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Recent observational studies from the Seeded and Natural Orographic Wintertime clouds: the Idaho Experiment (SNOWIE) project have collected unprecedented measurements that demonstrate seeding with silver iodide (AgI) produces ice crystals that grow and fall to the ground as snow. These measurements include in situ and remote sensing data as well as ground-based observations to document the microphysics and precipitation formation processes in both natural and seeded clouds. The measurements from SNOWIE provide a rich dataset for studying the impacts of cloud seeding on orographic precipitation, as well as for improving understanding of the natural physics in orographic clouds that influence precipitation formation.In parallel, new supercomputing and sophisticated modeling capabilities have recently advanced our ability to simulate orographic precipitation. This laid the foundation to develop the WRF-WxMod® model, which simulates the physical effects of AgI seeding. WRF-WxMod is an innovative capability to evaluate the impacts of cloud seeding in controlled numerical experiments. Furthermore, when combined with detailed observations, such as from SNOWIE, WRF-WxMod provides new opportunities to transform our understanding of cloud seeding impacts as well as to investigate the impacts of cloud seeding across a variety of scales.This presentation will provide highlights of the research from SNOWIE that has advanced our understanding of natural and seeded orographic clouds and demonstrate the utility of WRF-WxMod to quantify the impacts of cloud seeding., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

Aim: The purpose of this study is to develop a tool and assess patient satisfaction with inpatient Nurse Practitioner (NP) care.Design: A 10 item questionnaire was administered to hospital inpatients cared for directly by one of two NP’s.Methods: Patients were given a point of care tablet to answer the survey questions with yes or no. The primary outcomes measured were satisfaction with the hospital NP in quality of care, effective communication, and discharge planning.Results: A total of 19 patients participated in this study. 100% of patients felt that nurse practitioners communicated the plan of care effectively, clearly communicated medication information, answered all questions effectively, and knew what to expect when they went home. Between 80-89.5% of patients understood their discharge goals, what they may need after discharge, and understood what signs and symptoms to look for after discharge. Only 36% reported that they had a primary care provider appointment upon discharge.Conclusions: Results of the questionnaire showed a high level of satisfaction with NP care and identified specific discharge barriers among these hospitalized patients. Determining patient satisfaction with NP care can help direct important performance and improvement activities for healthcare institutions.

The solvent-mediated crystal morphologies of the α and β polymorphic forms of l-glutamic acid are presented. This work applies a digital mechanistically based workflow that encompasses calculation of the crystal lattice energy and its constituent intermolecular synthons, their interaction energies, and their key role in understanding and predicting crystal morphology as well as assessing the surface chemistry, topology, and solvent binding on crystal habit growth surfaces. Through a comparison between the contrasting morphologies of the conformational polymorphs of l-glutamic acid, this approach highlights how the interfacial chemistry of organic crystalline materials and their inherent anisotropic interactions with their solvation environments direct their crystal habit with potential impact on their further downstream processing behavior.

Groundworks to remove the footings of a demolished boundary wall were observed. Despite the high potential for archaeology given the site?s location adjacent to the line of a Roman road, no finds or features were identified. This is possibly due to the very heavy disturbance caused by roots of the three mature sycamore trees as well as by the construction of the original wall.

Job security can never be taken for granted, especially in times of rapid, widespread and unexpected social and economic change. These changes can force workers to transition to new jobs. This may be because new technologies emerge or production is moved abroad. Perhaps it is a global crisis, such as COVID-19, which shutters industries and displaces labor en masse. Regardless of the impetus, people are faced with the challenge of moving between jobs to find new work. Successful transitions typically occur when workers leverage their existing skills in the new occupation. Here, we propose a novel method to measure the similarity between occupations using their underlying skills. We then build a recommender system for identifying optimal transition pathways between occupations using job advertisements (ads) data and a longitudinal household survey. Our results show that not only can we accurately predict occupational transitions (Accuracy = 76%), but we account for the asymmetric difficulties of moving between jobs (it is easier to move in one direction than the other). We also build an early warning indicator for new technology adoption (showcasing Artificial Intelligence), a major driver of rising job transitions. By using real-time data, our systems can respond to labor demand shifts as they occur (such as those caused by COVID-19). They can be leveraged by policy-makers, educators, and job seekers who are forced to confront the often distressing challenges of finding new jobs.

Skill shortages are a drain on society. They hamper economic opportunities for individuals, slow growth for firms, and impede labor productivity in aggregate. Therefore, the ability to understand and predict skill shortages in advance is critical for policy-makers and educators to help alleviate their adverse effects. This research implements a high-performing Machine Learning approach to predict occupational skill shortages. In addition, we demonstrate methods to analyze the underlying skill demands of occupations in shortage and the most important features for predicting skill shortages. For this work, we compile a unique dataset of both Labor Demand and Labor Supply occupational data in Australia from 2012 to 2018. This includes data from 7.7 million job advertisements (ads) and 20 official labor force measures. We use these data as explanatory variables and leverage the XGBoost classifier to predict yearly skills shortage classifications for 132 standardized occupations. The models we construct achieve macro-F1 average performance scores of up to 83 per cent. Our results show that job ads data and employment statistics were the highest performing feature sets for predicting year-to-year skills shortage changes for occupations. We also find that features such as 'Hours Worked', years of 'Education', years of 'Experience', and median 'Salary' are highly important features for predicting occupational skill shortages. This research provides a robust data-driven approach for predicting and analyzing skill shortages, which can assist policy-makers, educators, and businesses to prepare for the future of work.

Three trenches were excavated. No features or finds of archaeological interest were encountered.

The evaluation identified mostly shallow ditches/gullies of uncertain date scattered across the site. A single worked flint, likely residual was all that was recovered from one of these features. The greatest archaeological potential derived from a mound with ring ditch, likely a tumulus. The only dating evidence for this was 13th-14th century North Devon medieval pottery coming from two features cut into the mound. However, the form of the earthworks and their proximity to known round barrows suggest an earlier date.

Monitoring was undertaken dung the stripping of areas for building footprints and slurry tank. The watching brief monitored groundworks in relation to the construction a dairy unit. Despite natural geology being reached only a modern field boundary was identified with no further finds or features of archaeological interest being identified.

Despite the site's location close to the Bronze Age and later Scheduled Monument of Stow Camp, and close proximity to the enclosure's ramparts, the trenching across the site identified only early modern features. These consisted of two quarry pits and two probable waste pits located towards the east side of site.

Eighteen evaluation trenches were excavated. Most of the features revealed were post-medieval drainage but one pit contained medieval pottery and three undated shallow pits were rich in charcoal. Most of the site contained no features.

The excavation of geotechnical test pits and boreholes was archaeologically monitored. Two test pits and 12 boreholes were monitored. No finds or features of archaeological interest were encountered.

The watching brief monitored ground works related to the construction of eleven dwellings. Two modern rubbish pits were identified along with two probable former field boundaries. No other features or finds of archaeological interest were identified.

The earthworks survey was carried out successfully, recording a low circular mound with ring ditch that matches findings of earlier geophysical survey and archaeological evaluation results. The form suggests a probable tumulus.

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