Search Results (11,028)

To address circularity and resource recovery in modern structural applications, industry is seeking materials that are sustainable and lightweight. Although natural fiber-reinforced composites offer sustainability advantages, their mechanical properties remain inferior to those of synthetic fiber systems, limiting practical deployment. Flax fibers were selected as reinforcement due to their high specific stiffness, biodegradability, and wide availability. This study implements a three-level strategy to enhance the flexural performance of flax fiber-reinforced composites: at the process level, curing under distinct heating rates to promote a more uniform polymer network; at the material level, incorporation of a carbonaceous additive derived from fuel–oil furnace waste to strengthen interfacial adhesion; and at the structural level, adoption of a sandwich configuration with a recycled PET core to increase section bending inertia. Specimens were fabricated via vacuum-assisted resin transfer molding (VARTM) and tested using a three-point bending method. Mechanical testing shows clear improvements in flexural performance, with the sandwich architecture yielding the highest values and increasing flexural strength by up to 4.52 × relative to the other conditions. For the curing series, FTIR indicates greater reaction extent, evidenced by lower intensities of the epoxide ring at 915 cm⁻¹ and glycidyl/oxirane band near 972 cm⁻¹, together with a more pronounced C–O–C stretching region, consistent with the higher flexural response. While SEM observations revealed interfacial debonding at 5% FCB, a hybrid mechanism with crack deflection appeared at 10%. This transition created tortuous crack paths, consistent with the higher flexural strength and modulus at 10% FCB. A distinctive feature of this work is the integration of three reinforcement strategies—controlled curing, waste-derived carbon additive, and recycled PET sandwich design. This integration not only enhances the performance of natural fiber composites but also emphasizes sustainability by valorizing recycled and waste-derived resources, thereby supporting the development of greener composite materials. Full article

This paper investigates the complex interplay between European and pre-Hispanic urban traditions in shaping colonial urbanism across the Americas, with particular emphasis on the transformation of the City of Mexico atop the remnants of the ancient city of Mexico-Tenochtitlan. It contends that the development of the viceregal capital was not merely a straightforward transplantation of the Castilian urban model, but rather a process profoundly influenced—and in many respects enabled—by the sophisticated spatial organisation of the Mexica metropolis. The research examines how the foundational urban layout of Mexico-Tenochtitlan informed the design of the colonial city, highlighting both continuities and divergences between indigenous and Castilian urban frameworks, and analysing the fusion of these traditions in the formation of a novel urban entity. Employing a historical-analytical methodology, this article combines documentary research, comparative analysis of urban configurations from both cultures, and case studies of early colonial settlements. The findings suggest that the City of Mexico evolved into a paradigm of hybrid urbanism, wherein European planning doctrines were adapted and interwoven with enduring indigenous spatial logics and symbolic systems—a synthesis that not only characterised the viceregal capital but also established a precedent for urban development throughout Spanish America. Full article

Background/Objectives: Urinary system stone disease is an important health problem both clinically and economically due to its high recurrence rates. In this study, an innovative hybrid approach based on deep learning is proposed to predict the recurrence risk of stone disease. Methods: Patient data were divided into three subsets: anthropometric measurements (Part A), derived body composition indices (Part B), and other clinical and demographic information (Part C). Each data subset was processed with autoencoder models, and low-dimensional, meaningful features were extracted. The obtained features were combined, and the classification process was performed using four different machine learning algorithms: Extreme Gradient Boosting (XGBoost), Cubic Support Vector Machines (Cubic SVM), k-Nearest Neighbor algorithm (KNN), and Decision Tree (DT). Results: According to the experimental results, the highest classification performance was obtained with the XGBoost algorithm. The suggested approach adds to the literature by offering a novel solution that makes early risk calculation for stone disease recurrence easier. It also shows how well structural feature engineering and deep representation can be integrated in clinical prediction issues. Conclusions: Prediction of the stone recurrence risk in advance is of great importance both in terms of improving the quality of life of patients and reducing the unnecessary diagnostic evaluations along with lowering treatment costs. Full article

The concept of accountability is central to understanding how sustainable corporate governance (SCG) structures shape organizational behavior, legitimacy, and firm performance in the pursuit of sustainability goals. While widely invoked, accountability is often treated inconsistently across governance contexts—oscillating between technical compliance and ethical legitimacy. This paper provides a structured conceptual review of how accountability is framed and operationalized within sustainability governance, with a specific focus on its implications for sustainable performance, corporate sustainability strategies, and governance effectiveness. Based on a qualitative analysis of thirteen peer-reviewed articles published between 2006 and 2025, the study identifies three dominant conceptual clusters: compliance-oriented, legitimacy-oriented, and hybrid approaches. Each cluster reflects different accountability logics and governance mechanisms—ranging from ESG metrics and sustainability reporting frameworks to participatory forums and stakeholder engagement processes that support sustainable development. The article synthesizes theoretical contributions from institutional theory, stakeholder theory, and deliberative democracy to explore how accountability serves as a bridge between formal governance mechanisms and legitimacy claims. A conceptual framework is proposed to illustrate the tensions and complementarities between compliance-driven and legitimacy-driven governance models in sustainability contexts. By deepening the theoretical understanding of accountability in corporate sustainability, this review contributes to the literature on ESG governance, social and environmental reporting, and the legitimacy–performance nexus in corporate settings. The findings offer a foundation for advancing more inclusive, transparent, and sustainability-oriented corporate governance practices in response to global sustainability challenges and the Sustainable Development Goals (SDGs). Full article

An assembly line is a manufacturing process commonly used in the production of commodity goods. The assembly process is divided into elementary tasks that are sequentially performed at serially arranged workstations. Among the various challenges that must be addressed during the design and operation of an assembly line, the assembly line balancing problem involves the assignment of tasks to different workstations. In its simplest form, this problem aims to distribute assembly operations among the workstations efficiently. An efficient line is one that optimizes a specific objective function, usually associated with maximizing throughput or minimizing resource requirements. In this study, we adopt a bi-objective approach to find a Pareto set of efficient solutions balancing throughput and resource requirements. To address this problem, we propose a multi-objective evolutionary method, complemented by single- and multi-objective local search procedures that leverage a polynomially solvable case of the problem. We then compare the results of these methods, including their hybridizations, through a computational experiment demonstrating the ability to achieve high-quality solutions. Full article

Sustainable construction requires bio-based insulation materials that achieve low thermal conductivity without compromising mechanical performance. Poplar wood, which is locally abundant in France, serves as an effective carbon sink and represents a promising resource. While recent research has explored bulk wood delignification, the characterization of such modified materials remains insufficient for practical implementation. In this work, we report the development of gradient-delignified poplar wood through partial delignification using alcoholysis and sodium chlorite bleaching. This process produced a hybrid structure with delignified outer layers and a lignified core. Microscopic analyses revealed that lignin removal led to cell wall swelling and the formation of nano-scale pores. Compared to native poplar, the modified material showed lower transverse thermal conductivity (0.057 W·m⁻¹·K⁻¹), higher specific heat capacity (1.4 kJ·K⁻¹·kg⁻¹ at 20 °C), increased hygroscopicity, and reduced longitudinal compressive strength (15.9 MPa). The retention of the lignified core preserved dimensional stability and load-bearing capacity, thereby overcoming the limitations of complete delignification. In contrast to synthetic foams or mineral wools, these findings demonstrate that partial delignification can produce anisotropic wood-based insulation materials that combine thermal efficiency, mechanical stability, and biodegradability. This work highlights the potential of wood modification nanotechnology to reduce the carbon footprint of building materials. Full article

The advent of Large Language Models (LLMs) has revolutionised natural language processing, providing unprecedented capabilities in text generation and analysis. This paper examines the utility of Artificial-Intelligence-assisted (AI-assisted) content analysis (CA), supported by LLMs, as a methodological tool for research in Information Science (IS) and Cyber Security. It reviews current applications, methodological practices, and challenges, illustrating how LLMs can augment traditional approaches to qualitative data analysis. Key distinctions between CA and other qualitative methods are outlined, alongside the traditional steps involved in CA. To demonstrate relevance, examples from Information Science and Cyber Security are highlighted, along with a new example detailing the steps involved. A hybrid workflow is proposed that integrates human oversight with AI capabilities, grounded in the principles of Responsible AI. Within this model, human researchers remain central to guiding research design, interpretation, and ethical decision-making, while LLMs support efficiency and scalability. Both deductive and inductive AI-assisted frameworks are introduced. Overall, AI-assisted CA is presented as a valuable approach for advancing rigorous, replicable, and ethical scholarship in Information Science and Cyber Security. This paper contributes to prior LLM-assisted coding work, proposing that this hybrid model is preferred over a fully manual content analysis. Full article

Prefabricated building projects represent industrialized and intelligent construction through factory production, standardized design, and mechanized assembly. This study presents a multi-agent simulation approach to model the prefabricated construction process, allowing for the concurrent optimization of the prefabricated component (PC) splitting design and the construction organization plan through iterative simulation. (1) Employing a questionnaire survey, it identifies critical factors affecting schedule and cost from a design–construction coordination perspective. (2) Based on these findings, an agent-based model was developed incorporating PC installation, crane operations, and storage yard spatial constraints, along with interaction rules governing these agents. (3) Data interoperability was achieved among Revit, NetLogo3D and Navisworks. This integrated environment offers project managers digital management of design and construction plans, simulation support, and visualization tools. Simulation results confirm that a hybrid resource allocation strategy utilizing both tower cranes and mobile cranes enhances resource leveling, accelerates schedule performance, and improves cost efficiency. Full article

Overcoming the strength–ductility trade-off in conventional aluminum matrix composites (AMCs) remains a significant challenge. This study employs dual-scale hybrid reinforcement particles comprising micron-sized Cu and nano-sized Ti, alongside bimodal micro-sized pure Al powders as matrix fillers. The AMCs were fabricated through ball milling (BM) combined with multi-pass friction stir processing (FSP). The homogenously distributed hybrid reinforcement particles generate an integrated composite region consisting of both coarse-grained (CG) and fine-grained (FG) structures, demonstrating enhanced material characteristics. The interwoven network of coarse- and fine-crystalline domains constructs a heterogeneous architecture that enables simultaneous improvement in both strength and ductility properties. The micron-Cu acts as a skeletal support within the matrix, enhancing load transfer efficiency and effectively hindering dislocation motion. The nano-Ti and in situ intermetallics facilitate grain refinement via the pinning effect and promote heterogeneous nucleation, which contributes to stress dispersion and dislocation obstruction. The addition of dual-scale micron-sized pure Al powder particles promotes the formation of the heterogeneous architecture, which enhances the balancing of strength and ductility in the composite. Following compositing (Al₁₀-5Cu-10Ti-10Al₂₀), the alloy exhibits an ultimate tensile strength (UST) of 267 MPa, a hardness of 98 HV, and an elongation of 16.7%, representing increases of 193.4%, 226.7%, and 9.9%, respectively, relative to the base metal. Full article

The growing demand for sustainable and eco-friendly technologies has driven the development of green and bio-based synthesis methods for metallic nanoparticles. Among these, the microbial synthesis of silver nanoparticles (AgNPs) has emerged as a promising alternative to conventional chemical methods, which often rely on hazardous reagents and harsh conditions. Bacteria and fungi are particularly attractive due to their ability to produce AgNPs with tunable size, shape, and surface properties through natural enzymatic and metabolic processes. This review provides a comparative analysis of bacterial and fungal synthesis routes, focusing on their distinct advantages, limitations, and optimal applications. Bacterial synthesis offers faster growth, simpler culture requirements, and greater potential for genetic manipulation, enabling precise control over nanoparticle (NP) characteristics. In contrast, fungal synthesis typically yields higher nanoparticle stability and is well suited for extracellular, scalable production. The review also summarizes key synthesis parameters (e.g., pH, temperature, reaction time), addresses reproducibility and scalability challenges, and highlights emerging research areas, including antibacterial bio-hybrid materials and bacterial-supported metallic catalysts. Overall, this comparative perspective provides a clear framework for selecting appropriate microbial systems for different technological applications and identifies future research directions to advance green nanotechnology. Full article

Rice blast, caused by Magnaporthe oryzae (M. oryzae), severely threatens global rice production with substantial yield losses, endangering food security and driving demand for resistant varieties. Fuhui2165 (FH2165), an elite restorer line with stable blast resistance, superior agronomic traits, and high grain quality, is valuable for hybrid breeding, but its resistance mechanisms remain unclear. In this study, we investigated the rice blast resistance and underlying mechanisms in FH2165 and its parental lines (Huahangsimiao/HHSM, Minghui86/MH86, and Shuhui527/SH527) using transcriptome sequencing analysis. Phenotypic analysis revealed that FH2165 and HHSM exhibited stronger resistance compared to MH86 and SH527. Differential expression analysis identified 3886, 2513, 3390, and 4678 differentially expressed genes (DEGs) in FH2165, HHSM, MH86, and SH527, respectively. Gene Ontology (GO) enrichment analysis highlighted DEGs associated with chloroplasts, plastids, thylakoids, and related cellular components. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis identified significant enrichment in pathways such as carbon metabolism, amino acid biosynthesis, and photosynthesis. This suggested that defense strategies could involve energy reprogramming and the synthesis of secondary metabolites. Additionally, the DEGs co-expressed specifically in FH2165 and HHSM were enriched in functions related to RNA processing, GTP binding, and L-ascorbic acid binding, with purine metabolism playing a role in the regulation of energy and signaling. These findings elucidated the critical metabolic and signaling networks that underlie the blast resistance of FH2165 and offered potential targets for breeding high-yield, disease-resistant hybrid rice varieties. Full article

Electrocoagulation (EC) employing aluminum–aluminum (Al–Al) electrodes was investigated for hospital wastewater treatment, targeting the removal of turbidity, soluble chemical oxygen demand (sCOD), and total dissolved solids (TDS). A hybrid modeling framework integrating response surface methodology (RSM) and artificial neural networks (ANN) was developed to enhance predictive reliability and identify energy-efficient operating conditions. A Box–Behnken design with 15 experimental runs evaluated the effects of pH, current density, and electrolysis time. Multi-response optimization determined the overall optimal conditions at pH 7.0, current density 20 mA/cm², and electrolysis time 75 min, achieving 94.5% turbidity, 69.8% sCOD, and 19.1% TDS removal with a low energy consumption of 0.34 kWh/m³. The hybrid RSM–ANN model exhibited high predictive accuracy (R² > 97%), outperforming standalone RSM models, with ANN more effectively capturing nonlinear relationships, particularly for TDS. The results confirm that EC with Al–Al electrodes represent a technically promising and energy-efficient approach for decentralized hospital wastewater treatment, and that the hybrid modeling framework provides a reliable optimization and prediction tool to support process scale-up and sustainable water reuse. Full article

Background: Angiogenesis, the physiological process by which new blood vessels originate from pre-existing ones, can be triggered by tumor cells to promote the growth, survival, and progression of cancer. Malignant tumors require a constant blood supply to meet their needs for oxygen and nutrients, making angiogenesis a key process in tumor development. Its pathologic role is caused by the dysregulation of signaling pathways, particularly those involving VEGFR-2, a key mediator of angiogenesis, and the K-RAS G12C mutant, a promoter of VEGF expression. Given their critical involvement in tumor progression, these targets represent promising candidates for new cancer therapies. Methods and Results: In this study, we applied an in silico hybrid and hierarchical virtual screening approach to identify potential dual VEGFR-2/K-RAS G12C inhibitors with anticancer and antiangiogenic properties. To this end, we screened the National Cancer Institute (NCI) database through ADME filtering tools. The refined dataset was then submitted to the ligand-based Biotarget Predictor Tool (BPT) in a multitarget mode. Subsequently, structure-based analysis, including molecular docking studies on VEGFR and K-RAS G12C, was performed to investigate the interactions of the most promising small molecules with both targets. Conclusions: Finally, the molecular dynamics simulations suggested compound 737734 as a promising small molecule with high stability in complex with both VEGFR-2 and K-RAS G12C, highlighting its potential as a dual-target inhibitor for cancer therapy. Full article

In the context of deep AI–design integration, traditional methods struggle to translate multi-source requirements into sustainable engineering solutions while balancing innovation with practicality. This study proposes AQTA, an intelligent design framework that integrates Analytic Hierarchy Process (AHP), Quality Function Deployment (QFD), Theory of Inventive Problem Solving (TRIZ), and AI-Generated Content (AIGC) to enable sustainable product development. AQTA employs a four-stage closed-loop process: requirement analysis, contradiction resolution, solution generation, and validation. QFD and AHP quantify user and sustainability requirements to identify key contradictions, TRIZ resolves technical conflicts and stimulates innovative solutions, while AIGC generates eco-efficient visual concepts through prompt engineering. Multi-criteria decision-making supports evaluation and optimization based on environmental and economic indicators. Empirical studies demonstrate that AQTA significantly enhances innovation quality, design efficiency, and sustainability performance. The framework provides a replicable, hybrid ‘theory-driven + AI-generated’ methodology, which is validated through the case study of urban fire trucks, contributing to sustainable manufacturing practices in the intelligent era. Full article

The pH shift generated by mixing alkaline shellac (SH) and acidic chitosan (CS) solutions may drive the formation of nanocomposites with interfacial activity. However, how the solution addition sequence affects their formation and properties remains unclear. In this study, we systematically investigated the influence of addition order on the formation, physicochemical properties, and interfacial activity of SH-CS nanocomposites. The results showed that pH variation during mixing promoted nanocomposite formation, with optimal electrostatic interactions occurring at a final pH near 5.0. The most efficient assembly was achieved at an SH: CS mass ratio of 2:3. FTIR and dissociation experiments confirmed that hydrogen bonding, hydrophobic effects, and electrostatic interactions jointly governed the assembly process. Importantly, the addition sequence determined the nanocomposite structure: adding SH to CS produced core–shell structures, whereas the reverse order yielded co-assembled hybrid nanocomposites. These distinct structures directly impacted interfacial behavior. The co-assembled nanocomposites effectively balanced the inherent hydrophobicity of SH and hydrophilicity of CS, achieving moderate wettability. This balance significantly reduced interfacial tension, thereby enhancing emulsifying performance. Overall, this study underscores the critical role of addition sequence in tailoring the properties of pH-driven SH-CS nanocomposites and highlights their strong potential as high-performance Pickering emulsifiers. Full article