Search Results (17,698)

Subsoil compaction is a major constraint in sugarcane production, limiting yields and reducing resource-use efficiency. This study presents WireDepth, an innovative cloud-connected monitoring system that leverages edge computing and IoT technologies for real-time, spatially aware analysis and visualization of subsoiling depth. The system integrates ultrasonic, laser, inclinometer, and potentiometer sensors mounted on the subsoiler, with on-board microcontroller processing and dual wireless connectivity (LoRaWAN and NB-IoT/LTE-M) for robust data transmission. A cloud platform delivers advanced analytics, including 3D depth maps and operational efficiency metrics. System accuracy was assessed using 300 reference depth measurements, with Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) calculated per sensor. The inclinometer and potentiometer achieved the highest accuracy (MAPE of 0.92% and 0.84%, respectively), with no significant deviation from field measurements (paired t-tests, p > 0.05). Ultrasonic and laser sensors exhibited higher errors, particularly at shallow depths, due to soil debris interference. Correlation analysis confirmed a significant effect of depth on sensor accuracy, with laser sensors showing the strongest association (Pearson r = 0.457, p < 0.001). Field validation in commercial sugarcane fields demonstrated that WireDepth improves subsoiling precision, reduces energy waste, and supports sustainable production by enhancing soil structure and root development. These findings advance precision agriculture by offering a scalable, real-time solution for subsoiling management, with broad implications for yield improvement in compaction-affected systems. Full article

The rapid growth of renewable energy integration in modern power systems brings new challenges in terms of stability and quality of electricity supply. Hybrid AC/DC microgrids represent a promising solution to integrate photovoltaic panels (PV), wind turbines, fuel cells, and storage units with flexibility and efficiency. However, maintaining adequate power quality (PQ) under variable conditions of generation, load, and grid connection remains a critical issue. This paper presents the modelling, implementation, and validation of a hybrid AC/DC microgrid equipped with a fuzzy-logic-based energy management system (EMS). The study combines PQ assessment, measurement architecture, and supervisory control for technical compliance and economic efficiency. The microgrid integrates a combination of PV array, wind turbine, proton exchange membrane fuel cell (PEMFC), battery storage system, and heterogeneous AC/DC loads, all modelled in MATLAB/Simulink using a physical-network approach. The fuzzy EMS coordinates distributed energy resources by considering power imbalance, battery state of charge (SOC), and dynamic tariffs. Results demonstrate that the proposed controller maintains PQ indices within IEC/IEEE standards while eliminating short-term continuity events. The proposed EMS prevents harmful deep battery cycles, maintaining SOC within 30–90%, and optimises fuel cell activation, reducing hydrogen consumption by 14%. Economically, daily operating costs decrease by 10–15%, grid imports are reduced by 18%, and renewable self-consumption increases by approximately 16%. These findings confirm that fuzzy logic provides an effective, computationally light, and uncertainty-resilient solution for hybrid AC/DC microgrid EMS, balancing technical reliability with economic optimisation. Future work will extend the framework toward predictive algorithms, reactive power management, and hardware-in-the-loop validation for real-world deployment. Full article

The Internet of Things (IoT) has become deeply embedded in modern society, enabling applications across smart homes, healthcare, industrial automation, and environmental monitoring. However, as billions of interconnected devices continuously collect and exchange sensitive data, privacy and security concerns have escalated. This survey systematically reviews the state-of-the-art privacy-preserving techniques in IoT systems, emphasizing approaches that protect user data during collection, transmission, and storage. Peer-reviewed studies from 2016 to 2025 and technical reports were analyzed to examine applied mechanisms, datasets, and analytical models. Our analysis shows that blockchain and federated learning are the most prevalent decentralized privacy-preserving methods, while homomorphic encryption and differential privacy have recently gained traction for lightweight and edge-based IoT implementations. Despite these advancements, challenges persist, including computational overhead, limited scalability, and real-time performance constraints in resource-constrained devices. Furthermore, gaps remain in cross-domain interoperability, energy-efficient cryptographic designs, and privacy solutions for Unmanned Aerial Vehicle (UAV) and vehicular IoT systems. This survey offers a comprehensive overview of current research trends, identifies critical limitations, and outlines promising future directions to guide the design of secure and privacy-aware IoT architectures. Full article

In this paper, the real-time decentralized integrated sensing, navigation, and communication co-optimization problem is investigated for large-scale mobile wireless sensor networks (MWSN) under limited energy. Compared with traditional sensor network optimization and control problems, large-scale resource-constrained MWSNs are associated with two new challenges, i.e., (1) increased computational and communication complexity due to a large number of mobile wireless sensors and (2) an uncertain environment with limited system resources, e.g., unknown wireless channels, limited transmission power, etc. To overcome these challenges, the Mean Field Game theory is adopted and integrated along with the emerging decentralized multi-agent reinforcement learning algorithm. Specifically, the problem is decomposed into two scenarios, i.e., cost-effective navigation and transmission power allocation optimization. Then, the Actor–Critic–Mass reinforcement learning algorithm is applied to learn the decentralized co-optimal design for both scenarios. To tune the reinforcement-learning-based neural networks, the coupled Hamiltonian–Jacobi–Bellman (HJB) and Fokker–Planck–Kolmogorov (FPK) equations derived from the Mean Field Game formulation are utilized. Finally, numerical simulations are conducted to demonstrate the effectiveness of the developed co-optimal design. Specifically, the optimal navigation algorithm achieved an average accuracy of $2.32\%$ when tracking the given routes. Full article

The transition toward renewable-dominated power systems is increasingly constrained by the shortage of flexible regulation resources. Hydropower, with its rapid response and strong load-adjustment capability, remains a cornerstone for enabling large-scale integration of intermittent wind and solar energy. Splitter-blade runners are widely employed in medium- and high-head conventional hydropower plants and pumped-storage stations due to their broad high-efficiency operating range and superior stability. In this study, based on a runner replacement project at an existing hydropower station, refined computational fluid dynamics (CFD) simulations were carried out to design a splitter-blade runner under strict dimensional constraints. The optimized runner expanded the unit’s stable operating range from 50–100% to 0–100% rated power, while also improving overall efficiency and reducing pressure pulsations. The optimized splitter-blade runner improved efficiency by 1–2%, reduced pressure pulsations in the draft tube by ≈25%, and decreased the runner radial force by ≈12% compared with the baseline configuration. Importantly, this work demonstrates for the first time that splitter-blade runners can be successfully applied at head ranges below 100 m, thereby extending their applicability beyond traditional limits. The results provide both theoretical and practical guidance for flexibility retrofits of existing Francis turbine units in China, offering a feasible pathway to support the adaptability of future renewable energy systems. Full article

The gas oil hydrocracking process is a cornerstone of modern refining, enabling the conversion of heavy fractions into high-value fuels such as diesel, kerosene, LPG, and naphtha. However, despite its economic significance, its considerable water requirements for cooling, washing, and steam generation lead to high utility costs, which may undermine profitability, representing the problem of the study. This study addresses the issue through a techno-economic assessment and resilience analysis of an industrial-scale, mass and energy-integrated gas oil hydrocracking process, utilizing the novel FP²O methodology. The process was modeled in Aspen HYSYS^® V14.0 with a capacity of 1.94 Mt/year, assuming a feedstock cost of USD 350/t and a primary product (diesel) price of USD 1539/t. The total capital investment (TCI) was estimated at USD 175.68 million, while utility expenses reached USD 1312.18 million/year, representing nearly half of the total product cost (TPC) of USD 2692.20 million/year. A set of twelve techno-economic and three financial indicators was determined, yielding a gross profit (GP) of USD 97.69 million, profitability after tax (PAT) of USD 64.96 million, and a net present value (NPV) of USD 229.62 million. The payback period (PBP) was 1.41 years, with a depreciable payback period (DPBP) of 2.99 years. The return on investment (ROI) was 36.97%, and the internal rate of return (IRR) reached 44.81%, evidencing strong profitability relative to comparable petrochemical operations. Resilience analysis highlighted sensitivities to fluctuations in product prices, feedstock costs, and normalized variable operating costs (NVOC), identifying a critical NVOC of USD 1435/t against the current operation at USD 1384.74/t, which suggests a narrow buffer before profitability deteriorates. Overall, the findings confirm that mass and energy integration enhances resource efficiency but does not fully mitigate exposure to feedstock and utility price volatility. This work constitutes the first application of FP²O to a mass and energy-integrated gas oil hydrocracking facility, establishing a benchmark for holistic techno-economic and resilience assessments in complex petrochemical systems. Full article

With the continuous increase in the proportion of wind and solar power, the strong randomness and volatility of distributed new energy output have brought great challenges to the planning, regulation, and operation of the new distribution system. Distributed energy storage, with its characteristics such as scattered location distribution, flexible installation, small capacity, and diverse forms and application scenarios, is increasingly becoming an important resource and technical means to enhance the consumption capacity of new energy and ensure the safe and reliable operation of the power system. This paper proposes a collaborative planning method for distributed energy storage based on differentiated demands. First, the typical application scenarios of distributed energy storage are analyzed; secondly, the source–load matching degree and modularity are proposed as cluster division indicators. Voltage fluctuation, load fluctuation, and the net income of distributed energy storage are combined into multiple optimization objectives. Based on differentiated demands, a two-layer optimal configuration model of distributed energy storage is proposed and solved by using the improved particle swarm optimization algorithm. Finally, the feasibility and effectiveness of the proposed method were verified through a modified IEEE33 node simulation example. Full article

Examining the changing patterns and underlying mechanisms of soil biomass carbon stocks constitutes a fundamental aspect of soil biology. Despite the potential influence of the sulfur cycle and the life strategies of organisms on community biomass, these factors have rarely been studied in tandem. Biocrusts are model systems for studying soil ecosystems. In this study, metagenomic analysis of biocrusts related to different life strategies from five batches over four consecutive years demonstrated that, in free-living communities, microbial biomass carbon (MBC) synthesis, via assimilatory sulfate reduction (ASR), is primarily coupled with the 3-hydroxypropionate/4-hydroxybutyrate and Calvin–Benson–Bassham cycles. These pathways are affected by the oxidation-reduction potential (Eh), pH, electrical conductivity, and nutrient levels. The decomposition of organic carbon (OC) via dissimilatory sulfate reduction (DSR) was accompanied by the production of dimethyl sulfide (DMS), which was influenced by the C/S ratio and moisture, whereas the synthesis of MBC by symbiotic communities was found to be affected by Eh and pH, and decomposition was affected by the C/S ratio. The MBC stock was influenced by all strategies, with resource strategies having the greatest impacts during the growing season, and the contribution of chemotrophic energy was most significant in free-living communities. In conclusion, the MBC in biocrusts is associated with both ASR and DSR and is facilitated by the A-, S-, and P-strategies under the regulation of the stoichiometric C/S ratio. The exploration of microbial life strategies and sulfur cycling in biocrusts within arid ecosystems in this study offers a new perspective on the patterns of change in soil biomass carbon stocks. Full article

Network slicing has emerged as a pivotal technology in addressing the diverse customization requirements of the Industrial Internet of Things (IIoT) within 5G networks, enabling the deployment of multiple logical networks over shared infrastructure. Efficient resource management in this context is essential to ensure energy efficiency and meet the stringent real-time demands of IIoT applications. This study focuses on the scheduling problem of minimizing average transmission power while maintaining Age of Information (AoI) tolerance constraints within 5G wireless network slicing. To tackle this challenge, an improved Dueling Double Deep Q-Network (D3QN) is leveraged to devise intelligent slicing schemes that dynamically allocate resources, ensuring optimal performance in time-varying wireless environments. The proposed improved D3QN approach introduces a novel heuristic-based exploration strategy that restricts action choices to the most effective options, significantly; reducing ineffective learning steps. The simulation results show that the method not only speeds up convergence considerably but also achieves lower transmit power while preserving strict AoI reliability constraints and slice isolation. Full article

Europe’s decarbonization calls for an increase in the resources used to ensure a fairer transition. The objective of this study is to evaluate the role of public finance in the decarbonization process, considering the context of various uncertainties. Data from 1995 to 2023 for selected European countries were analyzed in this sense. We used the cross-sectional dependence–consistent Driscoll–Kraay estimator as the main econometric approach and Feasible Generalized Least Squares (FGLS) as a robustness test. The results revealed a positive impact of public debt, world uncertainty, and gross domestic product on renewable energy usage in European countries. Additionally, general fiscal pressure is shown to have a negative impact on the renewable energy used during the analyzed period. The results showcase the importance of public finance tools adjustments in supporting the race to zero breakthroughs and dawdling climate change. Several policy recommendations were made in this regard. Full article

Between 51% and 72% of a bituminous roofing membrane used for structural waterproofing consists of organic material, predominantly bitumen—a derivative of crude oil refining—highlighting the strong dependence of this product on fossil resources. Considering that several tonnes of these membranes must be replaced every 30 to 50 years, substantial potential exists for emission reduction through the establishment of circular material systems. This study investigates this potential by analysing 26 Environmental Product Declarations (EPDs) and life cycle datasets from across Europe covering the period from 2007 to 2023. To ensure comparability, all data were normalised to a declared unit of 1 kg of roofing membrane. The reinforcement layers were categorised into glass and polyester & glass composites, and their differences were examined using Welch’s t-tests. Correlative analyses and linear as well as multiple regression models were then applied to explore relationships between environmental indicators and the shares of organic and mineral mass fractions. The findings reveal that renewable energy sources, although currently representing only a small share of total production energy, provide a major lever for reducing nearly all environmental impact categories. The type of reinforcement layer was also found to influence the demand for fossil resources, both materially and energetically. For most environmental indicators, only multiple regression models can explain at least 30% of the variance based on the proportions of organic and mineral inputs. Overall, the study underscores the crucial importance of high-quality, transparently documented product data for accurately assessing the sustainability of building products. It further demonstrates that substituting fossil energy carriers with renewable sources and optimising material efficiency can substantially reduce environmental burdens, provided that methodological consistency and clarity of indicator definitions are maintained. Full article

This study establishes a structured framework to identify and evaluate risk factors that may hinder the achievement of sustainable development goals in green buildings and sustainable infrastructure projects. Fifty-six risk factors are identified and categorized into four risk groups, including stakeholder and management, financial and economic, technological and resource, and process and regulatory risks. The risk factors are evaluated across four risk indices related to probability of occurrence, manageability, impact on building performance, and project cost. Further, the severity of risks based on combining the four indices’ effects is quantified using a new Green Risk Index (GRI), while the relationships among all risk indices are determined. The strongest positive correlation is observed between the probability and the impact on cost, whereas a negative relationship is found between the probability and manageability. The analysis demonstrates that a risk factor related to the lack of knowledge about energy-saving procedures and environmental concerns during the design phase is the most critical, as it has the highest severity based on the GRI. “Non-compliance with environmental standards in project design” is also identified as a critical risk factor due to its high effect on building performance. Additionally, the risk factor associated with unstable funds from investors shows the highest effect on manageability. Process and regulatory is identified as the most critical risk group, encompassing the maximum number of key risk factors, and has the highest average weight related to the GRI. These findings reveal crucial vulnerabilities and underline the importance of targeted strategies to strengthen the use of nature-based solution frameworks for mitigating the risk effects in green buildings and sustainable infrastructures. Full article

Low-permeability sandstone oil reservoirs, as an important type of oil and gas resource, feature high reservoir density and low permeability. The utilization of pore throats of different scales during their development process is crucial for enhancing oil recovery. Based on nuclear magnetic resonance and CT scanning techniques, this paper systematically studies the utilization limits and energy contribution of pore larynx under different displacement methods. The results show that during the water injection development process, the main pore–throat radius used by water flooding is between 1 and 20 μm. Among them, the contribution of the small pore tends to stabilize after the pressure rises to a certain stage, the contribution of the medium pore increases with the rise in pressure, while the contribution of the large pore gradually decreases with the increase in pressure. After switching to CO₂ gas flooding, the application range of the pore throat was further expanded to a smaller scale. The contribution of the small pore and the middle pore significantly increased in a specific pressure range, while the large pore made a greater contribution at a lower pressure. This paper has certain reference significance for the study of the limit and contribution of pore–throat exploitation in low-permeability sandstone oil reservoirs. Full article

Household waste systems are a frontline test of Australia’s circular economy transition, yet progress remains highly uneven and structurally constrained. Despite strong national targets for resource recovery and emissions reduction, local governments are expected to deliver circular outcomes without uniform access to infrastructure, funding, or technical capability. This study assesses the status, implementation, and progress of household waste management, energy recovery, and circular economy initiatives at the local government level in Australia. Using content analysis of data from 520 local government areas across six states, the study maps differences in service provision (e.g., general waste, mixed recycling, and food organics and garden organics [FOGO] collection), policy instruments, public-facing education, and participation in circular economy programs. The findings reveal that while a majority (92.5%) of councils provide general waste bins, 47% offer FOGO bins, and 78% supply mixed recyclable bins, only a small fraction (2.6%) offers a separate glass bin stream. Fewer than one in ten councils reference any form of energy recovery or waste-to-energy initiative, indicating that resource–energy integration remains emergent and geographically concentrated. Despite national policies such as the National Waste Policy Action Plan, significant regional disparities persist, particularly between metropolitan and rural councils. Guided by environmental governance theory and systems thinking, the study shows how policy fragmentation, funding limitations, and infrastructure inequities create systemic barriers to circularity. The study concludes by recommending targeted co-funding for rural councils, stronger policy support for organics and energy recovery infrastructure, and more coherent multi-level governance to achieve Australia’s 2030 waste and circular economy targets. This research contributes an evidence-based framework for understanding how governance structures and resource asymmetries shape local progress toward a circular economy. Full article

In response to the urgent need for sustainable energy solutions and efficient fossil resource utilization, the current research is conducted to examine the catalytic co-pyrolysis of four typical Chinese oil shales. The study assesses the ability of synergistic interactions, which are the result of organic and inorganic components, to improve the aspect of thermal behavior, decrease the activation energy and improve the shale oil quality. Thermogravimetric analysis in conjunction as Flynn–Wall–Ozawa (FWO), Kissinger–Akahira–Sunose (KAS) and integral master-plots approaches showed that there were low activation energies and better reaction kinetics in blended systems. Fischer assay and GC-MS were utilized in product distribution and product composition evaluation, respectively. Optimization increased gas yield and oil composition stabilization in the blended gas, which is found due to the catalytic functions of AAEMs and clay minerals. This contribution facilitates the development of catalytic co-processing solutions where better conversion and reduced carbon intensity are achieved in the production of fossil-based energy. Full article