Search Results (26)

Cultured meat is emerging as a sustainable alternative to conventional animal agriculture, with scaffolds playing a central role in supporting cellular attachment, growth, and tissue maturation. This review focuses on the development of gel-based hybrid biomaterials that meet the dual requirements of biocompatibility and food safety. We explore recent advances in the use of naturally derived gel-forming polymers such as gelatin, chitosan, cellulose, alginate, and plant-based proteins as the structural backbone for edible scaffolds. Particular attention is given to the integration of food-grade functional additives into hydrogel-based scaffolds. These include nanocellulose, dietary fibers, modified starches, polyphenols, and enzymatic crosslinkers such as transglutaminase, which enhance mechanical stability, rheological properties, and cell-guidance capabilities. Rather than focusing on fabrication methods or individual case studies, this review emphasizes the material-centric design strategies for building scalable, printable, and digestible gel scaffolds suitable for cultured meat production. By systemically evaluating the role of each component in structural reinforcement and biological interaction, this work provides a comprehensive frame work for designing next-generation edible scaffold systems. Nonetheless, the field continues to face challenges, including structural optimization, regulatory validation, and scale-up, which are critical for future implementation. Ultimately, hybrid gel-based scaffolds are positioned as a foundational technology for advancing the functionality, manufacturability, and consumer readiness of cultured meat products, distinguishing this work from previous reviews. Unlike previous reviews that have focused primarily on fabrication techniques or tissue engineering applications, this review provides a uniquely food-centric perspective by systematically evaluating the compositional design of hybrid hydrogel-based scaffolds with edibility, scalability, and consumer acceptance in mind. Through a comparative analysis of food-safe additives and naturally derived biopolymers, this review establishes a framework that bridges biomaterials science and food engineering to advance the practical realization of cultured meat products. Full article

In rapidly urbanizing environments, environmental stressors—such as air pollution, noise, heat, and green space depletion—substantially exacerbate public health burdens, contributing to the global rise of non-communicable diseases, particularly hypertension, cardiovascular disorders, and mental health conditions. Despite expanding research on green spaces and health (+76.9%, 2019–2025) and optimization and algorithmic approaches (+63.7%), the compounded and synergistic impacts of these stressors remain inadequately explored or addressed within current urban planning frameworks. This study presents a Mixed Methods Systematic Review (MMSR) to investigate the potential of AI-driven urban design optimizations in mitigating these multi-scalar environmental health risks. Specifically, it explores the complex interactions between urbanization, traffic-related pollutants, green infrastructure, and architectural intelligence, identifying critical gaps in the integration of computational optimization with nature-based solutions (NBS). To empirically substantiate these theoretical insights, this study draws on longitudinal 24 h dynamic blood pressure (BP) monitoring (3–9 months), revealing that chronic exposure to environmental noise (mean 79.84 dB) increases cardiovascular risk by approximately 1.8-fold. BP data (average 132/76 mmHg), along with observed hypertensive spikes (systolic > 172 mmHg, diastolic ≤ 101 mmHg), underscore the inadequacy of current urban design strategies in mitigating health risks. Based on these findings, this paper advocates for the integration of AI-driven approaches to optimize urban environments, offering actionable recommendations for developing adaptive, human-centric, and health-responsive urban planning frameworks that enhance resilience and public health in the face of accelerating urbanization. Full article

Machine learning (ML) algorithms are widely used in various fields, including cyber threat intelligence (CTI), financial technology (Fintech), and intrusion detection systems (IDSs). They automate security alert data analysis, enhancing attack detection, incident response, and threat mitigation. Fintech is particularly vulnerable to cyber-attacks and cyber espionage due to its data-centric nature. Because of this, it is essential to give priority to the classification of cyber-attacks to accomplish the most crucial attack detection. Improving ML models for superior prioritized recognition requires a comprehensive strategy that includes data preprocessing, enhancement, algorithm refinement, and customized assessment. To improve cyber-attack detection in the Fintech, CTI, and IDS sectors, it is necessary to develop an ML model that better recognizes the prioritized classes, thereby enhancing security against important types of threats. This research introduces adaptive incremental learning, which enables ML models to keep learning new information by looking at changing data from a data stream, improving their ability to accurately identify types of cyber-attacks with high priority. The Analytical Hierarchy Process (AHP) is suggested to help make the best decision by evaluating model performance based on prioritized classes using real multi-class datasets instead of artificially improved ones. The findings demonstrate that the ML model improved its ability to identify prioritized classes of cyber-attacks utilizing the ToN_IoT network dataset. The recall value for the “injection” class rose from 59.5% to 61.8%, the recall for the “password” class increased from 86.7% to 88.6%, and the recall for the “ransomware” class improved from 0% to 23.6%. Full article

Redox imbalance plays a pivotal role in the regulation of inflammation, influencing both the onset and progression of various inflammatory conditions. While the pro-inflammatory role of oxidative stress (OS) is well established, the impact of reductive stress (RS)—a condition marked by excessive reducing equivalents such as NADH, NADPH, and reduced glutathione (GSH)—remains underappreciated. This review offers a novel integrative perspective by analyzing how OS and RS act not merely in opposition, but as interconnected modulators of immune function. We explore the mechanisms through which OS activates inflammatory pathways, and how RS, when sustained, can paradoxically impair immune defense, alter redox-sensitive signaling, and contribute to disease progression. Emphasis is placed on the dynamic interplay between these redox extremes and their combined contribution to the pathogenesis of chronic inflammatory diseases, including autoimmune, cardiovascular, and neuroinflammatory disorders. Additionally, we evaluate therapeutic strategies that target redox homeostasis, arguing for a shift from antioxidant-centric treatments to approaches that consider the bidirectional nature of redox dysregulation. This framework may inform the development of more precise interventions for inflammation-related diseases. Full article

Interest in pulmonary/nasal routes for local delivery has significantly increased over the last decade owing to challenges faced in the delivery of molecules with poor solubility, systemic side effects, or new modalities such as biologics. This increasing interest has attracted new stakeholders to the field who have yet to explore inhaled drug product development. Contract development and manufacturing organizations (CDMOs) play a key role in supporting the development of drug products for inhalation, from early feasibility to post marketing. However, a critical gap exists for these newcomers: a clear, integrated, and a CDMO-centric roadmap for navigating the complexities of pulmonary/nasal drug product development. The purpose of this publication is to highlight the key aspects considered in the product development of inhaled dry powder products from a CDMO perspective, providing a novel and stepwise development strategy. A roadmap for the development of inhalable drug products is proposed with authors’ recommendations to facilitate the decision-making process, starting from the definition of the desired target product profile followed by dose selection in preclinical studies. The importance of understanding the nature of the API, whether a small molecule or a biologic, will be highlighted. Additionally, technical guidance on the choice of formulation (dry powder/liquid) will be provided with special focus on dry powders. Selection criteria for the particle engineering technology, mainly jet milling and spray drying, will also be discussed, including the advantages and limitations of such technologies, based on the authors’ industry expertise. Lastly, the paper will highlight the challenges and considerations for encapsulating both spray dried and jet milled powders. Unlike existing literature, this paper offers a unified framework that bridges preclinical, formulation, manufacturing, and encapsulation considerations, providing a practical tool for newcomers. Full article

Recent advancements in Natural Language Processing (NLP) technologies have been driven at an unprecedented pace by the development of Large Language Models (LLMs). However, challenges remain, such as generating responses that are misaligned with the intent of the question or producing incorrect answers. This paper analyzes various Prompt Engineering techniques for large-scale language models and identifies methods that can optimize response performance across different datasets without the need for extensive retraining or fine-tuning. In particular, we examine prominent Prompt Engineering techniques including In-Context Learning (ICL), Chain of Thought (CoT), Retrieval-Augmented Generation (RAG), Step-by-Step Reasoning (SSR), and Tree of Thought (ToT), and we apply these techniques to leading LLMs such as Gemma2, LlaMA3, and Mistral. The performance of these models was evaluated using the AI2 Reasoning Challenge (ARC), HellaSwag, Massive Multitask Language Understanding (MMLU), TruthfulQA, Winogrande, and Grade School Math (GSM8k) datasets across metrics such as BLEU, ROUGE, METEOR, BLEURT, and BERTScore. The experimental results indicate that the most suitable Prompt Engineering technique can vary depending on the characteristics of each dataset. Specifically, for datasets emphasizing mathematical and logical reasoning, Prompt Engineering strategies centered around CoT, SSR, and ToT were found to be advantageous. For datasets focusing on natural language understanding, ICL-centric strategies were more effective, while RAG-based strategies were beneficial for datasets where factual accuracy is crucial. However, it was also observed that the optimal combination of Prompt Engineering techniques could differ depending on the specific LLM, indicating that fine-tuning the Prompt Engineering approach to the model and dataset is essential for achieving the best performance. The findings indicate that as LLMs become more advanced, their reliance on Prompt Engineering (PE) techniques diminishes, yet the magnitude of their performance improvement when PE strategies are applied increases. Furthermore, these advanced models tend to depend less on ICL techniques while exhibiting a greater reliance on RAG strategies. It is also evident that implementing RAG with PE-based preprocessing yields superior performance enhancements compared to the mere application of RAG on raw data. Full article

This study examines the factors that lead students to consider or avoid a career in sales, focusing on behaviors and preferences during the transition period following the peak of the COVID-19 pandemic. Conducted in 2021, the study captures how the pandemic has changed traditional aspects of sales work, such as face-to-face interaction, and explores the lasting impact of these changes on young professionals. A sample of 671 business and engineering students participated in an online survey; data analysis was performed by using Partial Least Squares Structural Equation Modeling (SEM-PLS). Results show that intrinsic and social motivations enhance the perceived attractiveness of a sales career, which, in turn, impacts career intentions. Although empathy and COVID-19-related fears lack a direct effect on the intention to pursue a sales career, digital skills reinforce the connection between job attractiveness and career intentions in a digital-centric environment, having a moderating role. These findings emphasize the evolving nature of sales careers, highlighting the need to align career development strategies with young people’s intrinsic motivation and digital competencies. This study adds to the understanding of motivational factors in sales career decisions and offers valuable insights for employers seeking to attract motivated talent in a shifting industry landscape. Full article

Proteins are the foundation of life, and designing functional proteins remains a key challenge in biotechnology. Before the development of AlphaFold2, the focus of design was primarily on structure-centric approaches such as using the well-known open-source software Rosetta3. Following the development of AlphaFold2, deep-learning techniques for protein design gained prominence. This study proposes a new method to generate functional proteins using the diffusion model and ESM2 protein language model. Diffusion models, which are widely used in image and natural language generation, are used here for protein design, facilitating the controlled generation of new sequences. The ESM2 model, trained on the basis of large-scale protein sequence data, provides a deep understanding of the context of the sequence, thus improving the model’s ability to generate biologically relevant proteins. In this study, we used the Protein A-like peptide as a model study object, combined the diffusion model and the ESM2 model to generate new peptide sequences from minimal input data, and verified their biological activities through experiments such as the BLI affinity test. In conclusion, we developed a new method for protein design that provides a novel strategy to meet the challenges of generic protein generation. Full article

This paper reviews natural capital (NC) valuation approaches in the context of woodland, forest, and riparian ecosystems, emphasising the need for participatory methods to take priority over neoclassical economics approaches. Focusing on research carried out in Scotland, the study analyses findings according to a classification of natural capital initiatives that we have developed, building on ideas proposed by the UK ENCA initiative, a guideline proposed to help researchers and practitioners understand NC and take it into account in valuation, decision-making and policy. We have found that landscape-scale initiatives that address the relationships between people and place to inform value and decision-making beyond the economic (monetary) benefits generated by ecosystem services (ES) are becoming popular. For instance, recent methods employed to capture stakeholders’ non-utilitarian preferences include the use of participatory GIS mapping, scenario planning, and other participatory methods to identify, explore and quantify less tangible cultural ecosystem services (CES). The review shows that many studies provide information relevant to the formulation of a place-based NC approach, working towards the integration of contextual and relational values into land management decisions to help formulate management strategies that maximise ES delivery. Conversely, we have not found evidence of the integration of shared values arising from an eco-centric perspective of nature valuation into the more classical, instrumental value lens. Such an approach would help inform broader, overarching aspects of woodland and forest management that may foster more effective conservation and help to manage conflicts. Full article

Leveraging machine learning applications for predictive process control signifies a decisive advancement in manufacturing quality management, transitioning from traditional descriptive to predictive capability indices. This review highlights the growing importance of predictive process control, essential for quality assurance and the dynamic adaptability of production lines, which is paramount in satisfying stringent quality standards and evolving consumer demands. The investigation into the integration of comprehensive sensor networks and sophisticated algorithmic analytics enriches continuous improvement strategies, markedly enhancing the accuracy and efficiency of production quality monitoring and control mechanisms. By moving beyond the limits of statistical process control to predictive methods enabled by machine learning algorithms, the study presents a transformative leap in manufacturing processes. The presented findings illustrate the critical role of predictive algorithms in navigating the complexities of process variability, thereby ensuring consistent adherence to established quality specifications. This approach not only facilitates immediate and accurate product quality categorization, increasing overall operational efficiency, but also equips manufacturers to swiftly respond to the variable nature of manufacturing requirements. Furthermore, this research delves into the multifaceted impacts of predictive process control on the manufacturing ecosystem. The ability to predict process quality decrease before it occurs, the optimization of resource allocation, and the anticipation of production bottlenecks before they impact output are among the notable benefits of this technological evolution. These developments to predictive process control is instrumental in propelling the manufacturing industry toward a more agile, sustainable, and customer-centric future. This shift not only complements the industry’s drive toward comprehensive digitization but also promises significant strides in achieving superior process improvements and maintaining a competitive edge on the global market. Full article

Urban communities in environmentally sensitive areas face escalating challenges due to climate change and inadequate infrastructural support, particularly in underserved regions like southside Norfolk, Virginia. This area, characterized by its vulnerability to flooding and a predominantly low-income population, lacks equitable inclusion in broader urban flood protection plans. This research focuses on the development of community-centered resilience strategies through active engagement and collaboration with local residents. The methodology centered around building trust and understanding within the community through a series of interactions and events. This approach facilitated a two-way exchange of information, enabling the research team to gather crucial insights on community-valued assets, prevalent flooding issues, and preferred flood mitigation solutions. The engagement revealed a significant increase in community knowledge regarding climate change, sea level rise, and stormwater management. Residents expressed a strong preference for green infrastructure solutions, including rain gardens, permeable pavements, and living shorelines, alongside concerns about pollution and the need for infrastructure redesign. The outcomes of this community engagement have initiated plans to develop tailored, nature-based flooding solutions. These results are set to inform future urban planning and policy, offering insights to the City of Norfolk and the United States Army Corps of Engineers for potential redesigns of flood intervention strategies that are more inclusive and effective. A template for participatory research to inform coastal hazard management includes cross-sector collaboration, a long-term engagement commitment, and education and surveying opportunities to align solutions to lived, local experiences. This template allows for community trust building, which is especially important in environmental justice communities. The study highlights the importance of community involvement in urban resilience planning, demonstrating that local engagement is essential in shaping community-centric solutions and equitable environmental policies. Full article

In an era of rapidly advancing lighting technology and evolving public library roles, this study introduces a groundbreaking strategy for human-centric and integrative lighting asset management. Embracing both visual and non-visual effects, “integrative lighting” aims to enhance users’ physiological and psychological well-being. Despite technological progress, notably with LEDs, current asset management often lags, relying on reactionary measures rather than proactive strategies. As public libraries transform into dynamic learning hubs, the significance of indoor lighting, impacting both physical health and holistic well-being, cannot be understated. Yet, many existing solutions are based on controlled lab tests, bypassing the diverse real-world needs of public libraries. Aiming to explore and develop human-centric and integrative lighting asset management strategies to optimize lighting environments in public libraries, this research offers a cohesive approach encompassing context identification, a management framework, and a maturity assessment model. Additionally, this study highlights the synergy between the role of the lighting asset manager, ISO 55000 principles, and these foundational strategies. This holistic approach not only reinvents lighting in public libraries but also aligns it with the broader Sustainable Development Goals (SDGs), advocating for light as a conduit of comprehensive human betterment. The current study is primarily qualitative in nature. While this study is based on public libraries in Nordic countries, the implications and findings can be of interest and value to a broader international audience. Full article

Sustainable development and energy savings are crucial to the significant worldwide trend in smart city-related research and projects. In this regard, public lighting systems have great energy-saving potential. Nevertheless, while citizen engagement is a key element of most conceptualisations of smart cities, many smart lighting projects and systems fail to take account of the citizen’s viewpoint. Applying a citizen-centric lighting design model, the objective of this study is to examine the affective impressions of local residents of the luminous environments in their areas, taking account of the activities they carry out there. Kansei Engineering is employed to connect luminous design elements with citizens’ affective responses. Lighting environments in 18 urban spaces were evaluated by 310 local residents. The results show that subjective assessments in the evaluation of urban lighting environments can be explained by the following dimensions: Expressive-interesting, Innovative-efficient, Defined-sufficient, Formal-uniform and Glaring. The relationship of these dimensions to urban social activities shows that public lighting should generate, in local residents, sensations consistent with the nature of the activities. Urban lighting must create in the citizen a feeling of innovation (being up-to-date and contemporary) if it is to be seen as energy-saving and caring for the environment. These findings may be valuable for governments, architects, engineers, and lighting designers when developing strategies to ensure their designs are evaluated as being efficient, sustainable, and environmentally friendly. Full article

The coronavirus disease (COVID-19) pandemic has had widespread global effects. The advent of novel variants of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, along with the spread of diverse airborne viruses across different geographical locations, has caused reflective apprehension on a global scale. This resurgence emphasises the critical importance of carefully constructed structures installed with efficient ventilation systems, including both natural and mechanical ventilation techniques, as well as mixed-mode ventilation approaches in buildings. Building engineering and architectural designs must go beyond traditional considerations of economics and structural durability in order to protect public health and well-being. To attain a high quality of life, it is necessary to prioritise sustainability, energy efficiency, and the provision of safe, high-quality indoor environments. Empirical scientific investigations underscore the pivotal role played by conducive indoor environments in averting the transmission of viral diseases such as COVID-19 and mitigating challenges associated with sick building syndrome, primarily stemming from suboptimal indoor air quality. This work provides a summary and a SWOT (strength, weakness, opportunities, and threat) analysis of strategies designed for engineers, architects, and other experts in the field to implement. These strategies are intended for integration into new constructions and the retrofitting of extant structures. Their overarching objective is the minimisation of viral transmission within indoor spaces, accomplished in an energy-efficient manner consonant with sustainable development objectives. The significance of these strategies lies in their ability to impact changes to national and international building codes and regulations, strengthening infrastructures against probable airborne viral threats. Encompassing both object-centric and subject-centric approaches, these strategies collectively furnish a holistic framework for mitigating the dissemination of pathogens, exemplified by the SARS-CoV-2 virus and similar airborne viruses, across diverse typologies of buildings. Full article

This paper presents a comprehensive review of optimization practices in the corrugated board industry, which has recently experienced significant interest in using optimization methodologies driven by sustainable demands and increasing computational capabilities. The authors cover different review perspectives, including historical context, manufacturing applications, design optimization, and numerical optimization algorithms used. The main findings of this study indicate that the corrugated board industry has experienced a shift from trial-and-error and expert-driven approaches to data-centric strategies, particularly since the beginning of the 21st century. Interestingly, the industry has also adopted Multi-Disciplinary Optimization techniques from other fields, which demonstrates the importance of knowledge convergence across sectors. However, due to the complex nature of corrugated boards—including materials, design, and manufacturing processes—there is still much research to be done in this area. This work provides guidance for future research directions and encourages innovation and improvement in corrugated board optimization practices. In particular, the strong developments of material models for paper in recent years will boost the use of optimization tools in this field. Full article