Yu Du¹, Runwei Guan², Ho-Pun Lam¹, Jeremy Smith³, Yutao Yue^4,5, Ka Lok Man¹, Yan Li^6,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.072544 - 10 February 2026

Abstract As a cornerstone for applications such as autonomous driving, 3D urban perception is a burgeoning field of study. Enhancing the performance and robustness of these perception systems is crucial for ensuring the safety of next-generation autonomous vehicles. In this work, we introduce a novel neural scene representation called Street Detection Gaussians (SDGs), which redefines urban 3D perception through an integrated architecture unifying reconstruction and detection. At its core lies the dynamic Gaussian representation, where time-conditioned parameterization enables simultaneous modeling of static environments and dynamic objects through physically constrained Gaussian evolution. The framework’s radar-enhanced perception module… More >

Leyu Dai^1,2,3, Jindong Wang^1,2,3, Ming Zhou^1,2,3, Song Guo^1,2,3, Hengwei Zhang^1,2,3,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.072449 - 10 February 2026

Abstract In recent years, with the rapid advancement of artificial intelligence, object detection algorithms have made significant strides in accuracy and computational efficiency. Notably, research and applications of Anchor-Free models have opened new avenues for real-time target detection in optical remote sensing images (ORSIs). However, in the realm of adversarial attacks, developing adversarial techniques tailored to Anchor-Free models remains challenging. Adversarial examples generated based on Anchor-Based models often exhibit poor transferability to these new model architectures. Furthermore, the growing diversity of Anchor-Free models poses additional hurdles to achieving robust transferability of adversarial attacks. This study presents… More >

Fankun Meng^1,2,3, Yuyang Liu^1,2,*, Xiaohua Liu⁴, Chenlong Duan^1,2, Yuhui Zhou^1,2,3

FDMP-Fluid Dynamics & Materials Processing, Vol.22, No.1, 2026, DOI:10.32604/fdmp.2026.075865 - 06 February 2026

Abstract Carbonate gas reservoirs are often characterized by strong heterogeneity, complex inter-well connectivity, extensive edge or bottom water, and unbalanced production, challenges that are also common in many heterogeneous gas reservoirs with intricate storage and flow behavior. To address these issues within a unified, data-driven framework, this study develops a multi-block material balance model that accounts for inter-block flow and aquifer influx, and is applicable to a wide range of reservoir types. The model incorporates inter-well and well-group conductive connectivity together with pseudo–steady-state aquifer support. The governing equations are solved using a Newton–Raphson scheme, while particle More > Graphic Abstract

Antonios Chrysargyris^1,*, Demetris Antoniou², Timos Boyias², Nikolaos Tzortzakis^1,*

Phyton-International Journal of Experimental Botany, Vol.95, No.1, 2026, DOI:10.32604/phyton.2026.076565 - 30 January 2026

Abstract Cyprus has an extensive record in grape production and winemaking. Grapevine is essential for the economic and environmental sustainability of the agricultural sector, as it is in other Mediterranean regions. Intensive agriculture can overuse and exhaust natural resources, including soil and water. The current study evaluated how conservation strategies, including no tillage and semi-tillage (as a variation of strip tillage), affected grapevine growth and grape quality when compared to conventional tillage application. Two cultivars were used: Chardonnay and Maratheftiko (indigenous). Soil pH decreased, and EC increased after tillage applications, in both vineyards. Tillage lowered soil… More >

Mario D’Ambrosio¹, Elisabetta Bigagli^1,*, Lorenzo Cinci¹, Cecilia Brunetti^2,*, Edgardo Giordani³, Francesco Ferrini³, Cristina Luceri¹

Phyton-International Journal of Experimental Botany, Vol.95, No.1, 2026, DOI:10.32604/phyton.2026.073250 - 30 January 2026

Abstract Moringa oleifera (MO) is traditionally used to mitigate inflammatory-mediated disorders; however, the influence of ecotypic variation on its anti-inflammatory activity remains poorly understood. In this study, we compared the phytochemical composition and anti-inflammatory activity of ethanolic extracts obtained from fresh and dried leaves of four MO ecotypes (India, Paraguay, Mozambique, and Pakistan), all grown under the same outdoor conditions, as well as two commercial powders (Just Moringa and WISSA), using LPS-stimulated RAW 264.7 macrophages. Extracts from fresh leaves were 19–43% more cytotoxic than those from dried leaves, depending on the ecotype, likely due to higher cyanogenic… More >

Dilfuza Jabborova^1,2,3,*, Khurshid Sulaymanov¹, Muzafar Jabborov⁴, Nayan Ahmed⁵, Tatiana Minkina⁶, Olga Biryukova⁶, Nasir Mehmood^6,*, Vishnu D. Rajput⁶

Phyton-International Journal of Experimental Botany, Vol.95, No.1, 2026, DOI:10.32604/phyton.2026.072577 - 30 January 2026

Abstract The combined effects of macronutrients (Nitrogen, Phosphorus, and Potassium-N, P, K) and micronutrient fertilization on turmeric yield, soil enzymatic activity, microbial biomass, and nutrient dynamics remains poorly understood, despite their significance for sustainable soil fertility management and optimizing crop productivity across diverse agroecosystems. To investigate, a net house experiment on sandy loam Haplic Chernozem was conducted to 03 fertilizer regimes, viz. N₇₅P₅₀K₅₀ kg ha⁻¹ (T-2), N₁₂₅P₁₀₀K₁₀₀ kg ha⁻¹ (T-3), and N₁₀₀P₇₅K₇₅ + B₃Zn₆Fe₆ kg ha⁻¹ (T-4). Furthermore, the influence of these treatments was systematically assessed on soil nutrient status (N, P, K), enzymatic activities (alkaline phosphomonoesterase, dehydrogenase, fluorescein diacetate… More >

Hong Chen^1,2, Runze Yang^1,2, Jing Tian^1,2, Boyuan Xu^1,2, Qiang Chen³, Yuzong Chen^1,2, Ming-Xiao Zhao^1,2,*

Phyton-International Journal of Experimental Botany, Vol.95, No.1, 2026, DOI:10.32604/phyton.2025.072276 - 30 January 2026

Abstract Panax species are globally recognized for their high medicinal and economic value, yet large-scale cultivation is constrained by high production costs, progressive soil acidification, and persistent soil-borne diseases. Although various soil improvement strategies have been tested, a comprehensive synthesis of their comparative effectiveness has been lacking. Here, we conducted a meta-analysis of 1381 observations from 54 independent studies to evaluate the effects of conventional fertilizers, microbial fertilizers, organic amendments, and inorganic amendments on Panax cultivation. Our results demonstrate that microbial fertilizers, organic amendments, and inorganic amendments significantly increased soil pH, thereby ameliorating soil acidification. Among them,… More >

Monalisa Jena¹, Noman Khan^2,*, Mi Young Lee^3,*, Seungmin Rho³

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.075119 - 29 January 2026

Abstract Mental-health risk detection seeks early signs of distress from social media posts and clinical transcripts to enable timely intervention before crises. When such risks go undetected, consequences can escalate to self-harm, long-term disability, reduced productivity, and significant societal and economic burden. Despite recent advances, detecting risk from online text remains challenging due to heterogeneous language, evolving semantics, and the sequential emergence of new datasets. Effective solutions must encode clinically meaningful cues, reason about causal relations, and adapt to new domains without forgetting prior knowledge. To address these challenges, this paper presents a Continual Neuro-Symbolic Graph… More >

Jihoon Moon^*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.071052 - 29 January 2026

Abstract Predicting the behavior of renewable energy systems requires models capable of generating accurate forecasts from limited historical data, a challenge that becomes especially pronounced when commissioning new facilities where operational records are scarce. This review aims to synthesize recent progress in data-efficient deep learning approaches for addressing such “cold-start” forecasting problems. It primarily covers three interrelated domains—solar photovoltaic (PV), wind power, and electrical load forecasting—where data scarcity and operational variability are most critical, while also including representative studies on hydropower and carbon emission prediction to provide a broader systems perspective. To this end, we examined… More >

Weiqi Mao^1,2,3, Enbo Yu^1,*, Guoji Xu³, Xiaozhen Li³

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.069733 - 29 January 2026

Abstract Accurate wind speed prediction is crucial for stabilizing power grids with high wind energy penetration. This study presents a novel machine learning model that integrates clustering, deep learning, and transfer learning to mitigate accuracy degradation in 24-h forecasting. Initially, an optimized DB-SCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm clusters wind fields based on wind direction, probability density, and spectral features, enhancing physical interpretability and reducing training complexity. Subsequently, a ResNet (Residual Network) extracts multi-scale patterns from decomposed wind signals, while transfer learning adapts the backbone network across clusters, cutting training time by over… More >