Search Results (5,485)

Under deep mining conditions, coal and rock masses are subjected to high in situ stress and strong mining-induced disturbances, leading to intensified stress unloading, concentration, and redistribution processes. The stability of surrounding rock is therefore closely related to mine safety. Direct, real-time, and continuous monitoring of in situ stress magnitude, orientation, and evolution is a critical requirement for deep underground engineering. To overcome the limitations of conventional stress monitoring methods under high-stress and strong-disturbance conditions, a novel in situ stress monitoring device was developed, and its performance was systematically verified through laboratory experiments. Typical unloading–reloading and biaxial unequal stress paths of deep surrounding rock were adopted. Tests were conducted on intact specimens and specimens with initial damage levels of 30%, 50%, and 70% to evaluate monitoring performance under different degradation conditions. The results show that the device can stably acquire strain signals throughout the entire loading–unloading process. The inverted monitoring stress exhibits high consistency with the loading system in terms of evolution trends and peak stress positions, with peak stress errors below 5% and correlation coefficients (R²) exceeding 0.95. Although more serious initial damage increases high-frequency fluctuations in the monitoring curves, the overall evolution pattern and unloading response remain stable. Combined acoustic emission results further confirm the reliability of the monitoring outcomes. These findings demonstrate that the proposed device enables accurate and dynamic in situ stress monitoring under deep mining conditions, providing a practical technical approach for surrounding rock stability analysis and disaster prevention. Full article

Mapping individual-tree crowns (ITCs) along with extracting tree morphological attributes provides the core parameters required for estimating thermal stress and carbon emission functions. However, calculating morphological attributes relies on the prior delineation of ITCs. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) framework, this review synthesizes how deep-learning (DL)-based methods enable the conversion of crown geometry into reliable biometric parameter extraction (BPE) from high-resolution imagery. This addresses a gap often overlooked in studies focused solely on detection by providing a direct link to forest inventory metrics. Our review showed that instance segmentation dominates (approximately 46% of studies), producing the most accurate pixel-level masks for BPE, while RGB imagery is most common (73%), often integrated with canopy-height models (CHM) to enhance accuracy. New architectural approaches, such as StarDist, outperform Mask R-CNN by 6% in dense canopies. However, performance differs with crown overlap, occlusion, species diversity, and the poor transferability of allometric equations. Future work could prioritize multisensor data fusion, develop end-to-end biomass modeling to minimize allometric dependence, develop open datasets to address model generalizability, and enhance and test models like StarDist for higher accuracy in dense forests. Full article

Sustainable urban water system (UWS) management is vital for climate-resilient, resource-efficient cities. This study presents the first comprehensive life cycle assessment (LCA) of Seoul Metropolitan City (SMC)’s UWS, encompassing water abstraction, treatment, distribution, wastewater collection and treatment, and sludge management. Nine midpoint impact categories from ReCiPe 2016 (H) were analyzed to identify environmental hotspots and mitigation pathways. Results show that wastewater treatment dominates impacts, contributing 57.3% of global warming potential (GWP; 0.947 kg CO₂-eq per functional unit of 1 m³ of potable water supplied) and 71.1% of freshwater eutrophication (FE; 0.00066 kg P-eq/m³), driven by electricity use, sludge disposal, and direct CH₄/N₂O emissions. Electricity consumption is the leading driver across GWP, terrestrial acidification (TA), and fossil resource scarcity (FRS). Infrastructure construction notably influenced terrestrial ecotoxicity (TET) and human toxicity. Sensitivity analysis showed that SMC’s projected 2030 electricity mix could reduce GWP and FRS by up to 18%. Scenario evaluations revealed that sludge ash utilization in concrete and expanded wastewater reuse improve resource circularity, whereas biogas upgrading, solar generation, and heat recovery significantly lower GWP and FRS. The findings underscore the importance of energy decarbonization, resource recovery, and infrastructure longevity in achieving low-carbon and resource-efficient UWSs. This study offers a transferable framework for guiding sustainability transitions in rapidly urbanizing, energy-transitioning regions. Full article

Generative models play a pivotal role in the field of medical imaging. This paper provides an extensive and scholarly review of the application of generative models in medical image creation and translation. In the creation aspect, the goal is to generate new images based on potential conditional variables, while in translation, the aim is to map images from one or more modalities to another, preserving semantic and informational content. The review begins with a thorough exploration of a diverse spectrum of generative models, including Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Diffusion Models (DMs), and their respective variants. The paper then delves into an insightful analysis of the merits and demerits inherent to each model type. Subsequently, a comprehensive examination of tasks related to medical image creation and translation is undertaken. For the creation aspect, papers are classified based on downstream tasks such as image classification, segmentation, and others. In the translation facet, papers are classified according to the target modality. A chord diagram depicting medical image translation across modalities, including Magnetic Resonance Imaging (MRI), Computed Tomography (CT), Cone Beam CT (CBCT), X-ray radiography, Positron Emission Tomography (PET), and ultrasound imaging, is presented to illustrate the direction and relative quantity of previous studies. Additionally, the chord diagram of MRI image translation across contrast mechanisms is also provided. The final section offers a forward-looking perspective, outlining prospective avenues and implementation guidelines for future research endeavors. Full article

Amid the growing global environmental challenges, precise and efficient vehicle emission management plays a critical role in achieving energy-saving and emission reduction goals. At the same time, the rapid development of connected vehicles and autonomous driving technologies has generated a large amount of high-dimensional vehicle operation data. This not only provides a rich data foundation for refined emission accounting but also raises higher demands for the construction of accounting models. Therefore, this study aims to develop an accurate and efficient emission accounting model to contribute to the precise nitrogen oxide (NO_x) emission accounting for hybrid electric vehicles (HEVs). A systematic approach is proposed that combines incremental dimensionality reduction with advanced regression algorithms to achieve refined and efficient emission accounting based on multiple variables. Specifically, the dimensionality of the real driving emission (RDE) data is first reduced using the feature selection and t-distributed stochastic neighbor embedding (t-SNE) feature extraction method to capture key parameter information and reduce subsequent computational complexity. Next, an incremental dimensionality reduction method based on dictionary learning is employed to efficiently embed new data into a low-dimensional space through straightforward matrix operations. Given the computational cost of the dictionary learning training process, this study introduces the FISTA (Fast Iterative Shrinkage-Thresholding Algorithm) for accelerated iterative optimization and enhances the computational efficiency through parameter optimization, while maintaining the accuracy of dictionary learning. Subsequently, an NO_x emission factor correction factor prediction model is trained using the low-dimensional data obtained from t-SNE embeddings, enabling direct computation of the corresponding correction factor when presented with new incremental low-dimensional embeddings. Finally, validation on independent HEV datasets shows that parameter K improves to 1 ± 0.05 and R² increases up to 0.990, laying a foundation for constructing an emission accounting model with broad applicability based on multiple variables. Full article

Industrial packaging systems exert substantial environmental pressures, including material resource depletion, greenhouse gas emissions, and the accumulation of post-consumer waste. As global supply chains expand and sustainability regulations intensify, demand for environmentally responsible packaging solutions continues to rise. This study evaluates the environmental footprint of industrial packaging by integrating recent developments in life cycle assessment (LCA), ecological footprint (EF) methodologies, material innovations, and circular economy models. The assessment examines the sustainability performance of conventional and alternative packaging materials, plastics, aluminum, corrugated cardboard, and polylactic acid (PLA). Findings indicate that although corrugated cardboard is renewable, it still presents a measurable environmental burden, with evidence suggesting that incorporating solar energy into production can reduce its footprint by more than 12%. PLA-based trays demonstrate promising environmental performance when sourced from renewable feedstocks and directed to appropriate composting systems. Despite these advancements, several systemic challenges persist, including ecological overshoot in industrial regions where EF may exceed local biocapacity limitations in waste management infrastructure, and significant economic trade-offs. Transportation-related emissions and scalability constraints for bio-based materials further hinder large-scale adoption. Existing research suggests that integrating sustainable packaging across supply chains could meaningfully reduce environmental impacts. Achieving this transition requires coordinated cross-sector collaboration, standardized policy frameworks, and embedding advanced environmental criteria into packaging design and decision-making processes. Full article

Urban air pollution remains a critical public health issue, especially in densely populated cities where pedestrians experience direct exposure to traffic-related and environmental emissions. This study develops and tests a pollution-aware pedestrian routing framework for Thessaloniki, Greece, designed to minimize environmental exposure while maintaining route efficiency. The framework combines high-resolution air-quality data and computational techniques to represent pollution patterns at pedestrian scale. Air-quality is expressed as a continuous European Air Quality Index (EAQI) and is embedded in a network-based routing engine (OSRM) that balances exposure and distance through a weighted optimization function. Using 3000 randomly sampled origin-destination pairs, exposure-aware routes are compared with conventional shortest-distance paths across short, medium, and long walking trips. Results show that exposure-aware routes reduce cumulative AQI exposure by an average of 4% with only 3% distance increase, while maintaining stable scaling across all route classes. Exposure benefits exceeding 5% are observed for approximately 8% of medium-length routes and 24% of long routes, while short routes present minimal or no detours, but lower exposure benefits. These findings confirm that integrating high-resolution environmental data into pedestrian navigation systems is both feasible and operationally effective, providing a practical foundation for future real-time, pollution-aware mobility services in smart cities. Full article

Under the “dual carbon” strategy and green energy transition, traditional static accounting models have coarse temporal granularity. These models cannot meet the needs of fine-grained management and dynamic control for industrial parks. Therefore, it is urgent to develop high-resolution dynamic accounting systems and analyze model uncertainty. This study first defines the carbon source structure and establishes the accounting boundary for industrial parks. Second, it proposes dynamic accounting methods for both direct and indirect carbon emissions. Third, the study develops an uncertainty analysis model that considers parameter variability and error propagation. Finally, the feasibility and effectiveness of the proposed method are validated through a case study of a typical industrial park in Sichuan Province, China. The results indicate that the overall uncertainty of carbon emissions in the park is 28.9%, with electricity consumption identified as the primary driver of uncertainty (Spearman correlation coefficient of 0.986). The proposed framework effectively captures real-time emission fluctuations, providing a scientific basis for fine-grained carbon management. Full article

The article presents an extensive review of modern technological solutions for pulverized coal combustion, with emphasis on combustion modifiers and biomass co-firing. It highlights the role of coal in the national energy system and the need for its sustainable use in the context of energy transition. The pulverized coal combustion process is described, along with factors influencing its efficiency, and a classification of modifiers that improve combustion parameters. Both natural and synthetic modifiers are analyzed, including their mechanisms of action, application examples, and catalytic effects. Special attention is given to the synergy between transition metal compounds (Fe, Cu, Mn, Ce) and alkaline earth oxides (Ca, Mg), which enhances energy efficiency, flame stability, and reduces emissions of CO, SO₂, and NO_x. The article also examines biomass-coal co-firing as a technology supporting energy sector decarbonization. Co-firing reduces greenhouse gas emissions and increases the reactivity of fuel blends. The influence of biomass type, its share in the mixture, and processing methods on combustion parameters is discussed. Finally, the paper identifies directions for further technological development, including nanocomposite combustion modifiers and intelligent catalysts integrating sorption and redox functions. These innovations offer promising potential for improving energy efficiency and reducing the environmental impact of coal-fired power generation. Full article

Food security remains a critical challenge in Nigeria. As a result, this research examines the impact of fertilizer use, economic expansion, population growth, and methane emissions on food security in Nigeria from 1970 to 2022. The methodologies used include the Autoregressive Distributed Lag (ARDL) model, Wald Test, and the Spectral Granger Causality test. The ARDL results demonstrate that in the long run, fertilizer use spurs food security, although not significantly, while population growth reduces food security insignificantly. On the other hand, economic expansion and agricultural methane emissions are positively associated with food security, likely reflecting scale effects of agricultural production rather than a direct beneficial role of emissions. In the short run, fertilizer use and methane emissions drive food security. The Wald Test also confirms the short-run findings. Furthermore, the Spectral Granger Causality test showed that fertilizer use and economic expansion Granger-cause food security in the long, medium, and short term. Population growth, however, Granger-causes food security only in the long term, while methane emissions Granger-cause food security in the medium and long term. Based on these results, policies are recommended, and their further implications are discussed. Full article

Debates relating to the sustainability of the environment have emerged as a major goal of the global agenda in recent years. As a result, this research examines the impact of outward foreign direct investment (OFDI) on carbon dioxide emissions (CO₂) in Turkey from 1985 to 2022, using the autoregressive distributed lag model (ARDL) and frequency domain causality analysis (FDCA). In addition, economic growth (GDP) and trade in services (TROP) were used as control variables because they capture two big ways the economy interacts with environment. The empirical results are as follows: (i) The bounds test confirms a long-run association among the variables. (ii) The ARDL result confirms that in the long and short run, OFDI and GDP increase CO₂ in Turkey, while TROP contributes to the quality of the environment. (iii) The FDCA demonstrates that OFDI Granger causes CO₂ in the short and medium term, while TROP Granger causes CO₂ in the short, medium, and long-term. Based on these results, policies are recommended for implementation. Full article

Direct-starting of industrial motors has problems such as large current impact (five to eight times the rated current), mechanical stress damage, and low energy efficiency. This paper explores the technological innovations in motor soft-start driven by intelligent control and wide-bandgap semiconductors, and constructs a highly reliable and low energy consumption solution. Firstly, based on a material–device–algorithm system framework, a comparative study is conducted on the performance breakthroughs of SiC/GaN in replacing silicon-based devices. Secondly, an intelligent control model is established and a highly reliable system architecture is developed. A comprehensive review of recent literature indicates that SiC devices can reduce switching losses by up to 80%, and intelligent algorithms significantly improve control accuracy. System-level solutions reported in the industry demonstrate the capability to limit current to 1.5–3 times the rated current and achieve substantial carbon emission reductions. These technologies provide key technical support for the intelligent upgrading of industrial motor systems and the dual-carbon goal. In the future, development will continue to evolve in the direction of device miniaturization and other directions. Full article

Volatile organic compounds (VOCs) play a central role in plant communication and ecology, acting as a chemical language that mediates interactions with other organisms and responses to environmental stimuli. Analyzing changes in the plant volatilome enables the effective differentiation between biotic and abiotic stresses. Consequently, monitoring VOC emissions offers valuable insights into plant signaling pathways and health status. These insights position this approach as a promising strategy for improving crop protection. Direct infusion (DI) online analytical techniques, such as proton transfer reaction mass spectrometry (PTR-MS) and adduct ionization mechanism mass spectrometry (AIM-MS), have been developed to detect and characterize VOCs in real time. Here, we evaluated the suitability of PTR-MS and AIM-MS for monitoring VOC emissions in pea plants (Pisum sativum L.). Comparative analysis revealed that AIM-MS, a recently developed technology, detected a higher number of distinct signals than PTR-MS. Annotation of detected and significant AIM-MS signals indicated a predominance toward those that were putative lipids-derived and amino acids-derived, whereas PTR-MS signals were primarily associated with putative phenolic compounds. These findings suggest that the newly developed AIM reactor offers a broader detection range and may enhance our ability to monitor plant VOC emissions. Consequently, AIM-MS emerges as a promising tool for the real-time assessment of pea plant health and stress responses. Further efforts are needed to improve the portability of DI-MS techniques and to integrate them with GC-MS techniques. Overall, these efforts will allow this technology to be exploited for plant protection in compromised environments. Full article

The Mixed-Conduction Model for oxide films is used to quantitatively interpret in situ electrochemical and ex situ surface analytical results on the corrosion of AISI 316L (an internal reactor material) and Alloy 690 (a steam generator tube material) in small modular reactor primary coolant with the addition of soluble Zn. The model parameters of alloy oxidation and corrosion release are estimated with the time of exposure up to 168 h and anodic polarization potential (up to −0.25 V vs. standard hydrogen electrode) using fitting of the transfer function to experimental impedance spectra. Model parameters of individual alloy constituents are estimated by fitting of the model equations to the atomic fraction profiles of respective elements in the formed oxide obtained by Glow-Discharge Optical Emission Spectroscopy (GDOES). Conclusions on the effect of Zn addition on film growth and cation release processes in boron-free SMR coolant are drawn and future research directions are outlined. Full article

Advances in sensing technologies are increasingly transforming cutting operations by enabling data-driven condition monitoring, predictive maintenance, and process optimization. This review surveys recent developments in sensing modalities for cutting systems, including vibration sensors, acoustic emission sensors, optical and vision-based systems, eddy-current sensors, force sensors, and emerging hybrid/multi-modal sensing frameworks. Each sensing approach offers unique advantages in capturing mechanical, acoustic, geometric, or electromagnetic signatures related to tool wear, process instability, and fault development, while also showing modality-specific limitations such as noise sensitivity, environmental robustness, and integration complexity. Recent trends show a growing shift toward hybrid and multi-modal sensor fusion, where data from multiple sensors are combined using advanced data analytics and machine learning to improve diagnostic accuracy and reliability under changing cutting conditions. The review also discusses how artificial intelligence, Internet of Things connectivity, and edge computing enable scalable, real-time monitoring solutions, along with the challenges related to data needs, computational costs, and system integration. Future directions highlight the importance of robust fusion architectures, physics-informed and explainable models, digital twin integration, and cost-effective sensor deployment to accelerate adoption across various manufacturing environments. Overall, these advancements position advanced sensing and hybrid monitoring strategies as key drivers of intelligent, Industry 4.0-oriented cutting processes. Full article