Search Results (12,913)

The rapid expansion of peptide libraries and the increasing functional diversity of peptides have highlighted the significance of predicting the multifunctional properties of peptides in bioinformatics research. Although supervised learning methods have made advancements, they typically necessitate substantial amounts of labeled data for yielding accurate prediction. This study presents MvAl-MFP, a multi-label active learning approach that incorporates multiple feature views of peptides. This method takes advantage of the natural properties of multi-view representation for amino acid sequences, meets the requirement of the query-by-committee (QBC) active learning paradigm, and further significantly diminishes the requirement for labeled samples while training high-performing models. First, MvAl-MFP generates nine distinct feature views for a few labeled peptide amino acid sequences by considering various peptide characteristics, including amino acid composition, physicochemical properties, evolutionary information, etc. Then, on each independent view, a multi-label classifier is trained based on the labeled samples. Next, a QBC strategy based on the average entropy of predictions across all trained classifiers is adopted to select a specific number of most valuable unlabeled samples to submit them to human experts for labeling by wet-lab experiments. Finally, the aforementioned procedure is iteratively conducted with a constantly expanding labeled set and updating classifiers until it meets the default stopping criterion. The experiments are conducted on a dataset of multifunctional therapeutic peptides annotated with eight functional labels, including anti-bacterial properties, anti-inflammatory properties, anti-cancer properties, etc. The results clearly demonstrate the superiority of the proposed MvAl-MFP method, as it can rapidly improve prediction performance while only labeling a small number of samples. It provides an effective tool for more precise multifunctional peptide prediction while lowering the cost of wet-lab experiments. Full article

This study evaluates the effectiveness of the B Impact Assessment (BIA) as a catalyst for integrating sustainability into industrial firms through a qualitative case study of LC Paper, the first B Corp-certified tissue manufacturer globally and a pioneer in applying BIA in the pulp and paper sector. Based on semi-structured interviews, organizational documents, and direct observation, this study examines how BIA influences corporate governance, environmental practices, and stakeholder engagement. The findings show that BIA fosters structured goal setting and the implementation of measurable actions aligned with environmental stewardship, social responsibility, and economic resilience. Tangible outcomes include improved stakeholder trust, internal transparency, and employee development, while implementation challenges such as resource allocation and procedural complexity are also reported. Although the single-case design limits generalizability, this study identifies mechanisms transferable to other firms, particularly those in environmentally intensive sectors. The case studied also illustrates how leadership commitment, participatory governance, and data-driven tools facilitate the operationalization of sustainability. By integrating stakeholder and institutional theory, this study contributes conceptually to understanding certification frameworks as tools for embedding sustainability. This research offers both theoretical and practical insights into how firms can align strategy and impact, expanding the application of BIA beyond early adopters and into traditional industrial contexts. Full article

Widely employed in diverse engineering applications, stiffened steel plates are often subjected to biaxial compressive loads. Under these conditions, buckling may occur, initially within the elastic range but potentially progressing into the elasto-plastic domain, which can lead to permanent deformations or structural collapse. To increase the ultimate buckling stress of plates, the implementation of longitudinal and transverse stiffeners is effective; however, this complexity makes analytical stress calculations challenging. As a result, numerical methods like the Finite Element Method (FEM) are attractive alternatives. In this study, the Constructal Design method and the Exhaustive Search technique were employed and associated with the FEM to optimize the geometric configuration of stiffened plates. A steel plate without stiffeners was considered, and 30% of its volume was redistributed into stiffeners, creating multiple configuration scenarios. The objective was to investigate how different arrangements and geometries of stiffeners affect the ultimate buckling stress under biaxial compressive loading. Among the configurations evaluated, the optimal design featured four longitudinal and two transverse stiffeners, with a height-to-thickness ratio of 4.80. This configuration significantly improved the performance, achieving an ultimate buckling stress 472% higher than the unstiffened reference plate. In contrast, the worst stiffened configuration led to a 57% reduction in performance, showing that not all stiffening strategies are beneficial. These results demonstrate that geometric optimization of stiffeners can significantly enhance the structural performance of steel plates under biaxial compression, even without increasing material usage. The approach also revealed that intermediate slenderness values lead to better stress distribution and delayed local buckling. Therefore, the methodology adopted in this work provides a practical and effective tool for the design of more efficient stiffened plates. Full article

Background: Mediastinal staging plays a critical role in guiding treatment decisions for non-small cell lung cancer (NSCLC). While mediastinoscopy has been the gold standard for assessing mediastinal lymph node involvement, endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) has emerged as a minimally invasive alternative with comparable diagnostic accuracy. This systematic review evaluates the diagnostic performance, safety, cost-effectiveness, and feasibility of EBUS-TBNA versus mediastinoscopy for mediastinal staging. Methods: A systematic literature review was conducted in accordance with PRISMA guidelines, including searches in Medline, Scopus, EMBASE, and Cochrane databases for studies published from 2010 onwards. A total of 1542 studies were identified, and after removing duplicates and applying eligibility criteria, 100 studies were included for detailed analysis. The extracted data focused on sensitivity, specificity, complications, economic impact, and patient outcomes. Results: EBUS-TBNA demonstrated high sensitivity (85–94%) and specificity (~100%), making it an effective first-line modality for NSCLC staging. Mediastinoscopy remained highly specific (~100%) but exhibited slightly lower sensitivity (86–90%). EBUS-TBNA had a lower complication rate (~2%) and was more cost-effective, while mediastinoscopy provided larger biopsy samples, essential for molecular and histological analyses. The need for general anaesthesia, longer hospital stays, and increased procedural costs make mediastinoscopy less favourable as an initial approach. Combining both techniques in select cases enhanced overall staging accuracy, reducing false negatives and improving diagnostic confidence. Conclusions: EBUS-TBNA has become the preferred first-line mediastinal staging method due to its minimally invasive approach, high diagnostic accuracy, and lower cost. However, mediastinoscopy remains crucial in cases requiring posterior mediastinal node assessment or larger tissue samples. The integration of both techniques in a stepwise diagnostic strategy offers the highest accuracy while minimizing risks and costs. Given the lower hospitalization rates and economic benefits associated with EBUS-TBNA, its widespread adoption may contribute to more efficient resource utilization in healthcare systems. Full article

Ruminant livestock production plays a crucial role in the agricultural systems of Sub-Saharan Africa, significantly supporting rural livelihoods through income generation, improved nutrition, and employment opportunities. Despite its importance, the sector continues to face substantial challenges, such as low feed quality, seasonal feed shortages, and climate-related stresses, all of which limit productivity and sustainability. Considering these challenges, the adoption of natural feed additives has emerged as a promising strategy to enhance animal performance, optimise nutrient utilisation, and mitigate environmental impacts, including the reduction of enteric methane emissions. This review underscores the significant potential of natural feed additives such as plant extracts, essential oils, probiotics, and mineral-based supplements such as fossil shell flour as sustainable alternatives to conventional growth promoters in ruminant production systems across the region. All available documented evidence on the topic from 2000 to 2024 was collated and synthesised through standardised methods of systematic review protocol—PRISMA. Out of 319 research papers downloaded, six were included and analysed directly or indirectly in this study. The results show that the addition of feed additives to ruminant diets in all the studies reviewed significantly (p < 0.05) improved growth parameters such as average daily growth (ADG), feed intake, and feed conversion ratio (FCR) compared to the control group. However, no significant (p > 0.05) effect was found on cold carcass weight (CCW), meat percentage, fat percentage, bone percentage, or intramuscular fat (IMF%) compared to the control. The available evidence indicates that these additives can provide tangible benefits, including improved growth performance, better feed efficiency, enhanced immune responses, and superior meat quality, while also supporting environmental sustainability by reducing nitrogen excretion and decreasing dependence on antimicrobial agents. Full article

Constrained multi-objective optimization problems (CMOPs) require optimizing multiple conflicting objectives while satisfying complex constraints. These constraints generate infeasible regions that challenge traditional algorithms in balancing feasibility and Pareto frontier diversity. chemical reaction optimization (CRO) effectively balances global exploration and local exploitation through molecular collision reactions and energy management, thereby enhancing search efficiency. However, standard CRO variants often struggle with CMOPs due to the absence of specialized constraint-handling mechanisms. To address these challenges, this paper integrates the CRO collision reaction mechanism with an existing evolutionary computational framework to design a dual-stage and dual-population chemical reaction optimization (DDCRO) algorithm. This approach employs a staged optimization strategy, which divides population evolution into two phases. The first phase focuses on objective optimization to enhance population diversity, and the second prioritizes constraint satisfaction to accelerate convergence toward the constrained Pareto front. Furthermore, to leverage the infeasible solutions’ guiding potential during the search, DDCRO adopts a two-population strategy. At each stage, the main population tackles the original constrained problem, while the auxiliary population addresses the corresponding unconstrained version. A weak complementary mechanism facilitates information sharing between populations, which enhances search efficiency and algorithmic robustness. Comparative tests on multiple test suites reveal that DDCRO achieves optimal IGD/HV values in 53% of test problems. The proposed algorithm outperforms other state-of-the-art algorithms in both convergence and population diversity. Full article

This longitudinal study examines how perceived brand globalness (PBG) influenced sustainable purchase intentions (SPI) between 2021 and 2024, incorporating factors such as perceived brand quality (PBQ), perceived brand prestige (PBP), brand–cause fit (BCF), and the moderating effect of consumer ethnocentrism (CE). Using survey responses from 415 participants, the study employed partial least squares structural equation modeling (PLS-SEM) via SmartPLS4. The findings reveal that CE emerged as significant in 2024, while PBP’s impact on SPI weakened—suggesting a growing consumer association of prestige with sustainability. Heightened post-pandemic ethical awareness further underscores the importance of brand values. Contrary to earlier research indicating low CE in developing markets, the 2024 results demonstrate an unexpected rise in CE, highlighting its evolving significance. Overall, the study emphasizes the necessity for global brands to adopt sustainable, locally attuned strategies to succeed in developing countries. Full article

The cement industry is a significant contributor to global carbon dioxide emissions, primarily due to the energy demands of its production process and its reliance on clinker, a material formed through the high-temperature calcination of limestone. Strategies to reduce emissions include the adoption of low-carbon fuels, the use of carbon capture and storage (CCS) technologies, and the integration of supplementary cementitious materials (SCMs) to reduce the clinker content. The effectiveness of these measures depends on a complex set of interactions involving technological feasibility, market dynamics, and regulatory frameworks. This study presents a system dynamics model designed to assess how various decarbonization approaches influence long-term emission trends within the cement industry. The model accounts for supply chains, production technologies, market adoption rates, and changes in cement production costs. This study then analyzes a number of scenarios where there is large-scale sustained investment in each of three carbon mitigation strategies. The results show that CCS by itself allows the cement industry to achieve carbon neutrality, but the high capital investment results in a large cost increase for cement. A combined approach using alternative fuels and SCMs was found to achieve a large carbon reduction without a sustained increase in cement prices, highlighting the trade-offs between cost, effectiveness, and system-wide interactions. Full article

In scientific research and engineering practice, the design of deep spiking neural network (DSNN) architectures remains a complex task that heavily relies on the expertise and experience of professionals. These architectures often require repeated adjustments and modifications based on factors such as the DSNN’s performance, resulting in significant consumption of human and hardware resources. To address these challenges, this paper proposes an innovative evolutionary membrane algorithm for optimizing DSNN architectures. This algorithm automates the construction and design of promising network models, thereby reducing reliance on manual tuning. More specifically, the architecture of DSNN is transformed into the search space of the proposed evolutionary membrane algorithm. The proposed algorithm thoroughly explores the impact of hyperparameters, such as the candidate operation blocks of DSNN, to identify optimal configurations. Additionally, an early stopping strategy is adopted in the performance evaluation phase to mitigate the time loss caused by objective evaluations, further enhancing efficiency. The optimal models identified by the proposed algorithm were evaluated on the CIFAR-10 and CIFAR-100 datasets. The experimental results demonstrate the effectiveness of the proposed algorithm, showing significant improvements in accuracy compared to the existing state-of-the-art methods. This work highlights the potential of evolutionary membrane algorithms to streamline the design and optimization of DSNN architectures, offering a novel and efficient approach to address the challenges in the applications of automated parameter optimization for DSNN. Full article

This study aims to evaluate the level of awareness and practical adoption of green wall systems in the Nigerian construction industry. It seeks to examine the current state of green wall implementation and recommend strategies to enhance their integration into construction practices among Nigerian construction professionals. A thorough review of the existing literature was conducted to identify different types of green wall systems. Insights from this review informed the design of a structured questionnaire, which was distributed to construction professionals based in Lagos State. The data collected were analyzed using statistical tests. The study reveals that while there is generally high awareness of green wall systems among Nigerian construction professionals, the practical use remains low, with just 8 out of the 18 systems being actively implemented, eclipsing the mean value of 3.0. The findings underscore the need for targeted education, industry incentives, and increased advocacy to encourage the use of green wall systems in the Nigerian construction sector. The results have significant implications for the Nigerian construction industry. The limited awareness and adoption of green wall systems highlight the need for strategic actions from policymakers, industry leaders and educational institutions. Promoting the use of green walls could drive more sustainable building practices, improve environmental outcomes and support the broader goals of decarbonization and circularity in construction. This research adds to the body of knowledge on sustainable construction by offering a detailed evaluation of green wall awareness and adoption within the Nigerian context. While green wall systems have been studied globally, this research provides a regional perspective, which in this case focuses on Lagos State. The study’s recognition of the gap between awareness and implementation highlights an important area for future research and industry development. Full article

In this paper, we propose a fuzzy formulation of the classic Frankot–Chellappa algorithm by which surfaces can be reconstructed using normal vectors. In the fuzzy formulation, the surface normal vectors may be uncertain or ambiguous, yielding a fuzzy Poisson partial differential equation that requires appropriate definitions of fuzzy derivatives. The solution of the resulting fuzzy model is approached by adopting a fuzzy variant of the discrete sine transform, which results in a fast and robust algorithm for surface reconstruction. An adaptive defuzzification strategy is also introduced to improve noise handling in highly uncertain regions. In experiments, we demonstrate that our fuzzy Frankot–Chellappa algorithm achieves accuracy on par with the classic approach for smooth surfaces and offers improved robustness in the presence of noisy normal data. We also show that it can naturally handle missing data (such as gaps) in the normal field by filling them using neighboring information. Full article

Background: Rapid urbanization has intensified the challenges of freight transport, particularly in last-mile (LM) delivery, leading to rising costs and environmental externalities. Autonomous vehicles (AVs) have emerged as a promising innovation to address these issues. While much of the existing literature emphasizes business and operational perspectives, this study focuses on the acceptance of AVs from the standpoint of e-consumers—individuals who make purchases via digital platforms—in an emerging market context. Methods: Grounded in an extended Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), which is specifically suited to consumer-focused technology adoption research, this study incorporates five constructs tailored to AV adoption. Structural Equation Modeling (SEM) was applied to survey data collected from 304 e-consumers in Northeast Brazil. Results: The findings reveal that performance expectancy, hedonic motivation, and environmental awareness exert significant positive effects on acceptance and intention to use AVs for LM delivery. Social influence shows a weaker, yet still positive, impact. Importantly, price sensitivity exhibits a minimal effect, suggesting that while consumers are generally cost-conscious, perceived value may outweigh price concerns in early adoption stages. Conclusions: These results offer valuable insights for policymakers and logistics providers aiming to implement consumer-oriented, cost-effective AV solutions in LM delivery, particularly in emerging economies. The findings emphasize the need for strategies that highlight the practical, emotional, and environmental benefits of AVs to foster market acceptance. Full article

In UGC-based knowledge trading platforms, the abundance of personalized content often leads to varying quality levels. By incorporating embedded advertising, platforms can incentivize knowledge producers to produce high-quality content; however, the uncertainty in managing embedded advertisements increases the complexity of pricing knowledge products. This paper examines the impact of embedded advertising on the pricing of knowledge products, aims to maximize the profits of both knowledge producer and the platform. Based on Stackelberg game theory, two pricing decision models are developed under different advertising management modes: the platform-managed mode (where the platform determines the advertising intensity) and the advertiser-managed mode (where the advertiser determines the advertising intensity). The study analyzes the effects of UGC product quality, consumer sensitivity to advertising, and power structure on knowledge product pricing, and derives threshold conditions for optimal pricing. The results indicate that (1) When the quality of UGC knowledge product exceeds a certain threshold, platform-managed advertising becomes profitable. (2) Under the platform-managed mode, both the platform and knowledge producer can adopt price-increasing strategies to enhance profits. (3) Under the advertiser-managed mode, the platform can leverage differences in power structure to optimize revenue, while knowledge producer can actively enhance his pricing power to achieve mutual benefits with the platform. This study provides theoretical support and practical guidance for advertising cooperation mechanisms and pricing strategies for knowledge products in UGC-based knowledge trading platforms. Full article

The growing concern over environmental sustainability has intensified the focus on consumers’ perceptions of eco-friendly food packaging, especially among younger generations. This study aims to investigate the attitudes, preferences, and barriers faced by Greek university students regarding sustainable food packaging, a demographic considered pivotal for driving future consumption trends. An online questionnaire assessing perceptions, preferences, and behaviours related to sustainable packaging was administered to students, with responses measured on a five-point Likert scale. Three hundred and sixty-four students took part in this survey, with the majority (60%) of them being female. Principal component analysis was employed to identify underlying factors influencing perceptions, and k-means cluster analysis revealed two consumer segments: “Innovatives”, including one hundred and ninety-eight participants (54%), who demonstrate strong environmental awareness and willingness to adopt sustainable behaviours, and “Sceptics”, including one hundred sixty-six participants (46%), who show moderate engagement and remain cautious in their choices. Convenience, affordability, and clear product communication emerged as significant factors shaping student preferences. The findings suggest that targeted educational campaigns and transparent information are essential to converting positive attitudes into consistent purchasing behaviours. This research provides valuable insights for policymakers and marketers looking to design effective sustainability strategies tailored to the student population. Full article

The development of Industry 4.0 has accelerated the adoption of sophisticated technologies, including Digital Twins (DTs), Artificial Intelligence (AI), and cybersecurity, within Additive Manufacturing (AM). Enabling real-time monitoring, process optimization, predictive maintenance, and secure data management can redefine conventional manufacturing paradigms. Although their individual importance is increasing, a consistent understanding of how these technologies interact and collectively improve AM procedures is lacking. Focusing on the integration of digital twins (DTs), modular AI, and cybersecurity in AM, this review presents a comprehensive analysis of over 137 research publications from Scopus, Web of Science, Google Scholar, and ResearchGate. The publications are categorized into three thematic groups, followed by an analysis of key findings. Finally, the study identifies research gaps and proposes detailed recommendations along with a framework for future research. The study reveals that traditional AM processes have undergone significant transformations driven by digital threads, digital threads (DTs), and AI. However, this digitalization introduces vulnerabilities, leaving AM systems prone to cyber-physical attacks. Emerging advancements in AI, Machine Learning (ML), and Blockchain present promising solutions to mitigate these challenges. This paper is among the first to comprehensively summarize and evaluate the advancements in AM, emphasizing the integration of DTs, Modular AI, and cybersecurity strategies. Full article