Search Results (194)

Plant by-products have gained significant attention due to their rich content in bioactive compounds, which exhibit promising antioxidant, antimicrobial, and antitumor properties. In European countries, vegetable waste generation ranged from 35 to 78 kg per capita in 2022, highlighting both the scale of the challenge and the potential for valorization. This review provides an overview of key studies investigating the potential of plant residues in biomedicine, highlighting their possible contents of antioxidant compounds, their antimicrobial and antitumor properties, as well as their applications in dermocosmetics and nutraceuticals. However, despite their potential, several challenges must be addressed, such as the standardization of extraction protocols, as bioactive compound profiles can vary with plant source, processing conditions, and storage methods. Effective segregation and storage protocols for household organic waste also require optimization to ensure the quality and usability of plant by-products in biomedicine. Emerging 4.0 technologies could help to identify suitable plant by-products for biomedicine, streamlining their selection process for high-value applications. Additionally, the transition from in vitro studies to clinical trials is hindered by gaps in the understanding of Absorption, Distribution, Metabolism, and Excretion (ADME) properties, as well as interaction and toxicity profiles. Nonetheless, environmental education and societal participation are crucial to enabling circular bioeconomy strategies and sustainable biomedical innovation. Full article

The ability to study mature neuronal cells ex vivo is complicated by their non-dividing nature and difficulty in obtaining large numbers of primary cells from organisms. Thus, numerous transformed progenitor models have been developed that can be routinely cultured, then scaled, and differentiated to mature neurons. In this paper, we present a new method for differentiating one such model, the Lund human mesencephalic (LUHMES) dopaminergic neurons. This method is two days faster than some established protocols, results in nearly five times greater numbers of mature neurons, and involves fewer handling steps that could introduce technical variability. Moreover, it overcomes the problem of cell aggregate formation that commonly impedes high-resolution imaging, cell dissociation, and downstream analysis. While recently established for herpes simplex virus type 1, we demonstrate that LUHMES neurons can facilitate studies of other herpesviruses, as well as RNA viruses associated with childhood encephalitis and hemorrhagic fever. This protocol provides an improvement in the generation of large-scale neuronal cultures, which may be readily applicable to other neuronal 2D cell culture models and provides a system for studying neurotrophic viruses. We named this method the Streamlined Protocol for Enhanced Expansion and Differentiation Yield, or SPEEDY, method. Full article

With the continuous expansion of global trade, achieving sustainable maritime transport optimization and operations has become a key strategic direction for transforming maritime transport companies. To summarize the current state of research and identify emerging trends in sustainable maritime transport optimization and operations, this study systematically examines representative studies from the past decade, focusing on three dimensions, technology, management, and policy, using data sourced from the Web of Science (WOS) database. Building on this analysis, potential avenues for future research are suggested. Research indicates that the technological field centers on the integrated application of alternative fuels, improvements in energy efficiency, and low-carbon technologies in the shipping and port sectors. At the management level, green investment decisions, speed optimization, and berth scheduling are emphasized as core strategies for enhancing corporate sustainable performance. From a policy perspective, attention is placed on the synergistic effects between market-based measures (MBMs) and governmental incentive policies. Existing studies primarily rely on multi-objective optimization models to achieve a balance between emission reductions and economic benefits. Technological innovation is considered a key pathway to decarbonization, while support from governments and organizations is recognized as crucial for ensuring sustainable development. Future research trends involve leveraging blockchain, big data, and artificial intelligence to optimize and streamline sustainable maritime transport operations, as well as establishing a collaborative governance framework guided by environmental objectives. This study contributes to refining the existing theoretical framework and offers several promising research directions for both academia and industry practitioners. Full article

This perspective explores the use of biochar, a carbon-rich material derived from biomass, as a sustainable solution for mitigating volatile organic compounds (VOCs) emitted during asphalt production and use. VOCs from asphalt contribute to ozone formation and harmful secondary organic aerosols (SOAs), which negatively impact air quality and public health. Biochar, with its high surface area and capacity to adsorb VOCs, provides an effective means of addressing these challenges. By tailoring biochar’s surface chemistry, it can efficiently capture VOCs, while also offering long-term carbon sequestration benefits. Additionally, biochar enhances the durability of asphalt, extending road lifespan and reducing maintenance needs, making it a promising material for sustainable infrastructure. Despite these promising benefits, several challenges remain. Variations in biochar properties, driven by differences in feedstock and production methods, can affect its performance in asphalt. Moreover, the integration of biochar into existing plant operations requires the further development of methods to streamline the process and ensure consistency in biochar’s quality and cost-effectiveness. Standardizing production methods and addressing logistical hurdles will be crucial for biochar’s widespread adoption. Research into improving its long-term stability in asphalt is also needed to ensure sustained efficacy over time. Overcoming these challenges will be essential for fully realizing biochar’s potential in sustainable infrastructure development Full article

Generative artificial intelligence (AI) is rapidly transforming healthcare systems since the advent of OpenAI in 2022. It encompasses a class of machine learning techniques designed to create new content and is classified into large language models (LLMs) for text generation and image-generating models for creating or enhancing visual data. These generative AI models have shown widespread applications in clinical practice and research. Such applications range from medical documentation and diagnostics to patient communication and drug discovery. These models are capable of generating text messages, answering clinical questions, interpreting CT scan and MRI images, assisting in rare diagnoses, discovering new molecules, and providing medical education and training. Early studies have indicated that generative AI models can improve efficiency, reduce administrative burdens, and enhance patient engagement, although most findings are preliminary and require rigorous validation. However, the technology also raises serious concerns around accuracy, bias, privacy, ethical use, and clinical safety. Regulatory bodies, including the FDA and EMA, are beginning to define governance frameworks, while academic institutions and healthcare organizations emphasize the need for transparency, supervision, and evidence-based implementation. Generative AI is not a replacement for medical professionals but a potential partner—augmenting decision-making, streamlining communication, and supporting personalized care. Its responsible integration into healthcare could mark a paradigm shift toward more proactive, precise, and patient-centered systems. Full article

Efficient procurement processes are pivotal for strategic performance in digital organizations, requiring continuous refinement driven by automation, integration, and performance monitoring. This research investigates and demonstrates the potential for synergies between RPA and BPM in procurement processes. The primary objective is to analyze and evaluate a manual procurement-intensive process to enhance efficiency, reduce time-consuming interventions, and ultimately diminish costs and cycle time. Employing Design Science Research Methodology, this research yields a practical artifact designed to streamline procurement processes. An artifact was created using BPM methods and RPA tools. The RPA was developed after applying BPM Redesign Heuristics to the current process. A mixed-methods approach was employed for its evaluation, combining quantitative analysis on cycle time reduction with a qualitative Confirmatory Focus Group of department experts. The analysis revealed that the synergy between BPM and RPAs can leverage procurement processes, decreasing cycle times and workload on intensive manual tasks and allowing employees time to focus on other functions. This research contributes valuable insights for organizations seeking to harness automation technologies for enhanced procurement operations, with the findings suggesting promising enduring benefits for both efficiency and accuracy in the procurement lifecycle. Full article

Water management is a significant challenge, stimulating synergies between scientists and practitioners to create new tools and approaches to streamline decision making in this field. The assessment and monitoring of freshwater quality in surface water bodies are crucial for sustainable and safe water management. The main objectives of this study were to analyze the characteristics and properties of Chirita lake, assess seasonal variations in water quality, determine compliance with national environmental legislation, and perform a comparison with monitoring systems in other European lakes. The study used data that determined water quality indicators for a five-year period, from 2020 to 2024, considering temperature, turbidity, pH, conductivity, alkalinity, hardness, organic matter, nitrates, nitrites, ammonium, and chlorides. The statistical analysis technique based on the Pearson correlation coefficient was used to evaluate the seasonal correlations of water quality parameters in Chirita lake and to extract the essential parameters for assessing seasonal variations in river water quality. The results obtained indicated that the indicators considered important for water quality variation in one season may not be important in another season, except for organic matter and conductivity, which showed a significant contribution to water quality variation throughout the four seasons. This study demonstrated that lake water is classified as first class, according to national regulations. These results provide valuable support for local authorities to develop effective strategies for water quality management and the prevention of eutrophication processes in reservoirs. Full article

Over the past few years, there has been a need to discuss the strengthening of academic contributions to the 2030 Agenda as a vital facilitator for planning and evaluating sustainable goals. However, managing information in this field has become an internal institutional challenge for higher education organizations. Identifying the aspects of sustainable development goals within research projects is crucial for developing strategies and policies that promote collaboration in joint projects, ultimately strengthening research in SDGs. Recent advancements in computational methods have emerged as powerful tools to address the difficulties associated with utilizing information related to academic contributions to the 2030 Agenda. These methods offer innovative ways to process, analyze, and visualize data, enabling decision-makers to gain valuable insights and make informed decisions. This paper proposes a computational model to facilitate the identification of the 2030 Agenda for Sustainable Development within teaching, research, and extension projects at a Brazilian University. The model aims to align academic research and institutional actions with the 17 Sustainable Development Goals (SDGs) established by the United Nations. The developed model can extract and categorize SDG-related text data by employing keywords and natural language processing techniques. The development of this tool is driven by the need for universities to adapt their curricula and contribute to the 2030 Agenda. The model helps identify the potential impact of projects on the SDGs, assessing the alignment of research or actions with specific goals, and improving data governance. By utilizing the proposed model, educational institutions can efficiently manage their research, organize their work around the SDGs, foster collaboration internally and with external partners, and enhance their internationalization efforts. The model has the potential to increase the capabilities of educational institutes as vital mobilizing agents, reducing costs and streamlining the analysis of information related to the 2030 Agenda. This, in turn, enables more effective academic actions to integrate sustainable goals. Full article

Deep eutectic solvents (DESs) have emerged as prominent, environmentally benign substitutes for traditional solvents and catalysts in organic synthesis, notably in the synthesis of amines, pivotal structures in many industrial sectors. Their distinctive physicochemical attributes—including negligible volatility, exceptional thermal stability, and adjustable polarity—render them particularly advantageous for facilitating a broad spectrum of amination reactions. DESs can serve dually as reaction media and as intrinsic catalytic systems, accelerating reaction kinetics without necessitating supplementary catalysts or severe reaction conditions. They are especially efficacious in processes such as reductive amination, transamination, and multicomponent transformations, often affording superior yields and streamlining product isolation. The extensive hydrogen-bonding network intrinsic to DESs is believed to mediate crucial mechanistic steps, frequently obviating the requirement for external additives. Moreover, DESs are recyclable and exhibit compatibility with a diverse array of substrates, encompassing bio-derived and pharmaceutical intermediates. Full article

Business Continuity Management (BCM) is critical for organizations to mitigate disruptions and maintain operations, yet many struggle with fragmented and non-standardized self-assessment tools. Existing frameworks often lack holistic integration, focusing narrowly on isolated components like cyber resilience or risk management, which limits their ability to evaluate BCM maturity comprehensively. This research addresses this gap by proposing a structured Self-Assessment System designed to unify BCM components into an adaptable, standards-aligned methodology. Grounded in Design Science Research, the system integrates a BCM Model comprising eight components and 118 activities, each evaluated through weighted questions to quantify organizational preparedness. The methodology enables organizations to conduct rapid as-is assessments using a 0–100 scoring mechanism with visual indicators (red/yellow/green), benchmark progress over time and against peers, and align with international standards (e.g., ISO 22301, ITIL) while accommodating unique organizational constraints. Demonstrated via focus groups and semi-structured interviews with 10 organizations, the system proved effective in enhancing top management commitment, prioritizing resource allocation, and streamlining BCM implementation—particularly for SMEs with limited resources. Key contributions include a reusable self-assessment tool adaptable to any BCM framework, empirical validation of its utility in identifying weaknesses and guiding continuous improvement, and a pathway from initial assessment to advanced measurement via the Plan-Do-Check-Act cycle. By bridging the gap between theoretical standards and practical application, this research offers a scalable solution for organizations to systematically evaluate and improve BCM resilience. Full article

Quality management during construction is critical to ensuring compliance with technical specifications and quality standards. Traditional practices often rely on manual, paper-based documentation, leading to inefficiencies, data fragmentation, and poor traceability. This study presents QualiSite, a novel digital workflow that integrates Building Information Modeling (BIM) and Geographic Information Systems (GIS), aligned with ISO 9001:2015 requirements, to enhance quality management in building projects. The research is framed under the Design Science Research Method (DSRM), guiding the iterative development and validation of the tool. QualiSite was tested in a real-world case study involving the construction of reinforced concrete walls. The results demonstrated functional improvements in inspection traceability, consistency of quality records, and coordination between field data and BIM elements. Using structured digital forms contributed to more consistent data capture and greater efficiency in recording, organizing, and visualizing quality control statuses within the 3D environment. These outcomes enabled transparent inspection processes and clear visualization of quality status across construction elements. The digital workflow also facilitated the identification of nonconformities and streamlined communication between field inspectors and model managers. This approach advances traditional quality management by embedding inspection records into a Cyber-Physical Systems (CPS) framework, contributing to the digital transformation of the Architecture, Engineering, and Construction (AEC) industry and supporting the vision of Smart Industry. Full article

Pathogenic variants in breast cancer predisposition genes are associated with poor clinical outcomes but also offer an opportunity for more individualized therapeutic pathways. Given increasing knowledge, improvements in germline genetic testing efficiency, and the availability of novel systemic targeted treatment options, the importance of appropriately identifying patients for testing has never been greater. A pan-Canadian expert working group (EWG) consisting of 10 healthcare professionals (HCPs) was convened to review recent international guidelines for germline genetic testing in breast cancer and develop Canadian recommendations. The group identified four clinical questions to address which patients should undergo testing, what approaches should be used, how patients should be counselled, and what steps are needed for implementation. In response to these questions, the EWG agreed upon 12 recommendations that emphasized broader incorporation of germline genetic testing and more standardized, streamlined testing and counselling approaches. The group also offered multiple suggestions to support effective and equitable implementation across Canada. These recommendations provide guidance for HCPs and represent a call to action for the Canadian government and other organizations to support genetic testing pathways, drug access, and ultimately improved outcomes for patients with breast cancer and their families. Full article

The application of CAD/CAM technologies in modern production has revolutionized manufacturing processes, leading to significant improvements in precision, efficiency, and flexibility. These technologies enable the design and manufacturing of complex geometries with high accuracy, reducing errors and material waste. CAD/CAM integration streamlines workflows, enhances productivity, and facilitates rapid prototyping, accelerating the time-to-market for new products. Additionally, it supports customization and scalability in production, allowing for cost-effective small-batch and large-scale manufacturing. Without a 3D model of the product, it is not possible to use the advantages of applying advanced CAD/CAM technologies. Recognizing 3D models from engineering drawings is essential for modern production, especially for outsourcing companies in fluctuating market conditions, where the production process is organized with 2D workshop drawings on paper. This paper proposes a novel methodology for reconstructing 3D models from 2D engineering drawings, specifically those in DXF file format, leveraging a genetic algorithm. A core component of this approach is the representation of the 2D drawing as a symmetric adjacency matrix. This matrix serves as the foundational data structure for the genetic algorithm, enabling the evolutionary process to effectively optimize the 3D reconstruction. The experimental evaluation, conducted on multiple engineering drawing test cases (including both polyhedral and cylindrical geometries), demonstrated consistent convergence of the proposed GA-based method toward topologically valid and geometrically accurate 3D wireframe models. The approach achieved successful reconstruction in all cases, with fitness scores ranging from 1.1 to 112.2 depending on model complexity, and average execution times from 2 to 100 s. These results confirm the method’s robustness, scalability, and applicability in real-world CAD environments, while establishing a new direction for topology-driven 3D reconstruction using evolutionary computation. Full article

Amorphous carbon and its heteroatom-doped derivatives often exhibit wrinkled, defective, porous structures, and find wide applications in the fields of energy storage and catalysis. To date, although many methods for preparing doped carbon materials have been reported, the preparation process is relatively complex, and there are still few simple methods available. Therefore, it is necessary to further develop simple and feasible preparation methods. In this study, we employed commercially available manganese disodium ethylenediaminetetraacetate (EDTA-Na₂Mn, serving as both carbon and nitrogen sources) as the precursor. Through thermal decomposition under a nitrogen atmosphere, a nitrogen-doped carbon composite embedded with manganese monoxide (MnO) was initially obtained. Subsequently, hydrochloric acid etching was applied to remove the MnO phases, yielding the final product: nitrogen-doped carbon, denoted as C-N-Mn. Notably, the carbonization and nitrogen-doping processes were simultaneously accomplished during pyrolysis, thereby streamlining the synthesis route for nitrogen-doped carbons. To demonstrate the versatility of this approach, we extended the methodology to two additional metal–organic salts (EDTA-Na₂Zn and EDTA-NaFe), successfully synthesizing nitrogen-doped carbon materials (C-N-Zn and C-M-Fe) in both cases. The phase composition, morphology, microstructure, specific surface area, and pore volume of the products were systematically characterized using X-ray diffraction (XRD), scanning/transmission electron microscopy (SEM/TEM), Raman spectroscopy, X-ray photoelectron spectroscopy (XPS), and nitrogen adsorption/desorption analysis. These nitrogen-doped carbons exhibit high specific surface areas and tunable pore volumes, suggesting their potential applicability in energy storage systems. Full article

The discovery of bioactive natural products is often challenged by the complexity of isolating and characterizing active compounds within diverse mixtures. Previously, we introduced a ¹H NMR-based weighted gene correlation network analysis (WGCNA) approach to identify spectral features linked to growth inhibitory activity of Piper (Piperaceae) leaf extracts against model plant, fungal, and bacterial organisms. This method enabled us to prioritize specific spectral features linked to bioactivity, offering a targeted approach to natural product discovery. In this study, we validate the predictive capacity of the WGCNA by isolating the compounds responsible for the bioactivity-associated resonances and confirming their antifungal efficacy. Using growth inhibition assays, we verified that the isolated compounds, including three novel antifungal agents, exhibited significant bioactivity. Notably, one of these compounds contains a rare imidazolium heterocyclic motif, marking a new structural class in Piper. These findings substantiate the ¹H NMR-based WGCNA as a reliable tool for identifying structural types associated with biological activity, streamlining the process of discovering bioactive natural products in complex extracts. Full article