Spatiotemporal patterns of desertification sensitivity and influencing factors across the Western Inner Mongolia Plateau, China by Yang Chen, Long Ma, Xixi Wang, Tingxi Liu, Zixu Qiao

“…This study focuses on the Western Inner Mongolia Plateau in China as a case study to examine the evolution of desertification and its driving factors using a multifaceted approach, including the Mediterranean Desertification and Land Use (MEDALUS) model. Results show that the desertification sensitivity index (DSI) across the plateau ranged from 1.12 in prairie regions to 1.87 in desert areas, with a spatial gradient decreasing from west to east. …”
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Spatio-temporal evolution and driving mechanism of ecosystem services in typical hilly and gully areas of the loess Plateau: A case study in Yan’an Region, Shaanxi Province by Qiaoyang Yang, Hui Qian, Yanyan Gao, Yicheng Duan, Zhiming Cao, Panpan Tian, Kang Li, Shuhan Yang, Weijie Zhao, Qi Long

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Slope stability evaluation of mine rehabilitation project under different rainfall conditions by Meiyi TU, Shiyu YUAN, Jiangjun CHEN, Yunfeng GE

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Comparative analysis and selection of the geometry of the microphone array based on MEMS microphones for sound localisation by Andrii Riabko, Tetiana Vakaliuk, Oksana Zaika, Roman Kukharchuk, Yuriy Smorzhevsky

“…In future work, we will focus on improving array designs using SSL reduction methods to expand localization accuracy across a wider frequency range.…”
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Assessment of coastal zone ecosystem health in the context of tourism development: A case study of Jiaozhou Bay by Zhang Shengrui, Zhang Zhenqi, Zhang Tongyan, Ju Hongrun

“…Utilizing the “Vigor-Organization-Resilience (VOR)” model, the research establishes an index system for evaluating ecosystem health. …”
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Automotive DNN-Based Object Detection in the Presence of Lens Obstruction and Video Compression by Gabriele Baris, Boda Li, Pak Hung Chan, Carlo Alberto Avizzano, Valentina Donzella

“…The effects are discussed in terms of machine learning vehicle detection accuracy drop, and also visualising how detection performance spatially varies on the frames using the recently introduced metric, the Spatial Recall Index (SRI). …”
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A method for determining fracturing radius of blasting holes based on SF6 gas tracing by Dongxu JIA

“…In-situ blasting cracking tests were carried out at the 10102 top extraction roadway excavation face of Yuwang Coal Mine. The spatial concentration gradient analysis method was adopted to establish a quantitative evaluation model of SF6 diffusion field and cracking radius. …”
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Forest dynamics and local perceptions of conservation: the case of Bontioli forest in south-western Burkina Faso by Mohamed Awalo Traoré, Mohamed Awalo Traoré, Jean-François Bissonnette, Jean-François Bissonnette, Kossivi Fabrice Dossa, Kossivi Fabrice Dossa

“…Data were collected through semi-structured interviews and focus group discussions and analyzed using mixed thematic coding (both inductive and deductive) with NVivo 14 software.ResultsSpatial analysis revealed a steady pattern of deforestation, marked by significant agricultural expansion and a reduction in natural vegetation, primarily driven by human activities. …”
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Land use and land cover classification and terrestrial ecosystem carbon storage changes in Vietnam based on Sentinel images by Yang Liu, Kun Yang, Zongqi Peng, Tianle Zou, Danni Su, Run Sun, Jingcong Ma

“…Carbon stock data from 2015 to 2023 were generated using the InVEST model, and their spatial-temporal variations were analyzed. …”
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Combined application of FTAN and cross-spectra analysis to ambient noise recorded by a microseismic monitoring network by Ilaria Barone, Alessandro Brovelli, Giorgio Tango, Sergio Del Gaudio, Giorgio Cassiani

“…Surface wave tomography was run to obtain the spatial distribution of group and phase velocities for the same wavelengths. …”
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Spatiotemporal Analysis and Physicochemical Profiling of PM₁₀ and PM_2.5 in Slovenia by Maja Ivanovski, Ivan Anžel, Darko Goričanec, Danijela Urbancl

“…Meteorological data and statistical models were used to assess environmental influences on PM variability. …”
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PLIN2 promotes colorectal cancer progression through CD36-mediated epithelial-mesenchymal transition by Fan Yang, Ying Li, Xue Shang, Yun Zhu, Wenting Hou, Yi Liu, Qing Hua, Zhirong Sun

“…The challenge remains to construct reliable prognostic prediction models and to further elucidate the key molecular mechanisms of tumor progression. …”
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Multi-label Text Classification by Fusing Pseudo-label Generation and Data Augmentation by WANG Shuitao, ZHANG Sijia, SUN Xufei, YANG Jingjie, BI Tiantian, ZHANG Zhenglong

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Pre- and post-COVID-19 pandemic identification of dengue hotspots and exploration of population and environmental determinants of dengue in Quezon City, Philippines by John Robert Carabeo Medina, Shin’ya Kawamura, Rie Takeuchi, Rolando V. Cruz, Johnedel Mendoza, Paul Michael R. Hernandez, Fernando B. Garcia, Ernesto R. Gregorio, Jun Kobayashi

“…The association of dengue cases with greenness, surrounding greenness, transportation network hubs, small building ratios, and population density was determined through a generalized linear model (GLM). Results revealed that incidence rates of dengue across barangays were spatially heterogeneous, and the dengue hotspots were unstable as they varied quarterly each year. …”
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Cross-scale study of heat transfer performance in metal rubber with complex topological structures by Kequan Tang, Liangliang Shen, Linwei Shi, Weidong Yan, Qiang Song, Zhiying Ren

“…This approach comprehensively considers critical factors such as porosity, temperature, and interlayer thermal resistance, culminating in the development of a predictive numerical model for thermal conductivity in porous metal-based materials. …”
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CEDAR-GPP: spatiotemporally upscaled estimates of gross primary productivity incorporating CO₂ fertilization by Y. Kang, Y. Kang, Y. Kang, M. Bassiouni, M. Bassiouni, M. Gaber, M. Gaber, X. Lu, X. Lu, T. F. Keenan, T. F. Keenan

“…Our machine learning models effectively predict monthly GPP (<span class="inline-formula"><i>R</i>²</span> <span class="inline-formula">∼</span> 0.72), the mean seasonal cycles (<span class="inline-formula"><i>R</i>²</span> <span class="inline-formula">∼</span> 0.77), and spatial variabilities (<span class="inline-formula"><i>R</i>²</span> <span class="inline-formula">∼</span> 0.63) based on cross-validation at flux sites. …”
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Estimates of Lake Nitrogen, Phosphorus, and Chlorophyll‐a Concentrations to Characterize Harmful Algal Bloom Risk Across the United States by Meredith M. Brehob, Michael J. Pennino, Amalia M. Handler, Jana E. Compton, Sylvia S. Lee, Robert D. Sabo

“…We then used these RF models to extrapolate lake TN and TP predictions to lakes without nutrient observations and predict chlorophyll‐a for ∼112,000 lakes across the CONUS. …”
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A computational framework for IoT security integrating deep learning-based semantic algorithms for real-time threat response by Ripal Ranpara, Shobhit K. Patel, Om Prakash Kumar, Fahad Ahmed Al-Zahrani

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Intersectionality in residental solar adoption: Examining socio-demographics, racial diversity, geography, solar suitability, and policy in the United States by Gerald Jones, Chien-Fei Chen, Wei-An Chen, Lillian Lovingood, Xueping Li

“…The interplay of income and education significantly contributes to a higher adoption rate. …”
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Real-Time Facial Expression Recognition Based on Image Processing in Virtual Reality by Qingzhen Gong, Xuefang Liu, Yongqiang Ma

“…Hence, this paper suggests the Deep Automatic Facial Expression Recognition Model (DAFERM) for interactive virtual reality (VR) applications such as intelligent education, social networks, and virtual training. …”
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