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The world's rangelands and drylands are undergoing rapid change, and consequently are becoming more difficult to manage. Big data and digital technologies (digital tools) provide land managers with a means to understand and adaptively manage change. An assortment of tools—including standardized field ecosystem monitoring databases; web‐accessible maps of vegetation change, production forecasts, and climate risk; sensor networks and virtual fencing; mobile applications to collect and access a variety of data; and new models, interpretive tools, and tool libraries—together provide unprecedented opportunities to detect and direct rangeland change. Accessibility to and manager trust in and knowledge of these tools, however, have failed to keep pace with technological advances. Collaborative adaptive management that involves multiple stakeholders and scientists who learn from management actions is ideally suited to capitalize on an integrated suite of digital tools. Embedding science professionals and experienced technology users in social networks can enhance peer‐to‐peer learning about digital tools and fulfill their considerable promise. 

Abstract Monitoring livestock feeding behavior may help assess animal welfare and nutritional status, and to optimize pasture management. The need for continuous and sustained monitoring requires the use of automatic techniques based on the acquisition and analysis of sensor data. This work describes an open dataset of acoustic recordings of the foraging behavior of dairy cows. The dataset includes 708 h of daily records obtained using unobtrusive and non-invasive instrumentation mounted on five lactating multiparous Holstein cows continuously monitored for six non-consecutive days in pasture and barn. Labeled recordings precisely delimiting grazing and rumination bouts are provided for a total of 392 h and for over 6,200 ingestive and rumination jaw movements. Companion information on the audio recording quality and expert-generated labels is also provided to facilitate data interpretation and analysis. This comprehensive dataset is a useful resource for studies aimed at exploring new tools and solutions for precision livestock farming. 

This paper discusses the implementation of an introductory course to engineering established to provide students with knowledge about the roles of engineers, the engineering method, ethics, teamwork, and detailed information about each of the engineering majors offered in the College of Engineering (CoE) of the host institution. The course is offered as part of a larger initiative seeking to improve success indicators among low-income students. This paper provides details about the course structure, implementation context, metrics, and results measured via descriptive statistics among participant students. The results of a longitudinal implementation, suggest that early provision of career information and awareness can impact the engineering retention and persistence of students and their interest in their chosen majors, particularly in educational settings where students declare their major on the entrance to their first year. 

Pierolapithecus catalaunicus(~12 million years ago, northeastern Spain) is key to understanding the mosaic nature of hominid (great ape and human) evolution. Notably, its skeleton indicates that an orthograde (upright) body plan preceded suspensory adaptations in hominid evolution. However, there is ongoing debate about this species, partly because the sole known cranium, preserving a nearly complete face, suffers from taphonomic damage. We 1) carried out a micro computerized tomography (CT) based virtual reconstruction of thePierolapithecuscranium, 2) assessed its morphological affinities using a series of two-dimensional (2D) and three-dimensional (3D) morphometric analyses, and 3) modeled the evolution of key aspects of ape face form. The reconstruction clarifies many aspects of the facial morphology ofPierolapithecus. Our results indicate that it is most similar to great apes (fossil and extant) in overall face shape and size and is morphologically distinct from other Middle Miocene apes. Crown great apes can be distinguished from other taxa in several facial metrics (e.g., low midfacial prognathism, relatively tall faces) and only some of these features are found inPierolapithecus, which is most consistent with a stem (basal) hominid position. The inferred morphology at all ancestral nodes within the hominoid (ape and human) tree is closer to great apes than to hylobatids (gibbons and siamangs), which are convergent with other smaller anthropoids. Our analyses support a hominid ancestor that was distinct from all extant and fossil hominids in overall facial shape and shared many features withPierolapithecus. This reconstructed ancestral morphotype represents a testable hypothesis that can be reevaluated as new fossils are discovered. 

Improving the level of success of students from low socioeconomic backgrounds in science, technology, engineering, and mathematics (STEM) disciplines has been a prevailing concern for higher education institutions for many years. To address this challenge, a pilot initiative has been implemented with engineering students at the University of Puerto Rico Mayaguez, a recognized Hispanic-serving institution. Over the past four years, the Program for Engineering Access, Retention, and LIATS Success (PEARLS) has brought in an innovative intervention model that combines elements from socio-cognitive career theories and departure studies to impact students' success. PEARLS has established a comprehensive range of tools and services, including mentorship, professional readiness training, research opportunities, scholarships, and peer mentor activities. These efforts have led to impressive outcomes, including a significant increase in retention and persistence rates, increased graduation rates having quad-fold those observed in the general student population, and an impressive record of engagements in industry, research, and leadership experiences. This paper discusses the program structure and outcomes from five perspectives that include background experiences, the structure of provided services, the results of their execution, the elements of knowledge derived from its application, and the challenges experienced throughout its implementation. 

This study applies computationally efficient shallow neural networks to predict topographic effect multipliers directly from digital elevation data obtained from complex terrain, such as mountainous areas. Data were obtained from boundary layer wind tunnel (BLWT) modeling of surface wind flow over six regions in mainland Puerto Rico and its municipal islands. The results demonstrate an improvement over linear regression models, even for computationally efficient low neuron count and single hidden layer models. The paper proposes the development of a global BLWT data atlas to inform development of methods to predict topographic wind speedup for a diverse range of topography and surface roughness conditions. It also identifies knowledge gaps that could prevent standardization of data collected from different BLWT experimental designs. 

Abstract Understanding fluid flows in planetary cores and subsurface oceans, as well as their signatures in available observational data (gravity, magnetism, rotation, etc.), is a tremendous interdisciplinary challenge. In particular, it requires understanding the fundamental fluid dynamics involving turbulence and rotation at typical scales well beyond our day-to-day experience. To do so, laboratory experiments are fully complementary to numerical simulations, especially in systematically exploring extreme flow regimes for long duration. In this review article, we present some illustrative examples where experimental approaches, complemented by theoretical and numerical studies, have been key for a better understanding of planetary interior flows driven by some type of mechanical forcing. We successively address the dynamics of flows driven by precession, by libration, by differential rotation, and by boundary topography.