Acoustic vibration model for a composite shell with sound absorption material

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/76741/1/AIAA-1996-1348-145.pd

Redefining Sensory Preferences: Outcomes from a Pilot Design Collaboration for Individuals Living with Intellectual Disabilities to Promote Access, Awareness, and Acceptance in the Community through the Community Sensory Profile (CSP)

Research Question: What are the impacts of collaboration between occupational therapists, designers, and end users in promoting accessibility and use of strategies for individuals with intellectual disabilities

Australian consensus guidelines for the safe handling of monoclonal antibodies for cancer treatment by healthcare personnel

These consensus guidelines provide recommendations for the safe handling of monoclonal antibodies. Definitive recommendations are given for the minimum safe handling requirements to protect healthcare personnel. The seven recommendations cover: (i) appropriate determinants for evaluating occupational exposure risk; (ii) occupational risk level compared with other hazardous and non-hazardous drugs; (iii) stratification of risk based on healthcare personnel factors; (iv) waste products; (v) interventions and safeguards; (vi) operational and clinical factors and (vii) handling recommendations. The seventh recommendation includes a risk assessment model and flow chart for institutions to consider and evaluate clinical and operational factors unique to individual healthcare services. These guidelines specifically evaluated monoclonal antibodies used in the Australian cancer clinical practice setting; however, the principles may be applicable to monoclonal antibodies used in non-cancer settings. The guidelines are only applicable to parenterally administered agents

Australian consensus guidelines for the safe handling of monoclonal antibodies for cancer treatment by healthcare personnel

These consensus guidelines provide recommendations for the safe handling of monoclonal antibodies. Definitive recommendations are given for the minimum safe handling requirements to protect healthcare personnel. The seven recommendations cover: (i) appropriate determinants for evaluating occupational exposure risk; (ii) occupational risk level compared with other hazardous and non-hazardous drugs; (iii) stratification of risk based on healthcare personnel factors; (iv) waste products; (v) interventions and safeguards; (vi) operational and clinical factors and (vii) handling recommendations. The seventh recommendation includes a risk assessment model and flow chart for institutions to consider and evaluate clinical and operational factors unique to individual healthcare services. These guidelines specifically evaluated monoclonal antibodies used in the Australian cancer clinical practice setting; however, the principles may be applicable to monoclonal antibodies used in non-cancer settings. The guidelines are only applicable to parenterally administered agents

Beyond the imitation game: Quantifying and extrapolating the capabilities of language models

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 442 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 442 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting.Comment: 27 pages, 17 figures + references and appendices, repo: https://github.com/google/BIG-benc

Global economic burden of unmet surgical need for appendicitis

Background There is a substantial gap in provision of adequate surgical care in many low- and middle-income countries. This study aimed to identify the economic burden of unmet surgical need for the common condition of appendicitis. Methods Data on the incidence of appendicitis from 170 countries and two different approaches were used to estimate numbers of patients who do not receive surgery: as a fixed proportion of the total unmet surgical need per country (approach 1); and based on country income status (approach 2). Indirect costs with current levels of access and local quality, and those if quality were at the standards of high-income countries, were estimated. A human capital approach was applied, focusing on the economic burden resulting from premature death and absenteeism. Results Excess mortality was 4185 per 100 000 cases of appendicitis using approach 1 and 3448 per 100 000 using approach 2. The economic burden of continuing current levels of access and local quality was US $92 492 million using approach 1 and$73 141 million using approach 2. The economic burden of not providing surgical care to the standards of high-income countries was $95 004 million using approach 1 and$75 666 million using approach 2. The largest share of these costs resulted from premature death (97.7 per cent) and lack of access (97.0 per cent) in contrast to lack of quality. Conclusion For a comparatively non-complex emergency condition such as appendicitis, increasing access to care should be prioritized. Although improving quality of care should not be neglected, increasing provision of care at current standards could reduce societal costs substantially