pdf/human-resource-management-in-the-age-of-generative-lykbz2q9do.pdf

Human resource management in the age of generative artificial intelligence: Perspectives and research directions on ChatGPT

Nanocellulose provides a new materials platform for the sustainable production of high-perfor- mance nano-enabled products in an array of applications. In this paper, potential applications for cellulose nanoma- terials are identified as the first step toward estimating market volume. The overall study, presented in two parts, estimates market volume on the basis of estimated tonnage of cellulose nanomaterials rather than the dollar value of production or profits from production. In this paper, we first identified potential uses from literature, presentations, and patent reviews, and then categorized these under the broad headings of high-volume, low-volume, and emerg- ing/novel applications. For each application, the rationale for using nanocellulose is explained. The companion paper, Part 2, explains the assumptions and calculation of application-specific market estimates. High- and low-vol- ume consumption applications of cellulose nanomaterials were identified from published data as well as expert input. We categorized potential market sizes as high or low by considering applications where cellulose nanomateri- als would replace existing materials and be used at a published or estimated rate for some fraction of an entire exist- ing market. Novel applications for cellulose nanomaterials that are presently considered niche markets are also iden- tified, but volumes were not estimated because of a lack of published supporting data. Annual U.S. market potential for identified applications of nanocellulose is estimated as 6.4 million metric tons, with a global market potential of 35 million metric tons. The greatest volume potential for use of cellulose nanomaterials is currently in paper and packaging applications. Other potentially high-volume uses are in the automotive, construction, personal care, and textile sectors. Application: This study identifies the potential applications for nanoscale forms of cellulose and derives a methodology to estimate market volumes for its use in those applications. Applications are identified as high-vol- ume, low-volume, and emerging sector applications. The methodology and estimates may be used for planning and research decision-making.

https://www.fpl.fs.fed.us/documnts/pdf2014/fpl_2014_shatkin001.pdf

Market projections of cellulose nanomaterial-enabled products − Part 1: Applications

Cellulose Nanocrystals from Forest Residues as Reinforcing Agents for Composites: A Study from Macro- to Nano-Dimensions

A layer-by-layer (LbL) self-assembly of poly(3,4-ethylenedioxythiophene)?poly(styrenesulfonate) (PEDOT?PSS) on lignocellulose wood microfibres was used to make conductive fibres and paper. Polycations such as poly(allylamine hydrochloride) (PAH), and poly(ethyleneimine) (PEI) were used in alternate deposition with anionic conductive polythiophene (PEDOT?PSS) to construct the multilayer nanofilms on wood microfibres. Current?voltage characterization was measured on single fibres using a Keithley probe measurement system after deposition of every PEDOT?PSS monolayer to study the electrical properties of the coating. The conductivity of the microfibres increased linearly with increasing number of bilayers of PEDOT?PSS/polycation. The measured conductivities of the coated microfibres ranged from 1 to 10?S?cm?1. It was also observed that the conductivity of the fibres (i.e., coating of PEDOT?PSS) depends upon the type of polycations used to alternate with the polythiophene. In this work we have demonstrated successful scale integration from nano to micro and macroscale (nanocoating?microfibres?macropaper) in developing new paper material. The conductive paper that has been produced (and its fabrication method) can be used for the development of smart paper technology on monitoring of electrical, and optical/electrical signals.

https://iopscience.iop.org/article/10.1088/0957-4484/17/21/006/pdf

Conductive wood microfibres for smart paper through layer-by-layer nanocoating

The significant advancements in applying artificial intelligence (AI) to healthcare decision-making, medical diagnosis, and other domains have simultaneously raised concerns about the fairness and bias of AI systems. This is particularly critical in areas like healthcare, employment, criminal justice, credit scoring, and increasingly, in generative AI models (GenAI) that produce synthetic media. Such systems can lead to unfair outcomes and perpetuate existing inequalities, including generative biases that affect the representation of individuals in synthetic data. This survey study offers a succinct, comprehensive overview of fairness and bias in AI, addressing their sources, impacts, and mitigation strategies. We review sources of bias, such as data, algorithm, and human decision biases—highlighting the emergent issue of generative AI bias, where models may reproduce and amplify societal stereotypes. We assess the societal impact of biased AI systems, focusing on perpetuating inequalities and reinforcing harmful stereotypes, especially as generative AI becomes more prevalent in creating content that influences public perception. We explore various proposed mitigation strategies, discuss the ethical considerations of their implementation, and emphasize the need for interdisciplinary collaboration to ensure effectiveness. Through a systematic literature review spanning multiple academic disciplines, we present definitions of AI bias and its different types, including a detailed look at generative AI bias. We discuss the negative impacts of AI bias on individuals and society and provide an overview of current approaches to mitigate AI bias, including data pre-processing, model selection, and post-processing. We emphasize the unique challenges presented by generative AI models and the importance of strategies specifically tailored to address these. Addressing bias in AI requires a holistic approach involving diverse and representative datasets, enhanced transparency and accountability in AI systems, and the exploration of alternative AI paradigms that prioritize fairness and ethical considerations. This survey contributes to the ongoing discussion on developing fair and unbiased AI systems by providing an overview of the sources, impacts, and mitigation strategies related to AI bias, with a particular focus on the emerging field of generative AI. 

Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies

concert are a panacea against bias and each brings its own set of pitfalls. What is missing from current remedies is guidance from a broader SOCIO - TECHNICAL perspective that connects these practices to societal values. Experts in the area of Trustworthy and Responsible AI counsel that to successfully manage the risks of AI bias we must operationalize these values and create new norms around how AI is built and deployed. This document, and work by the National Institute of Standards and Technology (NIST) in the area of AI bias, is based on a socio-technical perspective.

Towards a Standard for Identifying and Managing Bias in Artificial Intelligence

A roadmap for Nanotechnology in the Forest Products Industries has been developed under the umbrella of the Agenda 2020 program overseen by the CTO committee. It is expected that the use of new analytical techniques and methodologies will allow us to understand the complex nature of wood based materials and allow the dramatically enhanced use of the major strategic asset the US has in renewable, recyclable resources based on its well managed Forests.

/pdf/nanotechnology-for-the-forest-products-industry-vision-and-11itnskfhj.pdf

Nanotechnology for the Forest Products Industry Vision and Technology Roadmap

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Towards a Standard for Identifying and Managing Bias in Artificial Intelligence

Nanotechnology for the Forest Products Industry Vision and Technology Roadmap