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The development of lightweight, corrosion-resistant metallic lattice structures has gained significant attention in aerospace, defense, and structural applications, where material durability and weight optimization are critical. This study investigates the corrosion behavior of Laser Powder Bed Fusion (LPBF)-fabricated A286 steel honeycomb, Body-Centered Cubic (BCC), and gyroid lattices, comparing their performance against conventional materials such as Rolled Homogeneous Armor (RHA), Maraging High Strength Steel (MHA), and High-Nitrogen Steel (HNS). Corrosion testing was conducted using accelerated salt spray exposure, and the results were validated through computed tomography (CT)-based structural integrity analysis and machine learning-based predictive modeling. The experimental findings revealed that lattice structures exhibited significantly lower corrosion rates than conventional bulk materials, with the honeycomb lattice demonstrating the highest corrosion resistance (1.218 mm/year), followed by BCC (1.311 mm/year) and gyroid (1.671 mm/year). Compared to RHA, the honeycomb lattice exhibited a 57.23% reduction in corrosion rate, confirming its superior electrochemical stability. CT scan evaluations further highlighted differences in density distribution and geometric fidelity, with honeycomb lattices showing the most uniform porosity, while BCC structures displayed localized density variations at nodal intersections. To enhance predictive capabilities, various machine learning (ML) algorithms were employed to model corrosion behavior based on weight-loss measurements and lattice topology. Bayesian Ridge regression outperformed other models, achieving an R² of 0.99849 and RMSE of 0.00049, confirming its robustness in capturing corrosion trends. Linear Regression also performed well, while ensemble models such as Random Forest and XGBoost exhibited higher error margins due to dataset linearity constraints. Residual analysis and graphical interpretations further validated the stability and predictive reliability of ML-based corrosion assessments, demonstrating their feasibility as an alternative to traditional experimental methods. This study presents a comprehensive framework for integrating experimental corrosion testing, computational modeling, and CT-based defect analysis, offering a scalable approach to optimizing micro-lattice designs for corrosion-sensitive applications. The findings highlight the potential of LPBF-fabricated metallic lattices for aerospace, defense, and marine structures, where enhanced corrosion resistance, reduced material degradation, and predictive maintenance strategies are essential for long-term operational performance.

Purpose – The purpose of this paper is to examine the extent to which service quality, perceived price and fairness and service convenience influence customer satisfaction and customer loyalty for Indian retail banking sector. It further explores the role of customer satisfaction as mediating variable between service quality dimensions, perceived price and fairness, service convenience dimensions and customer loyalty. Design/methodology/approach – A cross-sectional research on 445 retail banking customers through questionnaire is conducted. Population of study is valued retail urban customers of banks in Rajasthan, India, who frequently visit bank premises for transactions, have accounts in at least two banks and have availed of at least one information technology-based services. Responses are analyzed using factor analyses and regression analyses. Findings – Results reveal that service quality dimensions, perceived price and fairness and service convenience dimensions have positive impact on customer satisfaction and customer loyalty. Moreover, customer satisfaction acts as mediating variable between its antecedents and customer loyalty. Research limitations/implications – This study has taken into account a specific category of retail banking customers. Thus, it limits generalization of results to other banking population. Practical implications – This study explains the importance of customer satisfaction for achieving customer loyalty for Indian retail banking sector. Originality/value – The paper underlines the importance of customer satisfaction for achieving customer loyalty. Impact of SERVCON dimensions on customer loyalty is found rare in literature.

Graph burning is a process of information spreading through the network by an agent in discrete steps. The problem is to find an optimal sequence of nodes that have to be given information so that the network is covered in least number of steps. Graph burning problem is NP-Hard for which two approximation algorithms and a few heuristics have been proposed in the literature. In this work, we propose three heuristics, namely, Backbone Based Greedy Heuristic (BBGH), Improved Cutting Corners Heuristic (ICCH), and Component Based Recursive Heuristic (CBRH). These are mainly based on Eigenvector centrality measure. BBGH finds a backbone of the network and picks vertex to be burned greedily from the vertices of the backbone. ICCH is a shortest path based heuristic and picks vertex to burn greedily from best central nodes. The burning number problem on disconnected graphs is harder than on the connected graphs. For example, burning number problem is easy on a path where as it is NP-Hard on disjoint paths. In practice, large networks are generally disconnected and moreover even if the input graph is connected, during the burning process the graph among the unburned vertices may be disconnected. For disconnected graphs, ordering the components is crucial. Our CBRH works well on disconnected graphs as it prioritizes the components. All the heuristics have been implemented and tested on several bench-mark networks including large networks of size more than 50K nodes. The experimentation also includes comparison to the approximation algorithms. The advantages of our algorithms are that they are much simpler to implement and also several orders faster than the heuristics proposed in the literature.

News coverage of medical research is followed closely by many Americans and affects the practice of medicine and influence of scientific research. Prior work has examined the quality of media coverage, but no investigation has characterized the choice of stories covered in a controlled manner. We examined whether the media systematically covers stories of weaker study design. We compared study characteristics of 75 clinically-oriented journal articles that received coverage in the top five newspapers by circulation against 75 clinically-oriented journal articles that appeared in the top five medical journals by impact factor over a similar timespan. Subgroup analysis was performed to determine whether differences between investigations from both sources varied by study type (randomized controlled trial [RCT] or observational study). Investigations receiving coverage from newspapers were less likely to be RCTs (17% vs. 35%, p = 0.016) and more likely to be observational studies (75% vs. 47%, p<0.001). No difference was observed in number of people studied (median: 1034 vs. 1901, p = 0.14) or length of follow-up (median: 1.80 vs. 1.00 years, p = 0.22). In subgroup analysis, observational studies from the media used smaller sample sizes (median: 1984 vs. 21136, p = 0.029) and were more likely to be cross-sectional (71% vs. 31%, p<0.001), while no differences were observed for RCTs. Newspapers were more likely to cover observational studies and less likely to cover RCTs than high impact journals. Additionally, when the media does cover observational studies, they select articles of inferior quality. Newspapers preferentially cover medical research with weaker methodology.

Technological efforts are currently being used across a broad array of industries. Through the combination of consumer choice and matching principle, the goal of this paper is to investigate the prospective implications of artificial intelligence systems on businesses’ outcomes. From an entrepreneurship standpoint, the research revealed that artificial intelligence systems can help with better decision-making. What impact does the introduction of AI-based decision-making technologies have on organizational policymaking? The quirks of human and AI-based policymaking are identified in this research based on five important contextual factors: precision of the choice search area, contribution to the innovation of the policymaking process and result, volume of the replacement collection, policymaking pace, and generalizability. We create a novel paradigm comparative analysis of conventional and automation judgment along these criteria, demonstrating how both judgment modalities can be used to improve organizational judgment efficiency. Furthermore, the research shows that, by involving internal stakeholders, they can manage the correlation among AI technologies and improve decision for businessmen. Furthermore, the research shows that customer preferences and industry norms can moderate the link between AI systems and superior entrepreneurial judgment. The goal of this work is to conduct a thorough literature analysis examining the confluence of AI and marketing philosophy, as well as construct a theoretical model that incorporates concerns based on established studies in the areas. This research shows that, in a setting with artificial intelligence systems, customer expectation, industry standards, and participative management, entrepreneurial strategic decisions are enhanced. This research provides entrepreneurs with technology means for enhancing decision-making, illustrating the limitless possibilities given by AI systems. A conceptual approach is also formed, which discusses the four factors of profit maximization: relationship of AI tools and IT with corporate objectives; AI, organizational learning, and decision-making methodology; and AI, service development, and value. This study proposes a way to exploit this innovative innovation without destroying society. We show real-world examples of each of these frameworks, indicate circumstances in which they are likely to improve decision-making performance in organizations, and provide actionable implications into their constraints. These observations have a wide variety of implications for establishing new management methods and practices from both academic and conceptual viewpoints.

Citation score has become a very important metric to assess the quality of a publication in the current global ranking scenario. In this context, the study of citation networks gains importance as it helps in understanding the citation process as well as in analyzing citation trends in the research world. Citation networks are modeled as directed acyclic graphs in which publications of the authors are considered as nodes and citations between the papers form the links. In this paper, we propose an additive Time-Stamp based Network Evolution(TNE) model for citation networks, extending Price’s preferential attachment model by including the recency effect on the citation process without neglecting the impact of classical papers. We propose a more meaningful definition of clustering coefficient for citation networks in terms of ’citation triangles’. Further, the network simulated by the TNE model with best-fit parameters is compared with the real-world(DBLP) citation network. The results of various significance tests show that the simulated network matches very well with the DBLP citation network in terms of several network properties.

A blockchain is an advanced technology that can power over a decentralized network. The authors bring it up to design the autonomous transaction system for e-commerce applications; because of the dramatic increase in IoT devices, communication between physical things is enabled. This brings more efficiency and accuracy, which benefits the outsiders while human interaction reduces. There is a big challenge in data storage after payment in the e-commerce application. Blockchain presents an appropriate platform for the distributed data storage; it also protects the data from outsiders. The authors create blocks that check and record each transaction that took place in the e-commerce application. Blockchain is going to protect the user's privacy from outsiders/banks that are being violated. The authors deliver this research in this paper in terms of the method with detailed design and full implementation. The system captures the user data, processes it, and gives a visual representation of the processed data.

With recent technological advances, adding nanomaterials as a reinforcement material in concrete has gained immense attention. This review paper aims to report advances in the form of a one-stop shop catering to methods that focus on improving the quality of traditional concrete. Nanoparticles—the elementary form of nanomaterials—are proven to enhance the strength and longevity of concrete. Nanosilica, nanoalumina, nanometakaolin, carbon nanotubes, and nanotitanium oxide are modern nanomaterials that have demonstrated strong evidence of enhancing concrete quality, which supports infrastructure building and long-term monitoring. Nanoconcrete—an exciting prospect extending the boundaries of traditional civil engineering—exhibited increased compressive and flexural strength using elementary compounds. In particular, the rigorous research survey of many articles reveals an increase in compressive strength from 20% to 63% by replacing the cement with different nanomaterials in different percentages and flexural strength from 16% to 47%.

Cloud storage offers online storage services to back up data and enables easy access to real-time data at any time and on the fly. The primary issue of security arises as the data is outsourced to remote servers that cause data loss and modifications to occur. Data corruptions or misbehaviours in the cloud service providers (CSP) have to be detected at low cost and promptly. The data outsourced to CSP by the data owners might suffer from cloud service provider misbehaviour, in particular unauthorized deletion of data by CSP to save storage space towards attracting potential clients. Adapting continuous sampling of random blocks as batches reduce the computation workload on the servers, thus detecting the modifications or corruptions if any without time delays. The corrupted data blocks are located with continuous sampling methods and the probability of detection is high.

The internet of medical things (IoMT) ecosystem is highly vulnerable to malware attacks due to the vast number of connected devices and their continuous collection, transmission, and processing of sensitive data. Inadequate device management often makes each device a potential entry point, enabling malware to spread rapidly across networks with minimal detection. Given the resource constraints, privacy concerns, and distributed nature of IoT devices, there is a pressing need for lightweight and adaptive intrusion detection models. This paper proposes a federated learning (FL) based framework enhanced with TinyGAN, where the generator produces synthetic data to improve malware detection. The federated approach enables continuous, decentralized learning, allowing the model to adapt to emerging threats without requiring centralized retraining, thereby preserving privacy and reducing computational overhead. Experimental evaluations demonstrate significant improvements in both detection accuracy and efficiency compared to conventional centralized techniques. After 20 training rounds, the proposed model achieved a precision of 99.30%, a recall of 100%, and an F1-score of 99.52%. These results highlight the scalability, privacy-preserving nature, and effectiveness of the framework, offering a practical advancement in securing IoT environments against malware attacks. An experimental analysis of the IoT-23 dataset reveals that FL with TinyGAN consistently outperforms traditional models, such as MLP and FNN/LSTM, in terms of accuracy, convergence rate, and resource consumption, thereby establishing its effectiveness for practical IoT malware detection.