In times of real threats to the continuity of the human civilization resulting from environmental degradation, depletion of natural resources, overpopulation, and other adverse factors, the issue of sustainable development is the subject of interest of many scientific disciplines. As a leading objective of this paper, the authors take up the topic of sustainable development seen through the lenses of the library and information science, which is considered with special attention paid to its economic, social, environmental, and cultural dimensions. In addition to reviewing the most important literature, the authors also explore the subject matter from a quantitative perspective. As a result of the research, the authors identify the key areas that affect libraries as cultural and scientific institutions, in which work related to the sustainability concept is actively carried out. Quantitative research allowed to determine the proportions of efforts made by scientists within the previously selected areas, and to outline trends observed within those areas—that is, to identify which areas have recently been gaining importance, and which may have ceased to be exploited. The authors hope that the research results not only shed light on the landscape of world science in the subject matter, but above all, that they support contemporary researches of these fields by identifying potentially the most important works influencing the shape of particular research areas, and the identification of current trends, which are present within the mentioned areas as well. Further research directions, which are potentially worth undertaking, are also emphasized.

In the context of scaling a business-critical medical service that involves electronic medical record storage deployed in Kubernetes clusters, this research addresses the need to optimize the configuration parameters of horizontal pod autoscalers for maintaining the required performance and system load constraints. The maximum entropy principle was used for calculating a load profile to satisfy workload constraints. By observing the fluctuations in the existing workload and applying a kernel estimator to smooth its trends, we propose a methodology for calculating the threshold parameter of a maximum number of pods managed by individual autoscalers. The results obtained indicate significant computing resource savings compared to autoscalers operating without predefined constraints. The proposed optimization method enables significant savings in computational resource utilization during peak loads in systems managed by Kubernetes. For the investigated case study, applying the calculated vector of maximum pod count parameter values for individual autoscalers resulted in about a 15% reduction in the number of instantiated nodes. The findings of this study provide valuable insights for efficiently scaling services while meeting performance demands, thus minimizing resource consumption when deploying to computing clouds. The results enhance our comprehension of resource optimization strategies within cloud-based microservice architectures, transcending the confines of specific domains or geographical locations.

With the ever-increasing wide spread of the Internet, the number of web services, web applications, and IoT devices is growing every year. This brings a number of challenges, both in terms of network bandwidth and the ability to scale individual computing nodes, whether they are large systems running in computing clouds or smaller IoT devices running closer to their data sources (so-called edge computing). In both cases, the way to cope with handling large numbers of users/requests is horizontal scaling, the implementation of which today is using the concept of microservices. However, the concept itself is not enough—we need ready-made application frameworks that allow us to easily implement and deploy efficient services. In the case of the Java ecosystem, which is one of the most mature platforms for enterprise-class software development, several frameworks dedicated to the development of microservices have been engineered recently. These tools support system developers in implementing communication, computation, and data storage mechanisms. However, so far, there is a lack of comparative analysis of individual solutions in the scholarly discourse to assess their performance and production maturity, so the authors in this article try to fill this gap. Based on synthetic tests developed by the authors, the most promising frameworks (Spring Boot, Micronaut, Quarkus) were analyzed both in terms of computational, compilation, or deployment performance. The results obtained can help system architects make rational and evidence-driven choices of system architecture and technology stacks.

In an era marked by escalating concerns about digital security, biometric identification methods have gained paramount importance. Despite the increasing adoption of biometric techniques, keystroke dynamics analysis remains a less explored yet promising avenue. This study highlights the untapped potential of keystroke dynamics, emphasizing its non-intrusive nature and distinctiveness. While keystroke dynamics analysis has not achieved widespread usage, ongoing research indicates its viability as a reliable biometric identifier. This research builds upon the existing foundation by proposing an innovative deep-learning methodology for keystroke dynamics-based identification. Leveraging open research datasets, our approach surpasses previously reported results, showcasing the effectiveness of deep learning in extracting intricate patterns from typing behaviors. This article contributes to the advancement of biometric identification, shedding light on the untapped potential of keystroke dynamics and demonstrating the efficacy of deep learning in enhancing the precision and reliability of identification systems.

Despite the fact that the concept of sustainable development was born as early as the late 1980s, in the field of library and information science there has been a significant growth in interest only in recent years. This observation inspired the authors to explore this particular area with regards to productivity both in quantitative and qualitative terms. One of the important factors influencing scientific productivity is broadly understood research collaboration. Interestingly, in the scholarly literature dealing with the topics of scientific productivity, the researchers are most often representatives of the LIS field who study, also applying bibliometric methods, various scientific disciplines. However, so far there have been no studies on scientific collaboration concentrated around the discipline of sustainable library and information science (LIS) itself. Therefore, in this paper, the authors present the results of research into the phenomenon of co-authorship in this specific area. The results indicate not only the most important collaborating entities contributing to the research field and trends in research cooperation, but also verify certain general hypotheses put forward in the areas of the fundamental sustainable development of the discipline of sustainable LIS.