CNN Based Fault Classification and Predition of 33kw Solar PV System with IoT Based Smart Data Collection Setup by K. Punitha, G. Sivapriya, T. Jayachitra

“…Therefore, CNN is used as the fault prediction also. The model is implemented using Python programming language and demonstrated its effectiveness on test cases. …”
Get full text

Examining inter‐regional and intra‐seasonal differences in wintering waterfowl landscape associations among Pacific and Atlantic flyways by Matthew J. Hardy, Christopher K. Williams, Brian S. Ladman, Maurice E. Pitesky, Cory T. Overton, Michael L. Casazza, Elliott L. Matchett, Diann J. Prosser, Jeffrey J. Buler

“…We used 9 years (2014–2023) of data from the US NEXRAD network to model winter waterfowl relative abundance in the CVC and MA as a function of weather, temporal period, environmental conditions, and landcover characteristics using boosted regression tree modelling. …”
Get full text

Aerodynamic energy consumption analysis of divided evacuated tube transportation system by Ziwei Zhang, Yingxue Wang, Guanqing Wang, Feilong Li, Dengke Wang, Jianjun Luo

“…Theoretical discussions into reasonable parameter range including tube design and HST operation manners were conducted and then formed system-designing strategy. The spatial and temporal distribution law of tube gas circumstances were examined through model testing and numerical simulation. …”
Get full text

A CNN-LSTM Phase Compensation Method for Unidirectional Two-way Radio Frequency Transmission System by Jiahui Cheng, Zhengkang Wang, Yaojun Qiao, Hao Gao, Chenxia Liu, Zhuoze Zhao, Jie Zhang, Baodong Zhao, Bin Luo, Song Yu

“…The results demonstrate the CNN-LSTM model presents better prediction performance than the other eight previously proposed ML models. …”
Get full text

A systematic review of fishing impacts on the trophic level of fish populations and assemblages in the Mediterranean Sea by Audrey Marguin, Audrey Marguin, Simona Bussotti, Simona Bussotti, Paolo Guidetti, Paolo Guidetti, Francesca Rossi, Francesca Rossi

“…Interestingly, recent modelling studies used predictions from the model to explore the impact of different fishing pressure within global change scenarios. …”
Get full text

The Relative Constraining Power of the High-z 21 cm Dipole and Monopole Signals by Jordan Mirocha, Chris Anderson, Tzu-Ching Chang, Olivier Doré, Adam Lidz

“…This result holds for most of the available prior volume, which is set by constraints on galaxy luminosity functions, the reionization history, and upper limits from 21 cm power spectrum experiments. We also find that predictions for the monopole from a dipole measurement are robust to different choices of signal model. …”
Get full text

A Development of a Sound Recognition-Based Cardiopulmonary Resuscitation Training System by Dong Hyun Choi, Yoon Ha Joo, Ki Hong Kim, Jeong Ho Park, Hyunjin Joo, Hyoun-Joong Kong, Hyunju Lee, Kyoung Jun Song, Sungwan Kim

“…Each spectrogram was matched with the depth, rate, and release velocity of the compression measured at the same time interval by the ZOLL X Series monitor/defibrillator. Deep learning models utilizing spectrograms as input were trained using transfer learning based on EfficientNet to predict the depth (Depth model), rate (Rate model), and release velocity (Recoil model) of compressions. …”
Get full text

Lessons learned from the co-development of operational climate forecast services for vineyards management by N. Pérez-Zanón, V. Agudetse, E. Baulenas, P.A. Bretonnière, C. Delgado-Torres, N. González-Reviriego, A. Manrique-Suñén, A. Nicodemou, M. Olid, Ll. Palma, M. Terrado, B. Basile, F. Carteni, A. Dente, C. Ezquerra, F. Oldani, M. Otero, F. Santos-Alves, M. Torres, J. Valente, A. Soret

“…The workflow includes the downscaling of model outputs to increase their spatial resolution, the storage of sub-seasonal climate forecasts at daily frequencies to feed phenological impact models, the provision of past climate simulations to train the impact models, the bias-adjust of climate forecasts to reduce systematic model errors, the assessment of the climate forecasts to aware users on their quality and the deployment of a server to allow access to impact modellers.The variables provided are mean, minimum and maximum temperature, accumulated precipitation and solar radiation. …”
Get full text

Extraction of typical oyster pile columns in the Maowei Sea, Beibu Gulf, based on unmanned aerial vehicle laser point cloud orthophotos by Jinze Du, Jinze Du, Meiqin Huang, Zhenjun Kang, Zhenjun Kang, Zhenjun Kang, Yichao Tian, Yichao Tian, Yichao Tian, Jin Tao, Jin Tao, Qiang Zhang, Qiang Zhang, Yutong Xie, Yutong Xie, Yutong Xie, Jinying Mo, Jinying Mo, LiYan Huang, LiYan Huang, Yusheng Feng, Yusheng Feng

“…By employing band features and texture indices extracted from UAV’s multi-spectral images as data sources and combining them with a classification and prediction model based on deep learning convolutional neural networks (CNN), we successfully extract the desired oyster pile columns. …”
Get full text

Lithologically constrained velocity–density relationships and vertical stress gradients in the North Alpine Foreland Basin, SE Germany by P. Obermeier, F. Duschl, M. C. Drews

“…The same is true for the distribution of vertical stress gradients, a key input parameter for geomechanical modelling and the prediction of natural and induced seismicity. …”
Get full text

Multi-Source Rainfall Data Assimilation based on Broad Learning System over Yunnan Province by Yuanyuan ZHOU, Xiaohui YANG, Tiangui XIAO

“…The accurate estimation of rainfall is always a topic of concern， given its pivotal role in accurately predicting rainfall-related disasters.This study proposed a multi-source rainfall assimilation technology based on a broad learning system （BLS） to improve the accuracy of rainfall estimation.Yunnan Province， located in China's low-latitude plateau， was chosen as the geographical area of interest to establish a multi-source rainfall assimilation model within this region.In particular， the model utilizes five satellite-derived rainfall datasets （3B42V7， IMERG， GSMaP， CMORPH， PERSIANN） and the latitude and longitude information as the source data， and the ground-based rainfall gauge data serves as the reference data.The time span of all the datasets is from April 2014 to December 2017.A leave-one-year-out cross-validation （LOYOCV） method was applied to verify the performance of the established assimilation model， where statistical indicators including Pearson’s correlation coefficient （CC）， root-mean square error （RMSE）， mean absolute error （MAE）， Nash efficiency coefficient （NSE） and Kling-Gupta efficiency （KGE） were used to quantify the accuracy of assimilation rainfall at different spatiotemporal scales.Concurrently， assimilation models based on support vector machine （SVM） and deep neural network （DNN） were established to highlight the accuracy and efficiency of the BLS， respectively.Additionally， the effectiveness of the latitude and longitude information within the proposed assimilation model was examined.The results show that the daily average statistical index of assimilation rainfall based on BLS is better than that of the other five satellite-based products in LOYOCV.At the temporal scale， the proposed assimilation technique effectively reflects the temporal variations observed in gauge-recorded rainfall.Moreover， it can accurately estimate the rainfall amounts during rainstorms in Yunnan Province throughout 2017.It is worth noting that the rainfall data generated through the BLS method outperforms the CMORPH product （the most accurate one among the five satellite-derived rainfall products） in both rainy and dry seasons （May to October and November to April of next year， respectively）.At the spatial scale， BLS-based rainfall results in most areas of Yunnan Province showed higher CC and NSE as well as smaller RMSE and MAE than the satellite-based products.The evaluation of the assimilation models based on BLS， SVM， and DNN highlights that the BLS exhibits superior functional mapping capabilities compared to SVM and demands fewer computational resources than DNN.It is reasonable to conclude that the multi-source rainfall assimilation approach utilizing the BLS while incorporating latitude and longitude information can enhance the precision of rainfall estimates in Yunnan Province.The proposed method presents practical significance in multi-source rainfall data assimilation.…”
Get full text

Fast real-time detection and counting of thrips in greenhouses with multi-level feature attention and fusion by Zhangzhang He, Xinyue Chen, Ying Gao, Yu Zhang, Yuheng Guo, Tong Zhai, Xiaochen Wei, Huan Li, Haipeng Zhu, Yongkun Fu, Zhiliang Zhang, Zhiliang Zhang

“…Next, we design a lightweight channel-spatial hybrid attention mechanism to further refine multi-scale features, enhancing the model’s ability to extract global and local features with minimal computational cost. …”
Get full text

Calculation of capacity and optimization design-composite slab wall soil solidification foundation based on neural network by Linggang ZHOU, Yiting HU, Xinwei CHEN, Feng TU, Zhaofeng WU, Yang YU, Yanbing WANG, Yin MAN, Weichao LI

“…Objective The soil solidification technique is widely used in soft foundation treatment. To exploit spatial plasticity of this technique, composite slab wall soil solidification foundations have gradually been applied in engineering projects. …”
Get full text

Post-hoc Evaluation of Sample Size in a Regional Digital Soil Mapping Project by Daniel D. Saurette, Richard J. Heck, Adam W. Gillespie, Aaron A. Berg, Asim Biswas

“…Furthermore, the comparison of the optimal maps to the reference maps showed little difference in the global statistics (concordance correlation coefficient and root mean square error) and spatial trends of the data, confirming that the optimal sample size was sufficient for creating predictions of similar accuracy to the full calibration dataset. …”
Get full text

Urban morphology impacts on urban microclimate using artificial intelligence – a review by Ahmed Marey, Jiwei Zou, Sherif Goubran, Liangzhu Leon Wang, Abhishek Gaur

“…By synthesizing 111 studies over the last decade utilizing urban morphology and AI models to predict urban microclimate, this review aims to bridge these gaps and highlight AI’s unique potential to contribute to the field. …”
Get full text

Immune landscape and TAM density in endometrial cancer: implications for immune checkpoint inhibitors efficacy by Olivia Le Saux, Renaud Sabatier, Isabelle Treilleux, Léa-Isabelle Renaud, Pierre-Emmanuel Brachet, Alejandra Martinez, Jean-Sébastien Frenel, Cyril Abdeddaim, Justine Berthet, Sarah Barrin, Amélie Colombe-Vermorel, Laetitia Odeyer, Alexandra Lainé, Christophe Caux, Bertrand Dubois, Isabelle Ray-Coquard

“…Using a multivariate logistic regression model, we found that a low density of CD68 + tumor-associated macrophages (TAMs) in the stroma, was associated with response to ICI in EC (odds ratio = 11.67, 95% CI (1.69–237.45), p  = 0.033) and showed good accuracy in predicting response to ICI in the whole cohort (AUC = 0.75, 95% CI (0.59–0.91)). …”
Get full text

New insights into the role of mitophagy related gene affecting the metastasis of osteosarcoma through scRNA-seq and CRISPR-Cas9 genome editing by Sikuan Zheng, Mengliang Luo, Hong Huang, Xuanxuan Huang, Zhidong Peng, Shaowei Zheng, Jianye Tan

“…Results The distinct mitophagy activity of various mitochondria manifests in diverse spatial localization, cellular developmental trajectories, and intercellular interactions. …”
Get full text

Advancements in Medical Radiology Through Multimodal Machine Learning: A Comprehensive Overview by Imran Ul Haq, Mustafa Mhamed, Mohammed Al-Harbi, Hamid Osman, Zuhal Y. Hamd, Zhe Liu

“…This review may help clinicians and researchers comprehend the spatial distribution of the field, outline the current level of advancement, and identify areas of research that need to be explored regarding MMML in radiology.…”
Get full text

Effects of Land Use and Land Cover Change on Soil Organic Carbon Stock in Jira Watershed, Mid-Lands of Northern Ethiopia. by Guesh Assefa, Gebeyehu Taye, Kassa Teka

“…The highest SOC was gained due to the conversion of grazing land in to forest land (2007-2017) which has offered a total of 635.53 ton of SOC. A model was created to predict SOC of similar environments by using the observed SOC and NDVI values of the 2017 LULC types. …”
Get full text

Analysis on the Spatiotemporal Characteristics of the Postearthquake Reconstruction Efficiency of the Extremely Earthquake-Stricken Areas by the Wenchuan Earthquake Based on the DE... by Pin Lv, Bin Liu, Mingkang Yuan, Suyue Han, Di Zhang, Lv Zhang

“…In 2011, the reconstruction efficiency basically returned the areas to preearthquake levels, and there was a small fluctuation in efficiency due to the effects of earthquake-induced hazards and the reduction of external forces. Spatially, the reconstruction efficiencies of the 10 extremely stricken areas do not show a “convergence effect,” and the reconstruction efficiencies were closely related to the characteristics of the resources in the extremely stricken areas. …”
Get full text