Physical features and analysis of traditional mosques:the case of Quzzat quarter of Herat Old City,Afghanistan

This study examines the physical features of traditional mosques in the Quzzat(Bardrani)quarter of Herat Old City,Afghanistan.Traditional mosques are constructed with locally available materials and are planned based on cultural and climatic conditions.Mosques are categorised as modern or traditional.Traditional mosques are divided into three subcategories:preserved,damaged(defaced),and transformed.Transformed mosques are formerly traditional mosques reconstructed with modern or industrial materials(concrete and reinforcement).This study explores the distribution of mosques and analyses their plan typology.Mosques are categorised into five plan types,and three relative case studies are described in detail to provide a better understanding and an in-depth analysis of mosque typology.

Diagnosis of An Infrequent Regional Hail Weather Course in Gansu

Based on routine weather charts, numerical predication products and satellite cloud images, the causes of an infrequent regional hail weather process occurred in east of Gansu on Aug. 2, 2006 were diagnosed and analyzed. The results showed that the hail weather process occurred at the abnormal large-scale circulation leading the system to west. When the cold trough, which was separated by the north cold vortex, moved southward through Hetao and then intersected with 300 hPa jet stream and the surface cold front, that led to the strong convection. There were strong upward motion and unstable stratification in hail area, three MCS in satellite cloud, and a character of formed arch shape echo on radar echo charts.

Reservoir characteristics of Donghe well No.1 in Tarim Basin

Based on the techniques of X-ray diffraction analysis, identification of the thin sections of core cast, phys- ical analysis and scanning electron microscopy analysis, this paper studied the reservoir characteristics of the Carboniferous strata in Donghe well No.1 of Tarim region. The results show that the reservoir lithology is mainly the fine-grained quartz sandstone with ferrocalcite and pyrite, mud cement-based, the permeability concentrated in 5-40 × 10-3 μm2, a small part of the high permeability up to 150-327 ×10-3 μm2 and porosity ranged from 10% to 20%. The most part of the reservoirs is low perme- ability with a small part of the layer in moderate-high permeability. The types of reservoir space include intergranular pores, intra particle-molding pores, micro-pores and cracks, which mainly are intergranular pores with the pore diameter of 15-200 μm, 95.5μm on average. And the types of the throats are comolex with the main tvne of constricted l：hroats in this area and large contribution to the permeability.

Improved forecasting via physics-guided machine learning as exemplified using“21·7”extreme rainfall event in Henan

As a natural disaster,extreme precipitation is among the most destructive and influential,but predicting its occurrence and evolution accurately is very challenging because of its rarity and uniqueness.Taking the example of the“21·7”extreme precipitation event(17–21 July 2021)in Henan Province,this study explores the potential of using physics-guided machine learning to improve the accuracy of forecasting the intensity and location of extreme precipitation.Three physics-guided ways of embedding physical features,fusing physical model forecasts and revised loss function are used,i.e.,(1)analyzing the anomalous circulation and thermodynamical factors,(2)analyzing the multi-model forecast bias and the associated underlying reasons for it,and(3)using professional forecasting knowledge to design the loss function,and the corresponding results are used as input for machine learning to improve the forecasting accuracy.The results indicate that by learning the relationship between anomalous physical features and heavy precipitation,the forecasting of precipitation intensity is improved significantly,but the location is rarely adjusted and more false alarms appear.Possible reasons for this are as follows.The anomalous features used here mainly contain information about large-scale systems and factors which are consistent with the model precipitation deviation;moreover,the samples of extreme precipitation are sparse and so the algorithm used here is simple.However,by combining“good and different”multi models with machine learning,the advantages of each model are extracted and then the location of the precipitation center in the forecast is improved significantly.Therefore,by combining the appropriate anomalous features with multi-model fusion,an integrated improvement of the forecast of the rainfall intensity and location is achieved.Overall,this study is a novel exploration to improve the refined forecasting of heavy precipitation with extreme intensity and high variability,and provides a reference for the deep fusion of physics and artificial intelligence methods to improve intense rain forecast.

Estimating battery state of health with 10-min relaxation voltage across various charging states of charge

Battery capacity assessment is a crucial research direction in the field of lithium-ion battery applications.In the previous research,a novel data-driven state of health(SOH)estimation method based on the voltage relaxation curve at full charging is developed.The experimental results have shown the evidence of the superiority of accurate battery SOH estimation based on physical features derived from equivalent circuit models(ECMs).However,the earlier research has limitations in estimating battery capacity with a diversity of battery charging states of charge.This study represents an extension of the previous work,aiming to investigate the feasibility of this technology for battery degradation evaluation under various charging states so that the application capability in practice is enhanced.In this study,six ECM features are extracted from 10-min voltage relaxation data across varying charging states to characterize the battery degradation evolution.Gaussian process regression(GPR)is employed to learn the relationship between the physical features and battery SOH.Experimental results under 10 different state of charge(SOC)ranges show that the developed methodology predicts accurate battery SOH,with a root mean square error being 0.9%.