra-abdominal UDT.

Over the past decade, there has been extensive research conducted on complex networks, primarily driven by their crucial role in understanding the various real-world networks such as social networks, communication networks, transportation networks, and biological networks. Ranking influential nodes is one of the fundamental research problems in the areas of rumor spreading, disease research, viral marketing, and drug development. Influential nodes in any network are used to disseminate the information as fast as possible. Centrality measures are designed to quantify the node’s significance and rank the influential nodes in complex networks. However, these measures typically focus on either the local or global topological structure within and outside network communities. In particular, many measures limit their ability to capture the node’s overall impact on small-scale networks. To address these challenges, we develop a novel centrality measure called Isolating Clustering Distance Centrality (ICDC) by integrating the isolating and clustering coefficient centrality measures. The proposed metric gives a more thorough assessment of the node’s importance by integrating the local isolation and global topological influence in large-scale complex networks. We employ the SIR and ICM epidemic models to study the efficiency of ICDC against traditional centrality measures across real-world complex networks. Our experimental findings consistently highlight the superior efficacy of ICDC in terms of fast spreading and computational efficiency when compared to existing centrality measures.

Identifying vital nodes is important in disease research, spreading rumors, viral marketing, and drug development. The vital nodes in any network are used to spread information as widely as possible. Centrality measures such as Degree centrality (D), Betweenness centrality (B), Closeness centrality (C), Katz (K), Cluster coefficient (CC), PR (PageRank), LGC (Local and Global Centrality), ISC (Isolating Centrality) centrality measures can be used to effectively quantify vital nodes. The majority of these centrality measures are defined in the literature and are based on a network’s local and/or global structure. However, these measures are time-consuming and inefficient for large-scale networks. Also, these measures cannot study the effect of removal of vital nodes in resource-constrained networks. To address these concerns, we propose the six new centrality measures namely GRACC, LRACC, GRAD, LRAD, GRAK, and LRAK. We develop these measures based on the relative change of the clustering coefficient, degree, and Katz centralities after the removal of a vertex. Next, we compare the proposed centrality measures with D, B, C, CC, K, PR, LGC, and ISC to demonstrate their efficiency and time complexity. We utilize the SIR (Susceptible-Infected-Recovered) and IC (Independent Cascade) models to study the maximum information spread of proposed measures over conventional ones. We perform extensive simulations on large-scale real-world data sets and prove that local centrality measures perform better in some networks than global measures in terms of time complexity and information spread. Further, we also observe the number of cliques drastically improves the efficiency of global centrality measures.

With the advancement of the Internet and the world wide web (WWW), it is observed that there is an exponential growth of data and information across the internet. In addition, there is a huge growth in digital or textual data generation. This is because users post the reply comments in social media websites based on the experiences about an event or product. Furthermore, people are interested to know whether the majority of potential buyers will have a positive or negative experience on the event or the product. This kind of classification in general can be attained through Sentiment Analysis which inputs unstructured text comments about the product reviews, events, etc., from all the reviews or comments posted by users. This further classifies the data into different categories namely positive, negative or neutral opinions. Sentiment analysis can be performed by different machine learning models like CNN, Naive Bayes, Decision Tree, XgBoost, Logistic Regression etc. The proposed work is compared with the existing solutions in terms of different performance metrics and XgBoost outperforms out of all other methods.

Cropland products are of great importance in water and food security assessments, especially in South Asia, which is home to nearly 2 billion people and 230 million hectares of net cropland area. In South Asia, croplands account for about 90% of all human water use. Cropland extent, cropping intensity, crop watering methods, and crop types are important factors that have a bearing on the quantity, quality, and location of production. Currently, cropland products are produced using mainly coarse-resolution (250–1000 m) remote sensing data. As multiple cropland products are needed to address food and water security challenges, our study was aimed at producing three distinct products that would be useful overall in South Asia. The first of these, Product 1, was meant to assess irrigated versus rainfed croplands in South Asia using Landsat 30 m data on the Google Earth Engine (GEE) platform. The second, Product 2, was tailored for major crop types using Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m data. The third, Product 3, was designed for cropping intensity (single, double, and triple cropping) using MODIS 250 m data. For the kharif season (the main cropping season in South Asia, Jun–Oct), 10 major crops (5 irrigated crops: rice, soybean, maize, sugarcane, cotton; and 5 rainfed crops: pulses, rice, sorghum, millet, groundnut) were mapped. For the rabi season (post-rainy season, Nov–Feb), five major crops (three irrigated crops: rice, wheat, maize; and two rainfed crops: chickpea, pulses) were mapped. The irrigated versus rainfed 30 m product showed an overall accuracy of 79.8% with the irrigated cropland class providing a producer’s accuracy of 79% and the rainfed cropland class 74%. The overall accuracy demonstrated by the cropping intensity product was 85.3% with the producer’s accuracies of 88%, 85%, and 67% for single, double, and triple cropping, respectively. Crop types were mapped to accuracy levels ranging from 72% to 97%. A comparison of the crop-type area statistics with national statistics explained 63–98% variability. The study produced multiple-cropland products that are crucial for food and water security assessments, modeling, mapping, and monitoring using multiple-satellite sensor big-data, and Random Forest (RF) machine learning algorithms by coding, processing, and computing on the GEE cloud.

The lower catchment area of a Mak Hiao river system is vulnerable to flash floods and water stress. So it is important to construct irrigation structures in this area to minimize floods during the rainy season and store water for the winter season. The Asian Development Bank (ADB) has been supporting the Government of Laos in constructing such small reservoirs like Donkhuay schemes 1 & 2, Mak Hiao, Nalong 3 and Sang Houabor projects in lower catchment areas. Our study evaluated the impacts of small irrigation schemes in terms of land-use/land-cover (LULC), crop intensity, and productivity changes, using high resolution satellite imagery, socioeconomic, and ground data. We analyzed the temporal cropping pattern in the Vientiane prefecture of Laos using Planet and Sentinel-2 data. On the other hand, crop intensity and cropland changes were mapped using Sentinel-2 data and spectral matching techniques (SMTs). The crop classification accuracy based on field-plot data was 88.6%. Our results show that irrigation projects in the lower catchment areas brought about significant on-site changes in terms of cropland expansion and increased crop intensity. Remarkable changes in LULC were observed especially in the command areas owing to an increase of about 300% in crop area with access to irrigation and increase of water bodies by 31%. Our study found that interventions at the level of the command area do improved on-site soil, water and environmental services. They study emphasized underline the role of land-use regulations in reducing pressure on natural land-use systems and thereby serving the major goal of up-scaling sustainable natural resource management. The study documented the vital role of small/medium irrigation projects in restoring ecosystem services such as cropping patterns and LULC conversion.

A compact microstrip antenna for terahertz applications is proposed and its characteristic parameters are analyzed by utilizing several conducting and dielectric materials. The proposed terahertz antenna consists of a modified E-shaped patch and slot loaded T-shaped ground plane. The proposed THz antenna structure is designed on a 45 µm thick Rogers RT/Duroid 6010™ substrate with grapheme as a conducting material. The performance parameters are analyzed using High Frequency Structure Simulator and the optimized dimension of the antenna is 130 × 100 µm2. The graphene based designed antenna exhibits a wide impedance bandwidth (IBW) of 450 GHz from 0.57 to 1.02THz with a fractional impedance bandwidth (FBW) of 56.60%. The designed E-shaped antenna shows significant improvements in its FBW and bandwidth dimension ratio (BDR) due to the incorporation of rectangular slot in the T-shaped ground plane. The suggested antenna offers maximum reflection coefficient (S11) of −19.49 dB, adequate VSWR (

The elevated water tank comprises important structural elements which includes slabs, beams, columns, and footings, facilitating the transfer of loads amongst these contributors and subsequently to the subgrade of the soil. This paper goals is to comprehensively analyze the structural behaviours exhibited by elevated water tanks underneath various loading conditions. The behaviours of multiplied water tanks variety underneath various styles of loadings, inclusive of dead, live, and seismic loads, that are comprehensively analyzed. This paper primarily aims to conduct a hydrostatic evaluation of circular water tanks and emphasizes the necessity of a parametric study. To obtain this goal, 2, 2.5, and 3 lakh litters of tanks are being considered for the analysis which are all examined underneath area III seismic situations whilst keeping a normal height and varying diameters during the simulation. The examination focuses on carrying out a comparative evaluation of critical structural parameters, such as moment, maximum displacement, and maximum base shear. By means of analysing those parameters across various tank capacities, precious insights into the structural reaction of circular elevated water tanks under seismic loading conditions are gained. Those findings contribute to enhancing the design and overall performance of such structures, enhancing their resilience and protection in earthquake-susceptible regions.

Neurodegenerative diseases are characterized by progressive loss of neurons in specific regions of the brain. Neuronal death is often associated with the accumulation of misfolded proteins due to genetic mutations or abnormal protein homeostasis. An essential mechanism for regulating the clearance of misfolded proteins is neddylation, a post-translational modification closely related to ubiquitination. Neddylation is brought about by conjugating neural precursor cell-expressed developmentally downregulated protein 8 (NEDD8) to target substrates through a cascade of cellular events. Neddylation is crucial for many biological processes, and dysfunctional neddylation is implicated in several neurodegenerative diseases. This review discusses the current understanding of the role of neddylation pathways in neurodegenerative disorders and the emergence of neddylation signaling as a potential target for drug discovery and development in neurodegenerative diseases.

Abstract The use of nanoparticles has been growing in various industrial fields, and concerns about their effects on health and the environment have been increasing. Hence, characterization techniques for nanoparticles are essential. Here, we present a silicon dioxide microfabricated suspended microchannel resonator (SMR) to measure the mass and concentration of nanoparticles in a liquid as they flow. We measured the mass detection limits of the device using laser Doppler vibrometry. This limit reached a minimum of 377 ag that correspond to a 34 nm diameter gold nanoparticle or a 243 nm diameter polystyrene particle, when sampled every 30 ms. We compared the fundamental limits of the measured data with an ideal noiseless measurement of the SMR. Finally, we measured the buoyant mass of gold nanoparticles in real-time as they flowed through the SMR.

Objective: The aim of this study was to evaluate the post-marketing safety, tolerability, immunogenicity and efficacy of Bevacizumab (manufactured by Hetero Biopharma) in a broader population of patients with solid tumors. Patients And Methods: This phase IV, prospective, multi-centric clinical study was carried out in Indian patients with solid malignancies (metastatic colorectal cancer, non-squamous non-small-cell lung cancer, metastatic renal cell carcinoma) treated with Bevacizumab between April 2018 and July 2019. This study included 203 patients from 16 tertiary care oncology centers across India for safety assessment, of which a subset of 115 patients who have consented were also evaluated for efficacy and immunogenicity. This study was prospectively registered in the Clinical Trial Registry of India (CTRI), and was commenced only after receiving approval from the competent authority (Central Drugs Standard Control Organization, CDSCO). Results: Out of the 203 enrolled patients, 121 (59.6%) patients reported 338 adverse events (AEs) during this study. Of 338 reported AEs, 14 serious adverse events (SAEs) were reported by 13 patients including 6 fatal SAEs, assessed as unrelated to the study medication and 7 non-fatal SAEs, 5 assessed as related, and 3 unrelated to Bevacizumab. Most AEs reported in this study (33.9%) were general disorders and administration site conditions, followed by gastrointestinal disorders (29.1%). The most frequently reported AEs were diarrhea (11.3%), asthenia (10.3%), headache (8.9%), pain (7.4%), vomiting (7.9%), and neutropenia (5.9%). At the end of the study, 2 (1.75%) of 69 patients reported antibodies to Bevacizumab without affecting safety and efficacy. However, at the end of 12 months, no patient had reported antibodies to Bevacizumab. Complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD) were reported in 18.3%, 22.6%, 9.6%, and 8.7% of patients, respectively. The overall response rate (CR + PR) was reported in 40.9% of patients at the end of the study. Disease control rate (DCR), also known as the clinical benefit rate (CBR) was reported in 50.4% of patients. Conclusions: Bevacizumab (Cizumab, Hetero Biopharma) was observed to be safe, well tolerated, lacking immunogenicity, and efficacious in the treatment of solid tumors. The findings of this phase IV study of Bevacizumab, primarily as a combination therapy regimen suggest its suitability and rationality for usage in multiple solid malignancies. Clinical Trial Registry Number: CTRI/2018/4/13371 [Registered on CTRI http://ctri.nic.in/Clinicaltrials/advsearch.php : 19/04/2018]; Trial Registered Prospectively.

Abstract Changes in brain morphology have been reported during development, ageing and in relation to different pathologies. Brain morphology described by the shape complexity of gyri and sulci can be captured and quantified using fractal dimension (FD). This measure of brain structural complexity, as well as brain volume, are associated with intelligence, but less is known about the sexual dimorphism of these relationships. In this paper, sex differences in the relationship between brain structural complexity and general intelligence (g) in two diverse geographic and cultural populations (UK and Indian) are investigated. 3D T1-weighted magnetic resonance imaging (MRI) data and a battery of cognitive tests were acquired from participants belonging to three different cohorts: Mysore Parthenon Cohort (MPC); Aberdeen Children of the 1950s (ACONF) and UK Biobank. We computed MRI derived structural brain complexity and g estimated from a battery of cognitive tests for each group. Brain complexity and volume were both positively corelated with intelligence, with the correlations being significant in women but not always in men. This relationship is seen across populations of differing ages and geographical locations and improves understanding of neurobiological sex-differences.

Abstract Geographical weighted regression (GWR) can be used to explore the COVID-19 transmission pattern between cases. This study aimed to explore the influence from environmental and urbanisation factors, and the spatial relationship between epidemiologically-linked, unlinked and imported cases during the early phase of the epidemic in Singapore. Spatial relationships were evaluated with GWR modelling. Community COVID-19 cases with residential location reported from 21st January 2020 till 17th March 2020 were considered for analyses. Temperature, relative humidity, population density and urbanisation are the variables used as exploratory variables for analysis. ArcGIS was used to process the data and perform geospatial analyses. During the early phase of COVID-19 epidemic in Singapore, significant but weak correlation of temperature with COVID-19 incidence (significance 0.5–1.5) was observed in several sub-zones of Singapore. Correlations between humidity and incidence could not be established. Across sub-zones, high residential population density and high levels of urbanisation were associated with COVID-19 incidence. The incidence of COVID-19 case types (linked, unlinked and imported) within sub-zones varied differently, especially those in the western and north-eastern regions of Singapore. Areas with both high residential population density and high levels of urbanisation are potential risk factors for COVID-19 transmission. These findings provide further insights for directing appropriate resources to enhance infection prevention and control strategies to contain COVID-19 transmission.

This paper proposes a power system stabilizer (PSS) with optimal controller parameters for damping low-frequency power oscillations in the power system. A novel meta-heuristic, weighted grey wolf optimizer (WGWO) has been proposed, it is a variant of the grey wolf optimizer (GWO). The proposed WGWO algorithm has been executed in the selection of controller parameters of a PSS in a multi-area power system. A two-area fourmachine test system has been considered for the performance evaluation of an optimally tuned PSS. A multi-objective function based on system eigenvalues has been minimized for obtained optimal controller parameters. The damping characteristics and eigenvalue location in the proposed approach have been compared with the other state-of-the-art methods, which illustrates the effectiveness of the proposed approach.

People with diabetes will have some coexisting mental illness most of the time and its evaluation and management are essential for the well-being of the person. With this background, the Global Mental Health Assessment Tool (GMHAT) app was used to screen for mental illness among type 2 diabetes mellitus patients attending a tertiary care hospital in Mysore, India. Methods: A cross-sectional study was conducted on 100 patients who were on treatment for type 2 diabetes for at least 6 months from the Out-Patient Department of KR Hospital, Mysore. Interviews were conducted using a structured GMHAT. Data were entered in MS Excel and analysed using SPSS version 21.0. Chi-square, Independent T-test/Mann–Whitney tests were used to investigate associations between the variables. Results: The mean age of the study participants was 48.8 ± 11.6 years. Among the 100 diabetic patients, 39% (n = 39) had mental illness and among the 39 subjects, 21% had depression, 14% had anxiety disorder and 4% suffered from organic disorder. Apart from diabetes, 29, 17, 13, 27, 23 and 20% had hypertension, chronic kidney disease, liver disease, retinopathy, neuropathy and nephropathy, respectively, as comorbid conditions. Conclusion: There is a higher prevalence of mental illness in patients with diabetes mellitus. The prevalence rate increases with an increase in the chronicity of diabetes. The use of the GMHAT app could help in rapid assessment and accurate diagnoses.

Abstract Background The present work is aimed at quantification of organic volatile solvents like Methanol, Ethanol, Acetonitrile, Isopropyl alcohol (IPA), Dichloromethane (DCM), Methyl tert-butyl ether (MTBE), Hexane fractions, Ethyl acetate (EA), Tetra hydro furan (THF), 1,4-Dioxane, Dimethylformamide (DMF), and Toluene in Oseltamivir phosphate Active pharmaceutical ingredient (API) and pharmaceutical dosage forms. The method was developed using a thermal gradient elution program associated with a column having dimensions are DB-1,60 m × 0.32 mm × 5.0 μm with a flow rate of 1.0 mL/min and Nitrogen (N2) as a carrier gas. A flame ionization detector was used as a detector, and its temperature is at 290 °C whereas the injector temperature is at 180 °C. The total run time is 60.0 min. The developed method was validated according to International Council for Harmonization (ICH) guidelines. Results The linearity of the calibration curve for twelve impurities in the concentration range of Limit of Quantification (LOQ) to 150% was good. The curve was linear for twelve residual impurities of Oseltamivir phosphate. Relative standard deviation values for twelve residual impurities are not more than 15%. Limit of detection (LOD) and LOQ for twelve residual impurities were found to be very low-level concerning specification level. We found good results for all validated parameters for twelve residual impurities. Conclusions To quantify the residual organic volatile solvents (organic volatile impurities) in the Oseltamivir phosphate API, a novel, specific, and elevated sensitive headspace gas chromatography method was developed to separate twelve solvents that are accompanying with fifteen peaks. Out of fifteen peaks, critical components are Hexane fractions, Ethyl acetate, MTBE, and DCM. So, our method has capable of separating and simultaneous quantification such critical components. So, it can be successfully applied for routine analysis of quantification of these twelve residual impurities in Oseltamivir phosphate bulk and pharmaceutical dosage forms.

The microstructure of the fuel pellet plays an essential role in fission gas buildup and release and is critical for the safe and continued operation of nuclear power stations. Structural analysis of uranium dioxide (UO2)–molybdenum (Mo) composite fuel pellets prepared at a range of sintering temperatures from 1300 to 1800 °C was performed. Mo micro and nanoparticles were used in making the composite pellets. A systematic investigation into the influence of processing parameters during Spark Plasma Sintering (SPS) of the pellets on the microstructure, texture, grain size, and grain boundary characters of UO2-Mo is presented. UO2-Mo composite show significant differences in the fraction of general boundaries and also special/coincident site lattice (CSL) boundaries. EBSD orientation maps demonstrated that texturing was observed in the pellets fabricated at 1500 °C. The experimental investigations suggest that UO2-Mo composite pellets have favorable microstructural features compared to the UO2 pellet.

Matrix factorization is a long-established method employed for analyzing and extracting valuable insight recommendations from complex networks containing user ratings. The execution time and computational resources demanded by these algorithms pose limitations when confronted with large datasets. Community detection algorithms play a crucial role in identifying groups and communities within intricate networks. To overcome the challenge of extensive computing resources with matrix factorization techniques, we present a novel framework that utilizes the inherent community information of the rating network. Our proposed approach, named Community-Based Matrix Factorization (CBMF), has the following steps: (1) Model the rating network as a complex bipartite network. (2) Divide the network into communities. (3) Extract the rating matrices pertaining only to those communities and apply MF on these matrices in parallel. (4) Merge the predicted rating matrices belonging to communities and evaluate the root mean square error (RMSE). In our experimentation, we use basic MF, SVD++, and FANMF for matrix factorization, and the Louvain algorithm is used for community division. The experimental evaluation on six datasets shows that the proposed CBMF enhances the quality of recommendations in each case. In the MovieLens 100K dataset, RMSE has been reduced to 0.21 from 1.26 using SVD++ by dividing the network into 25 communities. A similar reduction in RMSE is observed for the datasets of FilmTrust, Jester, Wikilens, Good Books, and Cell Phone.

Reciprocating piezoelectric micropumps enable miniaturization in microfluidics for lab-on-a-chip applications such as organs-on-chips (OoC). However, achieving a steady flow when using these micropumps is a significant challenge because of flow ripples in the displaced liquid, especially at low frequencies or low flow rates (RFfl.), which is the ratio of the root mean square (RMS) value of the fluctuations present in the rectified output to the average fluctuation-free value of the discharge flow, as a metric to express the quality of the flow. The fluidic ripple factor was reduced by more than 90% by using three-phase rectification when compared to one-phase rectification in the 2–60 μL/min flow rate range. Analytical equations to estimate the fluidic ripple factor for a chosen number of pumps connected in parallel are presented, and we experimentally confirmed up to four pumps. The analysis shown can be used to design a frequency-independent multi-phase fluid rectifier to the desired ripple level in a flow for reciprocating pumps.

Major neonatal surgeries have been well reported to be associated with postoperative development of necrotizing enterocolitis (NEC). Incarcerated inguinal hernia has been rarely reported to develop postoperative NEC in infant. We report a case of postoperative NEC in a preterm infant at term equivalent age with incarcerated inguinal hernia. The infant developed abdominal distension, bilious gastric residue, and bleeding per rectum on day 3 postoperative period followed by pneumatosis intestinalis and pneumoperitoneum subsequently. Clinical, biochemical, hematological, and radiological findings were suggestive of postoperative NEC. In conclusion, incarcerated inguinal hernia should be included in the accusatory factors for the development of postoperative NEC in infants.