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Large-scale muscle injury in humans initiates a complex regeneration process, as not only the muscular, but also the vascular and neuro-muscular compartments have to be repaired. Conventional therapeutic strategies often fall short of reaching the desired functional outcome, due to the inherent complexity of natural skeletal muscle. Tissue engineering offers a promising alternative treatment strategy, aiming to achieve an engineered tissue close to natural tissue composition and function, able to induce long-term, functional regeneration after in vivo implantation. This review aims to summarize the latest approaches of tissue engineering skeletal muscle, with specific attention toward fabrication, neuro-angiogenesis, multicellularity and the biochemical cues that adjuvate the regeneration process.

Purpose This paper aims to propose a liquidity management solution for Islamic financial institutions (IFIs) that concurs with sustainable development and financial stability. Design/methodology/approach The study is a qualitative research. It uses the exploratory research methodology, specifically the content analysis approach, to gather primary data and identify and interpret relevant secondary data and Sharīʿah concepts. The purpose is to develop a liquidity management solution for IFIs. The proposal is based on the Unleveraged Green Investment Trust (UGIT) model, which is consistent with Basel III regulatory requirements. In developing the UGIT model, the exploratory research was complemented by a case study to examine the UGIT solution for the particular case of renewable energy. Findings The model demonstrates how financial innovation can meet both financial stability and sustainable development objectives, thereby achieving the spirit of Islamic finance. The structure further highlights the importance of regulatory and fiscal frameworks to enhance liquidity management and investor appeal for green financial instruments. Originality/value This study suggests a structure of UGIT to enable IFIs to meet their liquidity management needs while promoting sustainable development.

This open access book brings together a collection of cutting-edge insights into how action can and is already being taken against climate change at multiple levels of our societies, amidst growing calls for transformative and inclusive climate action. In an era of increasing recognition regarding climate and ecological breakdown, this book offers hope, inspiration and analyses for multi-level climate action, spanning varied communities, places, spaces, agents and disciplines, demonstrating how the energy and dynamism of local scales are a powerful resource in turning the tide. Interconnected yet conceptually distinct, the book’s three sections span multiple levels of analysis, interrogating diverse perspectives and practices inherent to the vivid tapestry of climate action emerging locally, nationally and internationally. Delivered in collaboration with the UK’s ‘Place-Based Climate Action Network’, chapters are drawn from a wide range of authors with varying backgrounds spread across academia, policy and practice.

The phenomena of climate change transcend all national and regional boundaries. To address this complex challenge, we must determine the areas of the country of interest, in this case, Greece, that have been most adversely affected by climate. Greece is surrounded by water, and a significant part of its GDP is derived from the marine and maritime industries, including tourism. Since the start of the IntelComp project, a Preparatory Living Lab (PLL) has been planned and delivered, feeding into the development of the IntelComp platform and the Living Lab on Climate Change Adaptation. The study's results lead to the conclusion that one of the most important challenges in tackling climate change is the decarbonisation challenge, specifically the shift to renewable energy sources and the investments that must be made. Several EU and national policy frameworks, including the European Green Deal, the Climate Law, the National Long-term Strategy for 2050 (on the Climate and Energy), highlight the decarbonisation as one of the major challenges in the climate change pledge. This will be the primary subject of the IntelComp climate change case study. PLLs also led to the identification of policy questions and useful data sources to aid the IntelComp project's launch. While previous research on co-production has primarily focused on involving citizens through public participation processes in order to gain their support, trust, and insights in structured decision-making processes, our approach opens a new channel for incorporating external knowledge into problem-solving processes. The IntelComp project will aid in policy development by providing pertinent tools co-developed with the final users that will provide insights and analysis in the field of STI (Science, Technology, Innovation) encompassing all of the Energy areas mentioned above.

Industrial producers face the task of optimizing production process in an attempt to achieve the desired quality such as mechanical properties with the lowest energy consumption. In industrial carbon fiber production, the fibers are processed in bundles containing (batches) several thousand filaments and consequently the energy optimization will be a stochastic process as it involves uncertainty, imprecision or randomness. This paper presents a stochastic optimization model to reduce energy consumption a given range of desired mechanical properties. Several processing condition sets are developed and for each set of conditions, 50 samples of fiber are analyzed for their tensile strength and modulus. The energy consumption during production of the samples is carefully monitored on the processing equipment. Then, five standard distribution functions are examined to determine those which can best describe the distribution of mechanical properties of filaments. To verify the distribution goodness of fit and correlation statistics, the Kolmogorov-Smirnov test is used. In order to estimate the selected distribution (Weibull) parameters, the maximum likelihood, least square and genetic algorithm methods are compared. An array of factors including the sample size, the confidence level, and relative error of estimated parameters are used for evaluating the tensile strength and modulus properties. The energy consumption and N2 gas cost are modeled by Convex Hull method. Finally, in order to optimize the carbon fiber production quality and its energy consumption and total cost, mixed integer linear programming is utilized. The results show that using the stochastic optimization models, we are able to predict the production quality in a given range and minimize the energy consumption of its industrial process.