The transformation of biomass into renewable fuels and chemicals has gained remarkable attention as a sustainable alternative to fossil-based resources. Metal-based catalysts, encompassing transition and noble metals, are crucial in these transformations as they drive critical reactions, such as hydrodeoxygenation, hydrogenation, and reforming. Transition metals, including nickel, cobalt, and iron, provide cost-effective solutions for large-scale processes, while noble metals, such as platinum and palladium, exhibit superior activity and selectivity for specific reactions. Catalytic advancements, including the development of hybrid and bimetallic systems, have further improved the efficiency, stability, and scalability of biomass transformation processes. This review highlights the catalytic upgrading of lignocellulosic, algal, and waste biomass into high-value platform chemicals, biofuels, and biopolymers, with a focus on processes, such as Fischer–Tropsch synthesis, aqueous-phase reforming, and catalytic cracking. Key challenges, including catalyst deactivation, economic feasibility, and environmental sustainability, are examined alongside emerging solutions, like AI-driven catalyst design and lifecycle analysis. By addressing these challenges and leveraging innovative technologies, metal-based catalysis can accelerate the transition to a circular bioeconomy, supporting global efforts to combat climate change and reduce fossil fuel dependence.

Facial expression recognition is very useful for effective human–computer interaction, robot interfaces, and emotion-aware smart agent systems. This paper presents a new framework for facial expression recognition by using a hybrid model: a combination of convolutional neural networks (CNNs) and a support vector machine (SVM) classifier using dynamic facial expression data. In order to extract facial motion characteristics, dense facial motion flows and geometry landmark flows of facial expression sequences were used as inputs to the CNN and SVM classifier, respectively. CNN architectures for facial expression recognition from dense facial motion flows were proposed. The optimal weighting combination of the hybrid classifiers provides better facial expression recognition results than individual classifiers. The system has successfully classified seven facial expressions signalling anger, contempt, disgust, fear, happiness, sadness and surprise classes for the CK+ database, and facial expressions of anger, disgust, fear, happiness, sadness and surprise for the BU4D database. The recognition performance of the proposed system is 99.69% for the CK+ database and 94.69% for the BU4D database. The proposed method shows state-of-the-art results for the CK+ database and is proven to be effective for the BU4D database when compared with the previous schemes.

Walking is an exercise that uses muscles and joints of the human body and is essential for understanding body condition. Analyzing body movements through gait has been studied and applied in human identification, sports science, and medicine. This study investigated a spatiotemporal graph convolutional network model (ST-GCN), using attention techniques applied to pathological-gait classification from the collected skeletal information. The focus of this study was twofold. The first objective was extracting spatiotemporal features from skeletal information presented by joint connections and applying these features to graph convolutional neural networks. The second objective was developing an attention mechanism for spatiotemporal graph convolutional neural networks, to focus on important joints in the current gait. This model establishes a pathological-gait-classification system for diagnosing sarcopenia. Experiments on three datasets, namely NTU RGB+D, pathological gait of GIST, and multimodal-gait symmetry (MMGS), validate that the proposed model outperforms existing models in gait classification.

Wheat (Triticum aestivum L.), a vital crop constituting approximately 20% of global caloric intake, faces significant threats from heavy metal contamination, particularly cadmium (Cd) and chromium (Cr), along with drought stress, jeopardizing global food security. This study aimed to investigate the combined effects of these stressors and the potential of plant growth enhancers such as gibberellic acid (GA3), biochar (BC), and rhizobacteria to improve wheat growth. Conducted in a controlled greenhouse environment at The Islamia University of Bahawalpur, the experiment utilized a completely randomized design with three replications across 72 pots, each filled with clay loam soil. The experimental layout included 24 treatment combinations involving cadmium stress (6 mg/kg), chromium stress (300 and 600 mg/kg), drought stress simulated at -0.8 MPa soil water potential, and various applications of GA3 (200 mg L− 1) and biochar (0.6% and 0.9% w/w). Seedlings of T. aestivum cv. Dilkash-21, treated with Agrobacterium fabrum, showed significant growth metrics, with root lengths of 9.36 cm under 6 mg/kg Cd stress compared to 5.53 cm in controls. The treatment also increased shoot and root fresh weights by 24.7% and 22.5%, respectively, while chlorophyll content peaked at 2.26 mg/g under 6 mg/kg Cd. Additionally, electrolyte leakage decreased to 10.5%, and the vigor index improved to 1586.05 under Cd stress. These findings indicate that utilizing GA3 and biochar can mitigate the adverse effects of environmental stressors on wheat. Future research should focus on the underlying mechanisms of these treatments and explore their application in field conditions to further enhance wheat productivity and resilience against environmental stress.

This study assessed the growth, biomass, and carbon storage potential of four urban tree species—Pinus roxburghii, Broussonetia papyrifera, Jacaranda mimosifolia, and Eucalyptus camaldulensis—in Islamabad’s greenbelts. Sampling was conducted using a quadrat method, covering five sites (F-9, G-8, H-8, I-8, Zero-Point), with 400 trees analyzed. Growth parameters such as diameter at breast height (DBH), height, aboveground biomass (AGB), aboveground carbon density (ACD), belowground carbon density (BCD), basal area (BA), and volume were measured. E. camaldulensis recorded the highest AGB (mean: 2.07 t ha⁻¹, range: 0.05–6.54 t ha⁻¹), ACD (mean: 971.43 t ha⁻¹), and BCD (mean: 233.14 t ha⁻¹), along with the largest DBH (mean: 47.31 cm) and height (mean: 20.10 m). B. papyrifera showed significant carbon storage potential, with mean ACD and BCD values of 373.94 t ha⁻¹ and 89.75 t ha⁻¹, respectively. In contrast, J. mimosifolia displayed the lowest mean AGB (0.2493 t ha⁻¹) but showed considerable variability, with DBH ranging from 7.68 to 59.83 cm. P. roxburghii exhibited moderate values, with a mean AGB of 0.45 t ha⁻¹ and ACD of 212.36 t ha⁻¹. Overall, mean AGB, ACD, and BCD across all species were 0.89 t ha⁻¹, 418.73 t ha⁻¹, and 100.49 t ha⁻¹, respectively, while basal area ranged from 12.07 to 154.94 m² ha⁻¹ (mean: 61.84 m² ha⁻¹). Statistical analysis revealed significant positive correlations among DBH, height, and biomass parameters (p < 0.05). The findings underscore E. camaldulensis as the most effective species for urban carbon sequestration, while B. papyrifera demonstrated positive soil fertility implications. Future research should explore long-term ecological impacts and management strategies for these species in urban landscapes.