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The AI Act and the Future of Work:Algorithms and AI literacy
No full textThe developments in artificial intelligence (AI) have important implications for the future of work. The rapid adoption and use of (generative) AI has led to important juridical and ethical concerns for the management of personnel, particularly in light of the EU’s novel AI Act. This paper is focused on the implications of the AI Act for HRM, by exploring how organizations can deal with these novel regulations and the requirements in terms of AI literacy. By integrating insights from law and HR, we analyse which juridical questions arise and how they influence decision-making. We first offer an overview of the juridical components of the use of (generative) AI in HR processes under the new AI Act, and then outline the crucial role of AI literacy in safeguarding responsible use of AI. We posit that AI literacy is essential for alleviating (juridical) risks while at the same time stimulating the adoption and use of fair and transparent HR practices. Our conceptual paper provides practical recommendations for (HR) managers and policymakers and offers directions for future research for (HR) management scholars
When Technology Becomes an Ideological Battleground:How Data Ideology affects Affordance Actualization in People Analytics
No full textDatafication technologies increasingly impact today’s workplaces, as employees’ behavioral data are collected and analyzed for organizational purposes. While datafication technologies can increase organizational efficiency, they come with the risk of employee surveillance and discrimination. As a result, their implementation is surrounded by controversy. Understanding the different perceptions and assumptions about these technologies from individuals with diverse functional roles is crucial to successfully implementing datafication technologies. Based on 43 interviews, we first investigated how individuals with different functional roles evaluate people analytics, as a manifestation of datafication technologies, using the well-known lens of affordances. Inconclusive results led us to explore further and investigate whether and how perceptions of datafication technologies, as well as affordance actualization, can be explained by data ideologies. Our findings from a critical realist analysis offer novel theoretical and empirical insights into the concept of data ideologies. Data ideologies offer a useful extension to the affordance theory and help explain the relationship between varied stakeholders and datafication technologies along three mechanisms: moderation, confirmation, and modulation. The theorized mechanisms have implications for deploying datafication technologies in practice
Economic complexity tools to analyze circular economy capabilities in global economy
Get PDFTo support the transition to circular economy (CE), companies are called to develop and implement CE initiatives through CE strategies. To be effective, CE capabilities are required, defined as appropriate skills, knowledge, abilities, organizational processes, and routines needed to develop CE strategies. This paper investigates two relevant issues concerning the assessment of CE capabilities possessed by country's companies and the identification suitable CE strategies to be adopted for different economic sectors. We adopt the economic complexity and develop two assessment schemes: the Country Circular Economy Capability Space, assessing the CE capabilities of countries’ companies, and the Circular Economy Capability Proximity Matrix, identifying effective CE strategies to implement by companies in different industries. To build our assessment schemes, we use the multi-regional input‐output tables provided by EXIOBASE3. Based on indices designed, our analyses offer a clear picture of CE capabilities possessed by countries and suggest which CE strategies to adopt for supporting the effective CE transition of economic industries.</p
Collective Sensemaking and Reframing in Futures Thinking Engagements:Lessons From a Responsible Futuring Learning Trajectory
Get PDFHow can we facilitate collective sensemaking and reframing processes to foster futures thinking? This paper explores the use of Responsible Futuring, a design-oriented approach, in a learning trajectory for professionals facing complex digital transformations in their organizations. Responsible Futuring supports collective sensemaking and reframing processes across four cognitive and experiential levels: (1) understanding values, (2) imagining, (3) tangibilizing, and (4) introspecting. While the trajectory enabled participants to incorporate diverse perspectives and embrace long-term thinking, our findings revealed a tendency towards solutionism, where participants prematurely focused on specific solutions rather than exploring broader, values-driven futures. In response, we propose four critical areas of attention, including (1) iterative guidance to redirect focus towards broader perspectives, (2) zooming processes to mitigate risks of actor and stakeholder inclusion/exclusion, (3) clearly defined goals in speculative activities to align with learning objectives, and (4) grounding speculative futures in everyday realities to enhance relevance. By addressing these areas, we offer insights for researchers and practitioners aiming to integrate futures-oriented activities into lifelong learning trajectories for communities navigating complex transformations.</p
A Hybrid Neural Model Approach for Health Assessment of Railway Transition Zones With Multiple Data Sources
Get PDFTransition zones in railway tracks often degrade faster than other locations, yet traditional health assessments rely on infrequent track geometry measurements, limiting early detection of dynamic changes. This research presents an approach for more frequent evaluation of transition zone health by integrating data sources from multiple monitoring technologies: track geometry, interferometric synthetic aperture radar (InSAR), and axle box acceleration (ABA). Missing InSAR data are addressed through spatiotemporal interpolation, and track longitudinal levels are predicted using a hybrid neural model that includes a hybrid convolutional neural network (CNN) with gated recurrent unit (GRU) network and a hybrid CNN with a long short-term memory (LSTM) network. The models fuse historical and interpolated data from InSAR and ABA, enabling high-frequency insights. A novel key performance index (KPI) based on predicted longitudinal levels is proposed to quantify track condition. The framework is validated on a transition zone at a railway bridge between Dordrecht and Lage Zwaluwe in The Netherlands. The results show that the hybrid model outperforms standalone methods and offers a good balance between accuracy and computational efficiency. The proposed approach enables earlier detection of irregularities, supporting prescriptive maintenance decisions.</p
DELTO Study:Delphi Consensus on Long-Term Textbook Outcome After Metabolic Bariatric Surgery
Get PDFBackground: This study aimed to create a comprehensive Core Outcome Set (COS) for assessing the long-term outcome (≥ 5 years) after Metabolic Bariatric Surgery (MBS), through the use of the Delphi method. Methods: The study utilized a three-phase approach. In Phase 1, a long list of items was identified through a literature review and expert input, forming the basis for an online Delphi survey. In Phase 2, Dutch healthcare professionals involved in MBS care, defined as having at least 1 year of experience in routine follow-up or managing issues arising during follow-up, rated the importance of these items over three Delphi rounds using a 5-point Likert scale. Participants had the option to suggest additional items. Consensus was defined as 75% agreement among panelists. In Phase 3, the final COS was validated at a national conference. Results: Thirty-one professionals participated in the first Delphi round. Of these, 28 (90%) completed the second round, and 24 (77%) completed the third round. The final COS, validated by 18 healthcare professionals, included various domains: short-term textbook outcome, weight loss, remission of comorbidities, quality of life, micronutrient deficiencies, lifestyle, psychopathology, long-term complications, and preoperative indication. Conclusions: The final COS offers a multidimensional approach to evaluate long-term outcomes after MBS. This COS is expected to enhance the measurement and benchmarking of MBS care, providing a more holistic view of patient outcomes.</p
Exact statistical analysis for response-adaptive clinical trials:A general and computationally tractable approach
Get PDFResponse-adaptive clinical trial designs allow targeting a given objective by skewing the allocation of participants to treatments based on observed outcomes. Response-adaptive designs face greater regulatory scrutiny due to potential type I error rate inflation, which limits their uptake in practice. Existing approaches for type I error control either only work for specific designs, have a risk of Monte Carlo/approximation error, are conservative, or computationally intractable. To this end, a general and computationally tractable approach is developed for exact analysis in two-arm response-adaptive designs with binary outcomes. This approach can construct exact tests for designs using either a randomized or deterministic response-adaptive procedure. The constructed conditional and unconditional exact tests generalize Fisher's and Barnard's exact tests, respectively. Furthermore, the approach allows for complexities such as delayed outcomes, early stopping, or allocation of participants in blocks. The efficient implementation of forward recursion allows for testing of two-arm trials with 1,000 participants on a standard computer. Through an illustrative computational study of trials using randomized dynamic programming it is shown that, contrary to what is known for equal allocation, the conditional exact Wald test based on total successes has, almost uniformly, higher power than the unconditional exact Wald test. Two real-world trials with the above-mentioned complexities are re-analyzed to demonstrate the value of the new approach in controlling type I errors and/or improving the statistical power.</p
Intramuscular tendon length in agonist–antagonist myoneural interface components in transtibial amputation:An anatomic study
Get PDFUnderstanding the role of tendons in muscle function and proprioception is crucial for enhancing amputation surgery. Muscle spindles and Golgi tendon organs provide essential feedback for muscle control. Preservation of tendon function in amputation surgery and the development of the agonist–antagonist myoneural interface (AMI) have shown promising results restoring muscle–tendon proprioception and in improving prosthetic control. However, challenges remain in constructing AMI due to anatomical limitations in residual limbs. A total of 25 lower legs from fresh-frozen human Caucasian donors were dissected, and the muscles relevant to the AMI technique, such as the gastrocnemius complex, the tibialis posterior, the tibialis anterior, and the peroneus longus, were analyzed. Demographic and anthropometric measurements, muscle preparation and weight, markings, imaging, and statistical analysis methods were described in detail. In all muscles examined, the intramuscular course of the tendon extended over more than 75% of the distal muscle belly. The muscle belly length of the peroneus longus muscle and the medial head of the gastrocnemius muscle showed a significant positive correlation with the weight and height of the donors. There were no significant correlations between the ratio of the intramuscular course of the tendon to muscle belly length and the weight or height of the donor. The AMI technique can enhance proprioceptive feedback for transtibial amputees wearing prostheses. The study indicates that gender does not impact muscle characteristics, but weight and height show correlations. These results offer valuable insights into muscle anatomy for informing future research on the functional effects of AMI and prosthetic limb design.</p
Designing Explainability Features for LLM-based Educational Chatbots to Promote Reflective Learning Behavior
Get PDFRecent advancements in large language models have enabled the development of artificial intelligence-based educational chatbots that are capable of supporting students in a wide range of tasks. However, concerns are growing about the student over-reliance on these tools, and the impact they have on critical thinking. This research explores how explainability features could be designed into educational chatbots to enhance student learning. The study adopts a mixed-methods approach, starting with a literature review to define the current state-of-the-art in explainability design, as well as learning and behavior theories that should be considered in their design. It continues with an empirical study, which gathers input from students and teachers to assess their expectations of explainability features and synthesizes the findings into a unified design framework. This research informs the design and development of AI-powered educational chatbot tools, specifically contributing to explainability features
Turán Numbers for Vertex-disjoint Triangles and Pentagons
Get PDFThe Turán number, denoted by ex (n, H), is the maximum number of edges of a graph on n vertices containing no graph H as a subgraph. Denote by kCℓ the union of k vertex-disjoint copies of Cℓ. In this paper, we present new results for the Turán numbers of vertex-disjoint cycles. Our first results deal with the Turán number of vertex-disjoint triangles ex (n, kC3). We determine the Turán number ex(n, kC3) for n≥k2+5k2 when k ≤ 4, and n ≥ k2 + 2 when k ≥ 4. Moreover, we give lower and upper bounds for ex (n, kC3) with 3k≤n≤k2+5k2 when k ≤ 4, and 3k ≤ n ≤ k2 + 2 when k ≥ 4. Next, we give a lower bound for the Turán number of vertex-disjoint pentagons ex (n, kC5). Finally, we determine the Turán number ex (n, kC5) for n = 5k, and propose two conjectures for ex (n, kC5) for the other values of n.</p