Abstract

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.

Abstract
Since the publication of the 2020 paper, Finding the Gaps About Uses of Immersive Learning Environments: A Survey of Surveys, the landscape of immersive learning environments (ILEs) has continued to evolve rapidly. This update aims to revisit the gaps identified in that previous research and explore emerging trends. We conducted an extensive review of new surveys published after that paper's cut date. Our findings reveal a significant amount of new published reviews (n = 64), more than doubling the original corpus (n = 47). The results highlighted novel themes of usage of immersive environments, helping bridge some 2020 research gaps. This paper discusses those developments and presents a consolidated perspective on the uses of immersive learning environments.

Abstract
Current residential electricity tariffs often combine a flat energy price with a fixed charge linked to contracted power, resulting in electricity bills that are weakly responsive to changes in consumption. This lack of proportionality reduces incentives for energy savings and may undermine demand-side efficiency.This paper proposes a novel Power-Conditioned Pricing (PCP) tariff, in which unit energy prices depend on the power level at which electricity is consumed. By associating higher prices with higher consumption intensity, the proposed tariff introduces progressivity while preserving transparency and regulatory feasibility. The tariff is calibrated to ensure revenue neutrality with respect to the current tariff for each contracted power level.Two complementary calibration strategies are analysed: a profile-based approach using representative regulatory load profiles, and an empirical approach based on statistical distributions derived from real consumer data. To assess consumer responsiveness, electricity bills are evaluated under both vertical and horizontal consumption adjustment models.Results show that bill elasticity increases from values between 0.43–0.73 under the current tariff to values close to unity under PCP, while maintaining revenue neutrality across contracted power levels. These findings suggest that power-conditioned pricing constitutes a promising alternative to current residential tariff structures, better aligned with energy-efficiency and conservation objectives.