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NUTRACEUTICALS IN PREGNANCY
Pregnancy is a phase in womens lives wherein they experience some physiological and bodily changes. In this period dietary supplements are crucial to meet the nutritional demand of the fetus and also to overcome certain gestational complications such as hypertension, diabetes, and anemia. Generally, the intake of micronutrients in the diet is low for women especially in developing countries while it becomes important during pregnancy as they enable uterus and placental growth, manage gestational obesity, and also avoid miscarriage. Research and clinical studies are in power in identifying various safe, plant bioactive substances, and efficient delivery systems to reduce morbidity and mortality rates of maternal, fetal, and neonatal. In recent years the growing awareness of nutraceuticals to fight against chronic diseases such as cancer, and heart disease has gained trust globally and is replacing pharmaceuticals. Knowledge of the macro- and micronutrients is necessary to modify the diet and supplement with nutraceuticals for the well-being of the mother and the fetus. The current chapter highlights the major health complications associated with pregnancy and the essential nutrients required during pregnancy and fetal growth with substantial clinical evidence. 2025 by Apple Academic Press, Inc. -
NUTRACEUTICALS IN PREGNANCY
Pregnancy is a phase in womens lives wherein they experience some physiological and bodily changes. In this period dietary supplements are crucial to meet the nutritional demand of the fetus and also to overcome certain gestational complications such as hypertension, diabetes, and anemia. Generally, the intake of micronutrients in the diet is low for women especially in developing countries while it becomes important during pregnancy as they enable uterus and placental growth, manage gestational obesity, and also avoid miscarriage. Research and clinical studies are in power in identifying various safe, plant bioactive substances, and efficient delivery systems to reduce morbidity and mortality rates of maternal, fetal, and neonatal. In recent years the growing awareness of nutraceuticals to fight against chronic diseases such as cancer, and heart disease has gained trust globally and is replacing pharmaceuticals. Knowledge of the macro- and micronutrients is necessary to modify the diet and supplement with nutraceuticals for the well-being of the mother and the fetus. The current chapter highlights the major health complications associated with pregnancy and the essential nutrients required during pregnancy and fetal growth with substantial clinical evidence. 2025 by Apple Academic Press, Inc. -
Extended-spectrum ?-Lactamase (ESBL) Producing Bacterial Pathogens Associated with Respiratory Tract Infections
Respiratory tract infections (RTIs) have been critically associated with health care problems globally. Subsequently, increased antibiotic resistance rates have limited treatment options that are further exaggerated due to lack of newer novel drugs and therapies. Current study highlights, antibiotic resistance profiling along with extended-spectrum beta-lactamase (ESBL) producers of RTI pathogens from Bengaluru. During June 2020-May 2021, 1016 clinical samples collected, prevalence rate of 22.4% was exhibited, with highest in male (74.5%). Following age group, 30-35 years displayed highest (24.1%) though, lowest was in 45-50 years (1.3%). The standard microbiological characterization revealed Klebsiella pneumoniae, Pseudomonas aeruginosa, Escherichia coli, Acinetobacter baumannii as predominant bacterial pathogens associated with RTIs. While, Antibiotic susceptibility test (AST) exhibited highest resistance rates for different antibiotics in the following pathogens, as K. pneumoniae for ampicillin (74.8%), P. aeruginosa for doripenem (66.6%), A baumannii to piperacillin/tazobactam (76.9%), E. coli for penicillin and ?-lactamase inhibitors ranging between 56-92%, E. cloacae to ticarcillin/clavulanic acid besides cefuroxime (100%). However, prevalence of Gram-positive strains were lowest and exhibited highest resistance to penicillin, and fluoroquinolone (83.3%). ESBL producers were predominantly K. pneumoniae, followed by E. coli, and E. cloacae with 21.9%, 6.5% and 1.3%, respectively. Notably, all the Gram-negative strains showed 100% sensitivity towards colistin with remarkable sensitivity was observed in oxazolidinone, glycopeptides by S. aureus and Coagulase-neagtive Staphylococcus aureus (CoNS). The study emphasizes increased antimicrobial resistance antimicrobial and ESBL resistance, suggesting AST as a systematic approach for apprising treatment guidelines in current scenario. The present study denotes polypeptide colistin as choice of drugs for treating RTI pathogens, however its not recommended in all cases. The Author(s) 2025. -
Phenotypic detection of ?-lactamases producing Klebsiella pneumoniae and Acinetobacter baumannii isolated from the respiratory tract infections
Introduction: With limited treatment options and emerging antimicrobial resistance amongst bacterial pathogens in hospital settings, the alarming condition needs to be addressed. Aim and Objective: The study aimed to determine the antibiotic susceptibility (AST) patterns, and extended spectrum ?-lactamases (ESBL) and Metallo ?-lactamases (MBL) production in Klebsiella pneumoniae and Acinetobacter baumannii isolated from respiratory tract infections (RTI). Materials and Methods: Current study, 1624 samples (sputum, tracheal secretions, suction tip) were collected during June 2020 to September 2022, phenotypically characterized by antibiotic sensitivity test (AST), Extended Spectrum Beta lactamases and Metallo Beta lactamases detection. Results: Of 12.3% (200 isolates), recorded prevalence rate of K. pneumoniae was 10.3% while, A. baumannii showed 1.9%. Following AST, K. pneumoniae displayed highest resistance against Ampicillin (95%), followed by Amoxycillin/clavulanic acid (91%), lowest recorded susceptibility for Tigecycline (39.2%). While, A. baumannii expressed susceptibility to Carbapenem group and Ciprofloxacin (84.3%), lowest Tigecycline (50%). Overall study exhibited Colistin retaining its activity with 100% sensitivity. ESBL production in K. pneumoniae was 31% and 0% in A. baumannii. While, MBL producers was 11%, K. pneumoniae and A. baumannii recorded at 9.5% and 25% respectively. Conclusion: The increasing rates of ESBL producers is concerning, and the current study highlights use of Colistin as the best available antimicrobial for treating such RTI pathogens. Therefore, continuous monitoring of antibiotic-resistant profiles will help to guide effective antimicrobial therapy and management. 2025 The Author(s), Published by Innovative Publications. This is an Open Access (OA) journal, and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms. https://creativecommons.org/licenses/by-nc-sa/4.0/ -
Stress and Decision-Making among Civil Aviation Pilots in India: Mediating Role of Cognitive Flexibility
Objective: The study aims to investigate the difference between stress, decision-making, and cognitive flexibility based on demographic factors and the mediating role of cognitive flexibility on the association of stress and decision-making among civil aviation pilots. Methods: Data was collected from 372 commercial pilots from India through an online survey. The survey comprises standardized tools, including perceived stress, decision-making, and cognitive flexibility. Results: No significant gender difference was found in stress, decision-making, and cognitive flexibility. Age and work experience influenced stress levels, with mid-career pilots reporting the highest stress. Stress has a negative impact on pilots' decision-making ability. Cognitive flexibility partially mediates this relationship. Conclusion: Integrating cognitive flexibility training and stress management interventions into pilot training programs could significantly improve decision-making under pressure for safer aviation practices. 2025 Indian Journal of Occupational and Environmental Medicine. -
Elusive Justice to Dalits in the 'Land of Social Justice'
The recent inhuman incident of mixing human faeces in the overhead tank supplying water to Dalit colony in Vengaivayal village in Pudukkottai district of Tamil Nadu refl ects the perpetuating violence against the Dalits. Locating this brutal violence within the larger framework of violence against Dalits in Tamil Nadu, the lackadaisical attitude of Dravidian parties when dealing with the issues related to Dalits is brought to the fore.. 2023 Economic and Political Weekly. All rights reserved. -
2D Metal-based Electrocatalysts: Properties and Applications
Metallic nanostructures with thickness ranging from a single atom up to 100 nanometers fall under the category of 2D metals. The modified electronic band structure due to quantum confinement effects leads to intriguing electrical and electronic properties. Moreover, the properties can be further altered by variations in their shape, thickness, and lateral size. The exceptionally high surface area to volume ratio of 2D metals and stretchability are beneficial in electrocatalysis. The exposed atoms on the outer surface of 2D metals with low coordination numbers, possess unique properties, forming numerous active sites on the surface. As a result, 2D metals demonstrate a high ability towards the activation of small molecules, including O2, H2, CO2, HCOOH, CH3OH, C2H5OH, etc. This exceptional oxidation reactivity enables 2D metals to be excellent electrocatalysts for hydrogen/oxygen evolution reaction (HER/OER), oxygen reduction reaction (ORR), and oxidation of small molecules (formic acid, methanol, and ethanol) for fuel-cell applications. As the localized surface plasmon resonance (SPRs) is sensitive to the size/shape of plasmonic 2D metals, the optical absorption enabled by SPRs offers additional advantages for photo-electrocatalytic processes. The stability of highly active catalytic 2D metals presents a challenge due to the propensity of metal surfaces with high reactivity to undergo oxidation. Recent developments in the synthesis, properties, and applications of 2D metal nanostructures for electrocatalytic processes are discussed. The challenges and opportunities in the electrocatalytic application of 2D metal nanostructures have been summarized. 2025 Ram K. Gupta. -
On Leech labelings of graphs and some related concepts
Let G=(V,E) be a graph and let f:E?{1,2,3,} be an edge labeling of G. The path weight of a path P in G is the sum of the labels of the edges of P and is denoted by w(P). The path number of G, tp(G) is the total number of paths in a graph G. If the set of all path weights S in G with respect to the labeling f is {1,2,3,,tp(G)}, then f is called a Leech labeling of G. A graph which admits a Leech labeling is called a Leech graph. Leech index is a parameter which evaluates how close a graph is towards being Leech. In this paper, the path number of the wheel graph Wn is obtained. We also determine a bound for the Leech index of Wn and a subclass of unicyclic graphs. A python program that gives all possible Leech labelings of a cycle Cn for n?3, if it exists, is also provided. 2023 Elsevier B.V. -
Homomorphic DNA Security in IoT Edge Data
The Internet of Things (IoT) based intelligent medical system possesses sensitive and private patient data. Most data relates to the patient's medical records and highly sensitive information. For this reason, safety and confidentiality of information are crucial. The preservation of patient privacy when sharing medical data is the primary concern of this study. Due to their excellent performance, biological notations based on deoxyribonucleic acid (DNA) are becoming increasingly admired for guaranteeing encryption and image protection. This paper proposes lightweight homomorphic with DNA-based medical image encryption (HDNA_MIE) for heterogeneous IoT in edge computing. The proposed approach contains two steps: In the first step, the secure DNA keys are generated using lightweight operations such as shifting and Josephus ring-based permutation (JRP). In the second step, the lightweight homomorphic cryptographic algorithm with DNA sequence-based encryption algorithm is suggested for secure encryption. The suggested strategy is evaluated using computational time and statistical analysis with several measures to determine its efficacy. The experimental findings of the proposed strategy exhibited a high level of security and a noticeable enhancement in the Number of Pixels Change Rate (NPCR), Unified Average Changing Intensity (UACI) and encryption processing time. The experiment outcomes demonstrate that our technique may be applied to highly confidential image encryption. 2024, Iquz Galaxy Publisher. All rights reserved. -
An analytical hierarchy process-based approach to building resilience in the Indian healthcare supply chain
Healthcare supply chains are constantly evolving and, thus, are subject to an array of disruptive risks. Apart from being crucial to the sustenance of human life, the healthcare supply chains are pivotal in adding value to the economy. The Indian healthcare supply chain is an intricate structure of indigenous and international entities functioning in unison to provide healthcare products to the masses. Bottlenecks along the healthcare supply chain can lead to disruptive consequences at any point. Thus, the identification of these disruptive risks is crucial in articulating risk mitigation strategies and ensuring business continuity. This study identifies supply chain risks along the Indian healthcare supply chain through unstructured interviews with industry experts. The identified risks are prioritised using the analytic hierarchy process (AHP) decision matrix. The results revealed that the Indian healthcare supply chain is facing obstacles with respect to import and export activities. Copyright 2026 Inderscience Enterprises Ltd. -
Stock Price Prediction using Deep Learning and FLASK
The forecasting of stock prices is one of the most explored issues, and it attracts the attention of both academics and business professionals. It is quite difficult to make predictions about the stock market, and it takes extensive research into the patterns of data. With the expansion of the internet and indeed the growth of social media, online media and opinions frequently mirror investor sentiment. The volatility and non-linear structure of the financial stock markets makes accurate forecasting difficult. One of the sophisticated analysis techniques that is being used by academics in a variety of fields is the neural network. In this paper, we proposed deep learning techniques for google stock price prediction. A dataset from Kaggle was collected and applied deep learning techniques RNN, LSTM variants. We achieved better results with Bidirectional LSTM. We also created a web app for stock prediction using Christ University python FLASK. 2022 IEEE. -
Airline Twitter Sentiment Classification using Deep Learning Fusion
Since the advent of the Internet, the way people express their ideas and beliefs has undergone significant transformation. Blogs, online forums, product review websites and social media are increasingly the primary means of distributing information about new products. Twitter, in particular, is giving people a platform to air their views and opinions about a variety of events and products. In order to continually enhance the quantity and quality of their products and services, entrepreneurs constantly need input from their customers. Businesses are always looking for ways to increase the quality of their products and services. As a result, it's tough to understand the consumer's sentiments because of the large volume of data. In this research work, a Kaggle dataset of airline tweets for sentiment analysis was used. The dataset contains 11,540 reviews. We proposed an ensemble CNN, LSTM architecture for sentiment analysis. For comparison of the proposed system, LSTM alone also tested for similar dataset. LSTM was given an accuracy of 91% and the proposed ensemble framework with LSTM and CNN was given an accuracy of 93%. The experiments showed that the proposed model achieved better accuracy when compared to conventional techniques. 2022 IEEE. -
Loan Default Prediction Using Machine Learning Techniques and Deep Learning ANN Model
Loan default prediction is a critical task in the financial sector, aimed at assessing the creditworthiness of borrowers and minimizing potential losses for lending institutions. Online loans continue to reach the public spotlight as Internet technology develops, and this trend is expected to continue in the foreseeable future. In this paper, the authors proposed loan default loan prediction system based on ML and DL models. This work makes use of the information on loan defaults provided by Lending Club. The dataset is preprocessed by applying various data preprocessing techniques and preprocessed dataset is generated. Later, we proposed four ML algorithms decision tree, random forest, logistic regression, K-NN and Feed forward neural network. The experimental results shown that proposed feed forward neural network achieved good accuracy for loan default prediction with an accuracy of 99%. 2023 IEEE. -
Role of AI in Computational Risk Modeling of Financial Stability and Portfolio Risk: A New Perspective
The need to assess climate change-related risks and their impact on the financial stability of banks is imperative. Innovations in technology, especially AI andML algorithms, have improved the efficiency and accuracy of risk analysis models. The obstacle for banks is assessing the climate risk exposure due to their lending portfolio. The climate data are uncertain and unavailable, and the granularity of the data is questionable. To overcome these issues, in this chapter, a hybrid risk predictive model is proposed. It uses a combination of ResNet-50 (to analyze and quantify spatial image data) and CoViaR (risk prediction) models. Using the ResNet-50 model, a climate change risk score is developed from images and feature extraction, which is correlated with the emission volume of the borrower firms. Then, using the proposed model, the impact of climate change-related risk on the lending portfolio is evaluated to understand the financial stability of banks through capital. 2025, Bentham Books imprint. -
An Efficient Approach for Gene Selection through Parallel Bio-Inspired Algorithms and Shapley Value Analysis
The fast development of microarray technology has significantly assisted in the use of gene expression analysis to forecast cancer subtypes. Analyzing high-dimensional microarray data is still challenging, as existing hybrid methods cannot find highly discriminative genes. This study aims to use Shapley value analysis and hybrid bio-inspired algorithms to develop a scalable, parallel gene selection technique to increase computing efficiency and classification accuracy in high-dimensional microarray data. This study used hybrid feature selection approaches inspired by bio-organisms to create a scalable parallel gene selection system. The dataset size is initially enlarged by Adaptive Synthetic Sampling (ADASYN). They use the Recursive Feature Elimination (RFE) approach to extract features and determine their Shapley values. In addition, the Whale Optimization Algorithm (WOA) works to determine which genes are most important. After that, Machine Learning (ML) techniques assist in classifying the chosen characteristics. According to the experiment results, the suggested strategy surpasses standard gene selection techniques with the same datasets, employing improved classification accuracy and reducing computing time. K-NN achieved an accuracy of 85.44%, while LR showed improved results with an accuracy of 91.72%. RF further increased accuracy to 94.69%. SVM demonstrated exceptional performance, reaching an accuracy of 97.63%. Ultimately, XGBoost excelled among all models with the highest accuracy of 98.49%, highlighting its robust ability to classify SRBCT samples effectively based on gene expression data. 2025, Ayandegan Institute of Higher Education. All rights reserved. -
An Extensive Time Series Analysis of Covid-19 Data Sets on the Indian States
Pandemic influenza coronavirus is causing a great loss to mankind. It is creating a chaos on the global economy. Fight against this unseen enemy is affecting all the sectors of the global economy. Mankind is quivering with fear and scared to do something. This study gives a detailed presentation of the current position of virus escalation in India. Sentiment analytics from Twitter data is evaluated on sentiment, emotions and fear opinions are analyzed in the study. The analysis is on red, orange and green zones in several states of India and also gave a comprehensive interpretation on various phases of lockdown. Confirmed, active, recovered and deceased cases in all states are modeled to predict the increase of number of cases. Textual, geographical and graphical analytics are extensively described in the research study. Time series analysis is broadly elaborated as a case study till July 22, 2020, forecasting the impact of virus on Maharashtra, Kerala, Gujarat, Delhi and Tamil Nadu. This study will favor the administrative system to control the disease spread across the nation. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Natural convection of a binary liquid in cylindrical porous annuli/rectangular porous enclosures with cross-diffusion effects under local thermal non-equilibrium state
The present article reports an analytical study of the double diffusive natural convection (DDNC) in cylindrical porous annuli (CPA) and rectangular porous enclosures (RPE), which are handled in a unified way using the curvature parameter, saturated by a binary liquid under the assumption of local thermal non-equilibrium (LTNE) state. The buoyancy forces (thermal and solutal) driving the flow are assumed to be induced by the maintenance of constant and uniform heat and mass fluxes applied along the vertical (radial) walls and insulation of both horizontal walls of the annuli/rectangular enclosures. The Darcy-Boussinesq equations with LTNE assumption between the fluid and solid phases are employed to model the problem of DDNC in a binary liquid-saturated porous medium with cross-diffusion effects. The analytical results are obtained by employing the Oseen-linearization transformation technique in the study. The influence of various dimensionless parameters on heat and mass transports of the system are depicted using the Nusselt and Sherwood numbers and isotherms plots, and the obtained results are analysed with the physical explanation. Special attention is given to understand the effect of LTNE parameter and cross-diffusion parameters on heat and mass transports of the system. Different aspect ratio values are chosen to obtain the results of three types of CPA/RPE (shallow, square and tall). Among these CPA/RPE, maximum and minimum heat and mass transports are achieved in the cases of shallow and tall CPA/RPE, respectively. The results of the pure thermal convection problem is obtained at the zero value of buoyancy ratio and solute Rayleigh number. The increasing value of N magnifies the heat and mass transports in the system due to the augmented buoyancy effect resulted from the thermal and solutal gradients. The increase of solid inner cylinder radius, by fixing its volume, makes the annulus slender which yields to decrease the heat and mass transports in the system. The effects of LTNE parameter and cross-diffusion parameters on heat and mass transports of the system are clearly brought out. The results of LTE model are obtained at the infinite value of ratio of porosity modified thermal conductivities, ?, as a particular case of the present model. From the study, we conclude that the shallow porous annulus and tall rectangular enclosure are best suited in the design of heat removal and heat storage systems, respectively. 2021 -
A study of the natural convection of water- AA 7075 nanoliquids in low-porosity cylindrical annuli using a local thermal non-equilibrium model
Natural convection in nanoliquid-saturated porous cylindrical annuli due to uniform heat and mass influxes from the solid cylinder and effluxes from the outer hollow cylinder is investigated analytically. The Darcy model and the modified version of the Buongiorno two-phase model are used, and local thermal non-equilibrium between the phases is assumed. A nanoliquid-saturated porous medium made up of glass balls with a dilute concentration of AA7075 alloy nanoparticles well-dispersed in water is considered. Out of three types of annuli considered, shallow annuli provide the best heat transport and tall annuli show the worst performance. The presence of a dilute concentration of nanoparticles significantly enhances the heat transport in the system. Of nine nanoparticle shapes considered, lamina-shaped nanoparticles enhance heat transport the most. Heat transport is enhanced in the case of heat-and-mass-driven convection compared to the case of purely heat-driven convection. The results for a rectangular enclosure are obtained as a particular case of the present study. Two asymptotic routes that take us to the results of thermal equilibrium are shown. The vanishing limit of the concentration Rayleigh number yields the result for a single-phase model. Results for the base-liquid-saturated porous medium form a limiting case of the present study. We conclude that a shallow cylindrical annulus saturated with water-AA7075 lamina-shaped alloy nanoparticles is best suited for heat transfer due to its high effective thermal conductivity in comparison with that of other shaped nanoparticles and a tall rectangular enclosure saturated by water is best suited for heat storage applications. 2021 Author(s). -
Theoretical Prediction of the Number of Bénard Cells in Low-Porosity Cylindrical/Rectangular Enclosures Saturated by a Fast Chemically Reacting Fluid
Many applications including chemical engineering and meteorology require the study of a chemically driven convection in cylindrical, as well as rectangular enclosures. The present paper reports a unified analysis of a chemically driven convection in densely packed porous cylindrical/rectangular enclosures saturated by a chemically reactive binary fluid mixture. Employing the degeneracy technique and the single-term Galerkin method involving Bessel functions in a linear stability analysis, an analytical expression for the critical Rayleigh number, (Formula presented.), was obtained. An analytical expression for the number of cells that manifest in a given enclosure, at the onset of convection, was derived from (Formula presented.). The connection between the stabilizing and destabilizing effects of various parameters and the size or the number of Bénard cells that manifest are described in detail. The results depicted that the chemical parameters related to the heat of reaction destabilize and the parameter depending inversely on the rate of the chemical reaction stabilizes the system. In the latter case, a greater number of smaller cells were formed in the system compared to the former case. Hence, we concluded that the chemically reactive fluid advances the onset of convection compared to the chemically non-reactive fluid. The results of a similar problem in rectangular enclosures of infinite horizontal extent and chemically non-reactive liquid-saturated porous medium were recovered as limiting cases. Thus, the present model presents a unified analysis of six individual problems. 2023 by the authors. -
Linear and weakly non-linear stability analyses of Rayleigh-Bard convection in a water-saturated porous medium with different shapes of copper nanoparticles
The Rayleigh-Bard convection of a nanoliquid-saturated porous medium confined in a very shallow enclosure is investigated theoretically using the modified Buongiorno - Brinkman model. In the study, the chosen nanoliquid-saturated porous medium is assumed to be made up of water well dispersed with copper(Cu) nanoparticles of five different shapes saturating in a 30% reinforced polycarbonate glass fiber(GF) porous material of high porosity and its effective thermophysical properties are calculated using the phenomenological laws or mixture theory. Two kinds of boundary conditions, viz., stress-free and rigid, are employed and the analytical solution is obtained in both cases. On the other hand, Rayleigh-Bard convection in a very shallow domain of height 5mm and width 5cm filled with water-liquid and bounded by the rigid boundaries is simulated. The simulation results are then compared with the analytical results in the case of rigid boundaries. We found that the analytical results are in good agreement with those of the simulation results and this validates results of the present study. Linear and weakly non-linear stability analyses are performed to find the onset and the heat transport of the system. The effects of various parameters on the onset and heat transport of the system are depicted graphically and the physical explanation is provided for all observed results in the study. We found that the addition of dilute concentration of nanoparticles advances the onset and thereby enhances the heat transport in the system. Among five different shapes of copper nanoparticles, maximum and minimum heat transports are observed in the cases of blade and spherical shaped nanoparticles, respectively. The porous medium parameters: Brinkman number and porous parameter, show a stabilizing effect in the system. The existence of subcritical motions is also predicted for the system. The results of the Khanafer-Vafai-Lightstone(KVL) single-phase model, nanoliquid, base liquid and base liquid-saturated porous medium are obtained as limiting cases of the present study. Since nanoparticles and porous medium, respectively, show a destabilizing and stabilizing nature of influence in the system, the present work has possible applications in both heat removal and heat retainment systems. 2022, The Author(s), under exclusive licence to SocietItaliana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature.