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Sustainable and Safe Last-Mile Delivery: A Multi-Objective Truck–Drone Matheuristic
Full text linkBackground: The rapid growth of e-commerce has intensified the need for last-mile delivery systems that can navigate urban congestion while minimizing environmental impact. Hybrid truck–drone networks offer a promising solution by combining heavy-duty ground transport with aerial flexibility; however, their deployment faces significant challenges in jointly managing operational risks, energy limits, and regulatory compliance. Methods: This study proposes a hybrid matheuristic framework to solve this multi-objective problem, simultaneously minimizing transportation cost, service time, energy consumption, and operational risk. A two-phase approach combines a metaheuristic for initial truck routing with a Mixed-Integer Linear Programming (MILP) formulation for optimal drone assignment and scheduling. This decomposition strikes a balance between exact optimization and computational scalability. Results: Experiments across various instance sizes (up to 100 customers) and fleet configurations demonstrate that integrating MILP enhances solution diversity and convergence compared to standalone strategies. Sensitivity analyses reveal significant impacts of drone speed and endurance on system efficiency. Conclusions: The proposed framework provides a practical decision-support tool for balancing complex trade-offs in time-sensitive, risk-constrained delivery environments, thereby contributing to more informed urban logistics planning
The Impact of Staff and Manager Training on Firm Productivity: Differential and Interaction Effects
Full text linkProductivity is a crucial goal for firms, yet training investments to develop employee skills and enhance productivity face scrutiny. Despite considerable research into training inputs and performance outcomes, several gaps remain. We investigate the differential effects of managerial and staff training on labour productivity, drawing on human capital theory to explain the value of training investment for workforce development. The analysis focuses on 19,289 firm-year observations from the UK Employer Skills Survey and Investment in Training Survey paired with the Business Structure Database in five waves over a 9-year period, accounting for potential selection bias. Examining several measures of training for different occupational categories and formal-informal modes of training, we find that productivity rose with greater training investment for both managers and staff. Among staff occupational categories, training for professionals and associate professionals yielded particular benefits for the firm. The interaction of staff and managerial training generated further gains, illustrating the value of complementary skill development for different employee levels, especially prioritizing intensity of training expenditure over broad coverage
On the dynamics of intersectional (in)visibility: women early career researchers negotiating authenticity at work
Full text linkHow do women negotiate and express authenticity in professional contexts where their presence and identities are largely rendered (in)visible? We draw on intersectional invisibility as our conceptual lens to explore how women early career researchers subjectively negotiate authenticity given prevailing conditions of visibility, invisibility and hypervisibility at work. Based on semi-structured interviews with recipients of the Organisation for Women in Science from Developing Countries (OWSD)-Elsevier award, we illuminate how (in)visible conditions shape the subjective negotiation of authenticity, informing the agentic capacity of women researchers to express themselves authentically in professional settings. Our findings reveal the negotiation of authenticity is closely tied to gender performance in a manner that aligns with perceived professionalism. This entails compartmentalising personal values when feeling invisible, experiencing a heightened awareness of context-specific boundaries when visibility increases and enacting adaptive agency when hypervisible. We thus posit authenticity as a continuous process of ongoing identity construction and negotiation rather than a static ideal
Outcomes and future activities of the ‘Pan-European network in Lipidomics and EpiLipidomics – EpiLipidNET’
Full text linkBackground: Lipidomics and its branch epilipidomics are rapidly advancing fields that explore the roles of native and modified lipids (e.g., oxidized, nitrated and halogenated lipid species), respectively, in biological systems. Dysregulation of lipid metabolism and signaling contributes to numerous diseases, including cardiovascular, metabolic, neurodegenerative, and inflammatory conditions. However, multiple challenges, including lack of standardization, limited data integration, and poor clinical translation, hinder progress. To address these, the COST Action EpiLipidNET (CA19105) established a pan-European network fostering collaboration across disciplines to accelerate lipid science and its application to health and disease. Aim of review: This review outlines the achievements of EpiLipidNET over its four-year duration, highlights key scientific contributions across five thematic working groups, and presents the future direction of its ongoing activities. The aim is to demonstrate how a collaborative, interdisciplinary framework can catalyze innovation in lipidomics and epilipidomics, enhance methodological harmonization, support early-career researchers, and bridge the gap between basic science, clinical translation, industry, stakeholders, and public engagement. Key scientific concepts of review: EpiLipidNET structured its networking activities around (i) harmonization of analytical workflows, (ii) development of epilipidomics tools and data integration strategies, (iii) translational studies for clinical lipid biomarkers, (iv) investigation of lipid signaling mechanisms, and (v) dissemination and outreach. The network supported over 460 members globally, launched multiple training schools and scientific missions, produced 110+ publications, and fostered new initiatives in endothelial membrane lipidomics, food lipidomics, plant and algae lipids, and redox lipid biology. Its integrative approach sets a foundation for continued progress toward precision medicine and sustainable health interventions through lipid science
Multidimensional analysis of interrelationships between money laundering risks, state vulnerability, and institutional transformation: Synergistic security effects
Full text linkThe article examines the interrelations among the key determinants of anti-money laundering (AML) systems, including money laundering risks, state vulnerability and institutional transformation, in the context of their legal regulation, aiming to identify systemic patterns in security challenges. An integrated methodological approach is applied, combining correlation analysis, correspondence analysis and quadratic modeling to explore the interdependencies between the quantitative measures of these determinants–the Basel AML Index (AMLI), the Fragile States Index (FSI), and the Bertelsmann Transformation Index (BTI). Statistical analysis revealed strong correlations: a positive relationship between AMLI and FSI (r = 0.70), and inverse relationships between AMLI and BTI (r = –0.81) and FSI and BTI (r = –0.77), all statistically significant (p = 0.00). A three-dimensional quadratic model demonstrates nonlinear threshold effects, where small changes in institutional capacity produce disproportionate shifts in risk and combined vulnerabilities amplify threats exponentially. Three critical risk zones were identified: ‘double weakness’, ‘institutional collapse’ and ‘pseudo-stability’. A paradoxical effect of ‘compensatory regulation’ was observed, where weaker states intensify formal AML measures to offset institutional deficiencies. The economic determinism of security risks was confirmed, as higher-income countries tend to perform better in AML
Perception of concurrent sentences: Within and across-formant grouping in harmonic and frequency-shifted speech
Full text linkKeyword identification in one of two simultaneous sentences is substantially improved when they differ in fundamental frequency (F0); this effect is greatest for almost continuously voiced speech. ΔF0 may act by improving voice identification [better first-formant (F1) definition or better across-formant grouping] or voice tracking. Sentences were monotonized and resynthesized to give a range of ΔF0s (0–10 semitones; F0s = 90–160 Hz). Sentences were additionally resynthesized after applying a frequency shift of 25% of F0 to the monotonized excitation source—making it inharmonic but with regularly spaced components—while preserving the original formant frequencies. Sentence pairs were created by embedding shorter targets within longer interferers. The large improvement with increasing ΔF0 found for harmonic sentences was reduced but still substantial for frequency-shifted sentences. In both cases, swapping target and interferer F0s across spectral regions (below vs above 800 Hz, F1 vs higher formants) caused substantial intelligibility loss only for large ΔF0s, indicating an important effect of changes in across-formant grouping. Listeners made few target-tracking errors, but these errors were more frequent for smaller ΔF0s and for stimuli with more ambiguous pitches. The results extend the range of perceptual phenomena usually attributed to harmonic processing to grouping by spectral regularity
Defining Quantum Agents: Formal Foundations, Architectures, and NISQ-Era Prototypes
Full text linkQuantum computing offers potential computational advantages, yet its integration into autonomous decision-making systems remains largely unexplored. This paper addresses the need for a unified framework that systematically combines quantum computation with agent-based artificial intelligence. We examine how quantum technologies can enhance the capabilities of autonomous agents and, conversely, how agentic AI can support the advancement of quantum systems. We analyze both directions of this synergy and present conceptual and technical foundations for future quantum–agentic platforms. Our work introduces a formal definition of quantum agents and outlines architectures that integrate quantum computing with agent-based systems. As concrete proof-of-concept implementations, we develop and evaluate three quantum agent prototypes: (i) a Grover-based decision agent for quantum search-driven action selection, (ii) a variational quantum reinforcement learning agent for adaptive policy learning in a multi-armed bandit setting, and (iii) an adaptive quantum image encryption agent that autonomously selects encryption strategies based on entropy-driven feedback. These prototypes demonstrate practical realizations of quantum agency in decision-making, learning, and security contexts under NISQ-era constraints. Furthermore, we discuss application domains including quantum-enhanced optimization, hybrid quantum–classical orchestration, autonomous quantum workflow management, and secure quantum information processing. By bridging these fields, we introduce a structured theoretical and architectural framework for quantum–agentic systems, providing formal definitions, system models, and early operational prototypes that illustrate the feasibility of quantum-enhanced agency under NISQ constraints
Haptic Teleoperation in Extended Reality for Electric Vehicle Battery Disassembly using Gaussian Mixture Regression
Full text linkWe present a comprehensive teleoperation framework for electric vehicle (EV) battery cell handling, integrating haptic feedback, extended reality (XR) visualization, and task-parameterized Gaussian mixture regression (TP-GMR) for adaptive, real-time trajectory generation. The system enables seamless switching between manual and autonomous operation through a variable autonomy mechanism, while constraint barrier functions (CBFs) enforce spatial safety constraints. A lightweight intent prediction module anticipates user deviation and precomputes corrective trajectories, reducing response time from 2.0 s to under 1 ms. The framework is implemented on an industrial KUKA robotic manipulator and validated in structured and real-world EV battery disassembly scenarios. Results show that combining XR and haptic feedback reduces task completion time by up to 48% and path deviation by 32%, compared to manual teleoperation without assistance. Predictive replanning improves continuity of force feedback and reduces unnecessary user motion. The integration of XR-based spatial computing, learning-from-demonstration, and real-time control enables safe, precise, and efficient manipulation in high-risk environments. This study demonstrates a scalable human-in-the-loop solution for battery recycling and other semi-structured tasks, where full automation is impractical. The proposed system significantly improves operator performance while maintaining safety and flexibility, marking a meaningful advancement in collaborative field robotics
Task-aware motion planning in constrained environments using GMM-informed RRT planners
Full text linkThis paper introduces a novel integration of Task-Parameterized Gaussian Mixture Models (TP-GMM) with sampling-based motion planners, specifically RRT, to improve planning efficiency and path optimality in constrained robotic manipulation tasks. The proposed GMM-RRT and GMR-RRT planners exploit a TP-GMM trained offline on human demonstrations to generate task-adaptive sampling distributions, effectively guiding the search toward feasible and high-quality solutions. The framework is implemented in the MoveIt motion planning framework and evaluated across five simulation experiments and 30 real-world trials, focusing on Electric Vehicle (EV) battery disassembly tasks. Compared to baseline sampling-based planners, the GMM-informed planners demonstrate superior performance in key planning metrics. In the path length aspect, GMM planners yield significantly shorter trajectories, averaging 0.8 meters versus over 2 meters for baseline planners. Similarly, in path simplification time, the near-optimal nature of the generated paths reduces post-processing efforts. While planning time is higher due to TP-GMM inference and projection stages, over 90% of that time is spent outside the RRT search itself, which completes quickly due to guided sampling. Path duration also remains competitive, with GMM-informed planners closely matching RRT*. These results highlight the effectiveness of task-conditioned sampling in unstructured manipulation scenarios. The proposed method maintains 100% success rate while improving efficiency, suggesting strong potential for integration in sequential and adaptive robotic systems. Future work will focus on extending generalization to broader task parameter spaces and addressing inverse kinematics challenges
Morphemes in the wild: Modelling affix learning from the noisy landscape of natural text
Full text linkMorphological knowledge serves as a powerful heuristic for vocabulary growth and contributes significantly to the speed and efficiency of reading. While research has long sought to explain how the knowledge of derivational morphology is acquired, previous approaches have struggled to capture the nuanced and complex ways in which derivational morphemes are used in written language, particularly that these morphemes contribute to meaning in a graded manner and that noise introduced by misleading forms (e.g., deliver) can impede learning. Our approach builds on earlier insights but moves beyond them by combining a large-scale analysis of vocabulary used in 1,200 popular books with computational modelling to explore how learning of derivational affixes may occur from text containing naturally occurring noise. We use a compositional distributional semantic model to investigate what can be learned about the meanings of individual English prefixes and suffixes through reading and evaluate the model’s performance against data from 120 adults in a lexical processing task. Our findings demonstrate that, despite the presence of noise, natural text contains sufficient structure to support the extraction of core affix semantics, and that readers are attuned to the complex patterns that shape affix use in the wild. This work contributes a new dimension to a more principled and psychologically grounded account of morpheme learning, and we discuss both this contribution and the broader insights it offers for language research
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