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ABSTRACTGlacial lakes in the Himalayas are increasing in size and number at an alarming rate, with the majority of lakes concentrated in the eastern and central parts of the mountain range. This research focuses on the future prediction of regional-scale glacial lake size in the entire Himalayan mountain range to avoid future glacial lake outburst flood (GLOF). Here, we used Landsat TM and OLI images, Google Earth imageries, Cartosat DEM, and SRTM DEM, and used some techniques: average lake size change rate, Markov model and glacial surface topography to extract glacial lake size change and future prediction of lake size. A total of 9675 individual glacial lakes were noticed during the study period (1990–2020), out of which the lakes present constantly throughout the study period (with size > 0.01 km2) were added in this study.1This study used two methods (i.e., Markov model and Glacier surface topography) to predict future lake size and identified 191 and 94 critical lakes that are most at risk of enlargement in Markov and glacial surface topography, respectively. Moreover, 443 critical lakes in Himalaya is distributed as 227 in the eastern Himalayas, 195 in the central Himalayas, and 21 in the western Himalayas. The findings suggest that the number and size of glacial lakes will continue to increase, posing an even greater risk of GLOFs in the future.

Abstract Dam numerical simulation is an important method to research the dam structural behavior, but it often takes a lot of time for calculation when facing problems that require many simulations, such as structural parameter back analysis. The surrogate model is widely used as a technology to reduce computational cost. Although various methods have been widely investigated, there are still problems in designing the surrogate model's optimal Design of Experiments (DoE). In addition, most of the current DoE focuses on establishing a single-output problem. Designing a reasonable DoE for high-dimensional outputs is also a problem that needs to be solved. Based on the above issues, this research proposes a sequential surrogate model based on the radial basis function model (RBFM) with multi-outputs adaptive sampling. The benchmark function demonstrates the applicability of the proposed method to single-input & multi-outputs and multi-inputs & multi-outputs problems. Then, this method is applied to establishing a surrogate model for dam numerical simulation with multi-outputs. The result demonstrates that the proposed technique can be sampled adaptively and samples can be targeted based on the function form of the surrogate model, which significantly reduces the required sampling and calculation cost.

Anti-sliding stability safety is a critical issue that must be given sufficient and widespread attention during the entire lifecycle of gravity dams. The calculated anti-sliding stability safety factor (ASS-SF) is usually compared with the allowable value required by the standards in the traditional method, which ignores the influence of material parameter uncertainties and leads to unreasonable safety evaluation results. Therefore, the nonlinear functional relationship between the stability safety factor (SF) and the random variable parameters is constructed based on the response surface equations, and the distribution types of SF sequences calculated by the Monte Carlo sampling are determined, then a probabilistic stability evaluation method for concrete gravity dams is proposed. Engineering application shows that the calculated SF obeys the normal distribution; the minimum guaranteed rate of different sliding paths in a gravity dam is 86.66%, and the guaranteed rate for the overload safety factor (OSF) is 36.00%. The results imply that a guaranteed rate for the allowable value of the ASS-SF should be provided when making the stability safety evaluation of the dams, especially the OSF. The outcome of this research will advance the understanding of stability evaluation of concrete dams, and reduce the potential risk of sliding instability of concrete dams.

The fashion apparel industry is facing an increasingly growing demand, compounded by the short sales lifecycle and strong seasonality of clothing, posing significant challenges to inventory management in the retail sector. Despite some retailers like Uniqlo and Zara implementing inventory management and dynamic pricing strategies, challenges persist due to the dynamic nature of fashion trends and the stochastic factors affecting inventory. To address these issues, we construct a mathematical model based on the mathematical expression of the deterministic fashion level function, where the geometric Brownian motion, widely applied in finance, is initially utilized in the stochastic fashion level function. Drawing on research findings from deteriorating inventory management and stochastic optimization, we investigate the fluctuation of inventory levels, optimal dynamic pricing, optimal production rates, and profits—four crucial indicators—via Pontryagin’s maximum principle. Analytical solutions are derived, and the numerical simulation is provided to verify and compare the proposed model with deterministic fashion level function models. The model emphasizes the importance of considering stochastic factors in decision-making processes and provides insights to enhance profitability, inventory management, and sustainable consumption in the fashion product industry.

High-voltage reactor is one of the critical equipment to ensure the safe and stable operation of power system. As the fault of high-voltage reactor is difficult to be identified accurately， a voiceprint recognition method based on LSTM （long short-term memory） neural network for high-voltage reactor faults is proposed. Firstly， the voiceprint signals generated during the operation of a high-voltage reactor are collected. Then the signals are divided into several segments， converted into a spectrogram， and the Mel time spectrum is used to reduce the dimensionality. Finally， the LSTM network is used to identify the high-voltage reactor faults in the spectrogram. The experimental results show that the proposed method can realize the intelligent diagnosis of high-voltage reactor faults， effectively improve the accuracy of fault identification， reduce the manpower required for fault detection， and improve the intelligent level of power grid safety monitoring.

This study was considered realizing the immense application in petroleum, chemical and fuel industry of the conjugate effect of heat transfer and chemical reaction on the laminar fluid flow through a channel with several obstacles. Numerical simulation was conducted using the finite element method with the aid of COMSOL multi-physics software to relate the fluid flow pattern, heat and mass transfer, and the reaction rate to the modification of shape, number, and arrangement of the impediments alongside the change of spacing ratio, the initial velocity of fluid and temperature of the obstacles. The results of this investigation point out a number of interesting phenomenon such as circular objects cause most prominent decomposition of water and the decomposition is found to be oppositely symmetrical to spacing ratio. However, the rise of temperature amplify the decomposition process where the increase in initial velocity influences contrarily. Moreover, increasing the spacing ratio of the obstacles reduced thermal decomposition of the fluid.

Debris flows propagating in natural environments often encounter irregular terrain features, such as bottom roughness and man-made structures like groundsills, which significantly influence their behavior and dynamics. In practice, groundsills are commonly used as debris flow mitigation structures. This study examines the effects of a beam-type groundsill array on the flow behavior of sediment mixtures in an inclined channel using numerical simulations. The sediment mixtures, modeled as Bingham fluids, were tested as they flowed over groundsill arrays with varying densities, characterized by the spacing-to-height ratio ( d / h ) ranging from 2 to 10. The findings indicate that interaction with the groundsills produces a hydraulic jump-like flow, reaching a height approximately 2.2 times the approach flow depth across different array densities. High-density arrays ( d / h ≤ 4 ) substantially hindered flow propagation, reducing front velocities but leading to sediment buildup upstream of the groundsills. Conversely, low-density arrays ( d / h > 4 ) facilitated smoother flow with higher velocities. These insights into the relationship between array density, flow behavior, and sediment trapping provide valuable guidance for optimizing groundsill array designs to effectively reduce the mobility of gravity-driven flows of non-Newtonian fluids (such as snow avalanches, debris, lava, or mudflows) and mitigate the associated risks. [ABSTRACT FROM AUTHOR]

Scour is often found along the shoulder of the road, especially the shoulder without pavement in suburban and rural areas. This can reduce the function of the road shoulder and increase the risk of traffic accidents. The mechanism and form of scour have not been widely discussed and need to be studied to improve road safety aspects. This study aims to investigate scour occurrence along the unpaved road shoulders. An experiment was conducted in the laboratory by creating a physical model to simulate the scouring process at unpaved road shoulder. The laboratory test used varying rainfall intensities, slopes, and sediment diameters. Results show that the scour depths are influenced significantly by those three factors. Scour depths resulting in 25% longitudinal slope is around twice deeper than these in 0% longitudinal slope. The scour depth in border between the paved road lane and the unpaved road shoulder is deeper than other part of the shoulder. It shows that the location is a susceptible-part to scouring.