Have you ever come across a situation where you want to predict a binary outcome like:. A very simple Machine Learning algorithm which will come to your rescue is Logistic Regression. In the Logistic Regression model, the log of odds of the dependent variable is modeled as a linear combination of the independent variables. Consider a situation where you are interested in classifying an individual as diabetic or non-diabetic based on features like glucose concentration, blood pressure, age etc.

Description of the data. We have the following eight independent variables. As a conservative measure, we can remove such observations. From the above histograms, it is evident that the variables — Pregnant and Age are highly skewed, we can analyze them in buckets.

For continuous independent variableswe can get more clarity on the distribution by analyzing it w. From the above plots, we can infer that the median glucose content is higher for patients who have diabetes. Similar inferences can be drawn for the rest of the variables. For categorical independent variableswe can analyze the frequency of each category w.

Analysis of Model Summary. The summary statistics helps us in understanding the model better by providing us with the following information:. Interpretation of Results. For continuous variablesthe interpretation is as follows:. For categorical variablesthe performance of each category is evaluated w.

The interpretation of such variables is as follows:. Variable Selection. There are multiple methodologies for variable selection.

Moreover, the shortlisted variables are highly significant. Analysis of the outcome.The boundaries, colors, denominations, and other information shown on this map do not imply any judgment on the part of member organizations of SuM4All concerning the legal status of any territory or the endorsement or acceptance of such boundaries. Skip to main content. Online Tool Toward Sustainable Mobility 2.

Mobility Performance Around the World. List Map.

towards a logic for performance and mobility

Select a Country. Search Countries. Bahamas, The. Bosnia and Herzegovina. Brunei Darussalam. Burkina Faso. Cabo Verde. Central African Republic. Congo, Dem. Congo, Rep. Costa Rica.

Cote d'Ivoire. Czech Republic. Dominican Republic. Egypt, Arab Rep. El Salvador. Equatorial Guinea. Gambia, The. Iran, Islamic Rep. Korea, Dem. People's Rep. Korea, Rep. Kyrgyz Republic. Lao PDR.Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: Zapf and M. Boon and P. Matteucci and N.

Binary Logistic Regression

Lovell and G. ZapfM. APPROACH Normally sighted volunteers were fitted with a wide-angle head-mounted display and carried out mobility tasks in photorealistic virtual pedestrian scenarios. View on PubMed. Save to Library. Create Alert. Launch Research Feed. Share This Paper. Top 3 of 9 Citations View All Assistive peripheral phosphene arrays deliver advantages in obstacle avoidance in simulated end-stage retinitis pigmentosa: a virtual-reality study.

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Zapf, M. Boon, … G. Suaning Journal of neural engineering Assistive peripheral prosthetic vision aids perception and mobility in outdoor environments: A virtual-reality simulation study. Initial mobility behaviors of people with visual impairment in a virtual environment using a mixed methods design. Natasha A.Yes, speed matters.

For workgroups, part of the learning process should be to continually step back and ask how refining our view of the destination might help us progress even faster. Action without reflection is a waste of time. Taking time to step back and reflect on actions, the results of those actions, and our expectations for actions can be a rich source of insight and learning. What seemed to have a greater impact? How can we do more of that and amplify it?

towards a logic for performance and mobility

This process of reflection and adaptation—before action, during action, after action, and outside action—is often very powerful. Explore the practices and case studies. Read an overview of the opportunity. Download the full report or create a custom PDF. Reflecting as a group holds unique potential for uncovering more insights, drawing more connections, and using them to build better solutions. Workgroups often need opportunities to pull out of the demands of the moment and revisit how near-term actions connect to improving the shared outcome.

Reflection can help workgroups break out of an incremental mind-set at a time when tried-and-true techniques may prove inadequate for the variety of new and unpredictable challenges and cases of first instances that workgroups will encounter. In reflecting on near-term initiatives and assessing whether they are accelerating us toward our destination, workgroups also learn more about the destination they are striving to reach. Part of the learning process should be to continually step back and ask how refining our view of the destination might help us progress even faster.

Reflection, for our purposes, is about understanding and interpreting information—in the form of results, observations, and data—to evolve our actions to get more impact. It is primarily a group activity. For accelerating performance improvement, we should create more opportunities for group reflection.

A diverse group of people willing to challenge each other can get much further than any individual sitting in a room with a mountain of data and trying to make sense of it.

Learning for the sake of learning. Reflection can be valuable when the workgroup uses it to learn more about impact and to catalyze action toward a destination. Finding fault or failure. Rather than run until something goes wrong, then fix the problem, and keep going, continuous reflection constantly seeks greater impact.

Just reflecting on the problem or the opportunity. To get better faster, the workgroup should reflect on its approach to problems and opportunities. In fact, the more you reflect on your approach, the more likely your biggest problems may become your biggest opportunities.

Taking the time to reflect is a conscious decision. Reflection for faster learning comes from first making a conscious decision to make it a priority for the group. A workgroup should focus attention on getting diverse and robust information to feed the reflection.

Members can practice reflection—at different levels of granularity and at different moments in time—to reexamine the status quo in light of the desired impact and trajectory. In order to learn how to get more and more impact, a workgroup needs new information and interactions, along with a growing base of new knowledge, upon which to reflect, draw insights, and determine new actions.

Capture what you can to feed reflection—data and formal metrics as well as the experiences and observations of group members and others—but try to keep data collection simple. For example, look for ways to exploit and analyze data that already exists, such as the digital exhaust that groups leave behind as they interact with people, technology, and equipment.

Our technology generates an increasing amount of data, such as the number of times we badge into work, or how we move and to whom we speak, or how much time we spend using a particular app, or the ways we link from one website to another while searching for information. Often invisible to us, this data can provide insight into the underlying factors that influence the effectiveness of a particular approach or opportunities to tinker with how the workgroup itself works to create more impact.

Collaboration tools can bring further visibility into the data around our work—interactions, queries, and searches, distribution of comments, usefulness of our contributions, and shared objects—for individuals or the group. Often this data is available in real time and can be combined with data pulled from other sources for dynamic feedback. More data—of all types, even if it involves just short back-and-forth conversations—means more transparency.

Look for ways to be radically transparent within the workgroup. More context supports more action, trust, and respect, all of which can fuel richer reflection.The high quality and safety requirements in the automotive industry demand high standards on plastic materials.

Technical properties and high functionality are key here. We have set up a global expert team on that topic. As well it offers excellent color stability at elevated temperatures, and therefore makes orange a longlasting signal color. Flame-retardant PBT has hitherto almost always used halogenated flame retardant systems. As new requirements, e.

These grades are available among other colors in orange RAL To allow our customers the production of connectors for a use at elevated temperatures and high humidity, BASF also set up some hydrolysis resistant HR grades. Contact with water in polyesters, even in the form of atmospheric humidity, leads to hydrolytic cleavage of the polymer chains and thus to a weakening of the material properties, particularly at elevated temperatures.

For critical applications such as those in automotive electronics, long life and reliability are basic requirements. Connectors in an electric car have to withstand up to V, which is even more challenging. The PBT used for these connectors needs to fulfill these demanding conditions guaranteeing safety as much as possible. To immediately identify the high voltage parts under the hood they need to be manufactured with a product which is colorable in orange RAL This color needs to be long-lasting even at elevated temperatures.

Compared to PA, Ultradur shows up a very low yellowing effect but even more stable coloring. Naturally, the plastic parts should be functional in all climate zones on earth, even in damp hot conditions.

If spray water and road salt play a role, this can also increase the demands on the plastic. Therefore, the level and duration of the stress are key factors as to whether or not an application is feasible using a PBT without improved hydrolysis resistance.

Online Tool Toward Sustainable Mobility 2.0

Today, the specifications for a number of plastic applications in the automotive sector include tests at elevated temperatures and humidity or tests on changing climatic conditions.

Thus, they allow automotive manufacturers to save on weight and design space while being an alternative to standard aluminum die-cast solutions.A lack of strong research evidence persists, however, about the most efficient and effective strategies to ensure optimal, sustained performance of CHWs at scale. To facilitate learning and research to address this knowledge gap, the authors developed a generic CHW logic model that proposes a theoretical causal pathway to improved performance.

Construction of the model entailed a multi-stage, inductive, two-year process. It began with the planning and implementation of a structured review of the existing research on community and health system support for enhanced CHW performance. It continued with a facilitated discussion of review findings with experts during a two-day consultation. The generic CHW logic model posits that optimal CHW performance is a function of high quality CHW programming, which is reinforced, sustained, and brought to scale by robust, high-performing health and community systems, both of which mobilize inputs and put in place processes needed to fully achieve performance objectives.

The model is a novel contribution to current thinking about CHWs. It places CHW performance at the center of the discussion about CHW programming, recognizes the strengths and limitations of discrete, targeted programs, and is comprehensive, reflecting the current state of both scientific and tacit knowledge about support for improving CHW performance.

The model is also a practical tool that offers guidance for continuous learning about what works. The final push toward achieving the Millennium Development Goals bycurrent post discussions, and the introduction of Universal Health Coverage [ 5 ] have prompted many LMICs to increasingly invest in CHW programming in the hope of creating more accessible, equitable, and people-centered health systems [ 6 ].

A core challenge of CHW programming is how to ensure sustained, optimal performance at scale of this important cadre of the health workforce. Available research evidence on the most efficient and effective strategies to ensure such performance, however, is weak [ 7 ]. Nevertheless, CHW programs continue to grow and expand, usually incorporating a variety of strategies to support performance with promising but uncertain effectiveness [ 8 ].

Consequently, more research is needed on how best to ensure optimal CHW performance at scale, particularly when LMIC governments are increasing domestic expenditures on health and donor support to the health sector is in a state of transition [ 9 ].

Towards a logic for performance and mobility

Although collective understanding of the definitive causal pathway to improved performance is limited, policy makers, program managers, practitioners, and the academic community can work together to develop theories about how to improve and sustain performance. Combining a somewhat patchwork collection of evidence from studies in the published and gray literature with the tacit knowledge of experts [ 10 ], and translating this knowledge into a practical tool for decision makers, is not unique to CHW performance, but is rather a recurrent challenge in on-going efforts to address the knowledge-practice gap in global public health [ 11 — 13 ].

Policy makers, program planners, project managers, and other analysts use logic models to communicate succinctly and visually the underlying theory of their policies and programs. A logic model maps the intended relationships and causal connections between what a program plans to do and what it hopes to achieve [ 1516 ].

Although the logic model traces its conceptual roots to program evaluation research [ 17 — 19 ], today its reach is far broader. It can guide program design, implementation, monitoring, operational research, and evaluation. During the last two decades, interest in the application of causal models has grown among academics, governmental agencies, non-governmental organizations, and practitioners of evaluation, primarily in industrialized countries [ 17 ].

The inclusion of this kind of causal thinking in the early stages of policy and program development is now making in-roads in non-industrialized countries [ 20 ]. The methods section describes how the generic CHW logic model was constructed, drawing explicitly on research in LMICs and the informed opinion of CHW experts with experience in these countries.

The results section presents a graphic display of the model and detailed explanations of its component parts, in both narrative and tabular form. In the discussion section, the authors examine the value and unique contribution of the model and its potential as a tool to guide continuous learning about what works.

towards a logic for performance and mobility

They also present challenges of translating potential learning into tangible learning and describe some inherent limitations of the model. The paper concludes that, despite these challenges and limitations, the model offers the global health community greater clarity about how to think about, learn about, and ultimately support improved CHW performance.Towards a logic for performance and mobility. N2 - Klaim is an experimental language designed for modeling and programming distributed systems composed of mobile components where distribution awareness and dynamic system architecture configuration are key issues.

StocKlaim [R. De Nicola, D. Latella, and M. ACM Press, In this paper, MoSL, a temporal logic for StocKlaim is proposed which addresses and integrates the issues of distribution awareness and mobility and those concerning stochastic behaviour of systems. The satisfiability relation is formally defined over labelled Markov chains. A large fragment of the proposed logic can be translated to action-based CSL for which efficient model-checkers exist. This way, such model-checkers can be used for the verification of StocKlaim models against MoSL properties.

An example application is provided in the present paper. AB - Klaim is an experimental language designed for modeling and programming distributed systems composed of mobile components where distribution awareness and dynamic system architecture configuration are key issues.

Overview Fingerprint. Abstract Klaim is an experimental language designed for modeling and programming distributed systems composed of mobile components where distribution awareness and dynamic system architecture configuration are key issues. Access to Document Logic Mathematics. Electronic notes in theoretical computer science2 In: Electronic notes in theoretical computer science. In: Electronic notes in theoretical computer scienceVol.

U2 - Electronic notes in theoretical computer science.

Towards a logic for performance and mobility

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