The Right Match: A Strong Principal in Every Public School

This report has one central premise: Keeping great principals starts with hiring the right principal. Even as Chicago fights to retain principals long enough to make student learning and school culture gains more permanent, we must recognize some principal attrition is inevitable.More than 70,000 students started the 2016-17 school year with a new principal, and at least 60 schools will need a new principal each year for the foreseeable future. The stakes are high: No great public school exists without great leadership. In fact, variation in principal quality accounts for about 25 percent of a school's total impact on student learning. Yet, more than four out of every 10 public school principals in Chicago leave before they begin their fifth year. To keep great principals, we have to make the right match from the start

Approach for model-based requirements engineering for the planning of engineering generations in the agile development of mechatronic systems

The crucial factor for a successful usage of modeling approaches of systems engineering is the interaction of language, method, and tool. For this, specific challenges arise for the application of MBSE in agile requirements engineering. From observations in agile development practice at a machine tool manufacturer, the challenges for model-based requirements engineering are described and each is assigned to its critical aspect of modeling: The language must formally represent the requirements data model, especially for planning engineering generations. The tool must support collaborative, interdisciplinary cooperation, and consider the dynamics of the requirements model during the development process. The method must individually support the requirements engineering activities, which are carried out several times in a sprint during the development process and must enable a target-oriented process for bundling the requirements into engineering generations. Taking these demands into account, an approach is then presented providing activity-based views in conjunction with activity steps based on a consistent ontology for the description of product requirements and verification activities. The activity steps are composed in activity patterns and support the user in making use of the views for modeling requirements for the engineering generations. The approach is implemented in the software JIRA at a machine tool manufacturer. The subsequent evaluation shows that the approach is used in development practice and offers the potential to plan engineering generation systematically and comprehensibly and to ensure a regular review of the implemented requirements

Uses of strength-based interventions for people with serious mental illness: a critical review

Background: For the past 3 decades, mental health practitioners have increasingly adopted aspects and tools of strength-based approaches. Providing strength-based intervention and amplifying strengths relies heavily on effective interpersonal processes. Aim: This article is a critical review of research regarding the use of strength-based approaches in mental health service settings. The aim is to discuss strength-based interventions within broader research on recovery, focussing on effectiveness and advances in practice where applicable. Method: A systematic search for peer-reviewed intervention studies published between 2001 and December 2014 yielded 55 articles of potential relevance to the review. Results: Seven studies met the inclusion criteria and were included in the analysis. The Quality Assessment Tool for Quantitative Studies was used to appraise the quality of the studies. Our review found emerging evidence that the utilisation of a strength-based approach improves outcomes including hospitalisation rates, employment/educational attainment, and intrapersonal outcomes such as self-efficacy and sense of hope. Conclusion: Recent studies confirm the feasibility of implementing a high-fidelity strength-based approach in clinical settings and its relevance for practitioners in health care. More high-quality studies are needed to further examine the effectiveness of strength-based approaches

The Vehicle, Fall 2003

Table of Contents Blame It on My BirthsignMichael Doizanpage 4 Like a BanjoKaitlyn Kingstonpage 6 A BubbleMaria Santoyopage 7 UntitledLiz Toyntonpage 8 She Said It Was Stuck in the FenceGreg Holdenpage 11 Thanksgiving Table CharactersKrystal Heringpage 12 This Is My LandKorah Winnpage 13 Bleeding HeartsKaitlyn Kingstonpage 14 SoldierEmily Rapppage 17 HomelessLaTasha Harrispage 18 InfinitiLindsey Nawojskipage 19 Gone Until ForeverAndy Whytepage 20 On My WayKristin Bornpage 27 GloryJay Popepage 28 Untitled (1)Trevor Moorepage 29 Nature\u27s MoratoriumMatt McCarthypage 29 Untitled (2)Trevor Moorepage 30 Eternal ChildAmy Towerypage 31 FingersJosh Sopiarzpage 31 She Likes JazzMario Podeschipage 32 Back Alley FarmsScott E. Lutzpage 33 Biographiespage 35https://thekeep.eiu.edu/vehicle/1078/thumbnail.jp

Correction to: Perceived economic self-sufficiency: a country- and generation-comparative approach

After online first publication of the article, it was noticed that the family name of author Panos Tsakloglou was erroneously misspelt as Tsakoglou

Multimodal neural and behavioral data predict response to rehabilitation in chronic post-stroke aphasia

BACKGROUND: Poststroke recovery depends on multiple factors and varies greatly across individuals. Using machine learning models, this study investigated the independent and complementary prognostic role of different patient-related factors in predicting response to language rehabilitation after a stroke. METHODS: Fifty-five individuals with chronic poststroke aphasia underwent a battery of standardized assessments and structural and functional magnetic resonance imaging scans, and received 12 weeks of language treatment. Support vector machine and random forest models were constructed to predict responsiveness to treatment using pretreatment behavioral, demographic, and structural and functional neuroimaging data. RESULTS: The best prediction performance was achieved by a support vector machine model trained on aphasia severity, demographics, measures of anatomic integrity and resting-state functional connectivity (F1=0.94). This model resulted in a significantly superior prediction performance compared with support vector machine models trained on all feature sets (F1=0.82, P<0.001) or a single feature set (F1 range=0.68–0.84, P<0.001). Across random forest models, training on resting-state functional magnetic resonance imaging connectivity data yielded the best F1 score (F1=0.87). CONCLUSIONS: While behavioral, multimodal neuroimaging data and demographic information carry complementary information in predicting response to rehabilitation in chronic poststroke aphasia, functional connectivity of the brain at rest after stroke is a particularly important predictor of responsiveness to treatment, both alone and combined with other patient-related factors.P50 DC012283 - NIDCD NIH HHShttps://www.ahajournals.org/doi/10.1161/STROKEAHA.121.036749Published versio

Perceived economic self‑sufficiency: a countryand generation‑comparative approach

We thank Michael Camasso and Radha Jagannathan as well as Asimina Christoforou, Gerbert Kraaykamp, Fay Makantasi, Tiziana Nazio, Kyriakos Pierrakakis, Jacqueline O’Reilly and Jan van Deth for their contribution to the CUPESSE project (Seventh Framework Programme; Grant Agreement No. 61325). CUPESSE received additional funding from the Mannheim Centre for European Social Research (MZES) and the Field of Focus 4 “Self-Regulation and Regulation: Individuals and Organisations” at Heidelberg University. We further acknowledge helpful comments on this article by two anonymous reviewers. Julian Rossello provided valuable research assistance.Electronic supplementary material The online version of this article (https ://doi.org/10.1057/ s4130 4-018-0186-3) contains supplementary material, which is available to authorized users.Existing datasets provided by statistical agencies (e.g. Eurostat) show that the economic and financial crisis that unfolded in 2008 significantly impacted the lives and livelihoods of young people across Europe. Taking these official statistics as a starting point, the collaborative research project “Cultural Pathways to Economic Self-Sufficiency and Entrepreneurship in Europe” (CUPESSE) generated new survey data on the economic and social situation of young Europeans (18–35 years). The CUPESSE dataset allows for country-comparative assessments of young people’s perceptions about their socio-economic situation. Furthermore, the dataset includes a variety of indicators examining the socio-economic situation of both young adults and their parents. In this data article, we introduce the CUPESSE dataset to political and social scientists in an attempt to spark a debate on the measurements, patterns and mechanisms of intergenerational transmission of economic self-sufficiency as well as its political implications.CUPESSE project (Seventh Framework Programme; Grant Agreement No. 61325