978 research outputs found
THE IMPACT OF FOOD RECALL ON THIRD-PARTY CERTIFICATION ADOPTION
Food safety problems have gained national attention, and food recall is one of the most important indications of this concern. Third-party certifications have become a popular way to improve the safety and quality of products for consumers. Publications related to third-party certification usually focus on the motives and benefits of a particular certification. However, to date, no existing research investigates the effects of food recalls on certification adoption.
This study uses Probit models with a binary endogenous explanatory variable to examine the relationship between food recalls and third-party certification, based on recalls occurring between January 1, 2015 and February 18, 2016. Marginal effects are used to interpret the impact of recalls and companies’ annual net sales on third-party certification adoption. Results reveal that past recalls significantly affect a firm’s likelihood of certification adoption
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Synthesis and Electrochemical Applications of Organo-Disulfide Redox Active Particles
The growing need for advanced, safe battery technologies across a spectrum of applications—from handheld devices to industrial-scale energy storage—demands innovative solutions beyond conventional metal-based systems. This dissertation investigates organic redox-active materials, with a specific focus on redox-active polymer particles (RAPs), as promising sustainable substitutes in battery technology. The application of these engineered polymers included lithium batteries and redox flow batteries, including cutting-edge redox targeting flow battery designs. Here in this dissertation, the synthesis and characterization of a series novel RAPs are investigated. Chapter 2 synthesizes redox-active polymer particles (RAPs) by crosslinking poly(glycidyl methacrylate) with redox-responsive disulfide groups, demonstrating superior electrochemical reversibility compared to small molecule analogues due to effective spatial confinement. Optimal electrolyte conditions, identified through galvanostatic cycling, showcased that a dimethyl sulfoxide/magnesium triflate mix enhances both stability and specific capacity. Notably, smaller particle sizes correlated with higher specific capacities. These findings highlight the potential of organosulfur-based materials for advanced multi-electron energy storage beyond lithium-ion systems. Chapter 3 introduces a novel electrode cleaning strategy for electrochemical flow cells using the similar RAPs in Chapter 2, specifically targeting electrode fouling, a prevalent issue in such systems. These RAPs can de-crosslink via electrochemical reduction or UV photoexcitation. In a custom flow cell with an artificially fouled ITO electrode, applying these stimuli resulted in 80% particle removal—six times more efficient than without stimulation. Furthermore, post-cleaning electrode performance restored accessible charge to levels comparable to a pristine electrode. This approach demonstrates the potential of stimuli-responsive RAPs to enhance maintenance and functionality in electrochemical flow cells. In Chapter 4, side chain modifications were applied to RAPs to enhance cycling stability and electrochemical performanceInitial modification with non-polar N-methylbutylamine (MBA) side chains (DS-RAPMBA) improved stability, while subsequent functionalization with polar, lithium-solvating oligoethylene glycol (OEG) and glycerol carbonate (GC) side chains significantly enhanced the electrochemical responses. DS-RAPGC exhibited the highest capacity, followed by DS-RAPOEG and DS-RAPMBA, with unmodified DS-RAP showing the lowest. Enhanced swelling in DS-RAPOEG and DS-RAPGC improved ion transport, contributing to their superior performance across various C rates and long-term cycling tests at 0.1C for 400 cycles, with minimal degradation. This study demonstrates the effectiveness of side chain modification adjacent to the redox center in enhancing the electrochemical properties of organic materials for energy storage applications. Chapter 5 details a novel synthetic method for creating poly-3,4-ethylenedioxythiophene (PEDOT) nanoparticles functionalized with a disulfide/thiolate redox couple, 2,5-dimercapto-1,3,4-thiadiazole (DMcT), to produce dual redox particles for use as organic cathode materials in Li-ion batteries. The nano-sized PEDOT-DMcT-Li particles, characterized by dense crosslinking with redox-active disulfides, exhibit significantly enhanced capacity due to improved electrochemical accessibility and a semiconducting backbone that reduces internal resistance. Comprehensive evaluations, including cyclic voltammetry (CV) and galvanostatic cycling, confirm the material's dual electrochemical behavior and exceptional long-term stability without degradation. Optimized asymmetrical cycling conditions further improved capacity retention, eliminating the need for additional chemical modifications. These results underscore the potential of the PEDOT-DMcT-Li dual-redox system for advancing battery performance. Chapter 6 utilized similar molecular design from Chapter 2 and 3, changing the topic of energy storage to trainable jamming. DS-RAPs are capable of forming amorphous structures with varying degrees of percolation in response to electrochemical stimuli or UV light. This adaptiveness, inspired by biological organisms, is achieved through a dynamic disulfide crosslinker in the particles, which supports tunable mechanical properties through reductive cleavage during repeated cycling in a toggled field. Additionally, our stimulus-responsive jamming network incorporates structural memory, controlled by external small amplitude oscillatory shear, allowing the complex modulus to be adjusted over several orders of magnitude based on specific training protocols. This mechanism also permits resetting by erasing structural memory. These advancements herald a new class of materials with trainable properties at both the molecular and mesoscopic scales. Chapter 7 highlights other redox active polymeric systems to further expand the library of RAPs, including nanosized DS-RAPs, dual functionalized polymer particles containing both disulfide and ferrocene moieties, RAP with diselenide as redox active components, and polyvinyl benzyl chloride (PVBC) particles with ferrocene as redox active components for redox targeting flow battery
Towards Real-World Federated Learning: Empirical Studies in the Domain of Embedded Systems
Context: Artificial intelligence (AI) has led a new phase of technical revolution and industrial development around the world since the twenty-first century, revolutionizing the way of production. Artificial intelligence (AI), an emerging information technology, is thriving, and AI application technologies are gaining traction, particularly in professional services such as healthcare, education, finance, security, etc. More machine learning technologies have begun to be thoroughly applied to the production stage as big data and cloud computing capabilities have improved. With the increased focus on Machine Learning applications and the rapid growth of distributed edge devices in the industry, we believe that utilizing a large number of edge devices will become increasingly important. The introduction of Federated Learning changes the situation in which data must be centrally uploaded to the cloud for processing and maximizes the use of edge devices\u27 computing and storage capabilities. With local data processing, the learning approach eliminates the need to upload large amounts of local data and reduces data transfer latency. Because Federated Learning does not require centralized data for model training, it is better suited to edge learning scenarios with limited data and privacy concerns. Objective: The purpose of this research is to identify the characteristics and problems of the Federated Learning methods, our new algorithms and frameworks that can assist companies in making the transition to Federated Learning, and empirically validate the proposed approaches. Method: To achieve these objectives, we adopted an empirical research approach with design science being our primary research method. We conducted a literature review, case studies, including semi-structured interviews and simulation experiments in close collaboration with software-intensive companies in the embedded systems domain. Results: We present four major findings in this paper. First, we present a state-of-the-art review of the empirical results reported in the existing Federated Learning literature. We then categorize those Federated Learning implementations into different application domains, identify their challenges, and propose six open research questions based on the problems identified in the literature. Second, we conduct a case study to explain why companies anticipate Federated Learning as a potential solution to the challenges they encountered when implementing machine learning components. We summarize the services that a comprehensive Federated Learning system must enable in industrial settings. Furthermore, we identify the primary barriers that companies must overcome in order to embrace and transition to Federated Learning. Based on our empirical findings, we propose five requirements for companies implementing reliable Federated Learning systems. Third, we develop and evaluate four architecture alternatives for a Federated Learning system, including centralized, hierarchical, regional, and decentralized architectures. We investigate the trade-o between communication latency, model evolution time, and model classification performance, which is critical for applying our findings to real-world industrial systems. Fourth, we introduce techniques and asynchronous frameworks for end-to-end on-device Federated Learning. The method is validated using a steering wheel angle prediction case. The local models of each edge vehicle can be continuously trained and shared with other vehicles to improve their local model prediction accuracy. Furthermore, we combine the asynchronous Federated Learning approach with Deep Neural Decision Forests and validate our method using important industry use cases in the automotive domain. Our findings show that Federated Learning can improve model training speed while lowering communication overhead without sacrificing accuracy, demonstrating that this technique has significant benefits to a wide range of real-world embedded systems. Future Work: In the future, we plan to test our approach in other use cases and look into more sophisticated neural networks integrated with our approach. In order to improve model training performance on resource-constrained edge devices in real-world embedded systems, we intend to design more appropriate aggregation methods and protocols. Furthermore, we intend to use the Federated Learning and Reinforcement Learning methods to assist the edge in evolving themselves autonomously and fully utilizing the computation capabilities of the edge devices
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