Rock failure is the root cause of geological disasters such as slope failure, civil tunnel collapse, and water inrush in roadways and mines. Accurate and effective monitoring of the loaded rock failure process can provide reliable precursor information for water inrushes in underground engineering structures such as in mines, civil tunnels, and subways. The water inrush may affect the safe and efficient execution of these engineering structures. Therefore, it is essential to predict the water inrush effectively. In this paper, the water inrush process of the roadway was simulated by laboratory experiments. The multiparameters such as strain energy field and infrared radiation temperature field were normalized based on the normalization algorithm of linear function transformation. On the basis of analyzing the variation characteristics of the original parameters, the evolution characteristics after the parameters normalization algorithm were studied, and the precursor of roadway water inrush was predicted comprehensively. The results show that the dissipation energy ratio, the infrared radiation variation coefficient (IRVC), the average infrared radiation temperature (AIRT), and the variance of successful minor infrared image temperature (VSMIT) are all suitable for the prediction of roadway water inrushes in the developing face of an excavation. The intermediate mutation of the IRVC can be used as an early precursor of roadway water inrush in the face of an excavation that is being developed. The inflection of the dissipation energy ratio from a declining amount to a level value and the mutation of VSMIT during rock failure can be used as the middle precursor of roadway water inrush. The mutation of AIRT and VSMIT after rock failure can be used as the precursor of roadway imminent water inrush. Combining with the early precursor and middle precursor of roadway water inrush, the graded warning of “early precursor–middle precursor–final precursor” of roadway water inrush can be obtained. The research results provide a theoretical basis for water inrush monitoring and early warning in the sustainable development of mine, tunnel, shaft, and foundation pit excavations.

The digital creative industry has reached a level of maturity, yet it still faces challenges such as dependence on input factors, weak enterprise connections, weak innovation capabilities, and low innovation efficiency. The use of the SEM-SD model can analyze the static and dynamic characteristics in the innovation ecosystem, which helps to improve the innovation capacity of the digital creative industry. This article employs Structural Equation Modeling (SEM) to examine how various factors within the digital creative industry influence the generation of value innovation output. Weibull distribution has been used to characterize the random statistical relationship between digital creative industry associations and capital. The characterization was used to derive a quantitative relationship between these associations and digital creative capital. Additionally, integrating non-linear optimal control principles within the digital creative industry economy, the article constructs a theoretical model for value co-creation within the innovation ecosystem of the digital creative industry. Building upon this framework, a System Dynamics (SD) model of the value co-creation system within the digital creative industry innovation ecosystem was developed. The findings of the study highlight the pivotal role of governmental coordination within the digital creative industry, significantly impacting innovation value output. Following closely is the importance of knowledge application ability, while knowledge spillover demonstrates the least influence. Moreover, within the digital creative industry economy, per capita consumption emerges as the primary driver of value innovation output, outshining both per capita digital creative capital and per capita material capital by a significant margin. These findings establish an important theoretical foundation for propelling the innovative progress of the digital creative industry.

Solar–hydrogen hybrid systems provide low-carbon and dispatchable energy, yet most existing configurations implicitly assume freshwater availability, thereby overlooking the role of water reuse in water-stressed regions. In semi-arid industrial contexts, the integration of clean energy systems with circular water management is essential for long-term sustainability. This study develops a closed-loop Solar–Green Hydrogen Hybrid System (SGHHS) in which industrial effluent is treated through a membrane bioreactor–reverse osmosis–deionization (MBR→RO→DI) sequence to satisfy proton exchange membrane (PEM) electrolyzer water-quality requirements, while water recovered from fuel-cell exhaust is captured as condensate, achieving an overall water recovery rate of approximately 90%. The proposed system consists of a 22.75 MW photovoltaic array, a 2.25 MW electrolyzer, 450 kg of hydrogen storage, and a 1 MW fuel cell, and is evaluated using a 25-year hourly-resolution simulation framework. Economic performance is assessed through discounted cash-flow analysis, while environmental impacts are quantified using life-cycle assessment. Results demonstrate that integrating water reuse reduces the levelized cost of electricity from 0.10 to 0.0866 USD/kWh, avoids approximately 157,000 tonnes of CO₂-equivalent (tCO₂-eq)emissions, and enables the recovery of nearly 400,000 L/day of process water. By explicitly internalizing water-treatment capital and operating costs alongside water-savings benefits within the energy cost formulation, the study presents a generalizable framework linking hydrogen-based energy systems with circular water infrastructure, supporting industrial decarbonization and Sustainable Development Goals related to clean energy, responsible resource use, and climate action.

The quest to reduce dangerous environmental emissions has led to the research and use of alternate and renewable energy sources. One of the major contributors to the dangerous environmental emissions is the automotive industry. The world is, therefore, quickly moving towards hybrid and electric vehicles. An alternate pollution-free automotive engine is a compressed-air engine, which is powered by compressed air and is more efficient than the electric engine since it requires less charging time than a traditional battery-operated engine. Furthermore, the tanks used in compressed-air engines have a longer lifespan in comparison to the batteries used in electric vehicles. However, extensive research is required to make this engine viable for commercial use. The current study is a step forward in this direction and shows the performance analysis of a single-cylinder compressed-air engine, developed from a four-stroke, single-cylinder, 70 cc gasoline engine. The results show that compressed-air engines are economic, environmental friendly and efficient.

Purpose. This study integrates unmanned aerial vehicles (UAVs) and Magnetic and 2D Electrical Resistivity Tomography (2D ERT) surveys to delineate subsurface laterite ore mineralization zones while reducing exploration costs. UAV imagery enabled the reconstruction of high-resolution digital elevation models and orthomosaics, providing detailed topographic information for survey planning. Methods. Magnetic survey integrated with 2D electrical resistivity tomography (ERT) profiles were applied to characterize subsurface lithology and identify layers such as topsoil, shale, and laterite. Findings. The ERT survey reveals that the topsoil is 3 meters thick and has a resistivity range of 10-50 Ω.m. The resistivity of shale varied between 50 and 150 Ω.m, with a thickness of two meters. The laterite ore was identified with resistivity values between 150 and 1200 Ω·m and a thickness of 5 m. Magnetic surveys identified magnetic anomalies of 200-600 nT, estimated at a depth of 3-5 m using forward modeling. Regional-scale interpretations from total magnetic intensity (TMI), reduced-to-pole (RTP), and continuation maps highlighted the detailed distribution of the magnetic anomalies throughout the study area, lithological variations, fault systems, and deep-seated magnetized bodies. Originality. This study demonstrates an integrated, low-cost workflow for lateritic mineralization that uses detailed geophysical data, including magnetic methods, 2D ERT, and UAV photogrammetry. Conventional techniques are quite expensive relative to the value of the ore. Practical implications. The results demonstrate that the integration of aerial photogrammetry, magnetic surveys, and 2D electrical resistivity tomography provides an efficient and cost-effective approach for delineating laterite ore mineralization zones and can serve as a viable alternative to conventional exploration methods.