Unsaturated soil mechanics for slope stabilization by Leong, Eng Choon, Rahardjo, Harianto, Satyanaga, Alfrendo

“…As a result, pore-water pressures in the unsaturated zone will become more negative, contributing to the shear strength of soil. During a rainy season, cracked soils with a higher permeability will increase rain infiltration into slopes and the groundwater table may rise, causing an increase in pore-water pressures in the zone above the groundwater table. …”
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Learning congestion propagation behaviors for traffic prediction by Sun, Yidan, He, Peilan, Jiang, Guiyuan, Lam, Siew-Kei

“…Traffic prediction is a challenging task as the traffic flow is influenced by many seasonal, stochastic, and structural factors. In addition, the spatial and temporal distribution of traffic flow can induce direct and indirect congestion propagation patterns. …”
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Reliability model for a distribution system incorporating snowfall as a severe weather event by Chaudhuri, Tanaya, Mitra, Sristi, Goswami, A. K.

“…The reliability assessment of an existing power system has been done on a seasonal basis.…”
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RobustLoc: robust camera pose regression in challenging driving environments by Wang, Sijie, Kang, Qiyu, She, Rui, Tay, Wee Peng, Hartmannsgruber, Andreas, Navarro, Diego Navarro

“…To deal with challenging driving environments that may have changing seasons, weather, illumination, and the presence of unstable objects, we propose RobustLoc, which derives its robustness against perturbations from neural differential equations. …”
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Green-aware workload scheduling in geographically distributed data centers by Chen, Changbing, He, Bingsheng, Tang, Xueyan

“…While green energy supply for a single data center is intermittent due to daily/seasonal effects, our workload scheduling algorithm is aware of different amounts of green energy supply and dynamically schedules the workload across data centers. …”
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Analysis of biofilm-resistance factors in Singapore drinking water distribution system by Li, Yuanzhe, Wang, Yilin, Xiao, Peng, Narasimalu, Srikanth, Dong, Zhili

“…Furthermore, other extrinsic factors, such as pipe age and material, hydraulic retention time, seasonal change, primary ultraviolet disinfection, etc. are also reviewed. …”
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Optimized integration of hydrogen technologies in island energy systems by Nastasi, Benedetto, Mazzoni, Stefano, Groppi, Daniele, Garcia, Davide Astiaso, Romagnoli, Alessandro

“…This is due to the weak connection to the mainland and, subsequently, the Power Grid as well as the strong changes in seasonal energy demand leading to congestion and stability issues. …”
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Droneport placement optimization and capacity prediction by Zeng, Yixi, Low, Kin Huat, Duong, Vu N., Schultz, Michael

“…This paper presents several contributions to the concept of droneport: (1) The Holt-Winters’ seasonal method was adopted to forecast future delivery drone demand based on historical online retailer data. (2) A multi-objective optimization model was established to determine the optimum placement and number of droneports considering both costs and societal value from three aspects: maximizing e-commerce demand coverage, minimizing drone service distance and maximizing area coverage. (3) Gaussian noise was introduced to the optimization model to make the measurement of service distance more practical. (4) The future capacity of each droneport was estimated. …”
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Future demand and optimum distribution of droneports by Zeng, Yixi, Low, Kin Huat, Schultz, Michael, Duong, Vu N.

“…We present several contributions to the concept of droneport: (1) The future delivery drone demand was forecasted using historical online retailer data and the Holt-Winters’ seasonal method. (2) The optimum number and distribution of droneports were determined by a multi-objective optimization model considering both costs and societal value from six aspects: maximizing e-commerce demand coverage, airtaxi demand coverage, subzone coverage, and area coverage, and minimizing service distance for both parcel and passenger delivery drones. (3) The optimization model integrates Gaussian noise to make the measurement of service distance more practical. (4) The future capacity of each droneport was estimated based on the number of droneports and their placement. …”
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Predicting aircraft landing time in extended-TMA using machine learning methods by Dhief, Imen, Wang, Zhengyi, Liang, Man, Alam, Sameer, Schultz, Michael, Delahaye, Daniel

“…The experimental results show that 4 sets of features play a significant impact on LDT prediction for primary runway-in-use, they are: (1) Control intent: traffic demand, current traffic density, and adjacent flow; (2) Weather: surface wind; (3) Trajectory: the position of aircraft; (4) Seasonality: parts of a day and a week. Moreover, comparing three Machine Learning algorithms, in our study case, Extra-Trees is the best prediction algorithm compared with other machine learning models in terms of Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). …”
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