Exploring Bottled Water Purchase Intention via Trust in Advertising, Product Knowledge, Consumer Beliefs and Theory of Reasoned Action

As the bottled water market is projected to grow continuously worldwide, so is the plastic waste that pollutes the environment. The beverage industry’s marketing campaigns have played an important role in sustaining the popularity of bottled water. Social science theory-based empirical research examining how consumers make bottled water consumption decisions remains limited. To help fill this literature gap, the current study tested a conceptual framework to explore the influence of trust in bottled water advertising and perceived product knowledge on consumer beliefs about bottled water, in conjunction with theory of reasoned action. The study surveyed a sample of college students in the U.S. (N = 445). Findings showed that greater trust in bottled water advertising as well as more false knowledge and less factual knowledge were significantly related to consumer beliefs about bottled water’s product content and image. Furthermore, more favorable cognitive beliefs, affective beliefs, attitude and perceived subjective norms toward bottled water consumption were positively related to purchase intention. To reduce bottled water purchase among young adults, it would be beneficial to utilize marketing strategies to popularize and normalize carrying a reusable water bottle as an environmentally friendly habit and a preferred lifestyle choice

Understanding and modeling food flow networks across spatial scales

We live in an increasingly global society, in which food commodity transfers enable production and consumption activities to be separated in space via complex supply chains. Here, we refer to the movement of food commodities from one location to another as ‘food flows’, reserving the term ‘food trade’ for the international exchange of food commodities between countries. Food flows underpin the complex food supply chains that are prevalent in our increasingly globalized world. Recently, much effort has been devoted to evaluating the resources (e.g. water, carbon, nutrients) embodied in food trade. Now, research is needed to understand the scientific principles of the food commodity flows that underpin these virtual resource transfers. What are the network properties of food flows within a country? How do food flows vary with spatial scale? How can we model food flows in locations without empirical information? This dissertation seeks to address these three overarching questions. First, this dissertation presents a novel application of network analysis to empirical information of domestic food flows within the USA, a country with global importance as a major agricultural producer and trade power. We find normal node degree distributions and Weibull node strength and betweenness centrality distributions. An unassortative network structure with high clustering coefficients exists. These network properties indicate that the USA food flow network is highly social and well-mixed. However, a power law relationship between node betweenness centrality and node degree indicates potential network vulnerability to the disturbance of key nodes. We perform an equality analysis which serves as a benchmark for global food trade, where the Gini coefficient = 0.579, Lorenz asymmetry coefficient = 0.966, and Hoover index = 0.442. These findings shed insight into trade network scaling and proxy free trade and equitable network architectures. Second, this dissertation presents an empirical analysis of food commodity flow networks across the full spectrum of spatial scales: global, national, and village. We discover properties of both scale invariance and scale dependence in food flow networks. The statistical distribution of node connectivity and mass flux are consistent across scales. Node connectivity follows a generalized exponential distribution, while node mass flux follows a Gamma distribution across scales. Similarly, the relationship between node connectivity and mass flux follows a power law across scales. However, the parameters of the distributions change with spatial scale. Mean node connectivity and mass flux increase with increasing scale. A core group of nodes exists at all scales, but node centrality increases as the spatial scale decreases, indicating that some households are more critical to village food exchanges than countries are to global trade. Remarkably, the structural network properties of food flows are consistent across spatial scales, indicating that a universal mechanism may underpin food exchange systems. Finally, we use our understanding of food flow networks across spatial scales to model food flows at resolutions for which empirical information is not available. Detailed spatial information on food flows is rare, but it is increasingly important to understand spatially resolved food flows to assess their embodied resources and vulnerability to supply chain disturbances. To this end, we develop the Food Flow Model, a data-driven methodology to estimate spatially explicit food flows for subnational locations without data. The Food Flow Model integrates machine learning, network properties, production and consumption statistics, mass balance constraints, and linear programming. We use the Food Flow Model to infer food flows between counties within the United States. Specifically, we downscale empirical information on food flows between 132 Freight Analysis Framework (FAF) locations (17,292 potential links) to the 3,142 counties and county-equivalents of the United States (9,869,022 potential links). Future work can build on these efforts to improve our understanding of vulnerabilities within a national food supply chain, determine critical infrastructures, and enable spatially detailed footprint assessments

ZOS: A Fast Rendezvous Algorithm Based on Set of Available Channels for Cognitive Radios

Most of existing rendezvous algorithms generate channel-hopping sequences based on the whole channel set. They are inefficient when the set of available channels is a small subset of the whole channel set. We propose a new algorithm called ZOS which uses three types of elementary sequences (namely, Zero-type, One-type, and S-type) to generate channel-hopping sequences based on the set of available channels. ZOS provides guaranteed rendezvous without any additional requirements. The maximum time-to-rendezvous of ZOS is upper-bounded by O(m1*m2*log2M) where M is the number of all channels and m1 and m2 are the numbers of available channels of two users.Comment: 10 page

Synthesis, characterization and antibacterial activity of cyclohexyltin N-(salicylidene)valinates

Abstract: Two new cyclohexyltin N-(salicylidene)valinates, [2-HOC6H4CH=NCH(CH(CH3)2)COO]SnCy3 (1) and [2-OC6H4CH=NCH(CH(CH3)2)COO]SnCy2 (2) (Cy = cyclohexyl), have been synthesized and characterized by elemental analysis, IR, and 1H NMR. The crystal structure of 2 has been determined by X-ray single crystal diffraction. In the complexes, the carboxylate is monodentate. Complex 1 is a four-coordinated tin compound, and 2 has a distorted trigonal bipyramidal geometry with the axial locations occupied by one carboxylate oxygen and a phenolic oxygen of the ligand. Bioassay results show that 1 and 2 have good in vitro antibacterial activity against Escherichia coli

Post-Quantum Key Exchange Protocols

If an eavesdropper Eve is equipped with quantum computers, she can easily break the public key exchange protocols used today. In this paper we will discuss the post-quantum Diffie-Hellman key exchange and private key exchange protocols.Comment: 11 pages, 2 figures. Submitted to SPIE DSS 2006; v2 citation typos fixed; v3 appendix typos correcte

Conversational Agents in Health Care: Expert Interviews to Inform the Definition, Classification, and Conceptual Framework

Background Conversational agents (CAs), or chatbots, are computer programs that simulate conversations with humans. The use of CAs in health care settings is recent and rapidly increasing, which often translates to poor reporting of the CA development and evaluation processes and unreliable research findings. We developed and published a conceptual framework, designing, developing, evaluating, and implementing a smartphone-delivered, rule-based conversational agent (DISCOVER), consisting of 3 iterative stages of CA design, development, and evaluation and implementation, complemented by 2 cross-cutting themes (user-centered design and data privacy and security). Objective This study aims to perform in-depth, semistructured interviews with multidisciplinary experts in health care CAs to share their views on the definition and classification of health care CAs and evaluate and validate the DISCOVER conceptual framework. Methods We conducted one-on-one semistructured interviews via Zoom (Zoom Video Communications) with 12 multidisciplinary CA experts using an interview guide based on our framework. The interviews were audio recorded, transcribed by the research team, and analyzed using thematic analysis. Results Following participants’ input, we defined CAs as digital interfaces that use natural language to engage in a synchronous dialogue using ≥1 communication modality, such as text, voice, images, or video. CAs were classified by 13 categories: response generation method, input and output modalities, CA purpose, deployment platform, CA development modality, appearance, length of interaction, type of CA-user interaction, dialogue initiation, communication style, CA personality, human support, and type of health care intervention. Experts considered that the conceptual framework could be adapted for artificial intelligence–based CAs. However, despite recent advances in artificial intelligence, including large language models, the technology is not able to ensure safety and reliability in health care settings. Finally, aligned with participants’ feedback, we present an updated iteration of the conceptual framework for health care conversational agents (CHAT) with key considerations for CA design, development, and evaluation and implementation, complemented by 3 cross-cutting themes: ethics, user involvement, and data privacy and security. Conclusions We present an expanded, validated CHAT and aim at guiding researchers from a variety of backgrounds and with different levels of expertise in the design, development, and evaluation and implementation of rule-based CAs in health care settings