The 2016 Planned Giving Study

Charitable bequests and other planned gifts have historically played a significant role in the funding of higher education institutions. Prominent institutions such as Harvard University, Johns Hopkins University, and the Julliard School have been established as a direct result of bequests, and these gifts continue to have a profound impact today. The field of planned giving has become more sophisticated over time. However, the complexity of various planned giving vehicles and the comparatively long time period required for planned gifts to be formalized make it difficult for researchers to systematically track and examine planned giving behavior. Existing studies, therefore, heavily rely on self-reported survey data or tax returns. This study is one of the first efforts that seek to understand the changing landscape of planned giving and to explore donor life-cycle trajectories at higher education institutions. This whitepaper is the first in what is hoped to be a series of reports based upon data on planned gifts and donors in the field of higher education. The whitepaper discusses findings from five case-study universities located across the U.S. As the study expands the sample to include more universities and colleges in the next phase, this report series will offer richer data and insights into more underexplored, yet important, questions in planned giving

The Influence of Mobile Technology on STEM Education Student Learning Outcomes

In many educational institutions, the adoption of mobile learning continues to be a growing topic. As has been considered recently, wireless technologies are currently employed by mobile technology to spread and exchange data via thinking, communicating, exchanging, and understanding. As a consequence, merging mobile technologies into teaching and learning can enhance the ambiance in higher education. Thus, the purpose of this investigation is to implement mobile learning to examine students’ applications in the framework of educational technology. The use of mobile technologies in STEM education is always efficient and engaging for the students. According to its potential to redefine traditional classroom learning paradigms, the inclusion of cellular phones into STEM (science, technology, engineering, and mathematics) education has drawn significant interest. Three artificial intelligence education (AIEd) paradigm structures are utilized to narrow our exploration of how AI is influencing the STEM sectors. An established cross-disciplinary topic of research dealing with leveraging artificial intelligence (AI) approaches to improve training is defined as AIEd. There seems to be an increasing desire to harness AIEd’s promise to tackle academic barriers in STEM fields. The implications of mobile phones on the educational outcomes of students in STEM education settings are explored in this study. By performing a deep review of existing scholarship and empirical investigation, we look for the impact of mobile devices, functions, and platforms on pupil engagement, understanding, and performance in multifaceted STEM fields. A learning approach entitled STEM Project-Based Learning merges project-based curriculum design with the STEM approach to education. As a whole, pupils’ science and technology literacy were improved by the STEM mobile learning package on the ecosystem. Certain learning packages deserve to be studied isolated, while others might be given outright during offline or personal conversations

New Open Conformation of SMYD3 Implicates Conformational Selection and Allostery

SMYD3 plays a key role in cancer cell viability, adhesion, migration and invasion. SMYD3 promotes formation of inducible regulatory T cells and is involved in reducing autoimmunity. However, the nearly “closed” substrate-binding site and poor in vitro H3K4 methyltransferase activity have obscured further understanding of this oncogenically related protein. Here we reveal that SMYD3 can adopt an “open” conformation using molecular dynamics simulation and small-angle X-ray scattering. This ligand-binding-capable open state is related to the crystal structure-like closed state by a striking clamshell-like inter-lobe dynamics. The two states are characterized by many distinct structural and dynamical differences and the conformational transition pathway is mediated by a reversible twisting motion of the C-terminal domain (CTD). The spontaneous transition from the closed to open states suggests two possible, mutually non-exclusive models for SMYD3 functional regulation and the conformational selection mechanism and allostery may regulate the catalytic or ligand binding competence of SMYD3. This study provides an immediate clue to the puzzling role of SMYD3 in epigenetic gene regulation

Wave Manipulations by Coherent Perfect Channeling

We report experimental and theoretical investigations of coherent perfect channeling (CPC), a process that two incoming coherent waves in waveguides are completely channeled into one or two other waveguides with little energy dissipation via strong coherent interaction between the two waves mediated by a deep subwavelength dimension scatterer at the common junction of the waveguides. Two such scatterers for acoustic waves are discovered, one confirmed by experiments and the other predicted by theory, and their scattering matrices are formulated. Scatterers with other CPC scattering matrices are explored, and preliminary investigations of their properties are conducted. The scattering matrix formulism makes it possible to extend the applicable domain of CPC to other scalar waves, such as electromagnetic waves and quantum wavefunctions

A New Method on Software Reliability Prediction

As we all know, relevant data during software life cycle can be used to analyze and predict software reliability. Firstly, the major disadvantages of the current software reliability models are discussed. And then based on analyzing classic PSO-SVM model and the characteristics of software reliability prediction, some measures of the improved PSO-SVM model are proposed, and the improved model is established. Lastly, simulation results show that compared with classic models, the improved model has better prediction precision, better generalization ability, and lower dependence on the number of samples, which is more applicable for software reliability prediction