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The rapid diagnosis of intraamniotic infection with nanopore sequencing
Background: Intraamniotic infection (defined as intraamniotic inflammation with microorganisms) is an important cause of the preterm labor syndrome. Methods for the detection of microorganisms in amniotic fluid are culture and/or polymerase chain reaction assay. However, both methods take time, and the results are rarely available for clinical decision-making. Nanopore sequencing technology offers real-time, long-read sequencing that can produce rapid results. Objective: To determine 1) the diagnostic performance of the 16S rDNA nanopore sequencing method for the identification of microorganisms in patients with intraamniotic inflammation and 2) the relationship between microbial burden and the intensity of the amniotic fluid inflammatory response. Study Design: We performed a prospective cohort study that included singleton pregnancies presenting with symptoms of preterm labor with intact membranes or of preterm prelabor rupture of the membranes. Amniotic fluid samples were obtained for the evaluation of bacteria in the amniotic cavity using cultivation and polymerase chain reaction-based 16S Sanger sequencing methods. Participants were classified into 4 groups according to the results of an amniotic fluid culture, 16S Sanger sequencing, and an amniotic fluid interleukin 6 concentration: 1) no intraamniotic infection and intraamniotic inflammation (interleukin 6 <2.6 ng/mL, and no microorganisms in the amniotic cavity, as determined by culture or 16S Sanger sequencing); 2) microbial invasion of the amniotic cavity without intraamniotic inflammation, defined by the presence of bacteria detected by culture or 16S Sanger sequencing; 3) sterile intraamniotic inflammation (interleukin 6 ?2.6 ng/mL without microbial invasion of the amniotic cavity); and 4) intraamniotic infection (interkeukin 6 ?2.6 ng/mL with microbial invasion of the amniotic cavity). Patients who underwent a mid-trimester amniocentesis, had no intraamniotic infection or intraamniotic inflammation, and delivered at term represented the control group. 16S rDNA nanopore sequencing was performed and the diagnostic indices for the identification of intraamniotic infection were determined. Bioinformatic analysis was carried out to identify microorganisms, and a read count of at least 100 or a read count exceeding that of the background species from the control group, along with a relative abundance of no less than 1%, was used. Results: 1) The 16S nanopore sequencing had a sensitivity of 88.9% (8/9), specificity of 95.4% (41/43), positive predictive value of 80.0% (8/10), negative predictive value of 97.6% (41/42), positive likelihood ratio of 19.1 (95% confidence interval, 4.875.4), negative likelihood ratio of 0.1 (95% confidence interval, 0.020.7), and an accuracy of 94.2% (49/52) for the identification of intraamniotic infection (prevalence, 17% [9/52]); 2) the microbial load determined by the 16S nanopore sequencing had a strong positive correlation with the intensity of an intraamniotic inflammatory response (amniotic fluid interleukin 6 concentration; Spearman's correlation 0.9; P=.002); and 3) a subgroup of patients with intraamniotic inflammation did not have bacteria determined by culture, Sanger sequencing, or nanopore 16S, thus confirming the existence of sterile intraamniotic inflammation. Conclusion: The 16S nanopore sequencing has high diagnostic indices, predictive values, likelihood ratios, and accuracy in the diagnosis of intraamniotic infection. 2025 The Author(s) -
Trust green, pay more: Decoding green brand loyalty and willingness to pay more for electric vehicles through green transparency and green perceived value
The StimulusOrganismResponse framework is applied in this study to explore the impact of Green Transparency (stimuli) and Green Perceived Value (stimuli) on Green Brand Trust (organism) and, subsequently, on Green Brand Loyalty (response) and Willingness to Pay More (response). Self-Brand Connection is examined as a moderator. An online survey was distributed to 557 EV consumers. We employed both PLS-SEM (SmartPLS 4) and CB-SEM (AMOS 29) to test the direct, mediating, and moderating effects, with CB-SEM used as a robustness check for model stability. The results show that both Green Transparency and Green Perceived Value are positive antecedents of Green Brand Trust. Green Brand Trust, in turn, positively influences Green Brand Loyalty and Willingness to Pay More and mediates the effects of the two stimuli. The results also confirm that Self-Brand Connection significantly and positively strengthens the Green Brand Trust?Green Brand Loyalty and Green Brand Trust?Willingness to Pay More relationships. This study establishes Green Brand Trust as a core green consumer behavior mechanism and identity alignment as a catalyst for Green Brand Loyalty and Willingness to Pay More, offering actionable guidance to EV brands for credibility building, customer retention, and sustainable consumption. 2026 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license. http://creativecommons.org/licenses/by-nc-nd/4.0/ -
Is connection the key? The mediating role of psychological safety in the relationship between relatedness to employee engagement
This study examines the influence of Relatedness Needs (RL) on Employee Engagement (EE) in Bengaluru's Information Technology (IT) Industry, with the mediating role of psychological safety (PS). As the information technology industry experiences continuous innovation and is associated with a high-pressure work environment, aligning the organizational needs with the employee's needs is critical to ensure organizational success. Employees with higher RL needs satisfaction will exhibit positive commitment and higher engagement, contributing to the long-term productivity and success of the organisation. This study examines the extent to which PS, indicating a safe environment promoting transparent communication, sharing ideas and engaging in collaborative decision-making without the fear of negative consequences, mediates the relationship between RL and EE. To test the study's hypothesis, AMOS, Smart-PLS, and structural equation modeling were used to analyse data collected from 304 employees working across companies in Bengaluru's Information Technology (IT) industry. Our findings suggest that having a stronger RL boosts EE through the mediating role of PS characterised by trustworthiness, a sense of safety and fairness at the workplace. The results suggest that fostering higher RL and ensuring a strong PS is vital for sustained EE and reducing turnover intention. This study offers valuable insights into the Information technology (IT) companies intending to boost workforce engagement in a highly pressured work environment. 2025 The Authors -
Balancing commercialization and sustainability in outer space: Addressing new challenges
One notable aspect of NewSpace is the growing influence of private non-state actors. Nation-states have also implemented policies that facilitate the involvement of private non-state players in space activities. The lack of progress in international space law since the Moon Agreement of 1979 highlights the need to reconsider and update the existing legal system. Therefore, it is necessary to create a different framework based on the concept of commercial reality. The article emphasises the impact of NewSpace on the technological and commercial aspects of the outer space industry. An analysis is conducted on the crucial requirement for regulatory reform in the field of outer space law and policy, which arises from these advancements in the future. The persistent obstacle in addressing the commercial development of NewSpace is the lack of clarity surrounding property rights in outer space, particularly concerning the ownership and utilisation of space resources. This article examines the issue presented by the national space laws, which have conflicting and inconsistent views on property rights in outer space. The article also aims to investigate the practicality of establishing an international framework for space cooperation. The importance of the New Age institutional mechanism for international collaboration and the agenda for the proposed mechanism are emphasized. The proposed institutional framework aims to achieve a balance between commercialization and profit-driven market pressures, considering their potential negative effects on the sustainable usage of outer space. In conclusion, the article discusses India's appropriate course of action in response to the emerging problems in the field of space exploration. 2025 IAA -
Unveiling the realm of AI governance in outer space and its importance in national space policy
This article explores the notable legal concerns that may arise from the growing utilisation of artificial intelligence and machine learning in outer space. Whether it is conducting space exploration, clearing orbital debris, or extracting resources from specific areas in space, these activities are becoming more popular. Therefore, it is necessary to establish a regulatory framework to ensure consistency and objective standards. In order for national space legislation to effectively address the challenges presented by activities involving robots with different levels of autonomy and numerous objectives, it is essential to appraise the nature of these challenges. The article aims to investigate the relationship between the Montreal Declaration for a Responsible Development of Artificial Intelligence, 2017, and outer space laws and principles. It also examines the legal status of autonomous space objects, such as planetary rovers that are currently in operation or will be in the near future. Ultimately, the article highlights the importance of national space policy in addressing the appropriate regulation of artificial intelligence in outer space. In conclusion, this article has also discussed the potential effectiveness of utilising artificial intelligence-based methodologies and strategies to enhance current space policy. 2024 IAA -
In-silico analysis of the mechanism of action ofNerium oleanderbioactive compounds againstHelicoverpa armigera
Helicoverpa armigera is one of the most destructive agricultural pests worldwide, noted for its wide host range, high fecundity, and rapid development of resistance to synthetic insecticides. To address this threat, sustainable botanical alternatives are urgently needed. In this study, Nerium oleander, a toxic ornamental plant rich in secondary metabolites, was evaluated as a potential botanical insecticide through in silico assays. Methanolic extracts were subjected to phytochemical screening, confirming the presence of alkaloids, saponins, cardiac glycosides, coumarins, and terpenoids. Gas Chromatography-Mass Spectrometry (GC-MS) profiling identified 20 major compounds, including terpenoids, fatty acids, sterols, and phenolics, with 2-methoxy-4-vinylphenol (2.7 %), neophytadiene (1.7 %), and phytol (0.9 %) among the key constituents. Cytochrome P450, a central detoxification enzyme in insects, was chosen as the molecular target. Docking analysis revealed strong binding affinities, with phytol (?6.92 kcal/mol, Ki 8.12 ?M), neophytadiene (?6.43 kcal/mol, Ki 14.57 ?M), and 2-methoxy-4-vinylphenol (?5.87 kcal/mol, Ki 45.13 ?M) demonstrating significant inhibitory potential. These findings indicate that N. oleander metabolites may disrupt detoxification pathways in H. armigera, providing a mechanistic basis for their insecticidal action and supporting the plant's promise as a candidate for integrated pest management. 2025 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. LtdThis is an open access article under the CC BY license. http://creativecommons.org/licenses/by/4.0/ -
Sentiment analysis of sentences used in Indian local languages using Transfer Learning
The growing number of online communication channels has led to a dramatic increase in the volume of user-generated content across all media. Sentiment analysis is given more force by having access to these varying perspectives and feelings. Due to a lack of standardised labelled data, sentiment analysis becomes much more difficult. The goal of sentiment analysis is to determine, from a text sample, an individual's likely emotional reaction to a given event or point of view. When estimating the tone of a text, polarity analysis is commonly used. Negative, Positive and neutral are the labels used by sentiment classifiers. This chapter provides a framework for effective sentiment analysis of sentences from four local language datasets, including Hindi, English, Bangla, and Marathi, using transfer learning. The results show that the BERT model achieves a high level of accuracy when evaluated using random forests, nae Bayes, and decision trees. 2026 -
The Role of Artificial Intelligence in Electric and Autonomous Vehicles
The?? use of Artificial Intelligence (AI) is changing the whole car landscape. Indeed, AI is the main driving force behind the most innovative electric and autonomous vehicles. The technology is making transportation environmentally friendly, intelligent, and cost-effective. The chapter demonstrates the role of AI in self-driving cars and electric vehicles through various examples, such as autonomous driving, battery performance, charging systems, predictive maintenance, safety, efficiency, and fleet management. AI is the reason that cars can now drive themselves, whereby it is the technology that enables navigation, object detection, and systems like Tesla Autopilot. Besides, it is heavily involved in battery management as it lowers the battery life through usage, overheating, and prolongs the battery life. The chapter also talks about AI technology that supports smart EV charging, allowing integration of renewable sources and even making charging more comfortable and hassle-free. Predictive maintenance is yet another significant area where the AI system is monitoring the health of the car, the earliest detection of the faults, and extending the lifespan of EV components. Implementation of AI in safety vehicles is a great advancement in this industry. In conjunction with this technology, AI-based safety systems, like driver assistance, hazard detection, and emergency response, provide safety to the cars. Moreover, the technology enhances energy efficiency, range prediction, and real-time vehicle performance. The chapter concludes with a discussion about the coming of reflection in the AI realm of environmental sustainability, intelligent fleet management, and challenges of the future. In summary, this article accentuates how AI is rewiring the future of electric and self-driving vehicles and why its role is key for researchers, industry professionals, and ??policymakers. 2026 -
Machine learning for healthcare
Machine learning currently drives healthcare innovation, enabling novelty in solving complex medical problems. This chapter will present an in-depth critical review of various machine learning techniques applicable in healthcare in general, focusing on practical applications and recent advancements. It will further discuss supervised and unsupervised learning to semi-supervised learning methods, thereby detailing their uses for disease prediction, segmentation of patients, and image analysis in medical science. Among the most important areas in ML includes data preprocessing and feature engineering issues in health-care datasets. This further includes treatments for missing data, dimensionality reduction, and class imbalance. This chapter also discusses extensive case studies with state-of-the-art approaches that give insight into how the ML approach is changing health care decision-making, increasing diagnostic precision, and improving patient outcomes. Interpretability, scalability, and the mitigation of bias are further discussed as some of the challenges in the implementation of ML in healthcare. Ethical considerations regarding the need to develop responsible AI in healthcare and regulatory compliance are also discussed. It aims to serve as a handbook for researchers, practitioners, and policy analysts operating at the intersection between ML and healthcare. 2026 -
Overcoming challenges in 5G performance evaluation and QoE management
As the 5G technology is being rolled out in phases, the world over, there is a need for efficient and effective 5G network planning and Quality of Experience. One way a country planning to adopt 5G could plan the logistics of laying out the network, could be through learning best practices from peers. Which peer to follow is subjective to each country in question. Therefore, in choosing these references/benchmark countries, a thorough knowledge of their performance is vital. Yet another factor that impacts 5G network planning is effective QoE management. Proper QoS provisioning over the network is very important even as countries strive to cater the best possible services to their citizens. This is made possible by identifying improvement needs of countries on its user QoE. By quantifying this need, suitable benchmarks could be set and achieved in the short or long terms thereby enabling the countries to provide better services to their people. Determining the right benchmarking and evaluation technique is the need of the hour. 2025 -
Demystifying the 5G-Advanced communication paradigm
With the massive surge in the increasing number of connected wireless devices, the demand for wireless data bandwidth keeps growing exponentially. Further on, in the beginning, many people were using only one computer. Today everyone has his own computer. But in the days ahead, there will be many digital devices to cogently and cognitively identify and deliver real-time and real-world services to a person. That is the world is tending towards a multi-device communication and computing era. Visualizing and realizing context-aware applications mandate for device computing. That is, devices have to be empowered to be computational, communicative, sensitive, vision-enabled, perceptive, decision-making, intelligently responsive, and action-taking. Leading research analysts and market watchers have forecast that there will be billions of IoT devices and trillions of IoT sensors in the years to unfold. Connecting them to instinctively share their unique capabilities and data with one another as well as with nearby and faraway cloud data platforms demands pioneering and powerful communication capabilities. With this projected growth in mind, the cellular industry looks for advanced wireless communication technologies. to other frequency bands that could possibly be utilized in the development of new 5G wireless technologies. This chapter is dedicated to telling all about the unique capabilities and use cases of 5G Advanced releases (Releases 17 and 18). Let us start with release 17 and then jump into the 18th release. 5G is all set to empower consumers and businesses to realise and use advanced applications and it allows a large number of devices to connect and exchange data faster than ever before. 2025 -
Enhanced mother optimization algorithm-based optimal reconfiguration to accommodate emerging electric vehicle demand
Radial configuration and high x/r ratio branches in electrical distribution systems (EDSs) result in significant power losses and diminished stability margins. Optimal network reconfiguration (ONR) is a highly flexible solution methodology for addressing these challenges. The identification of optimal branches or tie lines to modify their on/off status in relation to multiple objectives under radial constraints constitutes a complex optimization challenge. This paper presents a novel variant of the mother optimization algorithm (MOA) that incorporates dynamic learning techniques for the optimal placement and sizing of electric vehicle (EV) charging stations to enhance distribution system loadability. The proposed modifications enhanced the overall performance of the algorithm by improving the exploration and exploitation characteristics. This leads to superior global best results and faster convergence than with other competitive algorithms when addressing complex optimization problems. In addition, an enhanced mother optimization algorithm (EMOA) is employed to address the ONR problem in 7-, 12-, 33-, 69-, and 118-bus IEEE radial systems. The losses are reduced by 44.15%, 30.07%, 33.87%, 55.72%, and 33.04% when compared to the base case across the respective test systems. Moreover, the loadability is increased in the 33-bus and 69-bus configurations by 208.75% and 177.07% for the base and optimal configurations, respectively. The results indicate the appropriateness of the ONR for enhancing the loadability to accommodate the rising penetration levels of electric vehicles (EVs) in support of sustainability. The Author(s) 2025. -
On path-induced signed graphs
The path decomposition of a graph G is the process of decomposing it into edge-disjoint paths. An induced signed graph is a signed graph formed from an ordinary unsigned graph by assigning signs to its edges according to some protocol. In this paper, we introduce the notion of a path-induced signed graph as an induced signed graph whose edges receive a sign according to whether its end vertices are the end vertices of a path in a path decomposition of G. We also discuss some characteristics of this type of signed graph. The Author(s), under exclusive license to Sapientia Hungarian University of Transylvania 2026. -
Econometric investigation of sectoral contributions to remittance inflows in Kerala using the VECM framework
The sustained outmigration from Kerala has significantly contributed to the surge in remittance inflows, which have become a critical driver of the states economic advancement. As India maintains its position as the worlds largest recipient of remittances, Kerala remains one of the most prominent subnational beneficiaries. Although remittances have historically played a pivotal role in shaping Keralas developmental trajectory, there is a growing imperative to channel these financial inflows into productive investments across various sectors of the economy. Within this context, an undisturbed system analysis identifies agriculture, industry, and services as key sectors potentially influenced by remittance flows. The present study utilises the Vector Error Correction Model (VECM) framework to investigate the sector-specific contributions to Keralas remittance-induced economic growth. This econometric approach facilitates an examination of both the long-term equilibrium relationships and short-term adjustments among the variables. The empirical findings highlight the differentiated impact of each sector, with particular emphasis on the significant role played by the agricultural and industrial sectors in attracting and sustaining remittance flows. The Author(s) 2026. -
VAST-GCN: An Attention-Driven Graph Convolutional Network (GCN) for Robust Cluster Head Selection in Vehicular Ad-Hoc Networks
Vehicular Ad-Hoc Networks (VANETs) need smart and flexible communication protocols to deal with fast-moving vehicles and ever-changing network structures. Picking the right cluster head (CH) plays a key role to keep connections stable and reduce routing overhead. This paper presents VAST-GCN (Vehicular Attention-based Spatial-Temporal Graph Convolutional Network), a new model that uses attention to make vehicle grouping and CH selection better across different network sizes. VAST-GCN mixes Graph Convolutional Networks (GCNs) with Spatial, Temporal, and Channel Attention systems. Approach in vehicle settings with 100, 500, and 1000 vehicles has been tested using real-time info like speed and place. The design has transformer blocks to model time-based features and attention modules to improve space and feature relationships leading to better vehicle data. Data have been grouped using the K-Means method and checked with modularity score, silhouette score, and group density. At the time of comparison, it has been observed that VAST-GCN does better than regular GCN and MIXHOP GCN models in cutting down loss making better community structures, and keeping CHs stable when there are few vehicles or theyre moving fast. The proposed VAST-GCN framework exhibits clear advantages over existing spatio-temporal GNNs by delivering superior modularity, silhouette scores, and cluster head stability across diverse vehicular scenarios. Its attention-driven architecture not only improves clustering accuracy but also reduces packet delay and enhances throughput, highlighting its excellence as a robust and scalable solution for dynamic VANET environments. The Author(s) 2025. -
Deep Learning Based Multi Constraint Hybrid Optimization Algorithm for Transshipment-Based Inventory Routing with Dynamic Demands
The Inventory-Routing Problem (IRP) is considered a major issue in supply chain management as it comprises two areas: vehicle routing and inventory control. The existing techniqueswere unable to incorporate location details for enhancing the decision-making and it failed to consider the uncertainty of the demands. Hence to solve this issue, a Snake Artificial Ecosystem Optimization (SAEO) algorithm is proposed in this paper. The SAEO algorithm is developed to address the transshipment IRP with dynamic demands by combining the AEO model and SO to enhance the optimizer's performance. Further, a penalty strategy is proposed, where Deep Quantum Neural Network (DQNN) is employed for calculating the penalty for verifying the feasibility of the solution generated in case of violations in model constraints. In addition, the efficiency of the proposed SAEO-DQNN technique is examined by considering metrics, like transportation cost, transshipment cost, and total cost, and it achieved improved values of 0.391, 0.518, and 1.012 when compared to existing techniques such as Genetic Algorithm with Deep Reinforcement Learning (GA + Deep RL) and Kernel Search Multi-vehicle IRP (KSMIRP). The Author(s) 2024. -
Assessing the impact of perceived parenting on the self-concept of college going young women
Self-concept (SC) constitutes the idea and beliefs one has about oneself. As the primary agents of socialization, parents play a crucial role in the development of self-concept of their children. The present study sought to explore the relationship of SC of college going young women in India with perceived parenting. Furthermore, it investigated whether perceived parenting significantly predicted SC. Data was collected from 150 college going young women across Delhi NCR (Mage=19.62, Age Range = 1821 years) using standardized measures of Parenting Scale and Self-Concept Questionnaire. The scales demonstrated high reliability using Cronbachs alpha for the given population. Statistical analyses (correlation and regression analysis) of the data revealed that perceived parenting of mothers and fathers both positively and significantly correlate and predict several dimensions of SC. Particularly, for participants perception of their mothers, carelessness vs. protection significantly predicted the total SC. For fathers, rejection vs. acceptance and freedom vs. discipline were major predictors. For both mothers and fathers, marital conflict vs. marital adjustment was a common predictor. The findings are indicative of the importance of perceived parenting in the development of SC. It also highlights that parenting is a shared responsibility and necessitates a balanced approach as both mothers and fathers play a vital role in shaping an individuals view of self. The Author(s) 2026. -
A qualitative exploration of mental health experiences among IBDP students
The burgeoning concern surrounding mental health has become a prevailing issue. Considering the education sector, cut-throat competition among students has led them to experience concerns with their mental health. Although ample research exists on mental health awareness, escalating academic competition has impacted the holistic well-being of students. Research elucidates that the education sector beckons urgent attention to address the emerging mental health needs of the high school students. Thus, this study aims to probe the specific mental health requirements of students enrolled in the International Baccalaureate Diploma Program (IBDP) in Jaipur, Rajasthan. Conducted using a qualitative research paradigm, this study entailed in-depth interviews of 25 IBDP students. Subsequently, a thematic content analysis was deployed to interpret the emerging themes from the data gathered. The findings illustrated numerous themes such as mental health concerns, self-management, professional development, and other related themes discussed in the paper. This research seeks to enhance the awareness of the IBDP community regarding the mental health challenges experienced by the students. Additionally, it serves as an essential resource for educators, parents and students themselves, empowering them to acknowledge and address these challenges effectively. Furthermore, the results from this study can be utilized to develop a mental health framework tailor made to the needs of IBDP students. The Author(s) 2026.