Tinker Air Force Base

Abstract Education has always been the cornerstone of human progress, but the 21st century has accelerated its transformation in unprecedented ways. The global push towards digitalization, changing learner needs, and new policies have reshaped the aims, methods, and outcomes of education. In India, the National Education Policy (NEP) 2020 has provided fresh momentum to reforms that align with international standards while addressing local needs. This article explores the latest trends in education—ranging from technology integration, blended learning, skill-based education, and inclusive practices to mental health, value-based learning, and sustainability. It examines how these trends are influencing teachers, learners, and institutions, highlighting both opportunities and challenges. The article argues that education must now move beyond rote memorization to holistic, student-centered, and multidisciplinary approaches that prepare learners not only for employment but also for responsible citizenship and lifelong learning. Keywords: digitalization, student cantered, multidisciplinary, lifelong learning.

There has been a large increase in the deployment of drones in construction, and it is expected that there will be more interaction between construction professionals and drones on construction sites. Working in close proximity to these flying robots raises novel occupational safety and health issues that might make the construction industry more dangerous than before. While there has been a significant amount of research about how drone technology can be used for specific construction-related tasks, further research is required to empirically evaluate the potential safety impacts of working with or near drones on the jobsite. This study employs virtual reality technology to study the health and safety challenges of construction professionals working at different distances from drones. A user-centered experimental approach was adopted, and various subjective and objective measures were used to assess the impact of working with drones at different distances on the physiological, emotional, and attentional states of construction professionals, as well as their attitudes toward drones. The results revealed that drone presence has some physiological impacts and reduces construction professionals’ attention on the tasks at hand. Moreover, the results suggested that drones operating at some distance cause more distraction than drones operating in close proximity. No evidence was found indicating that drones either cause significant psychological or emotional distress or affect construction professionals’ attitude toward them. The safety knowledge generated through this study will not only help in better understanding safe drone integration in construction but could also lead to formalizing comprehensive regulations for drone use in this industry.

Equipping unmanned aerial systems (UASs) with light detection and ranging (lidar) has been made possible with recent advancements, which has made these sensors compact and gradually more cost-effective. Despite the increased proliferation of UAS-lidar in several fields, the geometric accuracy of lidar-generated point clouds, together with their visual qualities, needs to be explored for building surveying applications. Considering that red−blue−green (RGB) cameras are the most prevalent UAS sensors in building surveying, a lidar- and an RGB camera-equipped UAS was deployed on a mixed infrastructure to simultaneously collect data and generate corresponding point clouds. Different geometric features from both RGB and lidar point clouds were measured and compared quantitatively against benchmark field observations. A qualitative analysis on the point clouds’ visual qualities was also performed and a sensor recommendation matrix was proposed based on desired application accuracy. Lidar has proven to be a viable alternative, providing better geometric accuracy, data quality, and clarity in all three dimensions.

Tinker defines a new genre of storytelling called storyliving. Through interactive and embodied immersive experiences, participants can connect with narrative in a more meaningful way. In this live, bespoke virtual reality theatre experience, the participant becomes the main character of the story. Based upon the true story of Director Lou Ward and his relationship with his grandfather who developed Alzheimer's disease, Tinker places the participant in the role of the grandchild so that Ward's story, in turn, becomes their own. Over time, the participant creates new memories alongside their grandfather in his workshop, and their connection to the grandfather and the story grows stronger with time. Tinker is a cornerstone in compelling storyliving experiences; moreover, it is because of how it is designed that participants feel connected to the story.

<strong>Freesound Loop Dataset</strong> This dataset contains 9,455 loops from Freesound.org and the corresponding annotations. These loops have tempo, key, genre and instrumentation annotation. <strong>Dataset Construction</strong> To collect this dataset, the following steps were performed: Freesound was queried with "loop" and "bpm", so as to collect loops which have a beats-per-minute(BPM) annotations. The sounds were analysed with AudioCommons extractor, so as to obtain key information. The textual metadata of each sound was analysed, to obtain the BPM proposed by the user, and to obtain genre information. Annotators used a web interface to annotate around 3,000 loops. <strong>Dataset Organisation</strong> The dataset contains two folders and two files in the root directory: 'FSL10K' encloses the audio files and their metadata and analysis. The audios are in the 'audio' folder and are named '&lt;freesound_sound_id&gt;.wav'. The AudioCommons analysis of the loops is present in the 'ac_analysis' directory, while the Essentia analysis of the loops obtained through the Freesound API is on the 'fs_analysis' directory. The textual metadata for each audio can be found in the 'metadata.json'. Finally, the audio analysis provided by the algorithms which were benchmarked in the paper is on the 'benchmark' directory. 'annotations' holds the expert provided annotation for the sounds in the dataset. The annotations are separated in a folder for each annotator and each annotation is stored as a .json file, named 'sound-&lt;freesound_sound_id&gt;.json', with a key for each of the features extracted. <strong>Licenses</strong> All the sounds have some kind of Creative Commons license. The license of each sound in the dataset can be obtained from the 'FSL10K/metadata.json' file <strong>Authors and Contact</strong> This dataset was developed by António Ramires et. al. Any questions related to this dataset please contact: António Ramires antonio.ramires@upf.edu aframires@gmail.com <strong>References</strong> Please cite this paper if you use this dataset: <code>@inproceedings{ramires2020, author = "Antonio Ramires and Frederic Font and Dmitry Bogdanov and Jordan B. L. Smith and Yi-Hsuan Yang and Joann Ching and Bo-Yu Chen and Yueh-Kao Wu and Hsu Wei-Han and Xavier Serra", title = "The Freesound Loop Dataset and Annotation Tool", booktitle = "Proc. of the 21st International Society for Music Information Retrieval (ISMIR)", year = "2020" } </code> <strong>Acknowledgements</strong> This work has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 765068 (MIP-Frontiers).

SynopsisWe investigate the electron dynamics at various monocrystalline metal surfaces with stepped vicinal nanostructures. The unoccupied bands of the surface superlattice are resonantly excited via the charge transfer interaction of the surface with a H− ion which flies by at grazing angles. The ion survival exhibits modulations as a function of the vicinal-terrace size and shows peaks at energies that access the image state subbands.

Abstract. The effective applications of land surface model (LSM) and hydrologic models pose a varied set of data input and processing needs, ranging from ensuring consistency checks to more derived data processing and analytics. This article describes the development of the Land surface Data Toolkit (LDT), which is an integrated framework designed specifically for processing input data to execute LSMs and hydrological models. LDT not only serves as a pre-processor to the NASA Land Information System (LIS), which is an integrated framework designed for multi-model LSM simulations and data assimilation (DA) integrations, but also as a land surface-based observation and DA input processor. It offers a variety of user options and inputs to processing datasets for use within LIS and stand alone models. The LDT design facilitates the use of common data formats and conventions. LDT is also capable of processing LSM initial conditions, meteorological boundary conditions and ensuring data quality for inputs to LSMs and DA routines. The machine learning layer in LDT facilitates the use of modern data science algorithms for developing data-driven predictive models. Through the use of an object-oriented framework design, LDT provides extensible features for the continued development of support for different types of observational data sets and data analytics algorithms to aid land surface modelling and data assimilation.

A Damascus-style layered blade was made by incorporating bloomery iron and crucible steel. A bloomery furnace was constructed and charged with Black Hills, SD bog iron ore, alloys, and hardwood charcoal. At sufficient temperature, the furnace was bottom-tapped to produce a low carbon iron bloom. A high-carbon crucible steel was made in a natural gas-fired furnace using commercial hematite pellets and coke. The steel was cast into sand molds. The two types of iron/steel were forged together using traditional blacksmithing techniques in a coke-fired forge. The forging process continued until the metal could be evenly worked, signifying the homogenization of the two metals. Once homogenized, the metal was shaped into a blade and subsequently ground into near-final shape, heat-treated, and finish-machined. The microstructure and mechanical properties of the blade were characterized using optical microscopy, hardness and tensile testing. The grain structure of the material varied widely and was not entirely homogenous at the welded layers, but the layers themselves were well-homogenized. The finished blade was compared to a common steel (1095) used for bladesmithing and was found to have similar hardness but significantly lower tensile strength.

Background: Prior studies of metabolomic profiles and coronary heart disease (CHD) have been limited by relatively small case numbers and scant data in women. Methods: The discovery set examined 371 metabolites in 400 confirmed, incident CHD cases and 400 controls (frequency matched on age, race/ethnicity, hysterectomy status and time of enrollment) in the Women’s Health Initiative Observational Study (WHI-OS). All selected metabolites were validated in a separate set of 394 cases and 397 matched controls drawn from the placebo arms of the WHI Hormone Therapy trials and the WHI-OS. Discovery used 4 methods: false-discovery rate (FDR) adjusted logistic regression for individual metabolites, permutation corrected least absolute shrinkage and selection operator (LASSO) algorithms, sparse partial least squares discriminant analysis (PLS-DA) algorithms, and random forest algorithms. Each method was performed with matching factors only and with matching plus both medication use (aspirin, statins, anti-diabetics and anti-hypertensives) and traditional CHD risk factors (smoking, systolic blood pressure, diabetes, total and HDL cholesterol). Replication in the validation set was defined as a logistic regression coefficient of p&lt;0.05 for the metabolites selected by 3 or 4 methods (tier 1), or a FDR adjusted p&lt;0.05 for metabolites selected by only 1 or 2 methods (tier 2). Results: Sixty-seven metabolites were selected in the discovery data set (30 tier 1 and 37 tier 2). Twenty-six successfully replicated in the validation data set (21 tier 1 and 5 tier 2), with 25 significant with adjusting for matching factors only and 11 significant after additionally adjusting for medications and CHD risk factors. Validated metabolites included amino acids, sugars, nucleosides, eicosanoids, plasmologens, polyunsaturated phospholipids and highly saturated triglycerides. These include novel metabolites as well as metabolites such as glutamate/glutamine, which have been shown in other populations. Conclusions: Multiple metabolites in important physiological pathways with robust associations for risk of CHD in women were identified and replicated. These results may offer insights into biological mechanisms of CHD as well as identify potential markers of risk.