Policymakers and program managers are continually seeking ways to improve accountability in achieving an entity's mission. A key factor in improving accountability in achieving an entity's mission is to implement an effective internal control system. An effective internal control system helps an entity adapt to shifting environments, evolving demands, changing risks, and new priorities. As programs change and entities strive to improve operational processes and implement new technology, management continually evaluates its internal control system so that it is effective and updated when necessary. Section 3512 (c) and (d) of Title 31 of the United States Code (commonly known as the Federal Managers' Financial Integrity Act (FMFIA)) requires the Comptroller General to issue standards for internal control in the federal government.
Audits provide essential accountability and transparency over government programs. Given the current challenges facing governments and their programs, the oversight provided through auditing is more critical than ever. Government auditing provides the objective analysis and information needed to make the decisions necessary to help create a better future. The professional standards presented in this 2018 revision of Government Auditing Standards (known as the Yellow Book) provide a framework for performing high-quality audit work with competence, integrity, objectivity, and independence to provide accountability and to help improve government operations and services. These standards, commonly referred to as generally accepted government auditing standards (GAGAS), provide the foundation for government auditors to lead by example in the areas of independence, transparency, accountability, and quality through the audit process. This revision contains major changes from, and supersedes, the 2011 revision.
Author: United States. Congress. House. Committee on Appropriations. Subcommittee on the Treasury, Postal Service, and General Government Appropriations
The Go-To Resource for Large-Scale Organizations to Be Agile Rather than asking, “How can we do agile at scale in our big complex organization?” a different and deeper question is, “How can we have the same simple structure that Scrum offers for the organization, and be agile at scale rather than do agile?” This profound insight is at the heart of LeSS (Large-Scale Scrum). In Large-Scale Scrum: More with LeSS, Craig Larman and Bas Vodde have distilled over a decade of experience in large-scale LeSS adoptions towards a simpler organization that delivers more flexibility with less complexity, more value with less waste, and more purpose with less prescription. Targeted to anyone involved in large-scale development, Large-Scale Scrum: More with LeSS, offers straight-to-the-point guides for how to be agile at scale, with LeSS. It will clearly guide you to Adopt LeSS Structure a large development organization for customer value Clarify the role of management and Scrum Master Define what your product is, and why Be a great Product Owner Work with multiple whole-product focused feature teams in one Sprint that produces a shippable product Coordinate and integrate between teams Work with multi-site teams
Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data
The GHG Protocol Corporate Accounting and Reporting Standard helps companies and other organizations to identify, calculate, and report GHG emissions. It is designed to set the standard for accurate, complete, consistent, relevant and transparent accounting and reporting of GHG emissions.
The Institute of Medicine study Crossing the Quality Chasm (2001) recommended that an interdisciplinary summit be held to further reform of health professions education in order to enhance quality and patient safety. Health Professions Education: A Bridge to Quality is the follow up to that summit, held in June 2002, where 150 participants across disciplines and occupations developed ideas about how to integrate a core set of competencies into health professions education. These core competencies include patient-centered care, interdisciplinary teams, evidence-based practice, quality improvement, and informatics. This book recommends a mix of approaches to health education improvement, including those related to oversight processes, the training environment, research, public reporting, and leadership. Educators, administrators, and health professionals can use this book to help achieve an approach to education that better prepares clinicians to meet both the needs of patients and the requirements of a changing health care system.
This paper discusses the impact of the rapid adoption of artificial intelligence (AI) and machine learning (ML) in the financial sector. It highlights the benefits these technologies bring in terms of financial deepening and efficiency, while raising concerns about its potential in widening the digital divide between advanced and developing economies. The paper advances the discussion on the impact of this technology by distilling and categorizing the unique risks that it could pose to the integrity and stability of the financial system, policy challenges, and potential regulatory approaches. The evolving nature of this technology and its application in finance means that the full extent of its strengths and weaknesses is yet to be fully understood. Given the risk of unexpected pitfalls, countries will need to strengthen prudential oversight.
Systems' Verification Validation and Testing (VVT) are carried out throughout systems' lifetimes. Notably, quality-cost expended on performing VVT activities and correcting system defects consumes about half of the overall engineering cost. Verification, Validation and Testing of Engineered Systems provides a comprehensive compendium of VVT activities and corresponding VVT methods for implementation throughout the entire lifecycle of an engineered system. In addition, the book strives to alleviate the fundamental testing conundrum, namely: What should be tested? How should one test? When should one test? And, when should one stop testing? In other words, how should one select a VVT strategy and how it be optimized? The book is organized in three parts: The first part provides introductory material about systems and VVT concepts. This part presents a comprehensive explanation of the role of VVT in the process of engineered systems (Chapter-1). The second part describes 40 systems' development VVT activities (Chapter-2) and 27 systems' post-development activities (Chapter-3). Corresponding to these activities, this part also describes 17 non-testing systems' VVT methods (Chapter-4) and 33 testing systems' methods (Chapter-5). The third part of the book describes ways to model systems' quality cost, time and risk (Chapter-6), as well as ways to acquire quality data and optimize the VVT strategy in the face of funding, time and other resource limitations as well as different business objectives (Chapter-7). Finally, this part describes the methodology used to validate the quality model along with a case study describing a system's quality improvements (Chapter-8). Fundamentally, this book is written with two categories of audience in mind. The first category is composed of VVT practitioners, including Systems, Test, Production and Maintenance engineers as well as first and second line managers. The second category is composed of students and faculties of Systems, Electrical, Aerospace, Mechanical and Industrial Engineering schools. This book may be fully covered in two to three graduate level semesters; although parts of the book may be covered in one semester. University instructors will most likely use the book to provide engineering students with knowledge about VVT, as well as to give students an introduction to formal modeling and optimization of VVT strategy.
During the 2016 presidential election, America's election infrastructure was targeted by actors sponsored by the Russian government. Securing the Vote: Protecting American Democracy examines the challenges arising out of the 2016 federal election, assesses current technology and standards for voting, and recommends steps that the federal government, state and local governments, election administrators, and vendors of voting technology should take to improve the security of election infrastructure. In doing so, the report provides a vision of voting that is more secure, accessible, reliable, and verifiable.
