Shohreh Moradpour^1,#, Shuai Zhao^2,#, Mojgan Entezari¹, Shamsollah Ayoubi^3,*, Seyed Roohollah Mousavi⁴

Revue Internationale de Géomatique, Vol.34, pp. 809-829, 2025, DOI:10.32604/rig.2025.069538 - 06 November 2025

Abstract Accurate spatial prediction of soil organic carbon (SOC) and soil inorganic carbon (SIC) is vital for land management decisions. This study targets SOC/SIC mapping challenges at the watershed scale in central Iran by addressing environmental heterogeneity through a random forest (RF) model combined with bootstrapping to assess prediction uncertainty. Thirty-eight environmental variables—categorized into climatic, soil physicochemical, topographic, geomorphic, and remote sensing (RS)-based factors—were considered. Variable importance analysis (via) and partial dependence plots (PDP) identified land use, RS indices, and topography as key predictors of SOC. For SIC, soil reflectance (Bands 5 and 7, ETM+), topography, More > Graphic Abstract

Guiling Xu^1,#, Xiaoxuan You^1,#, Yuehua Feng^1,2,*, Xiaoke Wang¹, Yuqi Gao¹, Hongjun Ren¹, Zhili Han¹, Jiale Li¹

Phyton-International Journal of Experimental Botany, Vol.94, No.10, pp. 3251-3268, 2025, DOI:10.32604/phyton.2025.067997 - 29 October 2025

Abstract Nitrogen (N) reduction combined with organic fertilizer has become a highly popular fertilization method, meeting the sustainable development of agriculture. A field experiment was conducted to investigate the effects of N reduction (NR) and combined application of organic fertilizer (OF) on N utilization, yield, and quality of hybrid indica rice in the karst area. Using rice ‘Yixiangyou2115’ as the material, a split-plot design experiment was carried out with OF application rate as the main plots and NR rate as the subplots. The OF application rate had three levels: M0 (0 kg/ha), M1 (low OF, 1673… More >

Boyan Cui^1,2, Huijuan Wang^1,*, Yongjun Qi^1,*, Hongce Chen¹, Quanbo Yuan^1,3, Dongran Liu¹, Xuehua Zhou¹

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5531-5553, 2025, DOI:10.32604/cmc.2025.070101 - 23 October 2025

Abstract The proliferation of malware and the emergence of adversarial samples pose severe threats to global cybersecurity, demanding robust detection mechanisms. Traditional malware detection methods suffer from limited feature extraction capabilities, while existing Vision Transformer (ViT)-based approaches face high computational complexity due to global self-attention, hindering their efficiency in handling large-scale image data. To address these issues, this paper proposes a novel hybrid enhanced Vision Transformer architecture, HERL-ViT, tailored for malware detection. The detection framework involves five phases: malware image visualization, image segmentation with patch embedding, regional-local attention-based feature extraction, enhanced feature transformation, and classification. Methodologically,… More >

Carlos Quiterio Gómez Muñoz¹, Fausto Pedro García Márquez^2,*, Jorge Bernabé Sanjuán³

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3369-3386, 2025, DOI:10.32604/cmes.2025.069225 - 30 September 2025

Abstract Due to the continuous increase in global energy demand, photovoltaic solar energy generation and associated maintenance requirements have significantly expanded. One critical maintenance challenge in photovoltaic installations is detecting hot spots, localized overheating defects in solar cells that drastically reduce efficiency and can lead to permanent damage. Traditional methods for detecting these defects rely on manual inspections using thermal imaging, which are costly, labor-intensive, and impractical for large-scale installations. This research introduces an automated hybrid system based on two specialized convolutional neural networks deployed in a cascaded architecture. The first convolutional neural network efficiently detects More >

Rodrigo Rafael da Silva^1,*, Gabriela Carvalho Maia de Queiroz¹, Amanda Cibele da Paz Sousa¹, Antônio Gustavo de Luna Souto¹, Francisco Hélio Alves de Andrade ², Francimar Maik da Silva Morais¹, Rita Magally Oliveira da Silva Marcelino¹, Fagner Nogueira Ferreira¹, Alex Alvares da Silva³, Maria Isabela Batista Clemente¹, Baltazar Cirino Junior¹, Wedson Aleff Oliveira da Silva¹, Mateus de Freitas Almeida dos Santos¹, José Francismar de Medeiros^1,*

Phyton-International Journal of Experimental Botany, Vol.94, No.9, pp. 2797-2814, 2025, DOI:10.32604/phyton.2025.068089 - 30 September 2025

Abstract Due to its tolerance to water deficit and salinity, sorghum is considered a suitable crop for cultivation in regions affected by these stress conditions, enabling the efficient use of limited water resources. This study evaluated the resilience of the sweet sorghum cultivar BRS 506 under water deficit and salinity stress, focusing on water relations and yield performance in semiarid conditions. A randomized complete block design was employed in a 3 × 3 factorial arrangement with four replicates. Treatments consisted of three levels of irrigation water salinity (1.50, 3.75, and 6.00 dS m⁻¹) and three irrigation levels… More >

Sarab Almuhaideb^1,*, Najwa Altwaijry¹, Isra Al-Turaiki¹, Ahmad Raza Khan², Hamza Ali Rizvi³

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3095-3128, 2025, DOI:10.32604/cmc.2025.067390 - 23 September 2025

Abstract Many bioinformatics applications require determining the class of a newly sequenced Deoxyribonucleic acid (DNA) sequence, making DNA sequence classification an integral step in performing bioinformatics analysis, where large biomedical datasets are transformed into valuable knowledge. Existing methods rely on a feature extraction step and suffer from high computational time requirements. In contrast, newer approaches leveraging deep learning have shown significant promise in enhancing accuracy and efficiency. In this paper, we investigate the performance of various deep learning architectures: Convolutional Neural Network (CNN), CNN-Long Short-Term Memory (CNN-LSTM), CNN-Bidirectional Long Short-Term Memory (CNN-BiLSTM), Residual Network (ResNet), and… More >

Zeeshan Ali¹, Jihoon Moon², Saira Gillani³, Sitara Afzal⁴, Maryam Bukhari⁵, Seungmin Rho^6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2263-2286, 2025, DOI:10.32604/cmes.2025.067743 - 31 August 2025

Abstract Surveillance systems can take various forms, but gait-based surveillance is emerging as a powerful approach due to its ability to identify individuals without requiring their cooperation. In the existing studies, several approaches have been suggested for gait recognition; nevertheless, the performance of existing systems is often degraded in real-world conditions due to covariate factors such as occlusions, clothing changes, walking speed, and varying camera viewpoints. Furthermore, most existing research focuses on single-person gait recognition; however, counting, tracking, detecting, and recognizing individuals in dual-subject settings with occlusions remains a challenging task. Therefore, this research proposed a… More >

Manoharan Madhiarasan^*

Energy Engineering, Vol.122, No.8, pp. 2993-3011, 2025, DOI:10.32604/ee.2025.068358 - 24 July 2025

Abstract Accurate Global Horizontal Irradiance (GHI) forecasting has become vital for successfully integrating solar energy into the electrical grid because of the expanding demand for green power and the worldwide shift favouring green energy resources. Particularly considering the implications of the aggressive GHG emission targets, accurate GHI forecasting has become vital for developing, designing, and operational managing solar energy systems. This research presented the core concepts of modelling and performance analysis of the application of various forecasting models such as ARIMA (Autoregressive Integrated Moving Average), Elaman NN (Elman Neural Network), RBFN (Radial Basis Function Neural Network),… More >

Cui Zhang¹, Lieping Zhang^2,*, Huaquan Gan³, Hongyuan Chen³, Zhihao Li³

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3125-3148, 2025, DOI:10.32604/cmc.2025.064499 - 03 July 2025

Abstract In large-scale Wireless Rechargeable Sensor Networks (WRSN), traditional forward routing mechanisms often lead to reduced energy efficiency. To address this issue, this paper proposes a WRSN node energy optimization algorithm based on regional partitioning and inter-layer routing. The algorithm employs a dynamic clustering radius method and the K-means clustering algorithm to dynamically partition the WRSN area. Then, the cluster head nodes in the outermost layer select an appropriate layer from the next relay routing region and designate it as the relay layer for data transmission. Relay nodes are selected layer by layer, starting from the… More >

Abdullah A. Alharbi¹, Ahmad Y. Alqassim^1,*, Mohammad A. Jareebi¹, Ahmad A. Alharbi², Nada M. Makein¹, Fatimah H. Al Ghazwi³, Seba Y. Muzaiiadi³, Joud N. Refaei³, Revan A. Arishi³, Bashaer A. Al Rajhi³, Fatima A. Aqili³, Saleha M. Ayoub³, Mohammed A. Muaddi¹

International Journal of Mental Health Promotion, Vol.27, No.5, pp. 701-716, 2025, DOI:10.32604/ijmhp.2025.062924 - 05 June 2025

Abstract Background: Polycystic ovarian syndrome (PCOS), a common endocrine disorder in reproductive-aged women, has substantial physical and psychological impacts. While the physical manifestations of PCOS are well established, the psychological burden, especially anxiety, is understudied in Saudi Arabia. This study aimed to assess the prevalence of generalized anxiety disorder among females with PCOS compared to those without PCOS, and to identify the clinical and sociodemographic factors associated with anxiety in the Jazan region of Saudi Arabia. Methods: A cross-sectional study was conducted between January and March 2023 using an Arabic self-administered online questionnaire distributed via social… More >