EPOCA House: The Implementation Strategy

This capstone report provides a written strategic implementation plan for EPOCA House. EPOCA, Ex-Prisoners and Prisoners Organizing for Community Advancement, is a non-profit dedicated to creating better resources and opportunities for prisoners and ex-prisoners in Worcester. The organization’s most recent initiative, EPOCA House, is a transitional facility that will provide reentry services and temporary housing to ex-offenders in and around the Worcester area. Recently, Massachusetts passed a comprehensive criminal justice reform bill that will dramatically reduce the number of people being incarcerated; however, little attention is focused on what will happen to individuals after they leave prison. At the same time, funding for halfway houses and reentry programs have decreased across the country and there are very few adequate services offered for ex-offenders in the city. To address this need, Yoshada Kwaning, the Community Outreach Director at EPOCA came up with the idea for EPOCA House Inc. While EPOCA staff members have extensive knowledge about the needs of prison population, they lacked the time and resources to create a strategic action plan to implement their vision. This report employs a secondary data analysis methodology to compare ex-prisoner reentry programs across four specialization areas: education programming, vocational training, wellness programs, and sustainability initiatives. This report also draws on advice from experts and local practitioners in the field, who provide valuable insight into criminal justice issues in Worcester. Transcripts of these interviews can be found in the appendices

Imitation or Innovation? Research on the Path Selection of Enterprise Performance Improvement from the Perspective of Organizational Ecology

This paper analyzes how imitation and innovation strategies of high-tech small and medium-sized enterprises (SMEs) impact their sustainable performances and also the whole business ecosystem with the NK-model that mimics the fitness landscape and simulates enterprises’ choice of technological strategy in response to causal ambiguity and environmental complexity. Our study yielded three findings: (1) When the imitation barriers are low, the imitation strategy of high-tech SMEs has a better effect on the performance improvement in the early stage of the operation than the innovation strategy. In the long run, high-tech SMEs exhibit innovation, which plays a greater and more lasting role in enhancing sustainable performance. On the contrary, it is always difficult for imitators to realize significant performance improvement, (2) In a simple environment, imitation strategy plays a more effective role in improving high-tech SMEs’ performance, whereas in a complex environment, innovation strategy is more conducive to discovering opportunities, and it issues from high levels of competition, and (3) more importantly, the simulation finds that the innovation of high-tech SMEs contributes more to the performance of the business ecosystem as a whole. The introduction of the NK-model simulation method in the research of technological strategies and the new scope of looking at the strategies in the business ecosystem provide new research venues for the literature

Preference Prediction Based on Eye Movement Using Multi-layer Combinatorial Fusion

Face image preference is influenced by many factors and can be detected by analyzing eye movement data. When comparing two face images, our gaze shifts within and between the faces. Eye tracking data can give us insights into the cognitive processes involved in forming a preference. In this paper, a gaze tracking dataset is analyzed using three machine learning algorithms (MLA): AdaBoost, Random Forest, and Mixed Group Ranks (MGR) as well as a newly developed machine learning framework called Multi-Layer Combinatorial Fusion (MCF) to predict a subject’s face image preference. Attributes constructed from the dataset are treated as input scoring systems. MCF involves a series of layers that consist of expansion and reduction processes. The expansion process involves performing exhaustive score and rank combinations, while the reduction process uses performance and diversity to select a subset of systems that will be passed onto the next layer of analysis. Performance and cognitive diversity are used in weighted scoring system combinations and system selection. The results outperform the Mixed Group Ranks algorithm, as well as our previous work using pairwise scoring system combinations

Preference Prediction Based on Eye Movement Using Multi-layer Combinatorial Fusion

Face image preference is influenced by many factors and can be detected by analyzing eye movement data. When comparing two face images, our gaze shifts within and between the faces. Eye tracking data can give us insights into the cognitive processes involved in forming a preference. In this paper, a gaze tracking dataset is analyzed using three machine learning algorithms (MLA): AdaBoost, Random Forest, and Mixed Group Ranks (MGR) as well as a newly developed machine learning framework called Multi-Layer Combinatorial Fusion (MCF) to predict a subject’s face image preference. Attributes constructed from the dataset are treated as input scoring systems. MCF involves a series of layers that consist of expansion and reduction processes. The expansion process involves performing exhaustive score and rank combinations, while the reduction process uses performance and diversity to select a subset of systems that will be passed onto the next layer of analysis. Performance and cognitive diversity are used in weighted scoring system combinations and system selection. The results outperform the Mixed Group Ranks algorithm, as well as our previous work using pairwise scoring system combinations

Carbon neutrality and clean air acts can enable China to meet the Minamata Convention goals with substantial cost savings

China faces the concurrent challenges of carbon dioxide (CO2) and toxic mercury (Hg) emissions from coal combustion, with implications for environmental and human health. To address these problems, China has implemented carbon neutrality targets and air pollution controls and signed the Minamata Convention. However, how to best leverage these measures for optimal outcomes (i.e., effectively reduce emissions and pollution with the least cost) remains elusive. Here we examined the best-practice portfolio of climate, air pollution, and Hg reduction policies via an energy-environment-economic integrated assessment model. We found that the most cost-effective solution to simultaneously address these issues is coupling carbon neutrality strategies with clean air policies, which can further save 384 million Chinese yuan (CNY) in Hg abatement in 2060. Furthermore, carbon neutrality measures alone can achieve near-zero Hg emissions, whereas Hg policies will only achieve about one-third of the carbon neutrality target. These findings provide practical lessons to cost-effectively address multiple climate and pollution issues, especially for emerging economies that face similar challenges

Photochemical Reduction of Particle Bound Mercury in Atmospheric Aerosol Water

Particle bound mercury (PBM) deposition on the Earth’s surface threatens biota and humans. The photoreduction of PBM competes with deposition and thereby modifies global mercury cycling; yet, its pathway and mechanism remain poorly understood. Herein, we reveal the photoreduction process of PBM by comprehensively using field observation, mercury stable isotope analysis, and controlled experiment. We found the Δ199Hg values in wet haze episodes (0.34‰ ± 0.30‰) were significantly higher than those in clean periods (0.14‰ ± 0.19‰), majorly attributed to the elevated aerosol water content (AWC), which shifts the aerosol phase from the solid state to the liquid state, promoting soluble HgCl2 and HgBr2 photoreduction reactions. The carboxyl functional groups of water-soluble organic carbon (WSOC) were further identified as the crucial compounds that induce PBM photoreduction, whose reaction rates were ∼2 times higher than those of phenol and ketone ligands and 3–6 times higher than those observed in other atmospheric aqueous phases. Considering the ubiquitously distributed carboxyl ligands and significant positive Δ199Hg signals in the atmospheric aqueous phases, the PBM photoreduction mediated by carboxyl ligands is highlighted to significantly influence global mercury transformations, regional depositions, and isotopic compositions of atmospheric mercury pools