Search Results (6)

With the rapid development of big data and artificial intelligence technologies, the era of Industry 4.0 has driven large open-pit mines towards digital and intelligent transformation. This is particularly true in mature mining areas such as the Yanshan Iron Mine, where the depletion of shallow proven reserves and the increasing issues of mixed surrounding rocks with shallow ore bodies make it increasingly important to build intelligent mines and implement green and sustainable development strategies. However, previous mineralization predictions for the Yanshan Iron Mine largely relied on traditional geological data (such as blasting rock powder, borehole profiles, etc.) exploration reports or three-dimensional explicit ore body models, which lacked precision and were insufficient to meet the requirements for intelligent mine construction. Therefore, this study, based on artificial intelligence technology, focuses on geoscience big data mining and quantitative prediction, with the goal of achieving multi-scale, multi-dimensional, and multi-modal precise positioning of the Yanshan Iron Mine and establishing its intelligent mine technology system. The specific research contents and results are as follows: (1) This study collected and organized multi-source geoscience data for the Yanshan Iron Mine, including geological, geophysical, and remote sensing data, such as mine drilling data, centimeter-level drone image data, and high-spectral data of rocks and minerals, establishing a rich mine big data set. (2) SOM clustering analysis was performed on the elemental data of rock and mineral samples, identifying key elements positively correlated with iron as Mg, Al, Si, S, K, Ca, and Mn. TSG was used to interpret shortwave and thermal infrared hyperspectral data of the samples, identifying the main alteration mineral types in the mining area. Combined with spectral and elemental analysis, the universality of alteration features such as chloritization and carbonation, which are closely related to the mineralization process, was further verified. (3) Based on the spectral and elemental grade data of rock and mineral samples, a training model for ore grade–spectrum correlation was constructed using Random Forests, Support Vector Machines, and other algorithms, with the SMOTE algorithm applied to balance positive and negative samples. This model was then applied to centimeter-level drone images, achieving high-precision intelligent identification of magnetite in the mining area. Combined with LiDAR image elevation data, a real-time three-dimensional surface mineral monitoring model for the mining area was built. (4) The Bagged Positive Label Unlabeled Learning (BPUL) method was adopted to integrate five evidence maps—carbonate alteration, chloritization, mixed rockization, fault zones, and magnetic anomalies—to conduct three-dimensional mineralization prediction analysis for the mining area. The locations of key target areas were delineated. The SHAP index and three-dimensional explicit geological models were used to conduct an in-depth analysis of the contributions of different feature variables in the mineralization process of the Yanshan Iron Mine. In conclusion, this study successfully constructed the technical framework for intelligent mine construction at the Yanshan Iron Mine, providing important theoretical and practical support for mineralization prediction and intelligent exploration in the mining area. Full article

This study investigates the development of protein powder from honey bee drone broods using foam-mat drying, a scalable method suitable for community enterprises, as well as the preservation of bee broods as a food ingredient. Initially, honey bee broods were pre-treated by boiling and steaming, with steamed bee brood (S_BB) showing the highest protein content (44.71 g/100 g dry basis). A factorial design optimized the powder formulation through the foam-mat drying process, incorporating varying concentrations of S_BB, glycerol monostearate (GMS), and carboxymethyl cellulose (CMC). The physicochemical properties of the resulting powder, including yield, color spaces, water activity, solubility, protein content, and total amino acids, were evaluated. The results showed that foam-mat drying produced a stable protein powder. The binders (CMC and GMS) increased the powder’s yield and lightness but negatively affected the hue angle (yellow-brown), protein content, and amino acid content. The optimal quantities of the three variables (S_BB, GMS, and CMC) were determined to be 30 g, 6 g, and 1.5 g, or 80%, 16%, and 4%, respectively. Under this formulation, the protein powder exhibited a protein content of 19.89 g/100 g. This research highlights the potential of bee brood protein powder as a sustainable and nutritious alternative protein source, enhancing food diversification and security. Full article

Two different levels (5 and 10%) of male honey bees (drones) in powder form were incorporated into wheat flour, and their impact on dough properties and on bread-quality characteristics were investigated. The incorporation of the drone powder to the wheat flour caused a decrease in the extensibility and energy of the dough in the extensograph and an increase in the dough’s maximum resistance with increasing levels of the added drone powder. The elongational viscosity values of the dough fortified with drone powder were significantly higher than those of the control wheat flour dough. The breads supplemented with 10% drone powder exhibited lower lightness (L*) values compared to the control bread. The addition of drone powder led to an increase in the total dietary fiber content and insoluble dietary fiber content in the fortified bread. Significant differences in the specific volume values were observed between the control bread and the corresponding ones with 10% drone powder. Upon storage, the moisture content of the crumb of the control bread and of the fortified breads were both significantly decreased, while the addition of the drone powder to the wheat flour bread increased the crumb hardness and gumminess but decreased the cohesiveness of the breads. Full article

Edible insect ingredients have gained importance as environmental-friendly energy sources world-wide; the honeybee (Apis mellifera L.) drone pupae has gained prominence as a nutritional material. In this study, bee drone pupae were processed under different heating and drying conditions and incorporated into a puffed-rice snack with honey. The sensory, physicochemical, nutritional and microbial qualities of drone pupae powders were tested. The deep-fried and hot-air dried powder was selected; the values of 5.54% (powder) and 2.13% (honey) were obtained on optimization with honey by response surface methodology. Subsequently, the puffed-rice snack product enriched with drone pupae powder was stored at different temperatures for 180 days. The prepared product showed a higher content of proteins, fats, amino acids, and fatty acids compared to the control. The high content of a few minerals were maintained in the processed powder and the product, whereas heavy metals were not detected. The storage test indicated acceptable sensory qualities and safety results, considering important quality parameters. Thus, drone pupae powder and the developed product can be consumed as nutritional food materials; the quality characteristics can be improved through optimal processing. Full article

A micro needle trap sampler (NTS) was carried by a mini quadrotor drone (Mavic Pro, DJI) to collect volatile organic compounds (VOCs) from industries. The NTS was fabricated using a 7 cm long, 22-gauge stainless steel needle by packing powdered divinylbenzene (DVB) adsorbents (60–80 mesh diameters). The telescoping sampling shaft was installed on the drone to extend the NTS beyond the downward air turbulence that was caused by the rotation of its propellers. The total mass of the sampling device, including an NTS, a telescoping shaft, a mini-air pump, and an ABS (acrylonitrile butadiene styrene) rack, was not more than 200 g. The emitted VOCs, those from a steel processing plant, including aromatic hydrocarbons (toluene of 15 ppb, ethylbenzene of 9 ppb and p-xylene 12 ppb), and those from a semiconductor processing factory, including trace amounts of methanol (1.96–2.00 ppm), acetone (0.05–0.10 ppm), and toluene (1.04–2.00 ppm), were extracted by the NTS on the drone and identified using a gas chromatography-mass spectroscopy (GC-MS) system in the laboratory. According to the results of VOC detection during the sampling flight of a drone, the stationary pollution sources were successfully identified. Full article

This study aimed to determine the influence of ashes produced in the combustion of hard coal and eco-pea coal in individual household furnaces on the air quality in the region under analysis. To achieve this objective, we analysed the chemical and mineral composition of ashes, suspended and respirable dusts with particular attention being paid to phases containing potentially toxic elements (PTE) (As, Cd, Pb, Se, Ni, Ba, Tl, S, Th and U), and sulphur. The research methods used included powder X-ray diffraction, scanning electron microscopy and inductively coupled plasma mass spectrometry. Measurements were taken for PM concentrations, total suspended particulate matter (TSP), gaseous TVOC pollutants (volatile organic compounds) and soot at various altitudes and a mobile laboratory with measuring apparatus placed in the basket of a manned hot-air balloon was used for the analysis. The use of Poland’s unique laboratory allowed us to obtain real-time measurements up to an altitude of 1200 m above sea level. Measurements using unmanned units such as drones do not enable such analyses. The research confirmed that PTE concentrations in ash and its mineral composition are varied. The PM10 and PM2.5 ashes are dominated by sodium chloride, particles containing C, and a substance composed of S + C + O + N + Na. Trace amounts of Pb and Zn sulphides are also present. Full article