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Mineral composition in milk can affect its nutritional as well as physio-chemical properties of milk and is considered an important trait in the evaluation of milk quality. The composition and concentration of milk minerals could be altered with natural source of variation including nutrition and genetics. The effect of diet on milk minerals is well studied. However, genetic effects on the milk minerals have recently gained the attention. This review provides an overview of the genetic variation of milk minerals, and the genomic regions associated with mineral concentration in the milk are also discussed. The difference of milk minerals between breeds and the genetic parameters including heritability estimates and correlation among minerals indicates that milk minerals are under strong genetic control. Recently, the genome-wide association study (GWAS) has explored several regions associated with milk minerals and thus provides a new genetic source for improving the milk quality through genomics-assisted breeding. Hence, a combination of the qualitative and molecular approaches can be exploited to improving the nutritional quality of cattle milk in terms of its mineral composition.

Mung bean (Vigna radiataL.) holds significant importance as a pulse crop contributing to food and nutritional security. It is mainly cultivated in South Asia. Mung bean productivity is hampered by different abiotic stresses, particularly salinity. Salinity is a serious constraint that restricts the growth and development of mung bean. Identification and utilization of novel sources of variation for key stress tolerance traits is required in mung bean improvement breeding. A diverse set of thirty mung bean accessions were evaluated at the seedling stage for their response towards salinity stress using hydroponic culture with two treatments: control (0 mM NaCl), and salt stress (200 mM NaCl). The genotypes revealed significant variations for the fresh weight, dry weight, shoot length, surface area, and root volume. Also the root traits; root volume, root length, root surface area, and tip number were correlated to biomass under salinity stress. The salt tolerance index varied from 0.38 to 0.91, considering the seedling vigour. Four (PUSA0971, EC937886, IC282531, IC623909) genotypes were found promising for salinity tolerance based on seedling vigour and root traits. Identified genotypes may be validated in field conditions for their further use as trait donors to develop mung bean varieties with enhanced salt tolerance.

The application of nanotechnology in agriculture is driven by the pressing need to meet the increasing global demand for food production. Nanoparticles, owing to their incredibly small size, bridge the gap between macroscopic materials and atomic or molecular structures, making them ideal for various agricultural applications. They have the potential to revolutionize conventional farming practices by optimizing nutrient utilization, resource management, and environmental sustainability. The impact of nanotechnology on agriculture spans a wide range of areas, including nutrient delivery, pest management, soil fertility improvement, precision farming, water management, post-harvest preservation, environmental sustainability, smart delivery systems, genetic modification, and nanofertilizers (NFs). NFs, in particular, have garnered attention for their ability to improve nutrient delivery and enhance crop development, while minimizing environmental harm and reducing costs compared to traditional fertilizers. These nano-sized nutrients significantly enhance nutrient bioavailability to plants, ultimately promoting crop growth and yield. However, the application of nanomaterials in agriculture also raises concerns regarding their potential impact on soil microbial diversity, which plays a crucial role in maintaining soil health. In addition to NFs, this article discusses the role of carbon nanotubes (CNTs) in agriculture. CNTs possess unique properties that can improve plant growth, root development, and resistance to salinity and disease. Furthermore, the article also deals with nanobiosensors and their application in precision agriculture. Moreover, this article addresses the importance of considering the toxicity, biosafety, and regulatory aspects when implementing nanotechnology in agriculture to maximize its potential benefits while safeguarding natural and environmental resources.

The regular monitoring and accurate diagnosis of arrhythmia are critically important, leading to a reduction in mortality rate due to cardiovascular diseases (CVD) such as heart stroke or cardiac arrest. This paper proposes a novel convolutional neural network (CNN) model for arrhythmia classification. The proposed model offers the following improvements compared with traditional CNN models. Firstly, the multi-channel model can concatenate spectral and spatial feature maps. Secondly, the structural unit is composed of a depthwise separable convolution layer followed by activation and batch normalization layers. The structural unit offers effective utilization of network parameters. Also, the optimization of hyperparameters is done using Hyperopt library, based on Sequential Model-Based Global Optimization algorithm (SMBO). These improvements make the network more efficient and accurate for arrhythmia classification. The proposed model is evaluated using tenfold cross-validation following both subject-oriented inter-patient and class-oriented intra-patient evaluation protocols. Our model achieved 99.48% and 99.46% accuracy in VEB (ventricular ectopic beat) and SVEB (supraventricular ectopic beat) class classification, respectively. The model is compared with state-of-the-art models and has shown significant performance improvement.

Purpose: The recent development in the scholarship of cause-related marketing (CRM) highlights the growing popularity of this field of research. CRM is one of the forms of communicating corporate social responsibility (CSR). Although CSR is being extensively studied as a field of research, researchers have made limited efforts to review the development and progression of CRM research. Being an embryonic research field, a deeper understanding of the current state of CRM research and future research propositions is required. This study aims to fill this gap by providing a bibliometric review of the CRM research published in the Web of Science (WoS) database. Design/methodology/approach: A bibliometric literature review has been conducted of the papers published in WoS database to map the field of research in CRM. Various techniques, namely, citation analysis, co-citation analysis, co-authorship analysis, co-occurrence analysis and bibliographic coupling have been incorporated, and analysis has been provided. Findings: The findings highlight the important themes and research areas focused by CRM researchers. The study throws light on the important research avenues present in the field of CRM. Originality/value: The findings offer both academic and practical implications. The present study is a novel study exploring the scholarship of CRM using the bibliometric analysis technique.

In recent years, several machine learning models were successfully deployed in various fields. However, a huge quantity of data is required for training good machine learning. Data are distributivity stored across multiple sources and centralizing those data leads to privacy and security issues. To solve this problem, the proposed federated-based method works by exchanging the parameters of three locally trained machine learning models without compromising privacy. Each machine learning model uses the e-adoption of CT scans for improving their training knowledge. The CT scans are electronically transferred between various medical centers. Proper care is taken to prevent identify loss from the e-adopted data. To normalize the parameters, a novel weighting scheme is also exchanged along with the parameters. Thus, the global model is trained with more heterogeneous samples to increase performance. Based on the experiment, the proposed algorithm has obtained 89% of accuracy, which is 32% more than the existing machine learning models.

It is anticipated that during the next 3 decades, approximately two-thirds of the world’s population would live in urban regions. This represents a significant movement towards urbanisation on the part of the world’s population. Thus, urban planners need effective methods for regulating both the use of land and the management of infrastructure. The accessibility of data that is accurate, up to date, and insightful is an extremely important factor in the formation of effective methods of urban administration and smart cities. Combining technologies for pervasive sensing, intelligent computing, cooperative communication, and mass data management may enhance urban surroundings, quality of life, and smart city systems. To do this, we need to develop novel strategies for integrating computation and intelligence into urban environments. This field of study focuses on the intersection of computing and intelligence in urban settings.

Opioid Habit Disorder (OHD), which has become a mass health epidemic, is defined as the psychological or physical dependency on opioids. This study demonstrates how supervised machine learning procedures help us investigate and examine massive data to discover the hidden patterns in any disease to deliver adapted dealing and predict the disease in any patient. This work presents a generalized model for forecasting a disease in the healthcare sector. The proposed model was investigated and tested using a reduced feature-set of the Opioid Habit Disorder (OHD) dataset collected from the National Survey on Drug Use and Health (NSDUH) using an improved Iterative Dichotomiser 3 (pro-IDT) algorithm. The proposed healthcare model is also compared with further machine learning algorithms such as ID3, Random Forest, and Bayesian Classifier in Python programming. The performance of the proposed work and other machine-learning algorithms has estimated accuracy, precision, misclassification rate, recall, specificity, and F1 score.

In order to improve the sensor network, the nodes resources should be used in a well-organized way. The new cluster-based routing protocols and data aggregation approach have helped to increase the lifespan of the network. The methods of data aggregation eliminate the network's redundant data packets, which extends the lifetime of the network. A fault tolerant cluster head selection and data aggregation scheme (FT-CHSDA) that performs node clustering and data aggregation in the network is demonstrated in this study. The suggested method uses the energy level of the node to pick the most energy-efficient node as the head of the cluster and executes data aggregation to reduce redundant data packets. In addition, the use of a concept called backup node in a cluster has implemented a novel method to make the network accessible and run without any interruption. In the NS2 simulator, the simulation of the proposed scheme (FT-CHSDA) is being discussed. Using different performance metrics to assess its effectiveness, the proposed scheme (FT-CHSDA) is contrasted with existing proto-cols.

The Chebyshev polynomial approximation is a useful tool to approximate smooth and non-smooth functions. In fact, for a sufficiently smooth function, the partial sum of Chebyshev series expansion provides optimal polynomial approximation. Moreover, because the construction of these polynomial approximations is computational efficient, they are widely used in numerical schemes for solving partial deferential equations. Significant efforts have been devoted to establishing decay bounds for series coefficients, including Chebyshev, Jacobi, and Legendre series, for both smooth and non-smooth univariate functions. However, the literature lacks similar estimates for bivariate functions. This paper aims to address this gap by examining the decay estimates of bivariate Chebyshev coefficients, contributing both theoretically and practically to the understanding and application of Chebyshev series expansions, especially concerning functions with limited smoothness. Additionally, we derive L1-error estimates for the partial sum of Chebyshev series expansions of functions with bounded Vitali variation. Furthermore, we provide an estimate for the discrepancy between exact and approximated Chebyshev coefficients, leveraging a quadrature formula. This analysis leads to the deduction of an asymptotic L1-approximation error for finite partial sums of Chebyshev series with approximated coefficients.

Emerging markets (EMs) offer huge business opportunities for multinational corporations (MNCs). Owing to such a situation, EMs are gaining serious attention from the marketers. This call for an exploration of suitable marketing approaches by MNCs in order to gain competitive advantage in EMs. The present study concerns itself with the marketing strategy of an MNC (Amazon) in order to enter and expand its business operation in an EM (India). Adopting the quantitative approach to research, the authors collected primary data with the help of specifically finalised questionnaire for the study. Analysis was performed using SPSS software. The study reveals interesting findings regarding consumer's attitude towards Amazon India. Apart from theoretical contribution, the present study offers insights for the marketers regarding the processes in which the prevailing socio-economic conditions in India can be utilised as an opportunity. Lastly, the limitations and avenues for further research are provided.

With the rapid development of cloud advancement, a data security challenge has emerged. In this paper, a technique based on elliptical cryptography and cuckoo search algorithm is proposed. With this technique, data owners securely store their data files in the cloud server. Initially the user sends a file storage request to store a file in a cloud server provider (CSP). The input file is checked whether it is sensitive or non-sensitive by the user. If the file is sensitive, then it would be split and stored in different virtual machines (VMs), and if the file is non-sensitive, then it would be assigned in a single VM. This approach was used for the first time as per the survey. To add further security, the sensitive data retrieval needs an encryption process that is supported by the proposed algorithm. If the data owner stores the sensitive data to cloud server, the data owner's document is encrypted by the double encryption technique. Here RSA and optimal elliptic curve cryptography (OECC) algorithm is used to encrypt the document with high security. The authors have used cuckoo search algorithm to identify the optimal key in ECC. This paper has proposed a novel cryptography approach for delivering mass distributed storage by which user's original data cannot be directly reached by cloud operators. Hence, this research has proved that the proposed work will give better securable data storage solving the security issues.

Machine learning and data mining help companies to build a tool that can make and take actions based on customer knowledge and information. Customer information is the base of maintaining long term relationship with customers and also known as customer relationship and management (CRM). Classification and segmentation of customer data set is utilised to maintain efficient relation with customers and subsequently increase the profitability and productivity. In this paper, author proposed customer segmentation based on demographic properties like gender, age and spending score and analysed the data set for interesting fact. The derived attribute data set is investigated for classification. Classification is used to categorise each customer into a number of classes, i.e., 'gold', 'silver', 'elite' and 'occasional'. Comparison of different classification algorithm is simulated by WEKA tool. Multi-layer perceptron (MLP) is found as the best classification algorithm with an accuracy of 98.33% compared to Naïve Bayes, regression and J48.

India has been predicted to have the fastest increase in deaths attributable to tobacco in the first 2 decades of the twenty-first century. Consequently, it is imperative to examine the extent of nicotine dependency among adults in India. The main objective of the present article is to characterize nicotine dependency related to smoking and smokeless tobacco among adults according to age, education level, duration of use and other socioeconomic characteristics. We analyzed Global Adult Tobacco Survey (GATS) 2010 data for 9190 smokers and 13,357 smokeless tobacco users who were 15 years of age or older. Time to first tobacco use of the day was used as a measure of nicotine dependence. We employed a decision tree algorithm from SAS Enterprise Miner to conduct classification analysis to establish the relationship among nicotine dependency for smoking, smokeless tobacco use, and its predictors. More than 65% of smokers and 54.3% of smokeless tobacco users were nicotine dependent. The decision tree results showed that the most important explanatory variable for the prediction of smoking dependency was duration of smoking followed by education, gender and region. For smokeless tobacco use, duration of smokeless tobacco use was also the most important explanatory variable followed by education, occupation and age. Smoking and smokeless tobacco use dependencies are driven by different sets of demographic and socioeconomic predictors. Suitable plans for tobacco cessation need to be implemented based on the type of tobacco and particularly duration of use, which was found to be the most important determinant of quitting.

Unorganized development all over the world has resulted in the deterioration of the environment. The Mithi river located in Mumbai area is also one of the victims of this unorganized development. It has become a dumping site for industrial effluents, which contains various toxic materials including heavy metals such as mercury, chromium, arsenic. Physicochemical analysis of the Mithi river water samples has confirmed the deterioration of river water. Various parameters of Mithi river water such as electrical conductivity (EC), total dissolved salts (TDS), salinity, chemical oxygen demand (COD) and biological oxygen demand (BOD) were found to be higher than the normal values prescribed for the river water. In the present study, mercury-resistant bacteria were isolated from Mithi river and identified by biochemical and molecular analysis. The molecular identification of the mercury-resistant bacteria was done by 16S rDNA identification, which were identified as Bacillussp. strain CSB_B078, Klebsiella pneumoniaestrain FY2, Klebsiella pneumoniaeisolate 23, Enterobactersp. strain Amic_7, Enterobactersp. strain 08, Acinetobacter seohaensisstrain S34, Acinetobactersp. 815B5_12ER2A. Mercury-resistant bacteria were isolated from both low- and high-salinity sites of Mithi river. The minimum inhibitory concentration of bacterial isolates against mercury was determined. Bacterial isolates could tolerate and grow in the presence of 700 ppm mercury and could also tolerate a high salinity of 35 ppt of NaCl. Resistance of bacterial isolates to high concentration of mercury and high-salinity stress suggests that the bacterial isolates could be an efficient candidate for bioremediation of mercury.

Background: Computer-Assisted Diagnosis (CAD) has become a common practice of use in the healthcare industry due to its improved accuracy and reliability. The CAD systems are expected to improve the quality of medical care by assisting healthcare professionals with a wide range of clinical decisions. A CAD system is a combination of Computer-Assisted Detection (CADe) and Computer-Assisted Diagnosis (CADx) system. Objective: The objective of this research article is to generate an optimized rule-set for medical diagnosis capable of providing improved accuracy. It is evident from the literature that keeping a balance between these performance parameters is a real challenge. Methods: In order to achieve the desired objective, the following two contributions have been proposed to improve diagnosis accuracy: 1) an unsupervised feature selection approach based on ACO Meta-heuristic is used to design the CADe system, and 2) an ACO assisted decision tree classifier technique is employed to make CADx system. Results: Three popular UCI (Wisconsin Breast Cancer, Pima Indian Diabetes and Liver Disorder) medical domain datasets have been used to evaluate the performance of the proposed model. The exploratory result analysis shows the efficiency of the proposed work as compared to existing work.

Oral anticoagulation is underused in patients with atrial fibrillation. We assessed the impact of a multifaceted educational intervention, versus usual care, on oral anticoagulant use in patients with atrial fibrillation.

Wireless sensor networks (WSNs) have now become a substantial part of our life as it provides an incredible setup for monitoring environmental conditions such as glaciers melting, earthquakes, climate change, volcanic eruption, agriculture, and other natural calamities. One of the most important challenges in data gathering and examining accurate data is “energy consumption.” However, continuously changing environmental conditions, mobility in devices and extreme resource usage, such as battery capacity in WSNs collectively contribute to a high possibility of redundant data transmission thus making it tremendously hard to achieve a satisfactory network lifetime. Additionally, these environmental monitoring applications periodically senses and aggregate data which usually exhibits high temporal and spatial redundancies. As a result, exploiting these redundancies is essential for both resource utilization, as well as accurate data transmission. Since a huge amount of energy is exhausted in transmitting the redundant data, which has become a bottleneck in scaling such applications. In this context, this article provides a thorough examination of existing energy‐efficient redundant data transmission reduction techniques, as well as their strengths and drawbacks. The idea of redundant data transmission reduction concept is divided into three stages: the sensor nodes (SN) at the first stage, the cluster heads (CHs) at the second stage, and the base station (BS) at the third stage. Furthermore, this survey focuses on the current issues and challenges, as well as the future directions in reducing redundant data transmission for future investigation. The following are the contributions of this work. This article provides a thorough literature review and analysis of energy‐efficient data transmission reduction techniques for improving network lifetime. It discusses the methods and scheme for reducing redundant data transmission in cluster‐based wireless sensor network (WSN) with various stages.It examines the several methods, schemes, and techniques used for energy‐efficient redundant, identical, and correlated data transmission in WSNs, highlighting their proficiency metrics, dataset, tools, contributions, and limitations.Finally, a list of the proposed future works is compiled with the expectation that it will assist new researchers in following the novel and innovative directions in WSN.

This study assessed the significance of socio-demographic and contextual factors on the number of daily cigarette and bidi smoking in northeast India. This study is based on the data from the Global Adult Tobacco Survey-India (2009–2010). Community asset is measured as the totality of all households’ durables and community affinity to smoking as the proportion of households which allowed smoking in the house. High daily cigarette and bidi smokers constitute 20 and 30 % of the respective smokers and they smoke 35.8 cigarettes and 14.6 bidis daily, respectively, on the average. The higher is the community affinity to smoking, the higher is the intensity of smoking among the high daily cigarette smokers. Advancing age and educational attainment have significant deterrent effect on the intensity of cigarette and bidi smoking. Contextual factors are found to be important for regional tobacco control programmes. The need for reaching out to communities and the importance of promotion of public–private partnership under the provision of corporate social responsibility for effectiveness of tobacco control programme is recommended.

INTRODUCTION:Hyponatremia and hypernatremia are both possible outcomes.Changes in potassium, particularly Hypokalemia, as well as fluid content, are common in clinical practise. The most prevalent reasons are syndrome of inadequate anti-diuretic hormone secretion (SIADH), cerebral salt wasting (CSW), and the use of diuretics such as Furosemide and Mannitol.Another key factor that influences morbidity and mortality is age. Getting older has a negative impact. In most parts of the world, isotonic fluid may be provided without major fluid disruptions in the body, making effective fluid management of patients with traumatic brain injury (TBI) a difficulty.AIM:Study of RTA Patients with Serum Electrolytes and Serum Bilirubin LevelsMATERIAL AND METHOD:This study included total 40 patients comes in IPD and OPD department of medicine & psychiatrics with collaboration in Department of biochemistry Dr. Ulhas Patil Medical College and Hospital Jalgaon, Maharashtra RESULT:In table 1 show that comparison between RTA and traumatic subjects’ serum sodium level are elevated in RTA subjects compare to traumatic subjects. The P value are shows that statistically non-significant P >0.3546.Serum potassium levels are elevated in RTA subjects compare to traumatic subjects. The P value shows that non-significant P >0.8262.CONCLUSION:Our study shows that comparison of RTA and traumatic injury Electrolyte imbalances are particularly common in people who have suffered a brain injury. It is a significant and treatable cause of neurological decline. Serum potassium, calcium, and phosphorus levels should also be checked since they play a role in preventing secondary brain injuries, RTA preservation, and traumatic injury.

Acid‐free approach has been demonstrated for Friedel‐Crafts‐type reaction of (het)arenes with carbonyls using neutral ionic liquid (NIL). Methodology is enabled to afford a densely functionalized bis(indolyl)methanes in good to excellent yields. This conditions is also compatible to the synthesis of 3,3‐di(indol‐3‐yl)indolin‐2‐ones, bis(4‐hydroxycoumarines) and triarylmethanes (total 40 examples; up to 98% yields). Gram‐scale reactions and recycling study were carried out to demonstrate the practicality of present methodology. DFT studies illustrate the catalytic cycle involving simultaneous activation of C=O and indole C−H bond by NIL followed by C−C bond formation. A green and sustainable approach have been devised for Friedel‐Crafts‐type reaction of carbonyls under non‐acidic conditions using neutral ionic liquid for the first time. This approach enabled for the synthesis of bis(indolyl)methanes, bis‐ and tris‐arylmethanes in good to excellent yields.

Unlike phosphonium ylides used extensively for C═C bond formation, herein we disclose the direct use of phosphonium salts for site-selective alkylation to p-quinols via a 5-membered betaine-type intermediate. This strategy provides a novel and general approach for the synthesis of α-(m-aminoaryl) esters, amides and ketones under ambient conditions. The reaction proceeds through in situ generation of P-ylide, alkylation and aromatization. Reaction is highly compatible with diverse functional phosphonium salts and amines.