Search Results (12,848)

Forest volume is an important information for assessing the economic value and carbon sequestration capacity of forest resources and serves as a key indicator for energy flow and biodiversity. Although remote sensing technology is applied to estimate volume, optical remote sensing data have limitations in capturing forest vertical height information and may suffer from reflectance saturation. While LiDAR data can provide more detailed vertical structural information, they come with high processing costs and limited observation range. Therefore, improving the accuracy of volume estimation through multi-source data fusion has become a crucial challenge and research focus in the field of forest remote sensing. In this study, we integrated Sentinel-2 multispectral data, Resource-3 stereoscopic imagery, UAV-based LiDAR data, and field survey data to quantitatively estimate the forest volume in Saihanwula Nature Reserve, located in Inner Mongolia, China, on the southern part of Daxing’anling Mountains. The study evaluated the performance of multi-source remote sensing features by using recursive feature elimination (RFE) to select the most relevant factors and applied four machine learning models—multiple linear regression (MLR), k-nearest neighbors (kNN), random forest (RF), and gradient boosting regression tree (GBRT)—to develop volume estimation models. The evaluation metrics include the coefficient of determination (R²), root mean square error (RMSE), and relative root mean square error (rRMSE). The results show that (1) forest Canopy Height Model (CHM) data were strongly correlated with forest volume, helping to alleviate the reflectance saturation issues inherent in spectral texture data. The fusion of CHM and spectral data resulted in an improved volume estimation model with R² = 0.75 and RMSE = 8.16 m³/hm², highlighting the importance of integrating multi-source canopy height information for more accurate volume estimation. (2) Volume estimation accuracy varied across different tree species. For Betula platyphylla, we obtained R² = 0.71 and RMSE = 6.96 m³/hm²; for Quercus mongolica, R² = 0.74 and RMSE = 6.90 m³/hm²; and for Populus davidiana, R² = 0.51 and RMSE = 9.29 m³/hm². The total forest volume in the Saihanwula Reserve ranges from 50 to 110 m³/hm². (3) Among the four machine learning models, GBRT consistently outperformed others in all evaluation metrics, achieving the highest R² of 0.86, lowest RMSE of 9.69 m³/hm², and lowest rRMSE of 24.57%, suggesting its potential for forest biomass estimation. In conclusion, accurate estimation of forest volume is critical for evaluating forest management practices and timber resources. While this integrated approach shows promise, its operational application requires further external validation and uncertainty analysis to support policy-relevant decisions. The integration of multi-source remote sensing data provides valuable support for forest resource accounting, economic value assessment, and monitoring dynamic changes in forest ecosystems. Full article

Road networks are essential for access, resource distribution, and population evacuation during natural events. These challenges are pronounced in rural areas, where network redundancy is limited and communities may have social disparities. While traditional risk management systems often focus on the physical consequences of hazard events alone, specialized literature increasingly suggests the development of a more comprehensive approach for risk assessment, where not only physical aspects associated with infrastructure, such as damage level or disruptions, but also the social and economic attributes of the affected population are considered. Consequently, this paper proposes a Vulnerability Access Index (VAI) to support road network decision-making that integrates the social vulnerability of rural communities exposed to natural events, their accessibility to nearby critical infrastructure, and physical risk. The research methodology considers (i) the Social Vulnerability Index (SVI) calculation based on socioeconomic variables, (ii) Importance Index estimation (I_imp) to evaluate access to critical infrastructure, (iii) VAI calculation combining SVI and I_imp, and (iv) application to a case study in the influence area of the Villarrica volcano in southern Chile. The results show that when incorporating social variables and accessibility, infrastructure criticality varies significantly compared to the infrastructure criticality assessment based solely on physical risk, modifying the decision-making regarding road infrastructure robustness and resilience improvements. Full article

Nuclear power is a low-emission and economically competitive energy source, yet the effective disposal and management of its associated radioactive waste can be challenging. Radioactive waste can be categorised as high-level waste (HLW), intermediate-level waste (ILW), and low-level waste (LLW). LLW primarily comprises materials contaminated during routine clean-up, such as mop heads, paper towels, and floor sweepings. While LLW is less radioactive compared to HLW and ILW, the management of LLW poses significant challenges due to the large volume that requires processing and disposal. The volume of LLW can be significantly reduced through sorting, which is typically performed manually in a labour-intensive way. Smart management techniques, such as computer vision (CV) and machine learning (ML), have great potential to help reduce the workload and human errors during LLW sorting. This paper provides a comprehensive review of previous research related to LLW sorting and a summative review of existing applications of CV in solid waste management. It also discusses state-of-the-art CV and ML algorithms and their potential for automating LLW sorting. This review lays a foundation for and helps facilitate the applications of CV and ML techniques in LLW sorting, paving the way for automated LLW sorting and sustainable LLW management. Full article

This review outlines a comprehensive methodology for the research and development of heterogeneous catalytic technologies (R&D_HeCaTe). Emphasis is placed on the fundamental interactions between reactants, solvents, and heterogeneous catalysts—specifically the roles of catalytic centers and support materials (e.g., functional groups) in modulating activation energies and stabilizing catalytic functionality. Particular attention is given to catalyst deactivation mechanisms and potential regeneration strategies. The application of molecular modeling and chemical engineering analyses, including reaction kinetics, thermal effects, and mass and heat transport phenomena, is identified as essential for R&D_HeCaTe. Reactor configuration is discussed in relation to key physicochemical parameters such as molecular diffusivity, reaction exothermicity, operating temperature and pressure, and the phase and “aggressiveness” of the reaction system. Suitable reactor types—such as suspension reactors, fixed-bed reactors, and flow microreactors—are evaluated accordingly. Economic and environmental considerations are also addressed, with a focus on the complexity of reactions, selectivity versus conversion trade-offs, catalyst disposal, and separation challenges. To illustrate the breadth and applicability of the proposed framework, representative industrial processes are discussed, including ammonia synthesis, fluid catalytic cracking, methanol production, alkyl tert-butyl ethers, and aniline. Full article

Detection of soil pollution by petroleum products is necessary to remedy threats to economic and human health. Pollution by hydraulic oil often occurs through leaks from forestry machinery such as harvesters. Electronic noses equipped with gas sensor arrays are promising tools for applications of pollution detection and monitoring. A self-made, low-cost electronic nose was used for differentiation between clean and polluted samples, with two types of oils and three levels of pollution severity. An electronic nose uses the TGS series of gas sensors, manufactured by Figaro Inc. Sensor responses to changes in environmental conditions from clean air to measured odor, as well as responses to changes in sensor operation temperature, were used for analysis. Statistically significant response results allowed for the detection of pollution by biodegradable oil, while standard mineral oil was difficult to detect. It was demonstrated that the TGS 2602 gas sensor is most suitable for the studied application. LDA analysis demonstrated multidimensional data patterns allowing differentiation between sample categories and pollution severity levels. Full article

Poly(trimethylene terephthalate) (PTT) is a thermoplastic polyester with a unique structure due to having three methylene groups in the glycol unit. PTT competes with poly(ethylene terephthalate) (PET) and poly(butylene terephthalate) (PBT) in carpets, textiles, and thermoplastic materials, primarily due to the development of economically efficient synthesis methods. PTT is widely utilized in textiles, carpets, and engineering plastics because of its advantageous properties, including quick-drying capabilities and wrinkle resistance. However, its low melting point, resistance to chemicals, and brittleness compared to PET, have limited its applications. To address some of these limitations for targeted applications, PTT nanocomposites incorporating clay, carbon nanotube, silica, and ZnO have been developed. The distribution of nanoparticles within the PTT matrix remains a significant challenge for its potential applications. Several techniques, including sol–gel blending, melt blending, in situ polymerization, and in situ forming methods have been developed to obtain better dispersion. This review discusses advancements in the synthesis of various PTT nanocomposites and the effects of nanoparticles on the isothermal and nonisothermal crystallization of PTT. Full article

This study evaluated the antifungal potential of essential oils (EOs): oregano (Origanum vulgare), rosemary (Salvia rosmarinus), clove (Syzygium aromaticum), thyme (Thymus vulgaris), cinnamon (Cinnamomum verum), and basil (Ocimum basilicum). These oils were tested against fungi isolated from banana peels (Musa paradisiaca). The fungi tested were identified through macroscopic and microscopic analyses and DNA sequencing, after being isolated in potato dextrose agar (PDA) medium modified with 0.05% chloramphenicol. Subsequently, the antifungal properties of the tested essential oils were evaluated in vitro at concentrations of 200, 400, 600, 800, and 1000 ppm prepared in a 0.05% Tween 80 solution. Cinnamon EOs showed the highest antifungal activity, significantly inhibiting the growth of pathogens at a concentration of 400 ppm. Other EOs showed moderate effects at higher concentrations: rosemary inhibited fungal growth at 600 ppm, oregano at 800 ppm, and clove at 1000 ppm. These findings highlight the potential of EOs as eco-friendly alternatives to synthetic fungicides, contributing to the development of sustainable agricultural practices and the post-harvest management of bananas. It is recommended to conduct future research to assess the economic viability and practical impacts of large-scale applications. Full article

The genus Colletotrichum is widely known for its phytopathological significance, especially as the causative agent of anthracnose in diverse agricultural crops. However, recent studies have unveiled its ecological versatility and biotechnological potential, particularly among endophytic species. These fungi, which asymptomatically colonize plant tissues, stand out as high-yielding producers of bioactive secondary metabolites. Given their scientific and economic relevance, this review critically examines endophytic Colletotrichum species, focusing on the chemical diversity and biological activities of the metabolites they produce, including antibacterial, antifungal, and cytotoxic activity against cancer cells, and antioxidant properties. This integrative review was conducted through a structured search of scientific databases, from which 39 relevant studies were selected, highlighting the chemical and functional diversity of these compounds. The analyzed literature emphasizes their potential applications in pharmaceutical, agricultural, and industrial sectors. Collectively, these findings reinforce the promising biotechnological potential of Colletotrichum endophytes not only as sources of bioactive metabolites but also as agents involved in ecological regulation, plant health promotion, and sustainable production systems. Full article

Effective in situ H₂S removal is essential for the utilization of small, remote natural gas wells, where centralized treatment is often unfeasible. This study presents an integrated environmental–economic assessment of two such processes, LO-CAT^® and triazine-based absorption, using a scenario-based framework. Environmental impacts were assessed via the Waste Reduction Algorithm (WAR), considering both Potential Environmental Impact (PEI) generation and output across eight categories, while economic performance was analyzed based on equipment, chemical, energy, environmental treatment, and labor costs. Results show that the triazine-based process offers superior environmental performance due to lower toxic emissions, whereas LO-CAT^® demonstrates better economic viability at higher gas flow rates and H₂S concentrations. An integrated assessment combining monetized environmental impacts with economic costs reveals that the triazine-based process becomes competitive only if environmental impacts are priced above specific thresholds. This study contributes a practical evaluation framework and scenario-based dataset that support sustainable process selection for decentralized sour gas treatment applications. Full article

Hericium coralloides is a highly valued gourmet and medicinal species with growing market demand across East Asia, though industrial production remains limited by cultivation challenges. This study investigated the molecular characteristics, biological traits, domestication potential, and cultivation protocols of Hericium coralloides strains collected from the Changbaishan Nature Reserve (Jiling, China). Optimal conditions for mycelial growth included mannose as the preferred carbon source, peptone as the nitrogen source, 30 °C incubation temperature, pH 5.5, and magnesium sulfate as the essential inorganic salt. The fruiting bodies had a protein content of 2.43% g/100 g (fresh sample meter). Total amino acids comprised 53.3% of the total amino acid profile, while essential amino acids accounted for 114.11% relative to non-essential amino acids, indicating high nutritional value. Under optimized domestication conditions—70% hardwood chips, 20% cottonseed hulls, 8% bran, 1% malic acid, and 1% gypsum—bags reached full colonization in 28 days, with a 15-day maturation phase and initial fruiting occurring after 12–14 days. The interval between flushes was 10–12 days. The average yield reached 318.65 ± 31.74 g per bag, with a biological conversion rate of 63.73%. These findings demonstrate that Hericium coralloides possesses significant potential for edible and commercial applications. This study provides a robust theoretical foundation and resource reference for its artificial cultivation, supporting its broader industrial and economic utilization. Full article

(1) Background: This article aims to understand the forms and elements through which the inhabitants of the city of Medellin have configured their subjectivity in the context of the application of neoliberal policies in the last two decades. In this way, we can approach the frameworks of understanding that constitute a fundamental part of the individuation processes in which the incorporation of their subjectivities is evidenced in neoliberal contexts that, in the historical process, have been converging with authoritarian, antidemocratic and neoconservative elements. (2) Method: A qualitative approach with a hermeneutic-interpretative paradigm was used. In-depth semi-structured interviews were conducted with 41 inhabitants of Medellín who were politically identified with right-wing or center-right positions. Data analysis included thematic coding to identify patterns of thought and points of view. (3) Results: Participants associate success with individual effort and see state intervention as an obstacle to development. They reject redistributive policies, arguing that they generate dependency. In addition, they justify authoritarian models of government in the name of security and progress, from a moral superiority, which is related to a negative and stigmatizing perception of progressive sectors and a negative view of the social rule of law and public policies with social sense. (4) Conclusions: The naturalization of merit as a guiding principle, the perception of themselves as morally superior based on religious values that grant a subjective place of certainty and goodness; the criminalization of expressions of political leftism, mobilizations and redistributive reforms and support for policies that establish authoritarianism and perpetuate exclusion and structural inequalities, closes roads to a participatory democracy that enables social and economic transformations. Full article

The Union of the Comoros, located in the Indian Ocean, faces persistent energy challenges due to its geographic isolation, heavy dependence on imported fossil fuels, and underdeveloped electricity infrastructure. This study investigates the techno-economic optimization of a hybrid microgrid designed to supply electricity to a rural village in Grande Comore. The proposed system integrates photovoltaic (PV) panels, wind turbines, a diesel generator, and battery storage. Detailed modeling and simulation were conducted using HOMER Energy, accompanied by a sensitivity analysis on solar irradiance, wind speed, and diesel price. The results indicate that the optimal configuration consists solely of PV and battery storage, meeting 100% of the annual electricity demand with a competitive levelized cost of energy (LCOE) of 0.563 USD/kWh and zero greenhouse gas emissions. Solar PV contributes over 99% of the total energy production, while wind and diesel components remain unused under optimal conditions. Furthermore, the system generates a substantial energy surplus of 63.7%, which could be leveraged for community applications such as water pumping, public lighting, or future system expansion. This study highlights the technical viability, economic competitiveness, and environmental sustainability of 100% solar microgrids for non-interconnected island territories. The approach provides a practical and replicable decision-support framework for decentralized energy planning in remote and vulnerable regions. Full article

This article examines the physical properties, performance metrics, and cooling requirements of a range of superconducting materials, with a particular focus on their compatibility with hydrogen-based cryogenic systems. It analyses recent developments and challenges in this field, and considers how hydrogen cryomagnetic could transform superconducting technologies, making them economically viable and environmentally sustainable for a variety of critical applications. The discussion aims to provide insights into the intersection of metallic and ceramic superconductors with the hydrogen economy and to chart a path towards scalable and impactful solutions in the energy sector. Full article

Efficient planning of train crew rosters is a key factor in ensuring operational reliability and promoting long-term sustainability in railway transport, both economically and socially. This article presents a systematic approach to developing a crew rostering model in passenger rail transport, with a focus on the operational setting of the train crew depot in Česká Třebová, a city in the Czech Republic. The seven-step methodology includes identifying available train shifts, defining scheduling constraints, creating roster variants, and calculating personnel and time requirements for each option. The proposed roster reduced staffing needs by two employees, increased the average shift duration to 9 h and 42 min, and decreased non-productive time by 384 h annually. These improvements enhance sustainability by optimizing human resource use, lowering unnecessary energy consumption, and improving employees’ work–life balance. The model also provides a quantitative assessment of operational feasibility and economic efficiency. Compared to existing rosters, the proposed model offers clear advantages and remains applicable even in settings with limited technological support. The findings show that a well-designed rostering system can contribute not only to cost savings and personnel stabilization, but also to broader objectives in sustainable public transport, supporting resilient and resource-efficient rail operations. Full article

Floods pose a growing threat globally, causing tragic loss of life, billions in economic damage annually, and disproportionately affecting socio-economically vulnerable populations. This paper aims to improve flood-risk communication for community leaders by exploring the application of artificial intelligence. We categorize U.S. flood information sources, review communication modalities and channels, synthesize the literature on community leaders’ roles in risk communication, and analyze existing technological tools. Our analysis reveals three key challenges: the fragmentation of flood information, information overload that impedes decision-making, and the absence of a unified communication platform to address these issues. We find that AI techniques can organize data and significantly enhance communication effectiveness, particularly when delivered through infographics and social media channels. Based on these findings, we propose FLAI (Flood Language AI), an AI-driven flood communication platform that unifies fragmented flood data sources. FLAI employs knowledge graphs to structure fragmented data sources and utilizes a retrieval-augmented generation (RAG) framework to enable large language models (LLMs) to produce contextualized narratives, including infographics, maps, and cost–benefit analyses. Beyond flood management, FLAI’s framework demonstrates how AI can transform public service data management and institutional AI readiness. By centralizing and organizing information, FLAI can significantly reduce the cognitive burden on community leaders, helping them communicate timely, actionable insights to save lives and build flood resilience. Full article