The Scaled Wind Farm Technology (SWiFT) facility has been developed by Sandia National Laboratories to enable rapid, cost-efficient testing and development of transformative wind energy technology. As part of this effort, ATA Engineering was contracted by SNL to perform component and system level dynamic tests to acquire data for model updating and validation. The component work included modal and mass properties testing of the blades, towers, hubs, and nacelles; while the system level tests included modal tests of each tower mounted on its foundation, and two fully erected turbines, using both impact and wind-excitation. This paper presents an overview of all modal and mass property tests performed to support this project as well as key results used in aeroelastic model development for the modified V27 wind turbines.
The article examines the problem of optimizing memory and latency during the inference of large language models (LLMs) by efficiently managing the key–value cache, a central element of the autoregressive text generation mechanism. The relevance of the topic is driven by the exponential growth in the scale of modern LLMs and the sharp increase in computational and monetary costs in the inference phase, where memory capacity and bandwidth become the limiting factors of performance. The study aims to systematize and comparatively analyze contemporary approaches to KV-cache optimization at three levels, system, kernel, and algorithmic, while identifying their synergistic effects and engineering trade-offs. The novelty of the work lies in proposing a unified taxonomic structure that brings together heterogeneous methods traditionally considered in isolation: memory virtualization mechanisms (PagedAttention), IO-aware computational kernels (FlashAttention), cache compression methods via quantization (FP8, INT8), and intelligent token eviction policies (AhaKV, SAGE-KV, Attention-Gate). The study demonstrates that maximum efficiency is achieved by combining system and algorithmic techniques. Page-based memory organization eliminates fragmentation and increases GPU utilization, while IO-optimized attention kernels reduce latency. Additionally, quantization, combined with adaptive token eviction, ensures the scaling of context length while preserving generation quality. It is substantiated that further advances hinge on the creation of dynamic, self-learning policies for KV-cache management and on GPU architectures specialized for the memory access patterns of LLMs, as well as on integrating privacy-aware mechanisms that prevent data leakage when caches are shared among users. The article will be helpful to researchers and engineers involved in designing LLM inference systems, optimizing GPU performance, and developing highly efficient AI architectures.
The article presents the results of a numerical analysis of a selected configuration of a bolted end-plate beam-to-column connection, with particular emphasis on the influence of the failure model on the values and distribution of axial forces in the bolts.A definition of the joint rotation capacity is given, based on the criterion of the maximum force a bolt can sustain in laboratory testing. For the average maximum force in bolts (M20, grade 10.9), a reduction factor BT of 0.90 was adopted to eliminate the occurrence of plastic strain. The force value BT = 240 kN was taken as a reference for determining the ultimate rotation angle u.The analysis involved a column with a HEB500 cross-section, connected to beams with cross-sections HEA280, HEA300, HEA320, HEA340, HEA360, and HEA400. Numerical simulations were performed using end plates with thicknesses of 12, 15, and 20 mm. The model was built based on the results of a calibration procedure involving multi-stage hierarchical validation. The force distribution in bolts was analyzed for the three different plate thicknesses (20, 15, and 12 mm), allowing for a detailed assessment of the influence of joint stiffness on the load transfer efficiency by individual bolt rows.In configurations characterized by dominant end-plate yielding and significant elastic-plastic rotation, a favorable redistribution of forces among bolts is observed. Connections that exhibit high load-bearing capacity do not show significant increases in rotation capacity. These are cases where the failure mode, governed by bolt yielding, becomes the decisive factor.The introduction of a joint rotation angle definition, based on laboratory test results of the ultimate bolt force (90% of the average failure load for M20 bolts, grade 10.9, ISO 4014), enabled the establishment of an objective criterion for evaluating the rotation capacity of the connection
Global climate change has caused range shifts and population declines in various species. However, causal evidence from manipulative studies, particularly for vertebrates, remains scarce. Prolonged temperature increases, a direct consequence of climate change, pose significant challenges to species adaptation and survival. We examined the effects of prolonged temperature increases on reproduction, physiology, and behavior adaption in the greater long-tailed hamster using semi-natural enclosures where temperature was manipulated via plastic roofs and windows, creating a greenhouse effect without affecting rainfall. We analyzed data using grouped enclosures (low temperature, LT; high temperature, HT), which showed that prolonged temperature increases led to reduced reproductivity per capita during the breeding season. In addition, prolonged temperature increases reduced night-time activity in founder hamsters during overwintering, increased burrow depth during the breeding season, and raised testicular weights in founder males during the overwintering season. Our study provides experimental evidence that prolonged temperature increases negatively impact population growth of greater long-tailed hamsters by inducing temperature stress and impairing reproductive performance, highlighting the need to address heat stress in wildlife management under climate warming.
Purpose: The purpose of this article is to explore and adapt the google SRE principles for improving the reliability and performance of applications and APIs. This article explains the details of adapting google SRE principles with practical examples and decisions for proactive monitoring the applications. Methodology: The article explains a case study and analysis to demonstrate how Google SRE principles [1] help to improve the reliability, performance and decision on release of new functionalities to the critical application. Site Reliability Engineering at Google provides a practical leading toward that direction. Such principles are referred as SLOs, SLIs, error budgets, and proactive monitoring, come into play to balance system reliability and innovations for every organization. Findings: The findings show that by adapting Google SRE principles [2], reliability of the applications are improved and helps developers to prioritize the new features releases vs improving the reliability. This article takes a closer look at some of the ways in which SRE practices can help enhance the resiliency of an application, considering two very important examples: API availability and database reliability. Unique Contribution to Theory, Practice and Policy: This article makes valuable contributions to theory, practice, and policy. For theory, it expands the understanding of how google SRE principles helps to improve application reliability and performance. For practice, it provides clear, actionable steps for SRE teams to identify and resolve performance issues, helping organizations enhance reliability and user satisfaction. For policy, it highlights the importance of proactive network monitoring and metric-driven decision-making, encouraging organizations to adopt policies that prioritize resiliency, ensure consistent performance, and meet service-level agreements (SLAs). This article provides practical insights and examples to help teams implement SRE and achieve greater reliability and scalability.
