The impacts of human resource management practices and pay inequality on workers' job satisfaction

In this paper we investigate the relationship between Human Resource Management (HRM) practices and workers' overall job satisfaction and their satisfaction with pay. To investigate these issues we use British data from the 'Changing Employment Relationships, Employment Contracts and the Future of Work Survey' and the 'Workplace Employment Relations Survey'. After controlling for personal, job and firm characteristics, it is shown that several HRM practices raise workers overall job satisfaction and their satisfaction with pay, but these effects are only significant for non-union members. Satisfaction with pay is higher where performance-related pay and seniority-based reward systems are in place. A pay structure that is perceived to be unequal is associated with a substantial reduction in both non-union members' overall job satisfaction and their satisfaction with pay. Although HRM practices can raise worker job satisfaction, if workplace pay inequality widens as a consequence then non-union members may experience reduced job satisfaction.

AI-Based Innovation in B2B Marketing: An Interdisciplinary Framework Incorporating Academic and Practitioner Perspectives

Artificial intelligence (AI) rests at the frontier of technology, service, and industry. AI research is helping to reconfigure innovative businesses in the consumer marketplace. This paper addresses existing literature on AI and presents an emergent B2B marketing framework for AI innovation as a cycle of the critical elements identified in cross-functional studies that represent both academic and practitioner strategic orientations. We contextualize the prevalence of AI-based innovation themes by utilizing bibliometric and semantic content analysis methods across two studies and drawing data from two distinct sources, academics, and industry practitioners. Our findings reveal four key analytical components: (1) IT tools and resource environment, (2) innovative actors and agents, (3) marketing knowledge and innovation, and (4) communications and exchange relationships. The academic literature and industry material analyzed in our studies imply that as markets integrate AI technology into their offerings and services, a governing opportunity to better foster and encourage mutually beneficial co-creation in the AI innovation process emerges

Log Parsing Evaluation in the Era of Modern Software Systems

Due to the complexity and size of modern software systems, the amount of logs generated is tremendous. Hence, it is infeasible to manually investigate these data in a reasonable time, thereby requiring automating log analysis to derive insights about the functioning of the systems. Motivated by an industry use-case, we zoom-in on one integral part of automated log analysis, log parsing, which is the prerequisite to deriving any insights from logs. Our investigation reveals problematic aspects within the log parsing field, particularly its inefficiency in handling heterogeneous real-world logs. We show this by assessing the 14 most-recognized log parsing approaches in the literature using (i) nine publicly available datasets, (ii) one dataset comprised of combined publicly available data, and (iii) one dataset generated within the infrastructure of a large bank. Subsequently, toward improving log parsing robustness in real-world production scenarios, we propose a tool, Logchimera, that enables estimating log parsing performance in industry contexts through generating synthetic log data that resemble industry logs. Our contributions serve as a foundation to consolidate past research efforts, facilitate future research advancements, and establish a strong link between research and industry log parsing

Log Parsing Evaluation in the Era of Modern Software Systems

Due to the complexity and size of modern software systems, the amount of logs generated is tremendous. Hence, it is infeasible to manually investigate these data in a reasonable time, thereby requiring automating log analysis to derive insights about the functioning of the systems. Motivated by an industry use-case, we zoom-in on one integral part of automated log analysis, log parsing, which is the prerequisite to deriving any insights from logs. Our investigation reveals problematic aspects within the log parsing field, particularly its inefficiency in handling heterogeneous real-world logs. We show this by assessing the 14 most-recognized log parsing approaches in the literature using (i) nine publicly available datasets, (ii) one dataset comprised of combined publicly available data, and (iii) one dataset generated within the infrastructure of a large bank. Subsequently, toward improving log parsing robustness in real-world production scenarios, we propose a tool, Logchimera, that enables estimating log parsing performance in industry contexts through generating synthetic log data that resemble industry logs. Our contributions serve as a foundation to consolidate past research efforts, facilitate future research advancements, and establish a strong link between research and industry log parsing

Insights into the pathogenesis of nicotine addiction. Could a salivary biosensor be useful in Nicotine Replacement Therapy (NRT)?

Nicotine has gained the attention of the medical community due to its insidious addictive mechanisms which lead to chronic consumption. The multitude of compounds derived from tobacco smoke have local and systemic negative impacts, resulting in a large number of smoking-related pathologies. The present review offers insights into nicotine addiction physiopathology, as well as social and medical implications, with emphasis on its correlation with Advanced Glycation End Products (AGEs). Therapeutic strategies and new approaches to nicotine assessment and cessation treatment are discussed, noting that such strategies could take into account the possibility of slow and gradual nicotine release from a device attached to a prosthetic piece, based on salivary nicotine-concentration feedback. This approach could offer real-time and home-based self-therapy monitoring by the physician and the patient for follow-up and improve long-term cessation treatment success- Graphical abstract

A Relational Event Approach to Modeling Behavioral Dynamics

This chapter provides an introduction to the analysis of relational event data (i.e., actions, interactions, or other events involving multiple actors that occur over time) within the R/statnet platform. We begin by reviewing the basics of relational event modeling, with an emphasis on models with piecewise constant hazards. We then discuss estimation for dyadic and more general relational event models using the relevent package, with an emphasis on hands-on applications of the methods and interpretation of results. Statnet is a collection of packages for the R statistical computing system that supports the representation, manipulation, visualization, modeling, simulation, and analysis of relational data. Statnet packages are contributed by a team of volunteer developers, and are made freely available under the GNU Public License. These packages are written for the R statistical computing environment, and can be used with any computing platform that supports R (including Windows, Linux, and Mac).

Composition and distribution of the peracarid crustacean fauna along a latitudinal transect off Victoria Land (Ross Sea, Antarctica) with special emphasis on the Cumacea

The following study was the first to describe composition and structure of the peracarid fauna systematically along a latitudinal transect off Victoria Land (Ross Sea, Antarctica). During the 19th Antarctic expedition of the Italian research vessel “Italica” in February 2004, macrobenthic samples were collected by means of a Rauschert dredge with a mesh size of 500 m at depths between 85 and 515 m. The composition of peracarid crustaceans, especially Cumacea was investigated. Peracarida contributed 63% to the total abundance of the fauna. The peracarid samples were dominated by amphipods (66%), whereas cumaceans were represented with 7%. Previously, only 13 cumacean species were known, now the number of species recorded from the Ross Sea increased to 34. Thus, the cumacean fauna of the Ross Sea, which was regarded as the poorest in terms of species richness, has to be considered as equivalent to that of other high Antarctic areas. Most important cumacean families concerning abundance and species richness were Leuconidae, Nannastacidae, and Diastylidae. Cumacean diversity was lowest at the northernmost area (Cape Adare). At the area off Coulman Island, which is characterized by muddy sediment, diversity was highest. Diversity and species number were higher at the deeper stations and abundance increased with latitude. A review of the bathymetric distribution of the Cumacea from the Ross Sea reveals that most species distribute across the Antarctic continental shelf and slope. So far, only few deep-sea records justify the assumption of a shallow-water–deep-sea relationship in some species of Ross Sea Cumacea, which is discussed from an evolutionary point of view

Prototyping open digital tools for urban commoning

The paper will discuss an experimental co-design approach to the development of a digital toolkit prototype and a resulting set of co-design principles, which are put forward as a way of infrastructuring future design of digital tools for urban commoning. Focus is placed on the case study of a commoning hub in a Parisian suburb where the toolkit was co-designed through a series of prototyping workshops, carried out with hub users and addressing key hub needs. The prototyping process explored possibilities for re-appropriating and re-framing existing digital technologies as open toolkits, which can be further re-purposed by users, here and beyond, after the design of an initial toolkit prototype