Digital libraries have rapidly evolved from simple repositories of digitized texts to complex, user-centric platforms that leverage cutting-edge technologies. The paper follows the evolution of digital libraries across time, emphasising significant turning points like the creation of the World Wide Web, the emergence of mobile access, and the incorporation of artificial intelligence. It explores the technological advancements that have shaped digital libraries, including cloud comput

Football, recognized globally as the most popular sport, captivates millions of players and fans due to its fast-paced, dynamic gameplay. However, the intense physical activity involved in football inherently increases the risk of injuries. Players are frequently engaged in high-speed running, rapid changes in direction, sudden stops, and aggressive tackling—all of which contribute to the sport's physical demands. These elements, combined with frequent collisions and physical contact between pla

In the last decade, several research efforts have been directed to integrating performance analysis in the software development process. Traditional software development methods focus on software correctness, introducing performance issues later in the development process. This approach is not adequate since performance problems may be so severe that they may require considerable changes in the design, for example at the software architecture level, or even worse in the requirements analysis. Several approaches have been proposed to address early software performance analysis. Although some of them have been successfully applied, there is still a gap to be filled in order to see performance analysis integrated in ordinary software development. We present a comprehensive review of the recent developments of software performance research and point out the most promising research directions in the field.

The Chirawa region in Jhunjhunu District of Rajasthan lies within India's arid and semi-arid zone, known for its unique ecological diversity and adaptive flora. Despite its harsh climatic conditions and resource limitations, this region harbors several rare and threatened plant species of environmental, cultural, and medicinal importance. However, increasing anthropogenic pressures such as agricultural expansion, urbanization, overgrazing, and unsustainable harvesting practices have contributed significantly to habitat degradation and species decline. This preliminary study aims to document and analyze the current status of rare and threatened plant species in Chirawa through systematic field surveys and a thorough review of relevant literature and herbarium records. Field investigations conducted during the monsoon and post-monsoon seasons of 2018 covered varied habitats including scrublands, fallow lands, and semi-cultivated fields. A total of XX rare and threatened plant species were recorded, with many facing imminent risk due to their restricted distribution, ecological specificity, and economic exploitation. Among the key species identified were Ceropegia bulbosa, Tecomella undulata, Sarcostemma viminale, and Ephedra foliata—each recognized for their ecological and/or ethnobotanical value. The study also highlights the contributing threats, including land-use changes, climate variability, invasive species proliferation, and a lack of effective conservation frameworks. The findings underscore the urgent need for conservation strategies focused on in-situ and ex-situ preservation, policy-level interventions, and community-based stewardship. Public awareness, local participation, and integration of indigenous knowledge systems are essential to preserve the region’s botanical heritage. This research serves as a foundational step toward the long-term goal of ecological restoration and sustainable management of Chirawa’s native plant diversity.

This research compares four types of life distribution models, Exponential, Weibull, Log- Normal and Gamma, using examples from reliability data analysis. Different sample sizes and censoring levels were added to simulated data and this was analyzed with true failure time data to check how well the models worked. We evaluated the accuracy and appropriateness of the models with Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), log-likelihood values, Kolmogorov–Smirnov and the Anderson–Darling goodness-of-fit tests. The analysis indicated that the Weibull model fit best, since it could handle any type of hazard rate change. Even though the Log-Normal and Gamma models suited some data sets well, the Exponential model couldn’t be used much due to its rigid assumptions. The findings indicate that making the correct model choice, according to actual data features, is vital for achieving reliable and accurate failure predictions.