Getting to the Point: Bridging the Gap between Simple and Complex Catalytic Systems using Temporal Analysis of Products (TAP)

One of the key issues in the field of catalysis is to relate the catalyst structure/composition to its activity/selectivity. One way to understand this relationship is to understand the individual role each catalyst component plays in the chemical reaction. Industrial catalysts can be extremely complex in structure and to understand their reaction kinetics, researchers often study simpler surfaces such as single crystals using surface science techniques. This introduces a well-known problem in the field of catalysis commonly referred to as the pressure and materials gap. Typically, industrial catalyst research is performed under process conditions, which means operating pressures of one atmosphere or higher. Under these conditions, it is difficult to extract intrinsic kinetic properties of the catalyst which are properties that are directly related to the catalyst structure and composition. To find these intrinsic kinetic properties, scientists turn to surface science techniques using different types of spectroscopic tools to study reaction properties on single crystal surfaces under ultra-high vacuum: UHV) conditions. Experiments using single crystals and surface science techniques have helped establish that some crystal planes are more active and/or selective than others. Although surface science approaches are successful in obtaining fundamental information on a variety of catalytic reactions on the atomic level, current catalytic reactions are still carried out under atmospheric pressures or greater and on much more complex materials than single crystal surfaces. This dissertation introduces a new approach to characterize catalysts that vary in compositional/structural complexity in order to understand their performance in a conventional reactor/reaction environment under both atmospheric pressure and ultra-high vacuum conditions. Experiments performed under both pressure regimes were carried out using the same apparatus, the Temporal Analysis of Products: TAP) reactor. The catalysts under investigation are bulk transition metals: Pt), transition metals deposited on metal oxide supports: Pt/SiO2), and mixed metal oxides: VPO). The catalysts are applied to two types of reaction systems, CO oxidation and selective oxidation of hydrocarbons. The goal of the experiments is to understand and distinguish the role of each component of the catalyst during chemical reaction. Using the TAP reactor, the number of active sites, reaction mechanisms, adsorption/desorption rate constants, and rates of reaction can be determined

Generalized Batch Normalization: Towards Accelerating Deep Neural Networks

Utilizing recently introduced concepts from statistics and quantitative risk management, we present a general variant of Batch Normalization (BN) that offers accelerated convergence of Neural Network training compared to conventional BN. In general, we show that mean and standard deviation are not always the most appropriate choice for the centering and scaling procedure within the BN transformation, particularly if ReLU follows the normalization step. We present a Generalized Batch Normalization (GBN) transformation, which can utilize a variety of alternative deviation measures for scaling and statistics for centering, choices which naturally arise from the theory of generalized deviation measures and risk theory in general. When used in conjunction with the ReLU non-linearity, the underlying risk theory suggests natural, arguably optimal choices for the deviation measure and statistic. Utilizing the suggested deviation measure and statistic, we show experimentally that training is accelerated more so than with conventional BN, often with improved error rate as well. Overall, we propose a more flexible BN transformation supported by a complimentary theoretical framework that can potentially guide design choices.Comment: accepted at AAAI-1

Construction of Simulation Educational Digital Network Platform in Economics & Management Majors

Along with the informatization and the development of economic globalization, the competition among talent is increasingly intensified; the requirement of position includes both professional foundation theory and strong practical ability. The social integration of economics and management majors is very strong, with the important position in the enterprise and the social economic development. But the social position is determined by the characteristics of professional. Therefore, specialty practice teaching in economics and management majors is particularly important, especially the construction of simulation educational digital network platform in economics & management majors, which will improve the quality of cultivation and promote the strategy of practice teaching according to the characteristics of relative majors. We first analyze the problems in the construction of simulation educational digital network platform in economics & management majors and find what happened in the area. Second, we analyze the function of the platform which is suitable to the majors of economics and management. Third, we introduce the construction of the teaching platform. Finally, we put forward some of the suggestions and countermeasures which are suitable to the development based on the analysis of the problems

Occurrence and Removal of Pharmaceutical and Hormone Contaminants in Rural Wastewater Treatment Lagoons

With an increasing population in rural areas, the use of pharmaceuticals and personal care products (PPCPs) and naturally produced steroid hormones is also increasing. However, rural wastewater treatment plants were not specifically designed to remove PPCPs or hormones. Are these compounds being removed from water supplies? If they are, how effectively are they being removed and which treatment process is most effective? Samples from a rural wastewater treatment plant in Illinois were collected at each stage of the treatment process. Samples were taken twice, in September and November 2011. In addition, one sample each from upstream and downstream of the effluent discharge was taken in September. All the same samples were taken in November with the addition of a sample from the Mackinaw River downstream of the small stream to which the wastewater effluent discharges. Samples were extracted and analyzed in the lab for PPCPs and hormones via a new method developed by ISTC researchers. Results were published in Li et al (2013). Science of the Total Environment 445-446, 22-28. https://doi.org/10.1016/j.scitotenv.2012.12.035Ope

Assessing the Therapeutic Effect of 630 nm Light-emitting Diodes Irradiation on the Recovery of Exercise-induced Hand Muscle Fatigue with Surface Electromyogram

This paper aims to investigate the effect of light emitting diode therapy (LEDT) on exercise-induced hand muscle fatigue by measuring the surface electromyography (sEMG) of flexor digitorum superficialis. Ten healthy volunteers were randomly placed in the equal sized LEDT group and control group. All subjects performed a sustained fatiguing isometric contraction with the combination of four fingertips except thumb at 30% of maximal voluntary contraction (MVC) until exhaustion. The active LEDT or an identical passive rest therapy was then applied to flexor digitorum superficialis. Each subject was required to perform a re-fatigue task immediately after therapy which was the same as the pre-fatigue task. Average rectified value (ARV) and fractal dimension (FD) of sEMG were calculated. ARV and FD were significantly different between active LEDT and passive rest groups at 20%–50%, 70%–80%, and 100% of normalized contraction time (P \u3c 0.05 ). Compared to passive rest, active LEDT induced significantly smaller increase in ARV values and decrease in FD values, which shows that LEDT is effective on the recovery of muscle fatigue. Our preliminary results also suggest that ARV and FD are potential replacements of biochemical markers to assess the effects of LEDT on muscle fatigue

Religiosity and cross‐country differences in trade credit use

Using the firm‐level data over 1989–2012 from 53 countries, we find religiosity in a country is positively associated with trade credit use by local firms. Specifically, after controlling for firm‐ and country‐level factors as well as industry and year effects, we show that trade credit use is higher in more religious countries. Moreover, both creditor rights and social trust in a country enhance the positive association between religiosity and trade credit use, while the quality of national‐level disclosure mitigates the aforementioned positive association. These results are robust to alternative measures of religiosity, alternative sampling requirements and potential endogeneity concerns

Federated Unlearning via Active Forgetting

The increasing concerns regarding the privacy of machine learning models have catalyzed the exploration of machine unlearning, i.e., a process that removes the influence of training data on machine learning models. This concern also arises in the realm of federated learning, prompting researchers to address the federated unlearning problem. However, federated unlearning remains challenging. Existing unlearning methods can be broadly categorized into two approaches, i.e., exact unlearning and approximate unlearning. Firstly, implementing exact unlearning, which typically relies on the partition-aggregation framework, in a distributed manner does not improve time efficiency theoretically. Secondly, existing federated (approximate) unlearning methods suffer from imprecise data influence estimation, significant computational burden, or both. To this end, we propose a novel federated unlearning framework based on incremental learning, which is independent of specific models and federated settings. Our framework differs from existing federated unlearning methods that rely on approximate retraining or data influence estimation. Instead, we leverage new memories to overwrite old ones, imitating the process of \textit{active forgetting} in neurology. Specifically, the model, intended to unlearn, serves as a student model that continuously learns from randomly initiated teacher models. To preserve catastrophic forgetting of non-target data, we utilize elastic weight consolidation to elastically constrain weight change. Extensive experiments on three benchmark datasets demonstrate the efficiency and effectiveness of our proposed method. The result of backdoor attacks demonstrates that our proposed method achieves satisfying completeness