Abstract

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

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.

Abstract
Matrix manufacturing requires close coordination between collaborative workcells, mobile robots, and battery management resources to support the execution of heterogeneous human-robot operations in reconfigurable production environments. This paper presents a cyber-physical robotic coordination framework for human-robot teams deployed in an industrial matrix manufacturing system, integrating a collaborative workstation, a fleet of mobile programmable cobots, and an automatic battery changer through ROS/OPC UA communication. The framework coordinates task execution, intra-logistics, and energy management through a decision layer that assigns operations to human and robotic agents, relocates idle mobile robots, and triggers battery swaps. Three coordination modules-a Battery Management Module, a Task Allocation Module, and a Robot Relocation Module-implement this pipeline by computing feasible execution plans at each scheduling cycle, accounting for human and robot capabilities, workstation availability, transport times, and battery state. The approach is validated on a deployed industrial matrix manufacturing platform through a disassembly task comprising human-only, robot-only, and human-robot collaborative operations, demonstrating the feasibility of coordinating heterogeneous robotic and human resources in a physical reconfigurable manufacturing environment.