Search Results (219)

In the context of the mining industry, the belt conveyor is a critical piece of equipment. The motor constitutes the primary component of the belt conveyor apparatus, and its stable and accurate operation can significantly influence the performance of the belt conveyor apparatus. This paper introduces an integrated control approach combining vector control methodology with active disturbance rejection control (ADRC) for velocity regulation and model predictive control (MPC) for current tracking. The ADRC framework actively compensates for load disturbances and parameter variations during speed control, while MPC achieves precise current regulation with minimal tracking error. Validation involved comprehensive MATLAB/Simulink R2024a simulations modeling PMSM behavior under mining-specific operating conditions. The results demonstrate substantial improvements in dynamic response characteristics and disturbance rejection capabilities compared to conventional control strategies. The proposed methodology effectively addresses critical challenges in mining conveyor applications, enhancing operational reliability and system longevity. Full article

Natech events, involving multi-hazard coupling and cascading effects, pose serious threats to coal mine safety. This paper addresses flood-induced Natech scenarios in coal mining and introduces a two-stage cascading analysis framework based on hazard systems theory. A tri-layered network—comprising natural hazards, exposed elements, and secondary hazards—models hazard propagation. In Stage 1, an improved adjacency information entropy algorithm with multi-hazard coupling coefficients identifies critical exposed elements. In Stage 2, Dijkstra’s algorithm extracts key risk transmission paths. A dual-dimensional classification method, based on entropy and transmission risk, is then applied to prioritize emergency responses. This method integrates the criticality of exposed elements with the risk levels associated with secondary disaster propagation paths. Case studies validate the framework, revealing: (1) Hierarchical heterogeneity in the network, with surface facilities and surrounding hydrological systems as central hubs; shaft and tunnel systems and surrounding geological systems are significantly affected by propagation from these core nodes, exhibiting marked instability. (2) Strong risk polarization in secondary hazard propagation, with core-node-originated paths being more efficient and urgent. (3) The entropy-risk classification enables targeted hazard control, improving efficiency. The study proposes chain-breaking strategies for precise, hierarchical, and timely emergency management, enhancing coal mine resilience to flood-induced Natech events. Full article

The formation of acidic goaf water in abandoned coal mines poses significant environmental threats, especially in karst regions where the risk of groundwater contamination is heightened. This study investigates the geochemical processes responsible for the generation of acidic water through batch and column leaching experiments using coal mine surrounding rocks (CMSR) from Yangquan, China. The coal-bearing strata, primarily composed of sandstone, mudstone, shale, and limestone, contain high concentrations of pyrite (up to 12.26 wt%), which oxidizes to produce sulfuric acid, leading to a drastic reduction in pH (approximately 2.5) and the mobilization of toxic elements. The CMSR samples exhibit elevated levels of arsenic (11.0 mg/kg to 18.1 mg/kg), lead (69.5 mg/kg to 113.5 mg/kg), and cadmium (0.6 mg/kg to 2.6 mg/kg), all of which exceed natural crustal averages and present significant contamination risks. The fluorine content varies widely (106.1 mg/kg to 1885 mg/kg), with the highest concentrations found in sandstone. Sequential extraction analyses indicate that over 80% of fluorine is bound in residual phases, which limits its immediate release but poses long-term leaching hazards. The leaching experiments reveal a three-stage release mechanism: first, the initial oxidation of sulfides rapidly lowers the pH (to between 2.35 and 2.80), dissolving heavy metals and fluorides; second, slower weathering of aluminosilicates and adsorption by iron and aluminum hydroxides reduce the concentrations of dissolved elements; and third, concentrations stabilize as adsorption and slow silicate weathering regulate the long-term release of contaminants. The resulting acidic goaf water contains extremely high levels of metals (with aluminum at 191.4 mg/L and iron at 412.0 mg/L), which severely threaten groundwater, particularly in karst areas where rapid cross-layer contamination can occur. These findings provide crucial insights into the processes that drive the acidity of goaf water and the release of contaminants, which can aid in the development of effective mitigation strategies for abandoned mines. Targeted management is essential to safeguard water resources and ecological health in regions affected by mining activities. Full article

Deep-sea mining presents a critical sustainability paradox; it offers access to essential minerals for the technologies of the green transition (e.g., batteries, wind turbines, electric vehicles) yet threatens fragile marine ecosystems. As the terrestrial sources of these materials face mounting geopolitical, environmental, and ethical constraints, undersea deposits are increasingly being viewed as alternatives. However, the extraction technologies remain unproven at large scales, posing risks related to biodiversity loss, sediment disruption, and altered oceanic carbon cycles. This paper explores how deep-sea mining might be reconciled with sustainable development, arguing that its viability hinges on addressing five interdependent challenges—technological readiness, environmental protection, economic feasibility, robust governance, and social acceptability. Progress requires parallel advancements across all domains. This paper reviews the current knowledge of deep-sea resources and extraction methods, analyzes the ecological and sociopolitical risks, and proposes systemic solutions, including the implementation of stringent regulatory frameworks, technological innovation, responsible terrestrial sourcing, and circular economy strategies. A precautionary and integrated approach is emphasized to ensure that the securing of critical minerals does not compromise marine ecosystem health or long-term sustainability objectives. Full article

Understanding the interplay between plant leaf functional traits and plant and soil factors under different soil thicknesses is significant for quantifying the interaction between plant growth and the environment. However, in the context of ecological restoration of vegetation in mining areas, there has been a lot of research on trees, shrubs, and grasses, but the characteristics and correlations of leaf functional traits of vines have not been fully studied to a large extent. Here, we report the differences in leaf functional traits of six vine plants (Parthenocissus quinquefolia, Pueraria lobata, Hedera nepalensis, Campsis grandiflora, Mucuna sempervirens, and Parthenocissus tricuspidata) with distinct growth forms in different soil cover thicknesses (20 cm, 40 cm, and 60 cm). In addition, soil factor indicators under different soil cover thicknesses were measured to elucidate the linkages between leaf functional traits of vine plants and soil factors. We found that P. lobata showed a resource acquisition strategy, while H. nepalensis demonstrated a resource conservation strategy. C. grandiflora and P. tricuspidata shifted toward more conservative resource allocation strategies as the soil cover thickness increased, whereas M. sempervirens showed the opposite trend. In the plant trait–trait relationships, there were synergistic associations between specific leaf area (SLA) and leaf nitrogen content (LNC); leaf moisture content (LMC) and leaf nitrogen-to-phosphorus ratio (LN/P); and leaf specific dry weight (LSW), leaf succulence degree (LSD), and leaf dry matter content (LDMC). Trade-offs were observed between SLA and LSW, LSD, and LDMC; between leaf phosphorus content (LPC) and LN/P; and between LMC, LSW, and LDMC. In the plant trait–environment relationships, soil nutrients (pH, soil total phosphorus content (STP), and soil ammonium nitrogen content (SAN)) and soil enzyme activities (cellulase (CB), leucine aminopeptidase (LAP), enzyme C/N activity ratio, and enzyme N/P activity ratio) were identified as the primary drivers of variation in leaf functional traits. Interestingly, nitrogen deficiency constrained the growth of vine plants in the mining area. Our study revealed that the responses of leaf functional traits of different vines under different soil thicknesses have significant species specificity, and each vine shows different resource acquisition and conservation strategies. Furthermore, soil cover thickness primarily influences plant functional traits by directly affecting soil enzyme activities and nutrients. However, the pathways through which soil thickness impacts these traits differ among various functional traits. Our findings provide a theoretical basis and practical reference for selecting vine plants and optimizing soil cover techniques for ecological restoration in mining areas. Full article

Background/Objectives: Plexiform neurofibromas (pNFs) are one of the cardinal presentations of NF1 patients, often arising during early childhood. Since selumetinib was approved by the FDA in 2020, the long-term side effects and various responses of mitogen-activated protein kinase inhibitors (MEKi) in pediatric patients necessitate a new strategy. We propose that combining selumetinib with heat shock protein 90 inhibitors (HSP90i) can enhance the inhibitory effects as well as reduce the dosage of selumetinib in combination. We validated the synergistic effects and the significantly improved treatment effects of the combination of selumetinib and HSP90i in pNFs. Methods: We used drug screen data mining to predict the combination of selumetinib and HSP90i. Using cell lines and in vivo mouse models for pNFs, we tested a series of combinations with different concentrations. We validated the in vivo inhibitory effects using the transplanted tumors from DhhCreNf1^f/f mouse models. Results: We demonstrated that combining selumetinib and SNX-2112 or retaspimycin can achieve better tumor inhibition with synergistic effects. The combination significantly delays the progression of mouse pNFs. Conclusions: The combination of selumetinib and HSP90i has significant synergistic effects, provides therapeutic inhibitor effects, and reduces the selumetinib dosage in combination. Full article

To address the challenges of extracting rolling bearing degradation information and the insufficient performance of conventional convolutional networks, this paper proposes a rolling bearing degradation state identification method based on the improved monopulse feature extraction and a one-dimensional dilated residual convolutional neural network (1D-DRCNN). First, the fault pulse envelope waveform features are extracted through phase scanning and synchronous averaging, and a two-stage grid search strategy is employed to achieve FCC calibration. Subsequently, a 1D-DRCNN model is constructed to identify rolling bearing degradation states under different working conditions. The experimental study collects the vibration signals of nine degradation states, including the different sizes of inner and outer ring local faults as well as normal conditions, to comparatively analyze the proposed method’s rapid calibration capability and feature extraction quality. Furthermore, t-SNE visualization is utilized to analyze the network response to bearing degradation features. Finally, the degradation state identification performance across different network architectures is compared in pattern recognition experiments. The results show that the proposed improved feature extraction method significantly reduces the iterative calibration computational burden while effectively extracting local fault degradation information and overcoming complex working condition influence. The established 1D-DRCNN model integrates the advantages of dilated convolution and residual connections and can deeply mine sensitive features and accurately identify different bearing degradation states. The overall recognition accuracy can reach 97.33%. Full article

After a ship suffers an external strike, the system is often in a poor state of battle damage. Currently, the support capacity of the system in all aspects decreases dramatically, the operation interval narrows, and it is not easy to ensure the completion of the long-term mission chain, especially when it involves impact loads, which is more significant. Given this, this paper proposes a restoration strategy for the power system of battle-damaged ships based on the long-term mission chain. First, the Ship Power System (SPS) is modelled and analyzed to obtain the multi-case operating characteristics of various types of loads, including impact loads under the mission chain. Second, the frequency and power support capability of energy storage is mined and quantified, and the limitations of its frequency support, power interaction, and other multi-operating states are characterized, based on which the multi-operating state switching strategy of the system containing energy storage is formed, to enhance the active support capability of the system. Subsequently, a frequency response model of the system is established. This model takes into account the support provided by energy storage, analyzes the dynamic evolution of system frequency under the disturbance of directly connected impact loads. Based on this analysis, the safe operating boundary of the system is identified. Finally, a two-stage SPS optimization model is proposed based on the above, and the effectiveness and superiority of this paper’s strategy are verified through simulation analysis of typical scenarios and comparison of multiple strategies. Full article

This paper explores the implementation of battery electric vehicles (BEVs) in underground mining operations, focusing on their benefits, challenges, and safety considerations. The study examines the shift from traditional diesel-powered machinery to BEVs in response to increasing environmental concerns and stricter emission regulations. It discusses various lithium-ion battery chemistries used in BEVs, particularly lithium–iron–phosphate (LFP) and nickel–manganese–cobalt (NMC), comparing their performance, safety, and suitability for underground mining applications. The research highlights the significant benefits of BEVs, including reduced greenhouse gas emissions, improved air quality in confined spaces, and potential ventilation cost savings. However, it also addresses critical safety concerns, such as fire risks associated with lithium-ion batteries and the emission of toxic gases during thermal runaway events. The manuscript emphasises the importance of comprehensive risk assessment and mitigation strategies when introducing BEVs to underground mining environments. It concludes that while BEVs offer promising solutions for more sustainable and environmentally friendly mining operations, further research is needed to ensure their safe integration into underground mining practices. This study contributes valuable insights to the ongoing discussion on the future of mining technology and its environmental impact. Full article

Indonesia’s post-mining landscapes require an integrated governance approach to achieve equitable and sustainable reclamation. This study developed and evaluated the TILANG Framework (Triple-Bottom-Line Integrated Land Governance) as a multidimensional model that aligns ecological restoration, community empowerment, and institutional accountability. Based on a meta-synthesis of 773 academic and institutional remarks coded using NVivo 12, the study identified sustainable cacao agriculture as a viable compensation mechanism that supports livelihood recovery while restoring degraded land. The framework draws on six foundational theoretical components—Corporate Social Responsibility (CSR), Stakeholder Theory, Legitimacy Theory, the Theory of Planned Behavior, the Triple Bottom Line, and multi-level governance—and is operationalized through six implementation principles: Trust, Inclusivity, Legitimacy, Alignment, Norms, and Governance. The findings support performance-based land reclamation by embedding behavioral readiness and institutional co-financing into sustainability strategies. This model is particularly relevant to Indonesia’s ongoing land-use transformation, where post-extractive zones are shifting toward agroecological and community-centered recovery. The study found that (1) reframing land compensation as a restorative, performance-based mechanism enables more legitimate and inclusive post-mining governance; (2) sustainable cacao agriculture represents a viable and socially accepted strategy for ecological recovery and rural livelihood revitalization; and (3) the TILANG Framework advances land-use transformation by integrating corporate responsibility, behavioral readiness, and multi-level governance into a cohesive performance model. Full article

Dust emissions from unpaved haul roads in open-pit coal mining pose a significant risk to air quality, health, and operational efficiency of mining operations. This study integrated real-time field monitoring with numerical simulations using ANSYS Fluent 2023 R1 to investigate the generation, dispersion, and migration of particulate matter (PM) at the Ha’erwusu open-pit coal mine under varying meteorological conditions. Real-time measurements of PM2.5, PM10, and TSP, along with meteorological variables (wind speed, wind direction, humidity, temperature, and air pressure), were collected and analyzed using Pearson’s correlation and multivariate linear regression analyses. Wind speed and air pressure emerged as dominant factors in winter, whereas wind and temperature were more influential in summer (R² = 0.391 for temperature vs. PM2.5). External airflow simulations revealed that truck-induced turbulence and high wind speeds generated wake vortices with turbulent kinetic energy (TKE) peaking at 5.02 m²/s², thereby accelerating particle dispersion. The dust migration rates reached 3.33 m/s within 6 s after emission and gradually decreased with distance. The particle settling velocities ranged from 0.218 m/s for coarse dust to 0.035 m/s for PM2.5, with dispersion extending up to 37 m downwind. The highest simulated dust concentration reached 4.34 × 10⁻² g/m³ near a single truck and increased to 2.51 × 10⁻¹ g/m³ under multiple-truck operations. Based on spatial attenuation trends, a minimum safety buffer of 55 m downwind and 45 m crosswind is recommended to minimize occupational exposure. These findings contribute to data-driven, weather-responsive dust suppression planning in open-pit mining operations and establish a validated modeling framework for future mitigation strategies in this field. Full article

Mobile phone waste management is a growing environmental challenge, with improper disposal contributing to resource depletion, pollution and missed opportunities for material recovery. This study presents the findings of a dual-purpose survey (11,163 respondents) conducted in a wide academic context in Italy, aimed at both assessing mobile phones disposal behaviors and knowledge and raising awareness through structured educational prompts about sustainable e-waste management. The results reveal significant behavioral patterns and knowledge gaps across demographic groups. While most respondents (90.6%) own one phone, males tend to have more than females. Phones are replaced every 3–5 years by 48.8% of users and every 1–3 years by 36.7%, with students tending to replace them earlier. Only 20.2% replace their phone when irreparable while 46% replace them due to high repair costs. A large majority (92.3%) store old devices at home, forming an estimated urban mine of 29,799 unused phones. The awareness of hazardous components is higher than that of critical raw materials, with males more informed than females and students in scientific fields displaying greater awareness than those in humanities and health disciplines. The awareness of official take-back programs is particularly low, especially among younger generations. Notably, 90% reported increased awareness from the educational survey and 93.1% expressed willingness to use an on-campus e-waste collection system. These results highlight the role of universities as catalysts for sustainable behavior, supporting the design of targeted educational strategies and policy actions in line with circular economy principles and Sustainable Development Goal 12 “Responsible consumption and production”. Full article

This study examines the influence mechanism of mining depth evolution on dust distribution, using the An Tai Bao open-pit coal mine as the research subject. A spatial coordinate system of the mining area was established utilizing a GIS positioning system, and high-resolution topographic data were extracted using Global Mapper. The research team developed a three-dimensional geological model updating algorithm with depth gradient as the characteristic parameter, enabling dynamic monitoring of mining depth with a model iteration accuracy of 0.5 m per update. A Fluent-based numerical simulation method was employed to construct a depth-dependent dust migration field solving system, aiming to elucidate the three-dimensional coupling mechanism between mining depth and dust dispersion. The findings reveal that mining depth demonstrates a three-stage critical response to dust migration. When the depth surpasses the threshold of 150 m, the wind speed attenuation rate at the pit bottom exhibits a marked change, and the dust dispersion distance decreases by 62% compared to shallow mining conditions. The slope pressure field evolution shows a significant depth-enhancement effect, with the maximum wind pressure at the leeward step boundary increasing by 22–35% for every additional 50 m of depth, resulting in dust accumulation zones with distinct depth-related characteristics. The west wind scenario demonstrates a particularly notable depth amplification effect, with the dust dispersion range in a 200-meter-deep pit expanding by 53.7% compared to the standard west wind condition. Furthermore, the interaction between particle size and depth causes the dust migration distance to exhibit exponential decay as depth increases. This research elucidates the progressive constraining influence of mining depth, a critical control parameter, on dust migration patterns. It establishes a depth-oriented theoretical framework for dust prevention and control strategies in deep open-pit mines. Full article

This study highlights the importance of managing and analyzing customer reviews to gain a competitive advantage and improve customer experience in the hospitality industry. In this context, a deep learning-based sentiment analysis system of hotel customer reviews is developed to evaluate service quality within the scope of sustainable tourism. The study analyzed 15,522 customer reviews of five-star hotels in Antalya using text mining, topic modelling, and deep learning-based sentiment analysis. The reviews were classified as positive, negative, or neutral. The findings show that Hotel HB2 has the highest performance, with an F1 score of 97.9%. Overall customer satisfaction is 91%, while emotional satisfaction stands at 77%. Key factors, such as cleanliness, food quality, and staff professionalism, were found to play a critical role in customer loyalty. Additionally, this study integrates sustainability-orientated themes by identifying customer feedback related to environmentally friendly practices and sustainable hotel operations. The results provide evidence that customer satisfaction is not only influenced by service quality but also by the perceived environmental and social responsibility of the hotel. Machine learning techniques have emerged as effective tools for analyzing large-scale customer reviews, offering valuable insights to rapidly and accurately capture customers’ emotions, expectations, and perceptions. As a comprehensive application of sentiment analysis and text mining, this research offers hotel managers a practical framework to enhance service quality, foster customer loyalty, and develop sustainability-orientated strategies. This study contributes to the literature by linking AI-driven sentiment analysis with sustainability practices in the tourism sector. Full article

When a disaster occurs, a large number of social media posts on platforms like Weibo attract public attention with their combination of text and images. However, the consistency between textual descriptions and visual representations varies significantly. Consistent multi-modal data are crucial for helping the public understand the disaster situation and support rescue efforts. This study aims to develop a systematic framework for assessing the consistency of multi-modal disaster-related data on social media. This study explored how the congruence between text and image content affects public engagement and informs strategies for efficient emergency responses. Firstly, the Clip (Contrastive Language-Image Pre-Training) model was used to mine the disaster correlation, loss category, and severity of the images and text. Then, the consistency of image–text pairs was qualitatively analyzed and quantitatively calculated. Finally, the influence of graphic consistency on social concern was discussed. The experimental findings reveal that the consistency of text and image data significantly influences the degree of public concern. When the consistency increases by 1%, the social attention index will increase by about 0.8%. This shows that consistency is a key factor for attracting public attention and promoting the dissemination of information related to important disasters. The proposed framework offers a robust, systematic approach to analyzing disaster loss information consistency. It allows for the efficient extraction of high-consistency data from vast social media data sets, providing governments and emergency response agencies with timely, accurate insights into disaster situations. Full article