Abstract
White-light interferometry (WLI) enables the optical interrogation of interferometric sensors with different optical path lengths. However, its implementation using all-optical fiber technology is challenging when the interrogation dynamic range of approximately 1000 mu m or higher is required at sampling frequencies around or above 750 Hz. This paper presents an integration of the WLI technique with the optical Vernier effect (WLIVE), allowing an effectively expanded interrogation dynamic range to be achieved. To accomplish this, the optical interrogator is composed of two interferometers. The measurement window is determined by one interferometer operating at a high-frequency oscillation, while the second interferometer, characterized by a low-frequency oscillation, provides the tuning mechanism (tuning filter). The operation relies on the analysis of the carrier or envelope signal, which results from the beating between the interferometric sensor cavity and the tunable filter, when the sensor optical path difference (OPD) exceeds the physical limit of the measurement band. The experimental results confirm the repeatability of the system, achieving an average experimental error of 0.29 +/- 0.07%, as well as an expansion of the interrogation dynamic range from 180 +/- 2 mu m to 1352 +/- 2 mu m. These results demonstrate the feasibility of WLIVE combination to achieve high sampling frequencies and large interferometric dynamic range, while simultaneously reducing the complexity of sensor cavity fabrication.

Abstract

Abstract
Robotic fleet management systems are increasingly vital for sustainable operations in agriculture, forestry, and other field domains where labor shortages, efficiency, and environmental concerns intersect. We present FORMIGA, a fleet management framework that integrates human operators and autonomous robots into a collaborative ecosystem. FORMIGA combines standardised communication through the Robot Operating System with a user-centered interface for monitoring and intervention, while also leveraging large language models to generate executable task code from natural language prompts. The framework was deployed and validated within the FEROX project, a European initiative addressing sustainable berry harvesting in remote environments. In simulation-based trials, FORMIGA demonstrated adaptive task allocation, reduced operator workload, and faster task completion compared to semi-autonomous control, enabling dynamic labor division between humans and robots. By enhancing productivity, supporting worker safety, and promoting resource-efficient operations, FORMIGA contributes to the economic, and environmental dimensions of sustainability, offering a transferable tool for advancing human-robot collaboration in field robotics.

Abstract
Wheeled mobile robots are increasingly deployed in harsh environments where dense obstacles, traps, variable terrain, soil effects, tight energy budgets, and sensor noise often deem classical navigation stacks insufficient. This paper presents a PRISMA-guided systematic review of recent work on Deep Reinforcement Learning (DRL) for wheeled ground-robot navigation in harsh environments and organizes the field via a practical six-dimensional taxonomy: environmental challenges, navigation architecture, observation modality, action strategy, action space, and learning algorithm. The taxonomy is refined through an iterative, evidence-grounded coding process on the included studies, and applied under a transparent coding protocol to support reproducible categorization. Across the literature, DRL appears both as a planner module as well as end-to-end policy (behavior) implementer tool. Regarding observation, mapless navigation based on LiDAR or cameras are prevalent. Actions are predicted mostly one time step ahead and are continuous. Actor-critic methods are prevalent, notably PPO and SAC are the common DRL methods used. As for the evaluation methodology, it remains largely simulation-based, with only limited sim-to-real protocols. Building on these findings, we use the previously mentioned taxonomy to identify common design choices for navigation in harsh terrains, propose minimum reporting practices to enable reproducible comparison, and propose research directions including energy-aware learning, improved robustness to sensor degradation, all weather soil-vehicle interaction modeling, short-horizon look-ahead for stability and smoothness, standardized tasks and metrics. The proposed taxonomy and guidelines, as well as identified trends, intend to help researchers and practitioners select methods that best suits their own objectives and constraints, thus hopefully accelerating progress from promising simulation results to dependable, field-ready autonomy.

Abstract
This study aimed to identify how service operations managers perceive the effects of task duration variability and activity pooling on key performance indicators such as flow time, queue length, perceived service quality, and customer satisfaction. A scenario-based experiment was conducted with 229 professionals working in service operations in Brazil and Portugal. Participants evaluated fictional processes with varying levels of variability (low vs. high) and task allocation formats (specialized vs. pooled). All scenarios were validated through computer simulations prior to the experiment. The results reveal a gap between analytical models in the literature and managerial perceptions. While queuing theory associates increased variability with performance deterioration, respondents frequently attributed positive effects to higher variability and activity pooling, especially in relation to perceived quality. The study contributes by uncovering managerial interpretations that diverge from established operations management principles, suggesting the need for greater integration between analytical approaches and service-oriented perspectives. From a practical standpoint, the findings underscore the importance of strengthening managerial training in process analysis and promoting the use of computational tools as support for decision-making in complex service operations.

Abstract
We present a federated edge-intelligence framework for smart-mobility cybersecurity that integrates Edge AI, Federated Learning (FL), and blockchain anchoring, and we provide a runnable artifact for full reproducibility. Using a synthetic IDS-like workload with non-IID client splits, we benchmark centralised, edge-only, and FL (FedAvg) training while accounting for communication, a latency proxy, and a FLOPs-based energy index. FL maintained near-centralised accuracy (˜99.8%) and F1 (0.9932–0.9938), whereas edge-only degraded under client skew (˜85.3% accuracy; F1 ˜ 0). Training-time communication for FL was 98.96% lower than centralised at 5 clients/10 rounds (0.033 MB vs. 3.206 MB) and 97.99% lower at 10 clients/10 rounds (0.065 MB vs. 3.206 MB). The latency proxy grows linearly with FL rounds yet remains well below centralised inference (132 ms vs. 3,301 ms at 5 clients/10 rounds). Energy results follow expectations: edge-only lowest (0.000832), centralised mid (0.001040), and FL highest due to local training (0.001300–0.001560). Overall, the results quantify accuracy/communication/latency/energy trade-offs and show that federated-edge learning preserves accuracy under client heterogeneity while minimizing raw-data transfer; blockchain anchoring adds only a small, parameterized per-commit overhead. All configurations and logs are released to enable exact reproduction. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.