Maryville University of St. Louis is a private university in Town and Country, Missouri. It was originally founded on April 6, 1872 by the Society of the Sacred Heart and offers more than 90 degrees at the undergraduate, graduate, and doctoral levels to students from 50 states and 47 countries. The school's name is derived from the shortening and altering of "Mary's Villa" when the school opened as an all women-school in the country outside of the order's original downtown St. Louis location in 1872 (an area that today is within the boundaries of the city of St. Louis). In 1961 it moved to suburban St. Louis and in 1968 began admitting men. Since 1972 the university has been governed by a board of trustees consisting mostly of members of the laity, although five of the trustees are always associated with the Society of the Sacred Heart. The school's athletic nickname is now the Saints.
Design for Lean Six Sigma (DFLSS) is a powerful methodology adopted by many world-class companies today. It aims to build quality into products or services at the very early stages of the product development process. Ten Commandments of Design for Lean Six Sigma brings together the experience of six authors with more than 50 years of cumulative DFLSS deployment in the field. The book covers key topics such as alignment of DFLSS with organizational strategy; top management support and involvement; listening to the voice of the customers (VOC); effective training programme for right project teams; project selection and prioritization; linking DFLSS with ISO international standards; effective use of DFLSS methodology and the integrated tools within the methodology and reward and recognition schemes. The practical nature of the book makes this ideal reading for senior executives in all organizations with an interest in developing, implementing, and deploying DFLSS. It will be a great asset to many trainers and consultants who would like to deliver executive courses for senior managers in any organisation.
Seismic safety assessment of nuclear power plant (NPP) sites depends critically on the simultaneous characterisation of ground motion intensity and the capacity of the local soil to amplify that motion. While global probabilistic seismic hazard models and global shear-wave velocity (Vs30) compilations are now available, no prior study has combined these two open global datasets into a single, reproducible screening metric spanning the multi-country reactor-unit inventory. Deriving site-specific ground motion for an individual plant by convolving rock-outcrop hazard with site-response analysis is, by contrast, well-established regulatory practice. This paper introduces a Compound Seismic Screening Index (CSSI), a dimensionless 0–10 relative score that combines two independently normalised components: the 475-year return-period peak ground acceleration (PGA) on reference rock, extracted from the Global Earthquake Model (GEM) Ground Shaking Hazard Model version 2023 at 3-arc-minute resolution, and the inverted normalised Vs30 as a site-condition proxy. A higher score indicates greater regional hazard and/or softer, more amplifying ground. The CSSI is a relative screening tool and does not incorporate fragility, exposure, plant design level, safety-system robustness, soil–structure interaction, or multi-hazard probability; the Vs30-based amplification factor and surface PGA are reported separately as a descriptive site-response illustration and do not enter the index. The index is applied to 759 reactor units in a March-2024 snapshot of the GeoNuclearData v0.17.17 database, covering operational, shutdown, under-construction, and planned units. Results show that 50.7
Mathematics is a fundamental pillar for developing problem-solving abilities, fostering logical reasoning, and honing critical thinking skills, all of which are essential for achieving academic success in college and flourishing in personal and professional endeavours. However, the percentage of students dropping out of Science, Technology, Engineering, and Mathematics (STEM) disciplines after unsuccessful completion of a mathematics course has received much attention in recent decades. While grades of ‘B or C or D’ are common in mathematics, students tend to drop out of STEM-related disciplines after earning a C as a final grade. To boost retention and to encourage students to pursue a path in STEM disciplines, this paper introduces a new model in learning mathematics: (1) learning at your own pace with an individualized learning path and (2) combining two consecutive mathematics courses in the same classroom, where students have the flexibility to enroll in one course and do the other based on their progress. Although this model was built for mathematics courses, the changes could be implemented in any discipline with sequential courses. The study is not about an emporium model.
The design, monitoring, improvement, and control of processes of all types creates a continual flow of problems that must be solved for processes to perform as designed, and effectively and efficiently serve customers. As a result, various types of problems arise, and numerous problem-solving methods have been developed to address these problems. Using the principles of statistical engineering, this research develops a framework that integrates problem types and problem-solving strategies. The proposed framework introduces a structured decision logic based on several dimensions, including: the fundamental intent of intervention (fixing, improving, or creating), whether the solution direction is known or must be discovered, and the availability of sufficient problem-relevant data. The framework is designed to help practitioners choose the most effective problem-solving methodology for each unique challenge. This work emphasizes that the problem and its characteristics should drive the selection of tools, not the other way around. While this framework can be useful in practice, it should serve only as a guide to problem-solving, not the dictator of the approach. That is, the framework should work for the practitioner, not the other way around. The framework is illustrated using four real problems from our collective experience.
