The Case for Tipping and Unrestricted Tip-Pooling: Promoting Intrafirm Cooperation

No law in the United States requires or prohibits customers from tipping employees for satisfactory service. Tip income is typically regarded as belonging to employees and may not be appropriated by the employer. Tipping is a widespread phenomenon in certain settings–restaurants, hotels, and gambling casinos. It is a form of performance-based variable compensation that is generally not found elsewhere in this country, where employees generally prefer fixed incomes over a defined period. As a general matter, our laws allow tipping but regulate the sharing of tip income among employees. In the restaurant setting, tip-pooling occurs when tips received by one employee are shared to some extent with other employees. For example, waitstaff at a restaurant might pool tips only with other waiters; this is legally permitted. A broader arrangement, that is presently not allowed, would be an employer policy providing for the sharing of tips beyond the waitstaff to include those who bus the tables or work in the kitchen. The U.S. tipping norm is under challenge. A growing number of restaurant owners in big cities are moving to ban tipping and instead raise prices. They argue that existing law precludes them from sharing tips with back-of-the-house employees (like chefs and dishwashers), and thus makes it hard to compensate those employees fairly for their contribution to the joint endeavor. We argue that the movement against tipping is ill-advised. Voluntary tipping is a valuable social institution that allows customers to monitor service where management cannot readily do so. The better answer to a flat-out tipping ban is to remove legal restrictions on tip-pooling. Pooling tips among a broad swath of employees (other than ownership-level employees) helps promote the cooperative endeavor underlying the provision of service in settings like restaurants

Leadership characteristics in the immigrant community

The objective of this study is to understand the immigrant community perception of leadership and its characteristics. We want to know what qualities they would assign to leaders. The study also investigates the community perception of the following leadership characteristics: decisionmaking, associations, perception, and ease of leadership. Decision-making relates to how the decision-making is determined in general by immigrants. For example, if decisions are made with a conscious awareness of how it affects others, if the decisions are made for personal benefit, if decisions are made using a cost benefit analysis, etc. .We analyze associations based on past relationships that influence good leadership in immigrant. Ease of leadership is analyzed by the perceived amount of difficulty associated with leadership. All factors are related to their effect on the immigrant community. There are multiple studies that analyze and interpret leadership factors and qualities among immigrant or minority communities. Leader to Leader discusses whom different families coming from different cultural backgrounds and areas (Leadership, 2005). Another study focused on how cultural factors influence leadership among immigrants and how these characteristics affect operations in their respective organizations (Yun-His, 2011). This study is important in understanding other cultures and their views on leadership. It also allows people to help create better leaders based on the results we may find. (Author abstract)Tobin, P., Richard, N., Harrington, S., Remy, A., and Michaud, A. (2014). Leadership qualities and characteristics in the Manchester, NH immigrant community. Retrieved from http://academicarchive.snhu.ed

Key Technologies for Progressing Discovery of Microbiome-Based Medicines

A growing number of experimental and computational approaches are illuminating the “microbial dark matter” and uncovering the integral role of commensal microbes in human health. Through this work, it is now clear that the human microbiome presents great potential as a therapeutic target for a plethora of diseases, including inflammatory bowel disease, diabetes and obesity. The development of more efficacious and targeted treatments relies on identification of causal links between the microbiome and disease; with future progress dependent on effective links between state-of-the-art sequencing approaches, computational analyses and experimental assays. We argue determining causation is essential, which can be attained by generating hypotheses using multi-omic functional analyses and validating these hypotheses in complex, biologically relevant experimental models. In this review we discuss existing analysis and validation methods, and propose best-practice approaches required to enable the next phase of microbiome research

Logical modelling of cellular decision processes with GINsim

International audienc

Pilot testing methodologies, models, scenarios and validation approach

This report describes the pilot tests that will be carried out within GARPUR. The pilot tests aim at assessing and validating, as closely as possible to real-life conditions, the new Reliability Management Approach and Criteria (RMACs) developed in work package 2 and the socio-economic impact assessment framework developed in work package 3, while considering the methodologies and approaches for practical implementation in work packages 4, 5 and 6. The proposed RMACs are compared against the current N-1 practices and approaches determined in work package 1. In addition, some pilot tests will make use of the GARPUR Quantification Platform (GQP) that has been developed by work package 7. A total of eight pilot tests are proposed by five different transmission system operators (TSOs) and given different priority levels for implementation. Three pilot tests make use of the GQP. The five other pilot tests will be implemented at TSOs’ premises in near real-life context. The pilot tests cover real-time operations, short-term operation planning and long-term system development. Different indicators are proposed to assess the proposed reliability management approach and criteria and compare them with current practices. The diversity of the pilot tests in terms of time horizons and the number of involved TSOs demonstrate the general nature of the reliability management approach and criteria proposed within GARPUR

Rapid typing of Coxiella burnetii

Coxiella burnetii has the potential to cause serious disease and is highly prevalent in the environment. Despite this, epidemiological data are sparse and isolate collections are typically small, rare, and difficult to share among laboratories as this pathogen is governed by select agent rules and fastidious to culture. With the advent of whole genome sequencing, some of this knowledge gap has been overcome by the development of genotyping schemes, however many of these methods are cumbersome and not readily transferable between institutions. As comparisons of the few existing collections can dramatically increase our knowledge of the evolution and phylogeography of the species, we aimed to facilitate such comparisons by extracting SNP signatures from past genotyping efforts and then incorporated these signatures into assays that quickly and easily define genotypes and phylogenetic groups. We found 91 polymorphisms (SNPs and indels) among multispacer sequence typing (MST) loci and designed 14 SNP-based assays that could be used to type samples based on previously established phylogenetic groups. These assays are rapid, inexpensive, real-time PCR assays whose results are unambiguous. Data from these assays allowed us to assign 43 previously untyped isolates to established genotypes and genomic groups. Furthermore, genotyping results based on assays from the signatures provided here are easily transferred between institutions, readily interpreted phylogenetically and simple to adapt to new genotyping technologies

Application possibilities of artificial intelligence in facial vascularized composite allotransplantation—a narrative review

Facial vascularized composite allotransplantation (FVCA) is an emerging field of reconstructive surgery that represents a dogmatic shift in the surgical treatment of patients with severe facial disfigurements. While conventional reconstructive strategies were previously considered the goldstandard for patients with devastating facial trauma, FVCA has demonstrated promising short- and long-term outcomes. Yet, there remain several obstacles that complicate the integration of FVCA procedures into the standard workflow for facial trauma patients. Artificial intelligence (AI) has been shown to provide targeted and resource-effective solutions for persisting clinical challenges in various specialties. However, there is a paucity of studies elucidating the combination of FVCA and AI to overcome such hurdles. Here, we delineate the application possibilities of AI in the field of FVCA and discuss the use of AI technology for FVCA outcome simulation, diagnosis and prediction of rejection episodes, and malignancy screening. This line of research may serve as a fundament for future studies linking these two revolutionary biotechnologies

Towards a Responsible Innovation Agenda for HCI

In recent years responsible innovation has gained significant traction and can be seen to adorn a myriad of research platforms, education programs, and policy frameworks. In this workshop, we invite HCI researchers to discuss the relations between the CHI community and responsible innovation. This workshop looks to build provocations and principles for and with HCI researchers who are or wish to become responsible innovators. The workshop looks to do this by asking attendees to think about the social, environmental, and economic impacts of ICT and HCI and explore how research innovation frameworks speak to responsible HCI innovation. Through the workshop we look to examine 5 questions to develop a set of provocations and principles, which will help encourage HCI and computer science researchers, educators, and innovators to reflect on the impact of their research and innovatio

Application possibilities of artificial intelligence in facial vascularized composite allotransplantation—a narrative review

Facial vascularized composite allotransplantation (FVCA) is an emerging field of reconstructive surgery that represents a dogmatic shift in the surgical treatment of patients with severe facial disfigurements. While conventional reconstructive strategies were previously considered the goldstandard for patients with devastating facial trauma, FVCA has demonstrated promising short- and long-term outcomes. Yet, there remain several obstacles that complicate the integration of FVCA procedures into the standard workflow for facial trauma patients. Artificial intelligence (AI) has been shown to provide targeted and resource-effective solutions for persisting clinical challenges in various specialties. However, there is a paucity of studies elucidating the combination of FVCA and AI to overcome such hurdles. Here, we delineate the application possibilities of AI in the field of FVCA and discuss the use of AI technology for FVCA outcome simulation, diagnosis and prediction of rejection episodes, and malignancy screening. This line of research may serve as a fundament for future studies linking these two revolutionary biotechnologies