Results 171 to 180 of about 1,281,664 (278)

Trend analysis of dam inflow data using the trend accuracy index and the potential-evapotranspiration correction factor. [PDF]

Sci Rep
Wang WJ, Kim HS.
europepmc   +1 more source

Strengthening urban resilience in China through underground infrastructures management: Addressing global climate challenges with technological solutions

Deep Underground Science and Engineering, EarlyView.
This paper explores how climate‐resilient technologies, such as smart grids, digital twins, and self‐healing materials, can enhance urban resilience. It highlights the urgent need for proactive planning, public‐private collaboration, and data‐driven innovation to future‐proof underground infrastructure amid accelerating climate and urban pressures ...
Kai Chen Goh, Tonni Agustiono Kurniawan, Christia Meidiana, Abdelkader Anouzla, Faissal Aziz, Imran Ali, Joan Cecilia C. Casila, M. Imran Khan, Dongdong Zhang, Meihui Jiang, Mohd Hafiz Dzarfan Othman, Md Munir Hayet Khan, Kasun Kumara Dissanayake   +12 more
wiley   +1 more source

Object tracking algorithm based on deformable attention mechanism. [PDF]

Sci Rep
Liu Q, Yu N, Cheng J.
europepmc   +1 more source

Advancing mine pillar design: Evaluating traditional methods and integrating AI for enhanced stability of pillars in the Great Dyke, Zimbabwe

Deep Underground Science and Engineering, EarlyView.
B1 is bord width 1, B2 is bord width 2, L is the pillar length, W is the pillar width, red color and letter A represent the pillars, and white color and number 1 represent excavated areas. Pstress is the average pillar stress; σv is the vertical component of the virgin stress, MPa; and e is the areal extraction ratio. e = B o B o + B P ${\rm{e}}=\frac{{
Tawanda Zvarivadza, Hendrik Grobler, Peter Apata Olubambi, Moshood Onifade, Manoj Khandelwal   +4 more
wiley   +1 more source

DamCrack: A drone and smartphone image dataset for 2D and 3D damage assessment in concrete dams. [PDF]

Data Brief
Gharehbaghi V, Bennett C, Lequesne RD, Zhao H, Li J.   +4 more
europepmc   +1 more source

Dynamic geo‐hydrogeological monitoring‐driven situational awareness for real‐time floor water inrush risk prediction in deep mining

Deep Underground Science and Engineering, EarlyView.
The fused data extracted from the distributed monitoring system as the data basis, combined with dynamic geological data, are imported into a deep learning model. As the geological conditions of mining and excavation change, the risk of water inrush at the working face is retrieved in real time.
Yongjie Li, Huiyong Yin, Fangying Dong, Tao Wu, Chao Zhang   +4 more
wiley   +1 more source

Erratum: “Enhanced ion acceleration using the high-energy petawatt PETAL laser” [Matter Radiat. Extremes 6, 056901 (2021)]

Matter and Radiation at Extremes
D. Raffestin, L. Lecherbourg, I. Lantuéjoul, B. Vauzour, P. E. Masson-Laborde, X. Davoine, N. Blanchot, J. L. Dubois, X. Vaisseau, E. d’Humières, L. Gremillet, A. Duval, Ch. Reverdin, B. Rosse, G. Boutoux, J. E. Ducret, Ch. Rousseaux, V. Tikhonchuk, D. Batani   +18 more
doaj   +1 more source

Fault Diagnosis Method for Mechanical Components Fusing RPM, DAM-IResNet, and Transfer Learning. [PDF]

Sensors (Basel)
Ge X, Chen Z, Cao Y, Wu Z, Li Q.
europepmc   +1 more source

Experiencing Extreme Hunger in Anorexia Nervosa Recovery: A Qualitative Analysis of Reddit Narratives

International Journal of Eating Disorders, EarlyView.
ABSTRACT Objective Recovery from anorexia nervosa involves psychological and social adjustments that extend beyond weight restoration. Online forums increasingly serve as spaces where recovery experiences are openly shared, including accounts of “extreme hunger” during refeeding—a phenomenon that has not been reported in the clinical literature.
Léonie Langanay, Charlotte Meric, Margot Trimbur, Joël Charbit, Maxime Bubrovszky, Marion Hendrickx, Antoine Lamer   +6 more
wiley   +1 more source

Dam [PDF]

Choice Reviews Online, 2008
  +4 more sources