Abstract This study explores the development and optimization of polymer composite-based wind turbine blades, integrating glass fiber reinforced plastic (GFRP) with shape memory alloy (SMA) to enhance performance in wind energy harvesting. Advances in materials science, aerodynamics, computational modelling, and structural analysis have been leveraged to improve blade efficiency, durability, and self-adaptive capabilities. The research employs finite element analysis (FEA) and artificial neural networks (ANN) to evaluate the mechanical behaviour of composite blades under varying loads. A graded beam model was developed to assess the effects of ply drop-off and material distribution on structural integrity. Experimental validation confirmed that SMA integration enhances blade deformation recovery, mitigating stress accumulation and improving aerodynamic stability. The results demonstrate that GFRP-SMA blades achieve a performance coefficient approaching the Betz limit (0.5923), reducing deflections and improving load response. Despite these advancements, challenges remain in optimizing SMA wire placement, adhesion, and actuation efficiency. Future work should focus on refining material interfaces, developing adaptive control mechanisms, and validating the model in full-scale wind turbine applications. This study contributes to the next-generation smart wind turbine blade design, addressing structural limitations while enhancing energy efficiency and operational resilience.

Abstract Composite materials are tailor-made materials that constitute matrix and fiber-based reinforcements that are collectively bonded to obtain a material that possesses a combination of properties of both the precursor materials. The current article reviews the wide composite materials processes; furthermore, when it comes to manufacturing composites, cost is a very influential factor. The manufacture of composites is not cheap. The processes require resources, infrastructure, and energy. Therefore, energy-saving measures are worthwhile things to research. And said measures are also included in this work. Also present are information regarding existing conventional energy sources as well as potential alternative sources for the composite industry.

Abstract The research integrates chemical engineering principles with biological insights to overcome key barriers in glioblastoma multiforme (GBM) therapy. GBM remains one of the most aggressive and lethal brain tumours, characterised by its infiltrative growth, chemoresistance and poor prognosis. Conventional chemotherapy faces critical limitations, including restricted drug penetration across the blood–brain barrier, systemic toxicity and tumour resistance to classic treatment. Addressing these challenges, this study proposes an innovative, multiple emulsion–based drug delivery system designed to enhance the therapeutic effectiveness of GBM treatment through synergistic combinations of RNA-class molecules and chemotherapeutic agent (doxorubicin–DOX). The system utilises a pH-responsive biopolymer (carboxymethylcellulose sodium salt), facilitating controlled and selective drug release in the acidic microenvironment of tumour cells (pH 6.3), while preserving healthy tissues. The emulsion structures prepared using Couette–Taylor flow techniques, achieved high encapsulation efficiency of DOX, stability and precise control over release kinetics. The addition of siRNA targets the genetic pathways of tumour DNA repair, sensitising cancer cells to DOX and significantly reducing their viability. Experimental results demonstrated a substantial improvement in cytotoxic efficacy, with up to a 65 % reduction in cancer cell viability compared to conventional DOX solution, further amplified to about 81 % when combined with liposomal siRNA. A mathematical model of drug diffusion and chemical reaction expressing absorption by cancer cells highlights the systems’ potential for personalising therapy by optimising drug dose and release profiles. This approach not only minimises systemic side effects but also provides a platform for targeted, efficient and more patient-friendly cancer treatment. This study establishes multiple emulsion as a promising carrier for dual-drug delivery system, bridging the gap between biological complexity and engineering precision. Future work will focus on in vivo evaluation and clinical validation to realise the potential of this approach in improving GBM patient outcomes.

Abstract Von Willebrand disease (VWD) is a metabolic disease characterised by a qualitative and/or quantitative deficiency of von Willebrand factor (VWF) a multimeric glycoprotein that mediates platelet adhesion in haemostatic processes. Pharmacokinetic (PK) models have been developed to characterise VWD metabolic pathways and to achieve a model-based diagnosis based on clinical data. However, current PK models cannot be calibrated from infusion tests data, and their calibration requires stressful 24-h long tests to be carried out on subjects to achieve a statistically satisfactory estimation of the individual haemostatic parameters. The objectives of this review chapter are the following: i) to provide a review on physiological modelling of VWD starting from the analysis of basic VWF mechanisms in the body; ii) to describe methods and modelling tools used to provide a model-based diagnosis of VWD and, consequently, a classification of complex VWD types; iii) to illustrate how model-based design of experiments (MBDoE) techniques can be applied to maximise the information that can be obtained from advanced clinical tests (DDAVP) but also from infusion tests used in VWD treatment where blood analogues are administered through single or multiple injections. Results show how PK models calibrated from clinical data can be used to estimate key haemostatic parameters in the diagnosis of the disease. Promising results on the application of MBDoE to design infusion tests show how the duration of clinical tests for the identification of key haemostatic parameters can significantly be reduced from 24 h to 2.5 h, with the potential to increase the acquired test information if multiple infusions can be managed.

Abstract This book chapter explores the recent advancements and future prospects of carbon materials, specifically char reinforced composites, in the field of energy harvesting. Carbon materials have gained significant attention due to their unique properties, such as high electrical conductivity, thermal stability, and mechanical strength. The incorporation of char, a carbon-rich residue obtained from the incomplete combustion or pyrolysis of organic matter, as a reinforcing agent in composites, has shown promising results in energy harvesting applications. The chapter begins with an overview of chars and their key properties that make them suitable for energy conversion and storage, including their ability to facilitate electron transfer and withstand harsh environmental conditions. The focus then shifts to char, highlighting its abundance as a by-product of various industrial processes and its potential as a reinforcement material in composites. Furthermore, the chapter discusses recent development and limitations faced by char and its composites in energy harvesting applications, including solar cells, thermoelectric generators, and supercapacitors. Finally, the chapter outlines the future directions and the potential for exploring novel carbon sources and the integration of char composites in emerging energy harvesting technologies.

Abstract Computational techniques encompass all informatic tools that extract information from large databases. Some remarkable applications include chemoinformatics, geoinformatics, and bioinformatics, involving advanced computing tools to explore and analyse chemical, geological and biological data. Mass spectrometry has provided tremendous input regarding useful instrumentations that enhance the relevance of computational methods. Here, we reviewed computational methods for advanced mass spectrometry particularly the OMIC which comprises of technologies that measure some characteristics of a large family of cellular molecules, such as genes, proteins and small metabolites.

Abstract Battery casing is a vital part of the electrical vehicles. The material used in it plays an important role in deciding the safety of the vehicle. This chapter refers to the polymer composites, which gives a good replacement for the currently used materials. The chapter explains the concept of battery pack along with its safety issues. It also explores various features and characteristics behind the vitality of polymer composites. The usage of polymer composites as an energy storage gives this material an upper hand.

Abstract Solar panels are essential for harvesting clean energy from the sun, but dust collection can dramatically limit their efficiency and power output. This challenge has led to the development of automated solar panel cleaning robots, a promising solution to ensure the continuous and optimal performance of solar arrays. One concern is that the cleaning method can cause long-term damage or degradation to the panels, such as cracking, if the cleaning robot is not positioned correctly on the solar panel. The risk of damage or degradation to solar panels during the cleaning process is a significant consideration for both manufacturers and operators of solar installations. Automated solar panel cleaning robots are valuable tools in maintaining the efficiency and longevity of solar arrays. However, their deployment requires a balanced approach that prioritizes effective cleaning while minimizing the risk of damage to the panels.

Abstract Haemophilia B, also known as the Christmas disease, named after Stephen Christmas the first patient diagnosed with this disease, is an inherited disease caused by a defect in the Factor IX Gene (F9). This defect manifests in insufficient production of the blood coagulation factor IX, resulting in excessive bleeding. The therapy which is mainly used involves prophylactic infusions of factor IX concentrate to improve the quality of life by minimising the episodes of bleeds. The main limitations of such a treatment plan are repeat infusions, product half-life, costs and inhibitor formation. The FIX concentration in healthy individuals is typically 90 nM and based upon the experience of the clinicians increasing FIX activity to 1–5 % of normal values has significant impact on patients’ quality of life. Therefore, even a partial correction the FIX deficiency would result in improved clinical outcomes and increase the chances of patients living a near-normal life. Gene therapy has the potential to deliver this, and the fact that haemophilia B is monogenic in nature further encourages the exploration of gene delivery for this disease. In this chapter, an integrated Pharmacokinetic (PK) – Pharmacodynamic (PD) model that has been developed using the clinical data is reported. The key features of the model are that it considers the pharmacological response, i.e., plasma FIX coagulation activity level as well as the toxicological response, i.e., the level of serum alanine aminotransferase. The simulation-based PK-PD modelling approach is then used for the initial dose selection to provide clinicians with better tools to simplify the decision-making process for designing more effective treatment plans, which can be tailored to maximise efficacy while minimising toxicity for individual patients.