Search Results (875)

The decarbonisation of the construction sector represents a central pillar of sustainable development strategies, contributing simultaneously to climate change mitigation, energy efficiency, energy security, and long-term socio-economic resilience. In this context, the European regulatory framework increasingly recognises the role of digitalisation and smart technologies in improving building performance beyond static energy efficiency indicators. The Smart Readiness Indicator (SRI), introduced in Energy Performance of Buildings Directive IV (EPBD), is designed to evaluate a building’s ability to optimise energy usage, adapt to the needs of its occupants, and interact intelligently with energy networks through automation and control systems. However, the scientific literature has only partially explored its potential contribution to sustainability-oriented decision-making and decarbonisation governance. This study adopts a conceptual and methodological research approach to investigate the role of the SRI as a sustainability-oriented assessment and governance tool for building decarbonisation. The paper develops a multi-scale analytical framework based on a structured synthesis of the scientific literature, European policy documents and evidence emerging from national SRI test phases. The framework systematically links smart readiness functionalities with digital modelling tools, automation systems, and decarbonisation objectives across building, system, and policy levels. The results highlight that the SRI can be interpreted not only as a descriptive rating scheme, but also as a strategic instrument for assessing sustainability, capable of supporting environmentally, economically, and operationally sustainable decision-making in the built environment. This study contributes to the advancement of sustainability assessment tools that enable the monitoring, governance and long-term decarbonisation of the building stock in line with European climate and sustainability goals by reframing the SRI within a digital and decarbonisation-oriented methodological perspective. Full article

Urban sustainability heavily relies on efficient transportation systems, with dynamic bus lanes (DBL) being crucial components. However, traditional DBLs often face underutilization, leading to inefficient road usage. To this end, a novel IoT-Enabled Dynamic Bus Lane System (IoT-DBL) has been proposed, aimed at improving road utilization and reducing vehicle energy consumption. To assess the effectiveness of IoT-DBL, we developed a Markov chain-based queuing model and established a comprehensive evaluation framework through various performance metrics. Theoretical analysis reveals that the IoT-DBL system significantly improves intersection efficiency and reduces vehicle fuel consumption. Further optimization using a genetic algorithm (GA) identifies the optimal deployment length of IoT-DBLs to minimize fuel consumption. Numerical experiments demonstrate that the IoT-DBL strategy significantly outperforms traditional DBL methods, reducing queue lengths by 71.15%, vehicle delays by 69.48%, and fuel consumption by 70.42%, while increasing intersection efficiency by 100.11%. These results highlight that the IoT-DBL system can substantially improve traffic conditions, alleviate congestion, decrease fuel consumption, and enhance overall intersection efficiency, thereby providing a promising solution for sustainable urban transportation. Full article

Feature extraction and description are fundamental components of visual perception systems used in applications such as visual odometry, Simultaneous Localization and Mapping (SLAM), and autonomous navigation. In resource-constrained platforms, such as Unmanned Aerial Vehicles (UAVs), achieving real-time hardware acceleration on Field-Programmable Gate Arrays (FPGAs) is challenging. This work demonstrates an FPGA-based implementation of an adaptive ORB (Oriented FAST and Rotated BRIEF) feature extraction pipeline designed for high-throughput and energy-efficient embedded vision. The proposed architecture is a completely new design for the main algorithmic blocks of ORB, including the FAST (Features from Accelerated Segment Test) feature detector, Gaussian image filtering, moment computation, and descriptor generation. Adaptive mechanisms are introduced to dynamically adjust thresholds and filtering behavior, improving robustness under varying illumination conditions. The design is developed using a High-Level Synthesis (HLS) approach, where all processing modules are implemented as reusable hardware IP cores and integrated at the system level. The architecture is deployed and evaluated on two FPGA platforms, PYNQ-Z2 and KRIA KR260, and its performance is compared against CPU and GPU implementations using a dedicated C++ testbench based on OpenCV. Experimental results demonstrate significant improvements in throughput and energy efficiency while maintaining stable and scalable performance, making the proposed solution suitable for real-time embedded vision applications on UAVs and similar platforms. Notably, the FPGA implementation increases DSP utilization from 11% to 29% compared to the previous designs implemented by other researchers, effectively offloading computational tasks from general purpose logic (LUTs and FFs), reducing LUT usage by 6% and FF usage by 13%, while maintaining overall design stability, scalability, and acceptable thermal margins at 2.387 W. This work establishes a robust foundation for integrating the optimized ORB pipeline into larger drone systems and opens the door for future system-level enhancements. Full article

Growing pressure on electricity grids and the increasing availability of smart meter data have intensified the need for accurate, interpretable, and scalable methods to analyze and forecast household electricity consumption. In this context, this study presents a general, data-agnostic methodology for predicting and classifying household energy consumption. The proposed workflow unifies data preparation, feature engineering, and machine learning techniques (including clustering, classification, regression, and time series forecasting) within a single interpretable pipeline that supports actionable insights. Rather than proposing new prediction algorithms, this work contributes a fully reproducible, end-to-end methodological pipeline that enables the controlled evaluation of the impact of contextual variables, customer segmentation, and cold-start conditions on household energy forecasting. A distinctive aspect of the pipeline is the explicit use of household- and dwelling-level contextual variables to derive customer typologies via clustering and to enrich forecasting models. The models are evaluated for predictive accuracy, reliability under varying conditions, and suitability for operational use. The results show that incorporating contextual variables and clustering significantly improves forecasting accuracy, particularly in cold-start scenarios where no historical consumption data are available. Although numerous public datasets of residential electricity consumption exist, they rarely provide, in an openly accessible form, both detailed load histories and rich contextual attributes, while many are subject to privacy or licensing restrictions. To ensure full reproducibility and to enable controlled experiments where contextual variables can be switched on and off, the experiments are conducted on a synthetically generated dataset that reproduces realistic behavior and seasonal usage patterns. However, the proposed methodology is independent of the specific data source and can be directly applied to any real or synthetic dataset with similar structure. The approach enables applications such as short- and long-term demand forecasting, estimation of household energy costs, and forecasting demand for new customers. These findings demonstrate that the proposed pipeline provides a transparent and effective framework for end-to-end analysis of household electricity consumption. Full article

This paper presents an efficient parameter optimization approach to the plastic injection molding process to achieve high productivity. In collaboration with a company specializing in plastic injection-mold-based production, real process data was collected and used in this research. The result is an integrated framework, combining a genetic algorithm (GA) with a CatBoost-based surrogate model for multi-objective optimization of the injection molding machine parameters. The aim of the optimization is to minimize the cycle time and cycle energy while maintaining the product quality. Ten process parameters were optimized, which are machine-specific. An evolutionary optimization using the NSGA-II algorithm is used to generate the recommended parameter set. The proposed GA-surrogate hybrid approach produces the optimal set of parameters that reduced the cycle time by 4.5%, for this specific product, while maintaining product quality. Cycle energy was evaluated on an hourly basis; its variation across candidate solutions was limited, but it was retained as an optimization objective to support energy-based process optimization. A total of 95% of the generated solutions satisfied industrial quality constraints, demonstrating the robustness of the proposed optimization framework. While classical Design of Experiment (DOE) approaches require sequential physical trials, the proposed GA-surrogate framework achieves convergence in computational iterations, which significantly reduces machine usage for optimization. This approach demonstrates a practical way to automate data-driven process optimization in an injection mold machine for an industrial application, and it can be extended to other manufacturing systems that require adaptive control parameters. Full article

Machine tools are the major consumers of industrial energy, but their energy efficiency remains low, posing a serious challenge to sustainable manufacturing. The current literature predominantly focuses on isolated subsystems or specific operational phases (e.g., cutting parameters), lacking systematic evaluations of how different methodologies interact within the Life Cycle Assessment (LCA) framework. This paper provides a critical synthesis of three core methodologies—modeling methods, system parameter optimization, and machine learning (ML)—across the design/production, usage, and recycling stages. Unlike descriptive reviews, this study highlights the scientific contribution by defining the applicability boundaries and complementary mechanisms of these approaches. The analysis reveals that while modeling lays the theoretical basis for eco-design and remanufacturing assessments, and optimization effectively resolves multi-objective trade-offs, these static methods struggle with the dynamic complexity of real-time operations where ML excels. However, ML is identified to be constrained by high data dependency and poor generalization in heterogeneous environments. Consequently, this review shows that the ‘cross-application’ of modeling methods and machine learning to construct hybrid models is essential for addressing complex nonlinear relationships and achieving accurate energy prediction throughout the entire life cycle. Finally, future directions such as transfer learning and digital twins are proposed to overcome current generalization bottlenecks, providing a theoretical foundation for the industry’s transition from passive energy assessment to active, intelligent energy management. Full article

Efficient waste management is a critical challenge in urban areas. This paper explores the optimization of power consumption in a smart bin management system using LoRa (long-range) communication technology. LoRa’s low-power, wide-area capabilities make it an ideal choice for IoT-based waste management systems. However, energy efficiency remains a crucial factor for ensuring the long-term sustainability of such systems, to avoid frequent intervention and reduce operating costs. This study employs advanced optimization techniques to minimize the energy usage of LoRa nodes while maintaining a reliable data transmission and system performance. By integrating a dynamic scheduling algorithm based on the usage of bins, and a custom adaptive data rate and power algorithm, the proposed solution significantly reduces the system’s energy impact. The performance of the system is evaluated through simulations and real-world deployment, where the results demonstrate a significant reduction in energy usage, over 84%, a longer battery life, and fewer maintenance interventions. The findings provide a scalable and energy-efficient framework for deploying smart waste management systems in resource-constrained environments. Full article

Fenbendazole is an important anti-parasitic medicine widely used in the veterinary field and has recently been considered as a possible anti-cancer agent in humans by some researchers. Fenbendazole encounters challenges in its usage due to its limited aqueous solubility, which consequently impacts its therapeutic efficacy. In this work, an in vitro mechanistic investigation was conducted to evaluate the compatibility, amorphization behaviour and dissolution profile of fenbendazole dispersed in Soluplus^® using the solid dispersion approach via hot-melt extrusion. Three different fenbendazole/Soluplus^® ratios were formulated and characterised through systematic experimentation. Powder X-Ray Diffraction (PXRD), Differential Scanning Calorimetry (DSC), Scanning Electron Microscopy (SEM), Energy Dispersive X-Ray (EDX) and Fourier Transform Infrared Spectroscopy (FTIR) were employed for thermal, physical, chemical and morphological analyses. The solubility of the drug formulation during a dissolution test was investigated using Ultraviolet–Visible (UV–Vis) spectrophotometric measurements. In vitro dissolution testing in acidic and neutral media was employed as a controlled environment to compare dissolution behaviour among different loadings. The extrudates demonstrated markedly enhanced apparent solubility compared to neat fenbendazole, with the 5% formulation showing the highest dissolution rate (approximately 85% after 48 h). This improvement can be attributed to better wetting properties and drug dispersion within the Soluplus^® matrix. This innovative strategy holds promise in surmounting fenbendazole’s solubility limitations, presenting a comprehensive solution to enhance its therapeutic effectiveness. Full article

The growing demand for floating offshore structures calls for lightweight, impact-resilient, and sustainable design approaches. This study explores the optimization of composite fibree layup in a 30 m hull subjected to slamming-type hydrodynamic loads. Although based on a recreational vessel, the model serves as a transferable case for offshore applications such as wave energy devices, offshore wind platforms, and floating PV systems. A finite element method (FEM) model was developed using shell elements and a sinusoidal time-dependent pressure to simulate slamming events on the wet surface of the hull. The response was evaluated under different fiber orientation schemes, aiming to reduce structural mass while maintaining stress levels within safety margins. Results showed that strategic layup optimization led to a measurable reduction in total material usage, without compromising structural integrity. These outcomes suggest multiple advantages, including an approximately 14% reduction in raw material demand, which in turn facilitates for potential downsizing of propulsion systems and transportation energy due to lighter structures. Such improvements contribute indirectly to reduced emissions and operational costs. The methodology presented offers a replicable approach to composite optimization under transient marine loads, with relevance for sustainable offshore structural design. Full article

In the context of small, battery-powered systems, a lightweight, reusable architecture is needed for integrated measurement, visualization, and cloud telemetry that minimizes hardware complexity and energy footprint. Existing solutions require high resources. This limits their applicability in Internet of Things (IoT) devices with low power consumption. The present work demonstrates the process of design, implementation and experimental evaluation of a single-cell lithium-ion battery monitoring prototype, intended for standalone operation or integration into other systems. The architecture is compact and energy efficient, with a reduction in complexity and memory usage: modular architecture with clearly distinguished responsibilities, avoidance of unnecessary dynamic memory allocations, centralized error handling, and a low-power policy through the usage of deep sleep mode. The data is stored in a cloud platform, while minimal storage is used locally. The developed system combines the functional requirements for an embedded external battery monitoring system: local voltage and current measurement, approximate estimation of the State of Charge (SoC) using a look-up table (LUT) based on the discharge characteristic, and visualization on a monochrome OLED display. The conducted experiments demonstrate the typical U(t) curve and the triggering of the indicator at low charge levels (LOW − SoC ≤ 20% and CRITICAL − SoC ≤ 5%) in real-world conditions and the absence of unwanted switching of the state near the voltage thresholds. Full article

Accurate short-term forecasting of renewable energy sources is essential for stable and efficient microgrid operation. Existing models primarily focus on either solar or wind prediction, often neglecting their combined stochastic behavior within isolated systems. This study presents a comparative evaluation of three machine-learning models—Random Forest, ANN, and LSTM—for short-term solar and wind forecasting in microgrid environments. Historical meteorological data and power generation records are used to train and validate three ML models: Random Forest, Long Short-Term Memory, and Artificial Neural Networks. Each model is optimized to capture nonlinear and rapidly fluctuating weather dynamics. Forecasting performance is quantitatively evaluated using Mean Absolute Error, Root Mean Square Error, and Mean Percentage Error. The predicted values are integrated into a microgrid energy management system to enhance operational decisions such as battery storage scheduling, diesel generator coordination, and load balancing. Among the evaluated models, the ANN achieved the lowest prediction error with an MAE of 64.72 kW on the one-year dataset, outperforming both LSTM and Random Forest. The novelty of this study lies in integrating multi-source data into a unified ML-based predictive framework, enabling improved reliability, reduced fossil fuel usage, and enhanced energy resilience in remote microgrids. This research used Orange 3.40 software and Python 3.12 code for prediction. By enhancing forecasting accuracy, the project seeks to reduce reliance on fossil fuels, lower operational costs, and improve grid stability. Outcomes will provide scalable insights for remote microgrids transitioning to renewables. Full article

Over the past decades, global air traffic has increased continuously, with passenger kilometers roughly doubling every fifteen to twenty years, and this trend is estimated to continue, with some adjustments due to COVID-19 impact. In response to the resulting environmental challenges, the European initiatives Flightpath 2050 and Clean Sky serve as central drivers of technological development aimed at achieving ambitious sustainability goals. Flightpath 2050 targets, relative to a reference engine from the year 2000, include a 75% reduction in CO₂ emissions per passenger kilometer, a 90% reduction in NO_x emissions, and a 65% reduction in noise emissions. These objectives highlight the urgent need for emission reduction strategies across all manufacturing domains, including turbine component production. This study evaluates the environmental impacts of the preturning and roughing operations employed in turbine disk production. The analysis focuses on these specific processes rather than the entire product, as the approach of process-level Life Cycle Assessments (LCA) are more universally applicable across different products, and their systematic combination can ultimately form a comprehensive product-level LCA. Operational data, such as energy usage, cooling lubricants, and compressed air, were gathered and processed from the equipment involved in manufacturing. The collected data were analyzed and modeled in Spheras life cycle assessment software LCA for Experts (version 10.9.0.20) to quantify the environmental effects of each process. The findings of the current research emphasize notable patterns of resource utilization and their respective environmental impacts. Furthermore, the Industrial Digital Twin Association (IDTA) Product Carbon Footprint (PCF) template was utilized to present the findings in a standardized manner, enabling effective data transfer between stakeholders. The results demonstrate the critical need to leverage machine data for sustainability analysis, providing inputs for industry practice enhancement and progress toward better environmental performance. Full article

The increasing core counts and architectural heterogeneity of modern processors make performance optimization insufficient if energy consumption is not simultaneously considered. By providing a novel characterization of how the interaction between hybrid architectures and system software disrupts the traditional correlation between execution speed and energy efficiency, this research study analyzes the performance–energy trade-offs of parallel LU matrix decomposition algorithms implemented in Java, focusing on the Crout and Doolittle variants. This study is conducted on four different platforms, including ARM-based, Hybrid x86, and many-core accelerators. Execution time and speedup are evaluated for varying thread counts, while energy consumption is measured externally to capture whole-system energy usage. Experimental results show that the configuration yielding the maximum speedup does not necessarily minimize energy consumption. While x86 systems showed energy savings exceeding 80% under optimal parallel configurations, the ARM-based platform required distinct thread counts to minimize energy consumption compared with maximizing speed. These findings demonstrate that energy-efficient configurations represent a distinct optimization space that often contradicts traditional performance metrics. In the era of hybrid computing, green software optimization must transition from a simplistic “race-to-sleep” paradigm toward sophisticated, architecture-aware strategies that account for the specific power profiles of heterogeneous cores to achieve truly sustainable high-performance computing. Full article

Existing office buildings in Australia contribute to 24% of the nation’s electricity consumption and 10% of greenhouse gas emissions, with energy use projected to rise by 84%. Meeting the 2050 sustainability target and United Nations (UN) 17 Sustainable Development Goals (SDGs) requires improving sustainability within existing office buildings. This systematic review examines net zero energy and climate resilience strategies in these buildings by analysing 74 studies from scholarly literature, government reports, and industry publications. The literature search was conducted across Scopus, Google Scholar, and Web of Science databases, with the final search in early 2025. Studies were selected based on keywords and research parameters. A narrative synthesis identified key technologies, evaluating the integration of net zero principles with climate resilience to enhance energy efficiency through HVAC modifications. Technologies like heat pumps, energy recovery ventilators, thermal energy storage, and phase change materials (PCMs) have been identified as crucial in reducing HVAC energy usage intensity (EUI). Lighting control and plug load management advancements are examined for reducing electricity demand. This review highlights the gap between academic research and practical applications, emphasising the need for comprehensive field studies to provide long-term performance data. Current regulatory frameworks influencing the net zero transition are discussed, with recommendations for policy actions and future research. This study links net zero performance with climate adaptation objectives for existing office buildings and provides recommendations for future research, retrofit planning, and policy development. Full article

This research examines the feasibility of recovering and recycling condensate water, a waste byproduct generated by Atlas Copco ZR315 FF industrial air compressors utilizing oil-free rotary screw technology with integrated dryers. Given the growing severity of global water scarcity, finding alternative water sources is essential for sustainable industrial practices. This study specifically evaluates the potential of capturing and treating compressed air condensate as a viable method for water recovery. The investigation analyzes both the quantity and quality of condensate water produced by the ZR315 FF unit. It contrasts this recovery approach with traditional water production methods, such as desalination and atmospheric water generation (AWG) via dehumidification. The findings demonstrate that recovering condensate water from industrial air compressors is a cost-effective and energy-efficient substitute for conventional water production, especially in water-stressed areas like Morocco. The results show a significant opportunity to reduce industrial water usage and provide a sustainable source of process water. This research therefore supports the application of circular economy principles in industrial water management and offers practical solutions for overcoming water scarcity challenges within manufacturing environments. Full article