Various ensemble methodologies for building cooling energy use prediction

Due to the significant increase in cooling demand, accurately predicting cooling energy consumption in buildings has become essential. Machine learning techniques have been widely used for this purpose, with ensemble methods, which combine the outputs of multiple individual models, showing strong performance in handling its complexity. The primary objective of this study is to provide a systematic analysis of various ensemble aggregation strategies, including some novel combinations. Both homogeneous and heterogeneous ensembles are analyzed and tested using the same real-world measured dataset. Random forest and gradient boosting machine, which represent homogeneous ensembles that utilize bagging and boosting techniques, respectively, have proven to outperform individual models. Four prominent base models, namely feedforward neural network, decision tree, support vector regression, and gaussian process regression, are utilized to construct eight new heterogeneous ensembles. These base models are combined via weighted averaging, with various strategies for assigning weights systematically tested. The stacking/blending approach is also evaluated using all four base models along with multiple linear regression as meta learners. Results show that weighted averaging enhances prediction accuracy, with the best results obtained by solving an optimization problem that minimizes the mean squared error. Additional improvements are seen when an machine learning model acts as the aggregator, with the highest performance achieved using an feedforward neural network as the meta-learner. Despite its simplicity and low computational demands, multiple linear regression proves to be an effective meta-model, providing a solution that requires less extensive hyperparameter tuning compared to other nonlinear machine learning models.po Ugovoru 451-03-137/2025-03/ 20010

Travel Time and Performance Evaluation of the Multiple-Deep Shuttle-Based Storage and Retrieval Systems

under Contract 451-03-137/2025-03/ 200105 dated 02/04/2025; Phone. +381 11 3370-760, fax. +381 11 3370-364 www.mas.bg.ac.r

Dozator pigmenta za nijansiranje boje

Техничко решење припада области механика флуида – хидраулика и пнеуматика, подгрупа дозатори пигмента, а конкретније се ради о новом технолошком поступку захватања и дозирања пигмента у основну боју у циљу стварања одређене нијансе. Нијансирање боја је сложен технолошки процес током којег се од основне боје (базе) ствара боја жељене нијансе. Нијанса је дефинисана запреминским садржајем различитих пигмената убризганих у базу. Да би се обезбедила тачност нијансе и њена поновљивост у два одвојена процеса нијансирања неопходан је уређај за прецизно дозирање убризгане запремине пигмента у базу. Нова конструкција оваквог уређаја је предмет овог техничког решења

Novel Criterion on Finite-Time Stability of Fractional-Order Time Delay Human Balancing Systems

This paper studies the issues of human balancing and stability in the sagittal plane using fractional and integer order time delay feedback control. The neural-mechanical model of human balance is represented as an inverted pendulum controlled by torque. We present a finite-time stability (FTS) analysis for closed-loop neutral time delay systems (NFOTDSs) with fractional order 1<beta<alfa<= 2 . By employing a generalized Gronwall in equality, we derive new FTS criteria for these systems in terms of the Mittag-Leffler function. Finally, a suitable numerical example is presented to show the effectiveness of the proposed method.No. 51-03-34/2026-03/200105 od 05.02.2026

Hippopotamus Optimization for Dynamic Flexible Job Shop Scheduling under Machine Tool Breakdowns

Dynamic flexible job shop scheduling under machine tool breakdowns represents a complex and highly constrained combinatorial optimization problem in modern manufacturing systems. This research paper proposes an integrated optimization framework that simultaneously determines operation sequencing, machine tool assignment, tool selection, and tool orientation with the objective function of minimizing makespan. A unified multi-string solution representation is developed to simultaneously model all decision layers. Three biologically inspired metaheuristic algorithms, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Hippopotamus Optimization (HO), are implemented using the proposed encoding scheme. A rescheduling strategy is introduced to preserve completed operations while rescheduling the affected operations after machine tool failures. Experimental verification demonstrates that the integrated framework effectively handles dynamic disturbances and significantly improves scheduling performance. Comparative analysis shows that the hippopotamus optimization algorithm achieves superior convergence behavior and better objective function values than the other approaches. The proposed method provides a robust framework for resilient scheduling under multiple resource constraints

Real-Time Algorithm for Nonlinear Optimal Impact Angle Guidance

This paper proposes a computationally efficient algorithm for nonlinear optimal guidance with a predefined final flight path angle. Although numerous impact angle guidance methods based on optimal control theory exist, a lack of efficient calculation procedures remains for the exact nonlinear engagement model, leaving practical hardware implementation challenges for the end-user. A fixed-structure algorithm with deterministic computational burden is developed for real-time onboard integration. The performance and optimality of the algorithm are verified through a comparative study with established guidance laws. Unlike methods relying on line-of-sight rate or time-to-go estimations, the proposed approach uses a closed-feedback form based on standard navigation data. A closed-form solution is derived for the climb phase to the cruise altitude. Practical feasibility is demonstrated on a microcontroller-based onboard computer, with execution times analyzed for flight software compatibility. The robustness of the proposed framework is validated via high-fidelity hardware-in-the-loop tests for two distinct scenarios: a multi-phase cruise mission and a short-range ballistic trajectory subject to propulsion uncertainties. Results confirm high precision and accurate impact angles across vastly different flight regimes, ranging from low-altitude cruise to high-dynamic reentry.451-03-34/2026-03/20010

Operational Hypervisor: Anomaly Detection and Handling for Automated Electric Vehicles

The transition toward fully automated electric vehicles (AEVs) demands robust safety mechanisms capable of addressing unforeseen internal critical situations without reliance on human drivers. Conventional diagnostic systems remain constrained to predefined failure modes and cannot capture all safety-critical anomalies. This paper introduces an operational hypervisor framework, an integrated anomaly detection and fault-tolerant control advisor, designed to enhance unforeseen internal-system critical situations in AEVs. The proposed approach combines data-driven anomaly detection, including isolation forests, correlation analysis, and explainable AI (XAI) to capture anomalous patterns across heterogeneous AEV subsystems. Beyond anomaly detection, the operational hypervisor functions as a fault-tolerant advisory layer, attributing anomalies to their probable sources, quantifying their potential impact, and recommending context-aware corrective actions to ensure safe operation. To ensure robustness, the framework employs multistage data preprocessing, improving sensitivity to both shortterm and persistent anomalies. Designed for real-time execution, the hypervisor can be seamlessly integrated into the control architecture of AEVs, allowing interaction with decision-making and vehicle supervisory layers. Experimental evaluation with real occurrences of safety-critical AEVs anomalies verifies the system’s capability to extend detection beyond conventional diagnostic limits and provide interpretable feedback to controllers. By coupling explainable anomaly detection with anomaly handling, operational hypervisor advances the state of operational safety management in AEVs

On the Axial-Bending-Torsional Vibration Coupling Effect Occurring in a Cantilever Beam with an Asymmetrically Attached Spatial Rigid Body

Purpose The analysis pertains to the coupled axial-bending-torsional vibration of an axially functionally graded cantilever beam of a non-uniform cross-section to which a rigid spatial body is fixed at its free end. The centre of mass of the rigid body is positioned eccentrically in the space. Methods Both the Euler-Bernoulli and Timoshenko beam theories are employed in this investigation. For cross-sections that are not circular, the Saint-Venant theory of torsional vibrations is applied. By employing the extended Hamilton’s principle,the governing differential equations alongside the respective boundary conditions are derived. The task of determining the associated frequency equations and mode shapes is transformed into solving a corresponding two-point boundary value problem (TPBVP), which consists of a system of first-order ordinary differential equations subject to the relevant boundary conditions. The TPBVP is resolved utilising the symbolic-numeric method of initial parameters. Results and Conclusions The impact of the eccentricity parameters of the mass centre and the inertia products of the spatial rigid body on the kind of vibration coupling is scrutinised. All possible forms of vibration coupling effects are recognised. For the coupling of all vibration types (axial vibration, bending vibration, and torsional vibration), it is essential that all three type of the eccentricities of the rigid body mass center are concurrently non-zero, irrespective of the values associated with the inertia products of the rigid body. The proposed numerical approach for determining frequencies and the corresponding mode shapes is applicable to non-uniform axially functionally graded beams of arbitrary cross-sectional shape. The orthogonality conditions for the coupled axial-bending-torsional vibration modes of the scrutinised cantilever beam are established within the frameworks of both the Euler-Bernoulli and Timoshenko beam theories. A numerical example is provided in which a high degree of agreement is shown between the results obtained using the presented method and the corresponding results obtained using the finite element method

CFD approach to full-scale resistance: The Lucy Ashton case

Accurate prediction of ship resistance remains a major challenge in Computational Fluid Dynamics (CFD), particularly when translating results from model to full scale. This study investigates the prediction of total resistance for the historic vessel Lucy Ashton using CFD across six model scales and full scale. Experimental resistance data were harmonized using third-order polynomial fits, enabling consistent comparison with CFD results. Two full-scale approaches were evaluated: Setup 1 with prescribed inflow and Setup 2 incorporating surge motion with applied thrust to emulate deckmounted jets used during sea trials. Across all scales, CFD predictions showed strong agreement with experiments, with deviations typically within ±5%, consistent with accepted validation standards. Dynamic motions (heave and pitch) were also examined, and both setups produced nearly identical trends, with absolute differences negligible for resistance assessment. The results demonstrate that both CFD methodologies provide reliable full-scale resistance estimates.Project no. 451-03-137/2025-03/20010

Retention of quality and antioksidant capasity of the berry fruits by freezing

Freezing is widely used in the food industry, especially for long-term preservation of berry fruits. Freezing is considered a very suitable method for processing delicate fruits, due to the product tends to retain its original attributes, i.e., keeping its nutritional properties as close as possible to those of fresh fruit. In recent times, more attention has been focused on berry fruits, due to their very attractive sensory property closely related to popularity among consumers, as well as great economic importance. Furthermore, berry fruits are considered one of the richest sources of bioactive com-pounds and natural antioxidants. Their consumption has been linked to prevention of some chronic and degenerative diseases. In this paper, the effect of freezing on the quality and antioxidants capacity of the strawberry, raspberry and blackberry fruits was evaluated. The freezing was carried out under industrial conditions, discontinually in a chamber at an air temperature of -30 °C, until -18 °C was reached in the thermal center of the fruits. Physicochemical properties of the fresh berries, as well as after freezing were analyzed and the Quality Index (QI) was calculated. Antioxidants capacity of the strawberry, raspberry and blackberry fruits was determined before and after freezing by DPPH method. The obtained results indicated that no significant changes in the physicochemical parameters after freezing of the berry fruits. In addition, the determined antioxidants capacity was significantly higher in frozen raspberry and blackberry fruits. According to the examination and the obtained results, it may be concluded that the physicochemical properties of the berry fruits are maintained by freezing under industrial conditions, as well as their antioxidants capacity