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Abstract
Purpose:To synthesizethe availableevidenceaboutthe use of HealthInformationTechnology(HIT)to supportpatientsduringhemodialysis.Methods:TheJoannaBriggsInstitute's methodologicalguidelinesfor scopingreviewsandthe PRISMA-ScRchecklistwereemployed.BibliographicsearchesacrossMEDLINE (R), CINAHL (R), PsychologyandBehavioralSciencesCollection,Scopus,MedicLatina,and Cochraneyielded932 records.Results:Eighteenstudiespublishedbetween2003and2023wereincluded.Theyexploreda rangeof HITs,includingvirtualreality,exergames,websites,and mobileapplications,all specificallydevelopedfor use duringthe intradialyticperiod.Conclusion:Thisstudyhighlightsthe HITsdevelopedfor use duringhemodialysistreatment,supportingphysicalexercise,diseasemanagement,and enhancementof self-efficacyand self-care.

Abstract
The large gap between the generalization level of state-of-the-art machine learning and human learning systems calls for the development of artificial intelligence (AI) models that are truly inspired by human cognition. In tasks related to image analysis, searching for pixel-level regularities has reached a power of information extraction still far from what humans capture with image-based observations. This leads to poor generalization when even small shifts occur at the level of the observations. We explore a perspective on this problem that is directed to learning the generative process with causality-related foundations, using models capable of combining symbolic manipulation, probabilistic reasoning, and pattern recognition abilities. We briefly review and explore connections of research from machine learning, cognitive science, and related fields of human behavior to support our perspective for the direction to more robust and human-like artificial learning systems.

Abstract
When each patient of a kidney exchange program has a preference ranking over its set of compatible donors, questions naturally arise surrounding the stability of the proposed exchanges. We extend recent work on stable exchanges by introducing and underlining the relevance of a new concept of locally stable, or L-stable, exchanges. We show that locally stable exchanges in a compatibility digraph are exactly the so-called local kernels (L-kernels) of an associated blocking digraph (whereas the stable exchanges are the kernels of the blocking digraph), and we prove that finding a nonempty L-kernel in an arbitrary digraph is NP-complete. Based on these insights, we propose several integer programming formulations for computing an L-stable exchange of maximum size. We conduct numerical experiments to assess the quality of our formulations and to compare the size of maximum L-stable exchanges with the size of maximum stable exchanges. It turns out that nonempty L-stable exchanges frequently exist in digraphs which do not have any stable exchange. All the above results and observations carry over when the concept of (locally) stable exchanges is extended to the concept of (locally) strongly stable exchanges.

Abstract
Nowadays, the world's fishing fleet uses 20% more fuel to catch the same amount offish compared to 30 years ago. Addressing this negative environmental and economic performance is crucial due to stricter emission regulations, rising fuel costs, and predicted declines in fish biomass and body sizes due to climate change. Investment in more efficient engines, larger ships and better fuel has been the main response, but this is only feasible in the long term at high infrastructure cost. An alternative is to optimize operations such as the routing of a fleet, which is an extremely complex problem due to its dynamic (time-dependent) moving target characteristics. To date, no other scientific work has approached this problem in its full complexity, i.e., as a dynamic vehicle routing problem with multiple time windows and moving targets. In this paper, two bi-objective mixed linear integer programming (MIP) models are presented, one for the static variant and another for the time-dependent variant. The bi-objective approaches allow to trade off the economic (e.g., probability of high catches) and environmental (e.g., fuel consumption) objectives. To overcome the limitations of exact solutions of the MIP models, a greedy randomized adaptive search procedure for the multi-objective problem (MO-GRASP) is proposed. The computational experiments demonstrate the good performance of the MO-GRASP algorithm with clearly different results when the importance of each objective is varied. In addition, computational experiments conducted on historical data prove the feasibility of applying the MO-GRASP algorithm in a real context and explore the benefits of joint planning (collaborative approach) compared to a non-collaborative strategy. Collaborative approaches enable the definition of better routes that may select slightly worse fishing and planting areas (2.9%), but in exchange fora significant reduction in fuel consumption (17.3%) and time at sea (10.1%) compared to non-collaborative strategies. The final experiment examines the importance of the collaborative approach when the number of available drifting fishing aggregation devices (dFADs) per vessel is reduced.

Abstract
A notable shift from an internal combustion engine vehicles (ICEVs) fleet to an electric vehicles (EVs) fleet is expected in the medium term due to increasing environmental concerns and technological breakthroughs. In this context, this paper conducts a systematic literature review on life cycle assessment (LCA) research of EVs compared to ICEVs based on highly impactful articles. Several essential aspects and characteristics were identified and discussed, such as the assumed EV types, scales, models, storage technologies, boundaries, lifetime, electricity consumption, driving cycles, combustion fuels, locations, impact assessment methods, and functional units. Furthermore, LCA results in seven environmental impact categories were gathered and evaluated in detail. The research indicates that, on average, battery electric vehicles are superior to ICEVs in terms of greenhouse gas (GHG) emissions (182.9 g CO2-eq/km versus 258.5 g CO2-eq/km), cumulative energy demand (3.2 MJ/km versus 4.1 MJ/km), fossil depletion (49.7 g oil-eq/km versus 84.4 g oil-eq/km), and photochemical oxidant formation (0.47 g NMVOC-eq/km versus 0.61 g NMVOC-eq/km) but are worse than ICEVs in terms of human toxicity (198.1 g 1,4-DCB-eq/km versus 64.8 g 1,4-DCB-eq/km), particulate matter formation (0.32 g PM10-eq/km versus 0.26 g PM10-eq/km), and metal depletion (69.3 g Fe-eq/km versus 19.0 g Fe-eq/km). Emerging technological developments are expected to tip the balance in favor of EVs further. Based on the conducted research, we propose to organize the factors that influence the vehicle life cycle into four groups: user specifications, vehicle specifications, local specifications, and multigroup specifications. Then, a set of improvement opportunities is provided for each of these groups. Therefore, the present paper can contribute to future research and be valuable for decision-makers, such as policymakers.