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Ore Texture and Mineral Dissemination in a Yunnan Sulfide Copper Ore: Implications for Beneficiation
No full textThis study focuses on a low-grade sulfide copper ore from Yunnan, China. Through chemical multi-element analysis, reflected-light microscopy, and Tescan Integrated Mineral Analyzer (TIMA), systematic process mineralogy research was conducted. Results indicate that the ore contains 0.49% Cu, with chalcopyrite (1.32%) and pyrite (17.57%) as the primary metallic minerals, and quartz (67.07%) as the dominant gangue mineral. Chalcopyrite and pyrite are closely associated, exhibiting medium to fine dissemination sizes (0.2–5.0 mm) and complex intergrowth relationships. Based on these characteristics, the study suggests that the key to copper recovery via flotation lies in optimizing the grinding fineness to achieve effective liberation of target minerals, while enhancing selective collection of chalcopyrite and depression of pyrite to improve separation efficiency. This research provides essential mineralogical guidance for optimizing beneficiation strategies for similar ores
Construction of an Evaluation Model for the Influence of Smart Agriculture Development on Agroforestry Economic Growth
No full textTo scientifically quantify the correlation mechanism between smart agriculture development and agroforestry economic growth, this study takes technology innovation theory as the logical support, constructs a multi-dimensional evaluation model based on the extended Cobb-Douglas production function, conducts empirical verification using standardized simulated evaluation data, synthesizes the smart agriculture development evaluation index through the entropy weight method, and verifies the model's adaptability by combining methods such as the fixed effects model, robustness test, and heterogeneous scenario comparison. The experimental results show that the peak goodness-of-fit of the constructed evaluation model reaches 0.912, and the evaluation coefficient of smart agriculture development on agroforestry economic growth ranges from 0.24 to 0.31, all passing the significance test. This provides a replicable technical path for the quantitative research on the economic impact of smart agriculture
Comparative analysis of particulate matter emissions and health risks from biomass and LPG cooking fuels
No full textIndoor air pollution from biomass fuel combustion remains a major health concern in developing countries, particularly affecting women and children. This study investigates emissions of size-fractionated particulate matter (PM1, PM2.5, PM10) from five commonly used cooking fuels i.e., wood, cow dung, crop residue, coal, and liquefied petroleum gas (LPG) under controlled indoor conditions. Biomass fuels emitted 1.4–1.8 times higher PM2.5 and PM10 concentrations than LPG, with cow dung showing the highest levels (PM10 = 757 μg/m3, PM2.5 = 701 μg/m3, PM1 = 638 μg/m3). Exposure assessment revealed that women and children received greater particulate deposition, with coarse particles settling mainly in the head region and fine particles in the alveolar region. Non-carcinogenic risk analysis indicated hazard quotient (HQ) values above unity for all biomass fuels, implying potential adverse health impacts, especially among children and infants. Elemental characterization identified Na, Al, Si, and K as dominant elements, while toxic Ba was detected in all fuels except LPG. Morphological analysis showed predominantly spherical and irregular particles. Overall, the findings highlight that biomass fuels significantly elevate indoor PM levels and associated health risks, underscoring the urgent need for cleaner cooking technologies, better ventilation, and policy measures to protect exposed populations
Justification of the acceptability of a hybrid approach to passenger and freight flows' predictive modelling in the context of sustainable development
No full textThe relevance of the study is determined by the need for more accurate long-term forecasting in modeling passenger and freight flows. The methodological basis of the study consists of methods of analysis and forecasting of time series, methods of recurrent neural networks, and various methods of expert assessments. A hybrid SARTMAX/LSTM method for long-term forecasting of freight and passenger traffic flows has been implemented, and a scheme for long-term forecasting and a scheme for applying the hybrid SARIMAX/LSTM method have been proposed. An assessment of the overall accuracy of long-term forecasting using the SARIMAX/LSTM hybrid method for the transport and logistics market in general, for railway and air transport in particular has been carried out
A Performance-Enhanced Water Distribution Model via EPANET Integrated with R
No full textAn efficient water distribution system is fundamental to achieving sustainable water management and ensuring consistent service delivery in both urban and rural communities. This study focuses on the development of an efficient water distribution system through an amalgamation of EPANET, Coefficient of Determination (R2), and Percent Bias (PBIAS) to evaluate and enhance hydraulic performance. The main objective is to design, simulate, and assess the efficiency and reliability of the distribution network in delivering adequate pressure and flow under varying demand conditions. The methodology involved developing a hydraulic model using EPANET based on actual system data, including pipe characteristics, nodal elevations, and water consumption patterns. Model calibration and validation were conducted by comparing simulated and field-observed pressure and flow data. Statistical indicators were applied—R2 measured the degree of correlation between simulated and observed values, while PBIAS quantified the average tendency of the model to over- or underestimate system performance. Results indicated a strong coefficient of determination (R2 > 0.9) and low percent bias, suggesting that the model effectively represents real system behavior with minimal deviation. Hence, the integration of EPANET with R2 and PBIAS provides a robust framework for designing and evaluating water distribution efficiency. It is recommended that the calibrated model be adopted for future expansion planning, leakage control, and operational optimization to ensure sustainable and equitable water supply management
Smart Locker Solutions for Sustainable Urban Logistics: Experimental Research in Ho Chi Minh City
No full textThis study explores the factors that influence the intention to use smart lockers in sustainable last-mile delivery. This solution addresses urban challenges such as carbon emissions, traffic congestion, and high costs. A survey of 302 students in Ho Chi Minh City, analyzed using PLS-SEM, identified three main factors impacting usage intentions: convenience, perceived security, and reliability. Convenience had the most significant effect on perceived value (β = 0.472), followed by perceived security (β = 0.236). Although reliability did not significantly affect perceived value, it did directly influence intention to use (β = 0.143). Perceived value also played a key mediating role (β = 0.474). This research advances the application of self-service technology in urban logistics. It highlights how smart lockers can help reduce impacts such as air pollution, noise, and congestion, supporting last-mile delivery amid rapid urban growth and expanding e-commerce
A posteriori error control for a finite volume scheme for a cross-diffusion model of ion transport
No full textWe derive a reliable a posteriori error estimate for a cell-centered finite volume scheme approximating a cross-diffusion system modeling ion transport through nanopores. To this end, we derive a stability framework that is independent of the numerical scheme and introduce a suitable (conforming) reconstruction of the numerical solution. The stability framework relies on some simplifying assumptions that coincide with those made in weak uniqueness results for this system. Additionally, when electrical forces are present, we assume that the solvent concentration is uniformly bounded from below. This is the first a posteriori error estimate for a cross-diffusion system. Along the way, we derive a pointwise a posteriori error estimate for a finite volume scheme that approximates the diffusion equation. We conduct numerical experiments showing that the error estimator scales with the same order as the true error
Molecular Line Parameters, Impact on Climate Models and the Quantum-Pascal
No full textInfrared absorption spectroscopy is one of the most powerful techniques for determining gas compositions. A key input parameter for this method is the absorption line strength, which also has a significant impact on atmospheric and climate models. Line strengths can either be determined experimentally, as demonstrated in this work for three of the strongest absorption lines of carbon monoxide (CO), or calculated using ab-initio theoretical methods. Improving and experimentally validating theoretical line strengths offers the fundamental advantage that complete absorption spectra can be calculated with higher accuracy, leading to more reliable spectroscopic knowledge for climate models. At the Physikalisch-Technische Bundesanstalt (PTB), CO absorption line strengths in the fundamental band in the 4.5 µm wavelength range (R8-R10) were experimentally determined at 296 K, yielding values of 4.452(16)·10-19, 4.217(16)·10-19, and 3.851(15)·10-19 cm molecule-1 respectively. These results show excellent agreement with the latest theoretical values recently reported by University College London, at sub-percent level. In contrast, line strengths currently used in widely applied databases such as HITRAN deviate from the experimental results by 0.66 %, 1.32 %, and 1.21 %, respectively
Integrated Analytical Measurement Systems for Safe and Efficient Green Hydrogen Production
No full textHydrogen, which is produced from renewable energies, is of fundamental importance in making industry climate-neutral. Despite technical advances and political support, there are still a number of challenges regarding the safety, reliability, efficiency and cost-effectiveness of green hydrogen production facilities. This article deals with the use of process analyser systems in hydrogen production and purification. In particular, the focus is on the implementation of process analytics in the plant, its significance for plant safety and hydrogen quality, and aspects of sample conditioning to ensure the functionality of process analysers in electrolysis plants, specifically using alkaline and proton exchange membrane (PEM) technologies. It emphasises the importance of powerful, fully-integrated process analyser systems. This guarantees stable processes, minimises downtime and ensures product quality. In turn, it contributes to an increasing market ramp-up of green hydrogen technologies
Bulgeless Evolution And the Rise of Discs (BEARD)
No full textWe study the formation and evolution of bulgeless galaxies within the Milky Way-Andromeda analogue sample of the TNG50 simulation. Through kinematic decomposition with MORDO