Abstract
Measuring water motion is essential for oceanography, coastal engineering, and marine environmental monitoring. A wide range of sensing technologies is used to quantify water velocity, wave motion, and flow dynamics, each suited to specific spatial and temporal scales. This paper presents a comprehensive review of modern sensor technologies for marine flow measurement, covering mechanical, electromagnetic, pressure-based, acoustic, optical, MEMS-based, inertial, Lagrangian, and remote-sensing approaches. The operating principles, strengths, and limitations of each technology are examined alongside their suitability for different environments and deployment platforms, including moorings, buoys, vessels, autonomous underwater vehicles, and drifters. Special attention is given to rapidly advancing fields such as MEMS flow sensors, multi-sensor fusion, and hybrid systems that combine inertial, acoustic, and optical data. Applications range from high-resolution turbulence measurements to large-scale current mapping and wave characterization. Remaining challenges include biofouling, performance degradation in energetic shallow waters, uncertainties in indirect velocity estimation, and long-term calibration stability. By synthesizing the state of the art across sensing modalities, this review provides a unified perspective on current technological capabilities and identifies key trends shaping the future of marine flow measurement.

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Abstract
The advent of real-time telematics and advanced analytics has transformed maintenance in heavy-duty transport. Predictive maintenance systems now demand both reliable short-horizon failure alerts and precise Remaining Useful Life (RUL) forecasts to optimize service schedules, minimize operational risk, and support sustainability goals.This work tackles two complementary prognostic tasks under realistic deployment constraints: imminent-failure classification and continuous RUL estimation, using a recently released Scania truck dataset. The classification task must cope with extreme class imbalance and a cost structure that heavily penalizes overlooked failures far more than false alarms. Meanwhile, RUL estimation faces its own challenges: highly skewed target distributions, right-censored data, and shifting degradation dynamics across a diverse fleet.We propose a framework that integrates (1) a cost-sensitive Light-GBM classifier to minimize real-world misclassification expenses, and (2) a three-stage XGBoost regression ensemble in which each model specializes in one of three phases - healthy, early-degradation, or late-degradation - and uses log-transformed targets along with upweighted late-stage samples to stabilize training and prioritize critical short-horizon accuracy.Under a deployment-like validation protocol, the classifier achieved an AUC of 0.8024 and cut average misclassification cost by 27% versus a "one-class-early"benchmark. The RUL ensemble reached a global MAE of 19.8 time steps (10.4 within the final 20 steps) and demonstrated steadily improving precision as failure approached. These results confirm that cost-driven, health-stage-specialized models can deliver robust prognostics for industrial applications. © 2026 Copyright held by the owner/author(s).

Abstract
The wine production, included in the primary sector is a great cultural and economic deal, both nationally and internationally matters. However, it is highly dependent on natural resources, and traditionally involves high energy and water consumption. Given the global climate change scenario and the need for efficient resource management, it is necessary to implement a sustainable plan for the wine sector to realize sustainable practices. Data from the International Organization of Vine and Wine (OIV), states that global wine production exceeded 260 million hectoliters, in 2022. These has resulted in significant water and energy consumption, with around 500-1200 m(3) of water used per hectare for irrigation and 1.2 gigajoules per hectoliter of wine produced, concluding that more than 80% of total water consumption is associated with irrigation, while more than 90% of energy consumption, is associated with winery processes. In this context, the scarcity of water or the need to achieve carbon neutrality by 2050 makes it essential to adopt energy and water efficiency measures that allow for the sustainable management of resources without endangering the sector's viability. With this in mind, a case study applied to a Portuguese wine industry is presented, including data analysis from water and energy consumption. Also, efficiency metrics will be analyzed, proposing management and decision-support tools based on monitoring and sensor-based techniques. In fact, one example of these efficiency measures deals with the adoption of systems that provide real-time data on consumption patterns and resource availability in order to improve sustainability of the global process production.

Abstract
This work examines the impact of time-slice resolution on renewable energy integration outcomes in long-term energy planning using OSeMOSYS. The analysis focuses on the Portuguese power system over the period 2024-2050, analysed under three scenarios: one coarse (six time slices) and two finer (twelve and twenty-four time slices), all evaluated under strict cost optimisation. Key outputs include system costs, technology deployment, dispatch behaviour, and emissions trajectories. Results indicate that temporal structure directly shapes long-term planning outcomes. The coarse scenario smooths short-term variability and promotes investment in technologies, particularly solar photovoltaic and wind, while reducing the share of natural gas combined cycle (NGCC), presenting an optimistic decarbonisation pathway. Finer resolutions capture intra-day and seasonal fluctuations, revealing operational constraints, increasing NGCC capacity (1.3 to 2 GW), and moderating Solar PV and wind output. Overall, the findings demonstrate that temporal resolution is not a secondary modelling choice but a critical determinant of the credibility of long-term energy planning. Appropriate temporal segmentation is therefore essential for robust evaluation of policy options, system flexibility requirements, and sustainable energy transition strategies © 2026 IEEE.

Abstract
?Accurate recording of employee working hours is fundamental for workforce management, operational planning, and regulatory compliance. Despite the widespread adoption of digital time-tracking systems, timesheet records remain susceptible to irregularities that can distort labor metrics, productivity indicators, and cost estimations. This study proposes a domain-informed analytical framework for detecting, classifying, and interpreting anomalous entries in employee attendance data.The methodology integrates outlier detection with operational context in a structured workflow. First, six relative deviation features are engineered to capture directional differences between planned and recorded work and lunch periods, including start times, end times, and durations. These features are normalized to ensure comparability across heterogeneous shifts. Second, univariate Tukey’s fences are applied to identify mild and extreme outliers for each deviation feature. Extreme outliers are interpreted as potential measurement errors, whereas mild outliers are classified according to domain-defined directional rules as either operationally acceptable or operationally detrimental deviations. Third, unauthorized deviations are analyzed using Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) to reveal recurring behavioral patterns within the multidimensional deviation space. Finally, employee-level behavioral risk is quantified through a normalized Severity Index based on the frequency of unauthorized deviations relative to attendance frequency, enabling both global ranking and temporal monitoring.Applied to 4,726 anonymized timesheet records, the proposed approach effectively distinguishes measurement errors, acceptable deviations, and operationally detrimental behaviors while revealing structured patterns of noncompliance. By integrating robust statistics with domain knowledge, it enables scalable attendance analytics and workforce governance.