Search Results (1,161)

Fuel consumption in heavy-duty off-road machinery depends on a wide range of interacting factors related to the operating environment, the technical characteristics and condition of the machine, and the behaviour, experience and state of the operator. Existing studies typically address only fragments of this relationship, focusing on vehicle parameters, selected environmental factors or individual aspects of driving style. The method proposed in this work provides a general and transferable framework for assessing fuel consumption in any type of machine or vehicle. The Integrated Fuel Consumption Assessment Model (IFCAM) combines environmental, vehicle and human domains into a coherent structured formula that can be used across different operational contexts. The model was developed using continuous short-term measurements and long-term operational data collected during real industrial work. Its universal structure makes it applicable not only to mining equipment, but also to construction machinery and transport vehicles, as well as conventional passenger cars, where it offers a systematic procedure for estimating fuel demand under variable operating conditions. The results demonstrate that integrating multi-domain data improves predictive accuracy and opens new possibilities for analysing operator influence and overall energy efficiency. Full article

Map matching is a fundamental technique for aligning noisy GPS trajectory data with digital road networks and constitutes a key component of Intelligent Transportation Systems (ITS) and Location-Based Services (LBS). Nevertheless, existing approaches still suffer from notable limitations in complex environments, particularly urban and urban-like scenarios characterized by heterogeneous GPS noise and sparse observations, including inadequate adaptability to dynamically varying noise, unavoidable trade-offs between real-time efficiency and matching accuracy, and limited generalization capability across heterogeneous driving behaviors. To overcome these challenges, this paper presents a Meta-learning-driven Progressive map-Matching (MPM) method with a symmetry-aware design, which integrates a two-layer pattern-mining-based noise-robust meta-learning mechanism with a dynamic weight adjustment strategy. By explicitly modeling topological symmetry in road networks, symmetric trajectory patterns, and symmetric noise variation characteristics, the proposed method effectively enhances prior knowledge utilization, accelerates online adaptation, and achieves a more favorable balance between accuracy and computational efficiency. Extensive experiments on two real-world datasets demonstrate that MPM consistently outperforms state-of-the-art methods, achieving up to 10–15% improvement in matching accuracy while reducing online matching latency by over 30% in complex urban environments. Furthermore, the symmetry-aware design significantly improves robustness against asymmetric interference, thereby providing a reliable and scalable solution for high-precision map matching in complex and dynamic traffic environments. Full article

Methane emissions from underground coal mines are a significant source of greenhouse gases (GHGs) and a major safety concern. In highly methane-prone operations, a large proportion of emissions comes from low-content abandoned mine methane (LCAMM) accumulated in post-mining goafs, where concentrations usually stay below 30% CH₄. Building on the Research Fund for Coal and Steel (RFCS) REM project, this paper presents a cost–benefit analysis of a comprehensive scheme for capturing, transporting, and utilising LCAMM from post-mining goafs for electricity generation. The concept involves long-reach directional boreholes drilled behind isolation dams, a dedicated methane-reduced drainage system connected to a surface methane drainage station, and four 2 MWe gas engines designed to run on a 20–40% CH₄ mixture. Greenhouse gas performance is evaluated by comparing a “business-as-usual” scenario in which post-mining methane is combusted in gas engines to produce electricity without further GHG cost–benefit consideration. The results indicate that the project can achieve a positive net present value, highlighting the role of LCAMM utilisation for methane-intensive coal mines. The paper also explores the monetisation of non-emitted methane using the European Union Emissions Trading System (EU ETS), as well as social cost benchmarks and penalty levels consistent with the emerging EU Methane Emissions Regulation (EU MER). Full article

With the increasing requirements for “Green Mine” construction, Cemented Tailings Backfill (CTB) has emerged as the preferred strategy for solid waste management and ground pressure control in underground metal mines. However, full tailings, characterized by wide particle size distribution and high fine-grained content, exhibit complex physicochemical properties that lead to significant non-linear behavior in slurry rheology and strength evolution, posing challenges for accurate prediction using traditional empirical formulas. Addressing the issues of significant strength fluctuations and difficulties in mix proportion optimization in a specific lead–zinc mine, this study systematically conducted physicochemical characterizations, slurry sedimentation and transport performance evaluations, and mechanical strength tests. Through multi-factor coupling experiments, the synergistic effects of cement type, cement-to-tailings (c/t) ratio, slurry concentration, and curing age on backfill performance were elucidated. Quantitative results indicate that solids mass concentration is the critical factor determining transportability. Concentrations exceeding 68% effectively mitigate segregation and stratification during the filling process while maintaining optimal fluidity. Regarding mechanical properties, the c/t ratio and concentration show a significant positive correlation with Uniaxial Compressive Strength (UCS). For instance, with a 74% concentration and 1:4 c/t ratio, the 3-day strength increased by 1.4 times compared to the 68% concentration, with this increment expanding to 2.0 times by 28 days. Furthermore, a comparative analysis of four cement types revealed that 42.5# cement offers superior techno-economic indicators in terms of reducing binder consumption and enhancing early-age strength. This research not only establishes an optimized mix proportion scheme tailored to the operational requirements of the lead–zinc mine but also provides a quantitative scientific basis and theoretical framework for the material design and safe production of CTB systems incorporating high fine-grained full tailings. Full article

High rock slopes are extensively distributed in areas of major engineering constructions, such as transportation infrastructure, hydraulic projects, and mining operations. The stability and failure evolution mechanism during their multi-stage excavation process have consistently been a crucial research topic in geotechnical engineering. In this paper, a series of two-dimensional rock slope models, incorporating various combinations of slope height and slope angle, were established utilizing the Discrete Element Method (DEM) software PFC2D. This systematic investigation delves into the meso-mechanical response of the slopes during multi-stage excavation. The Parallel Bond Model (PBM) was employed to simulate the contact and fracture behavior between particles. Parameter calibration was performed to ensure that the simulation results align with the actual mechanical properties of the rock mass. The research primarily focuses on analyzing the evolution of displacement, the failure modes, and the changing characteristics of the force chain structure under different geometric conditions. The results indicate that as both the slope height and slope angle increase, the inter-particle deformation of the slope intensifies significantly, and the shear band progressively extends deeper into the slope mass. The failure mode transitions from shallow localized sliding to deep-seated overall failure. Prior to instability, the force chain system exhibits an evolutionary pattern characterized by “bundling–reconfiguration–fracturing,” serving as a critical indicator for characterizing the micro-scale failure mechanism of the slope body. Full article

Soil heavy metal contamination poses significant risks to ecological safety and human health, particularly in rapidly industrializing cities. Effectively identifying pollution sources is crucial for risk management and remediation. GIS coupled with data mining techniques, provide a powerful tool for quantifying and visualizing these sources. This study investigates the concentration, spatial distribution, and sources of heavy metals in urban soils of Bengbu City, an industrial and transportation hub in eastern China. A total of 139 surface soil samples from the urban core were analyzed for nine heavy metals. Using integrated GIS and PCA-APCS-MLR data mining techniques, we systematically determined their contamination characteristics and apportioned sources. The results identified widespread Hg enrichment, with concentrations exceeding background levels at all sampling sites, and a Cd exceedance rate of 28.06%, leading to a moderate ecological risk level overall. Spatial patterns revealed significant heterogeneity. Quantitative source apportionment identified four primary sources: industrial source (37.1%), which was the dominant origin of Cr, Cu, and Ni, primarily associated with precision manufacturing and metallurgical activities; mixed source (26.7%) governing the distribution of Mn, As, and Hg, mainly from coal combustion and the natural geological background; traffic source (22.3%) significantly contributing to Pb and Zn; and a specific cadmium source (13.9%) potentially originating from non-ferrous metal smelting, electroplating, and agricultural activities. These findings provide a critical scientific basis for targeted pollution control and sustainable land-use management in analogous industrial cities. Full article

The prediction of deep-sea mining sediment plumes is essential for assessing and mitigating the environmental impacts on vulnerable deep-sea ecosystems. In this paper, the numerical simulation method is adopted to predict the sediment plume transportation. Fluid dynamics are governed by the incompressible Navier–Stokes equations, coupled with the Standard k–ε turbulence model to capture turbulent diffusion. The air–water free surface is tracked by a high-resolution Volume of Fluid (VOF) method. The pressure–velocity coupling utilizes the PISO algorithm. Sediment transport is governed by the advection–diffusion equation. The mathematical model is validated through experiments. There is a good consistency between the experiment results and the numerical results, which proves that the numerical method can be applied. The study calculates the diffusion range and characteristics of plumes under different free stream velocities, injection velocities and discharge densities. The results indicate that an increase in free stream velocity enhances the development of turbulence, but conversely restricts the expansion of the mixing zone between the plume and the ambient water. A greater injection velocity leads to a wider distribution range of the plume, while inhibiting the development of local turbulence. A higher plume discharge density results in a larger horizontal distribution range, while hindering the effective mixing between the plume and the ambient water body. Full article

Agricultural soils surrounding mining areas are often polluted with heavy metals (HMs) due to long-term mining activities and high geological background values. In this study, we investigated the distribution and transport of Cu, Cr, Zn, Cd, Pb, and As in a soil–rice system near a century-old mining site, evaluated their toxic effects on rice (Oryza sativa L.) throughout the growth period, and assessed the associated health risks using the Nemerow index and potential ecological risk index. The results showed that HM contents in rice grown in contaminated soils were significantly higher than in the control. HMs mainly accumulated in roots, with the lowest contents in grains. Cd exhibited the highest enrichment capacity, with bioconcentration factors of 0.79, 1.04, and 1.95 at the tillering, heading, and maturity stages, respectively, and its accumulation increased with rice growth. Transport from stems to leaves was relatively strong. HM exposure significantly inhibited rice growth, reducing plant height, biomass, tiller number, and panicle emergence. In addition, oxidative stress indicators and antioxidant enzyme activities, as well as root amino acid exudation, were markedly altered under HM stress. According to soil–rice HM contents, the pollution level of agricultural soils reached a high class, with As, Pb, Cd, and Zn as the main contributors. The potential ecological risk reached a moderate level, with Cd identified as the dominant factor. Notably, the health risks to children were substantially higher than those to adults, and Monte Carlo simulation indicated a 100% probability of non-carcinogenic and carcinogenic risks for adults and children. The above results highlighting the urgent need for risk management in mining-affected regions. Full article

The green synthesis of silver nanoparticles (AgNPs) by bacteria is a strategic route for sustainable nanobiotechnology; however, the genomic and biochemical mechanisms that make it possible remain poorly defined. In this study, bacteria native to silver-bearing mine tailings in Taxco (Mexico) were isolated, capable of tolerating up to 5 mM of AgNO₃ and producing extracellular AgNPs. Spectroscopic (430–450 nm) and structural (XRD, fcc cubic phase) characterization confirmed the formation of AgNPs with average sizes of 17–21 nm. FTIR evidence showed the participation of extracellular proteins and polysaccharides as reducing and stabilizing agents. Genomic analyses assigned the isolates as Lysinibacillus fusiformis 31HCl and L. xylanilyticus G1-3. Genome mining revealed extensive repertoires of genes involved in uptake, transport, efflux and detoxification of metals, including P-type ATPases, RND/ABC/CDF transporters, Fe/Ni/Zn uptake systems, and metal response regulators. Notably, homologues of the silP gene, which encode Ag⁺ translocator ATPases, were identified, suggesting convergent adaptation to silver-rich environments. Likewise, multiple nitroreductases (YodC, YdjA, YfKO) were detected, candidates for mediating electron transfer from NAD(P)H to Ag⁺. These findings support the role of Lysinibacillus as microbial nanofactories equipped with specialized molecular determinants for silver tolerance and AgNP assembly, providing a functional framework for microorganism-based nanobiotechnology applications. Full article

Efficient truck–shovel allocation is essential for optimizing open-pit mining operations, but the integration of heterogeneous diesel and electric fleets introduces complex scheduling challenges, including charging requirements, range limitations, and equipment capacity constraints. This study proposes an integrated allocation framework tailored to heterogeneous fleets, formulating a multi-objective optimization model that minimizes transportation cost and waiting time under realistic constraints. An enhanced multi-objective particle swarm optimization algorithm with adaptive penalty mechanisms is developed, providing superior convergence and computational efficiency compared to traditional methods. A case study demonstrates that heterogeneous fleets achieve a better trade-off, with a balanced fleet configuration reducing transportation cost by 26.1% and waiting time by 19.2% compared to pure diesel and electric fleets, respectively. Sensitivity analyses reveal that fluctuations in fuel and electricity prices reshape the trade-off, while faster charging enhances electric truck competitiveness but increases diesel idle time. These findings offer practical insights for configuring heterogeneous fleets and adapting scheduling strategies in dynamic energy and technology environments, supporting sustainable mining operations. Full article

Conveyor belts are among the most critical components of material transport systems across various industrial sectors, including mining, energy, cement production, metallurgy, and logistics. Their reliability directly affects the continuity and operational costs. Traditional methods for assessing belt condition often require downtime, are labor-intensive, and involve a degree of subjectivity. In recent years, there has been a growing interest in non-destructive and remote diagnostic techniques that enable continuous and automated condition monitoring. This paper provides a comprehensive review of current diagnostic solutions, including machine vision systems, infrared thermography, ultrasonic and acoustic techniques, magnetic inspection methods, vibration sensors, and modern approaches based on radar and hyperspectral imaging. Particular attention is paid to the integration of measurement systems with artificial intelligence algorithms for automated damage detection, classification, and failure prediction. The advantages and limitations of each method are discussed, along with the perspectives for future development, such as digital twin concepts and predictive maintenance. The review aims to present recent trends in non-invasive diagnostics of conveyor belts using remote and non-destructive testing techniques, and to identify research directions that can enhance the reliability and efficiency of industrial transport systems. Full article

This study presents the phenotypic characterization and genomic mining of uric acid catabolism genes in Lactiplantibacillus plantarum YC, a novel food-grade lactic acid bacterium isolated from traditional fermented vegetables with potent uric acid-lowering activity. YC is non-hemolytic, catalase- and gelatinase-negative, exhibits strong adhesion and broad antibacterial activity, and degrades 29.22% of uric acid in vitro, along with complete (100%) degradation of inosine and guanosine. Whole-genome sequencing revealed a 3,214,448 bp chromosome encoding 3026 protein-coding genes. Comparative genomics-based functional annotation highlighted abundant CAZy-related genes and antimicrobial factors, including lysozyme and monooxygenase. Crucially, genomic mining identified a complete uric acid degradation gene cluster, comprising pucK (uric acid permease), hpxO (uric acid hydroxylase), eight copies of hiuH (5-hydroxyisourate hydrolase), allB (allantoinase), and purine nucleoside transport/metabolism genes (rihA, rihB, rihC, pbuG). This work provides the first comparative genomic insight into the genetic architecture and distribution of uric acid metabolism in L. plantarum, elucidating YC’s dual urate-lowering mechanism and delivering key molecular markers for developing enzyme-based functional foods and microbial therapeutics against hyperuricemia. Full article

Waste haulage represents one of the most critical and cost-intensive operations in surface mining, accounting for up to 50% of the total operating costs. Under such operating conditions, the implementation of continuous systems such as In-Pit Crushing and Conveying (IPCC) is an alternative to truck haulage, as it demonstrates a higher degree of economic efficiency. In a theoretical and practical sense, due to its direct impact on the extraction plan, defining the optimal position of the crusher and consequently the system of conveyors is often the most challenging problem of this methodology. This paper introduces an innovative approach to determining the optimum waste haulage configuration by comparing conventional truck-based transport with IPCC systems. The model is formulated as a Mixed-Integer Linear Programming (MILP) problem, explicitly incorporating spatial dimensions and the relocation costs of semi-mobile crushers. The model situates the crusher in a way that reduces transfer costs throughout production periods and it has been tested on a hypothetical open pit. Full article

Open-pit coal mining is characterized by multiple pollution sources, diverse types, and extensive affected areas, leading to complex air pollution with wide diffusion. Traditional fixed monitoring methods cannot address these limitations. Taking a coal mine in Xinjiang as a case study, this study developed a drone-mounted mobile atmospheric monitoring system, focusing on nitrogen dioxide (NO₂) and suspended particulate matter (PM_2.5 and PM₁₀) to explore their distribution, diffusion patterns, and influencing factors. The results show distinct seasonal pollutant characteristics: NO₂ and ozone (O₃) dominate in summer, while particulate matter prevails in winter. The temporal distribution exhibits a bimodal pattern, with high levels in the early morning and evening hours. Spatially, higher pollutant concentrations accumulate vertically below ground level, while lower levels are observed above it. Horizontally, elevated concentrations are found along northern transport corridors; however, these levels become more uniform at greater heights. A spatiotemporal prediction model integrating convolutional neural network (CNN) and long short-term memory (LSTM) network was successfully applied to real-time pollutant prediction in open-pit coal mining areas. This study provides a reliable mobile monitoring solution for open-pit coal mine air pollution and offers valuable insights for targeted pollution control in similar mining areas. Full article

The genus Kocuria includes Gram-positive and environmentally versatile bacteria, which are of biotechnological interest due to their ability to synthesize secondary metabolites. In this study, the genome of Kocuria sp. KH4, isolated from alkaline mine tailings (southeastern Mexico), was sequenced and analyzed to determine its taxonomic affiliation and explore its metabolic and adaptive potential. The assembled genome showed a size of 3.89 Mb, a GC content of 73.2%, and 3609 coding genes. Phylogenomic analyses and genomic relationship indices (ANI, AAI, and dDDH) confirmed that strain KH4 represents a novel genomospecies within the genus Kocuria. Functional analysis revealed broad metabolic diversity, with genes associated with the transport and metabolism of amino acids, carbohydrates, and inorganic ions. A total of 165 genes linked to metal resistance and homeostasis mechanisms were identified, including ABC-type transport systems and ATPases, as well as specific genes for Fe, Ni, Zn, Cu, As, and Hg. Forty-eight genomic islands were also identified, encoding a wide variety of functions and mobile genetic elements (MGEs). Furthermore, six biosynthetic gene clusters (BGCs) involved in the production of nonribosomal peptides, type III polyketides, terpenes, and siderophores were detected, suggesting a remarkable potential for the synthesis of bioactive compounds. Taken together, the results highlight this strain as a promising source of secondary metabolites with potential applications in environmental, pharmaceutical, and industrial biotechnology, underscoring the importance of Kocuria genomes as natural reservoirs of new biosynthetic pathways. Full article