Safe and intuitive human robot interaction (HRI) requires precise regulation of contact forces and torques while adapting to dynamic and uncertain human behavior. Traditional impedance and admittance control strategies rely on fixed parameters and accurate system modeling, which often limit their performance in unstructured or collaborative environments. This paper presents an AI-enabled force and torque control framework that integrates machine learning techniques with conventional control methods to enhance adaptability, compliance, and safety in physical human robot interaction. The proposed approach employs deep neural networks and reinforcement learning to learn human intent and interaction dynamics directly from multi-modal sensor data, including force torque sensors, joint encoders, and inertial measurements. By continuously adjusting control gains in real time, the system achieves stable interaction while minimizing excessive contact forces and undesired torques. Experimental evaluations conducted on a collaborative robotic platform demonstrate significant improvements over classical control schemes, including reduced interaction force peaks, smoother torque profiles, and improved task execution efficiency during cooperative manipulation tasks. The results indicate that AI-driven force and torque control can substantially improve robustness, adaptability, and user comfort in human robot collaboration, making it a promising solution for applications in rehabilitation robotics, assistive devices, and industrial cobots.
Parkinson's disease (PD) is a progressive neurodegenerative disorder traditionally characterized by dopaminergic neuronal loss in the substantia nigra and the accumulation of misfolded α-synuclein (α-syn) aggregates. While genetic susceptibility and environmental exposures are well-recognized contributors to PD, growing evidence indicates that disease initiation and progression may also involve peripheral mechanisms originating in the gastrointestinal (GI) tract. Early non-motor symptoms such as constipation, along with the presence of α-syn pathology in the enteric nervous system, have led to increasing interest in the gut-brain axis as a critical modulator of PD pathogenesis. Recent literatures reveal that gut microbiota dysbiosis can influence neurodegeneration through immune activation, intestinal barrier dysfunction, and altered production of microbial metabolites, including short-chain fatty acids, bile acids, lipopolysaccharides, and tryptophan-derived compounds. However, the precise molecular mechanisms by which these microbial factors modulate α-syn aggregation, propagation, and clearance remain incompletely understood. In this article, we review current clinical and experimental literature linking gut microbiota alterations to α-syn pathology, with particular emphasis on inflammatory signaling, microbial metabolites, and impaired proteostatic pathways that promote α-syn misfolding. We further integrate emerging concepts of "body-first" and "brain-first" PD subtypes and discuss proposed routes of α-syn transmission from the enteric to the central nervous system, including vagal, hematogenous, and immune-mediated pathways. By highlighting underexplored mechanistic connections between gut dysbiosis and α-syn biology, this review underscores the potential of microbiome-targeted strategies for early diagnosis and disease modification. A deeper understanding of gut-brain communication may ultimately enable personalized therapeutic approaches and reshape current paradigms of PD pathogenesis.
Medicinal plants produce essential secondary metabolites which have immense potential in human welfare, including pharmaceutical and nutraceutical applications. Over the last few decades, hairy root cultures have emerged as a promising biotechnological platform for the sustainable synthesis of pharmacologically important secondary metabolites. This approach exploits the inherent capability of Agrobacterium rhizogenes to transfer root-inducing (Ri) plasmid T-DNA into the host plant genome, resulting in the formation of genetically stable, fast-growing, and hormone-independent hairy roots. Transformed hairy roots are particularly advantageous due to their high biosynthetic potential, genetic stability, and competence to replicate or even surpass the parent plant in producing bioactive metabolites such as phenolics, alkaloids, terpenoids, and flavonoids. This approach not only conserves plant biodiversity but also enables controlled manipulation of biosynthetic pathways through elicitor treatments, precursor feeding, and metabolic engineering. The integration of advanced technologies including transcriptomics, proteomics, and metabolomics further enhances our understanding of metabolic regulation and supports pathway optimization for improved metabolite yield. Despite its tremendous potential, challenges remain in optimizing transformation protocols, strain selection, and scaling up production using bioreactor systems. Nevertheless, hairy root cultures constitute an efficient and environmentally sustainable platform for the large-scale production of high-value metabolites across diverse plant species. This review explores the biological basis, influencing factors, and applications of hairy root cultures, emphasizing their role in modern plant biotechnology and their commercial relevance in the pharmaceutical and nutraceutical industries. We also critically discuss and analyze recent trends and future perspectives.
This review aims to integrate current insights into the interactive roles of biochar and zinc (Zn) in nutrient management and phytohormone regulation, emphasizing their combined potential for climate-smart and sustainable agriculture. It highlights how Zn-biochar interactions affect soil fertility, nutrient cycling, phytohormonal balance and overall plant growth. It also addresses global challenges such as micronutrient malnutrition and environmental degradation. Relevant peer-reviewed studies were critically examined to assess how biochar applications affect Zn availability, soil physicochemical traits and mechanism of phytohormone signalling. The review integrates findings across soil-plant-microbe interactions, highlighting mechanistic connections between Zn bioavailability, auxin-cytokinin modulation and root system architecture. Evidence shows that biochar enhances soil structure, cation exchange capacity and Zn retention, while also promoting microbial activity and beneficial root-microbe associations. Zinc supplementation contributes to enzyme function, antioxidant defence and phytohormone-mediated stress resilience. Together, Zn-biochar systems enhance nutrient-use efficiency, crop productivity and Zn biofortification. They also contribute to carbon sequestration, reduced fertilizer inputs and lower greenhouse gas emissions. The combined use of Zn and biochar offers a valuable strategy for developing nutrient-efficient, low-emission and resilient agricultural systems. However, further interdisciplinary research is needed to elucidate biochar-Zn-phytohormone interactions across soil types and cropping systems. Translating these findings into scalable, field-based nutrient management strategies remains essential for achieving global sustainability goals.
This review examines recent advances in semi-analytical and hybrid solution methodologies for nonlinear ordinary and partial differential equation (ODE/PDE) systems. Its primary focus is on the Adomian Decomposition Method (ADM), the Homotopy Perturbation Method (HPM), and the Akbari–Ganji Method (AGM). These approaches are categorized as semi-analytical because they combine analytical series expansions with iterative or numerical steps to obtain approximate solutions. Thereby occupying an intermediate position between fully analytical methods (i.e. exact closed-form solutions) and fully numerical techniques. The discussion systematically addresses their theoretical properties, including convergence behavior, computational requirements, and the role of hybrid extensions. Selected engineering applications are highlighted, with emphasis on nonlinear oscillators, heat transfer systems, biosensor modeling, and reaction–diffusion problems. A comparative assessment of strengths, limitations, and failure scenarios (e.g. stiffness, slow convergence) is provided to guide method selection. This study presents a structured synthesis that identifies problem classes, recommends appropriate solution methods, and highlights scenarios in which hybrid strategies can enhance stability and computational efficiency. Overall, this review provides researchers with a practical framework for selecting and applying semi-analytical methods to solve nonlinear problems efficiently.
