Search Results (62)

To address the challenges of decarbonization in the global transportation sector and disposal of waste tires, warm asphalt rubber (WAR) with low viscosity and high performance was prepared. In particular, the preparation and rheological behavior of WAR incorporating composite warm mix systems at relatively high crumb rubber contents have not been thoroughly documented. In this study, WAR prepared under such conditions was systematically examined. A five-factor, three-level segmented orthogonal experimental design (OED) was employed to investigate the effects of preparation parameters on hot mix asphalt rubber (AR) properties. Based on the optimized AR formulation, a composite warm mix system combining Ultra-Warm Mix additive (UWM) and Sasobit was developed, and control groups containing 5% UWM only and 1.5% Sasobit only were prepared for comparison. Conventional physical tests together with rheological characterization, including Dynamic Shear Rheometer (DSR), Multiple Stress Creep Recovery (MSCR), and Bending Beam Rheometer (BBR) tests, were conducted to evaluate the high- and low-temperature performance of WAR. Results show that the optimal preparation process consisted of aromatic oil content 5%, crumb rubber content 30%, shear temperature 220 °C, shear time 120 min, and reaction time 90 min. The composite warm mix system notably enhanced WAR performance, with the WAR-5U1.5S group exhibiting the most balanced properties. A marked reduction in rotational viscosity was achieved while maintaining a stable softening point, and satisfactory ductility and elastic recovery were also retained. DSR and MSCR tests confirmed improved high-temperature deformation resistance, an increase in percent recovery R, and a decrease in non-recoverable creep compliance J_nr. BBR test further verified that the composite system maintained good low-temperature cracking resistance, meeting all specification requirements. Overall, these results indicate that, compared with the optimized AR, WAR can reduce mixing viscosity without sacrificing rutting or cracking performance, while alleviating the limitations observed for single warm mix additives. This study provides essential technical support for promoting WAR that integrates low-carbon construction with superior pavement performance. Full article

Soft robots often suffer from insufficient load capacity due to the softness of their materials. Existing variable stiffness technologies usually introduce rigid components, resulting in decreased flexibility and complex structures of soft robots. To address these challenges, this work proposes a novel wrist-gripper composite soft end-effector based on the weaving jamming principle, which features a highly integrated design combining structure, actuation, and stiffness. This end-effector is directly woven from pneumatic artificial muscles through weaving technology, which has notable advantages such as high integration, strong performance designability, lightweight construction, and high power density, effectively reconciling the technical trade-off between compliance and load capacity. Experimental results demonstrate that the proposed end-effector exhibits excellent flexibility and multi-degree-of-freedom grasping capabilities. Its variable stiffness function enhances its ability to resist external interference by 4.77 times, and its grasping force has increased by 1.7 times, with a maximum grasping force of 102 N. Further, a grasping force model for this fiber-reinforced woven structure is established, providing a solution to the modeling challenge of highly coupled structures. A comparison between theoretical and experimental data indicates that the modeling error does not exceed 7.8 N. This work offers a new approach for the design and analysis of high-performance, highly integrated soft end-effectors, with broad application prospects in unstructured environment operations, non-cooperative target grasping, and human–robot collaboration. Full article

This study examines the link between post-construction constructability and perceived service quality in a 15-building campus at Ricardo Palma University (Lima, Peru). The research combined technical assessments, user surveys, and document analysis. Constructability was operationalized across six dimensions: compliance with quality requirements, adherence to design, valuation, execution time, social impact, and environmental impact. Service quality was measured through user satisfaction surveys. The instruments were validated through expert judgment, while data were analyzed using descriptive statistics, Spearman’s correlations, and non-parametric bootstrap procedures to address the limitations of a small sample size (N = 15). Results indicate positive associations between constructability efficiency and service quality, highlighting that projects with higher constructability scores achieved better user satisfaction levels. The findings provide a replicable framework for evaluating university facilities and offer practical implications for facility management and institutional sustainability. Full article

Ensuring consistent quality and safety in agri-food processing is a strategic priority for firms seeking compliance with international standards such as ISO 9001 and ISO 22000. Traditional inspection practices in Ecuador’s food industry remain largely destructive, labor-intensive, and subjective, limiting real-time decision-making. This study developed a non-destructive, ISO-aligned framework for process-level quality control by integrating digital (RGB) imaging for surface-level inspection, hyperspectral imaging (HSI) for internal-quality prediction (e.g., moisture, firmness, and freshness), near-infrared spectroscopy (NIRS) for compositional and authenticity analysis, and deep learning (DL) models for automated classification of ripeness, maturity, and defects. Experimental results across four flagship commodities—bananas, cacao, coffee, and shrimp—achieved classification accuracies above 88% and ROC AUC values exceeding 0.90, confirming the robustness of AI-driven, multimodal (RGB–HSI–NIRS) inspection under semi-industrial conveyor conditions. Beyond technological performance, the findings demonstrate that digital inspection reinforces ISO principles of evidence-based decision-making, conformity verification, and traceability, thereby operationalizing the Plan–Do–Check–Act (PDCA) cycle at digital speed. The study contributes theoretically by advancing the conceptualization of Quality 4.0 as a socio-technical transformation that embeds AI-driven sensing and analytics within management standards, and practically by providing a roadmap for Ecuadorian SMEs to strengthen export competitiveness through automated, real-time, and auditable quality assurance. Full article

Evaluating the efficacy of available technology for pollutant treatment is critical for formulating environmental management policies and standards. To address the lack of systematic quantitative methods for evaluating available technology, we propose a method based on the Entropy Weight TOPSIS model which integrates technology, economic efficiency, environmental benefits, and operational feasibility. We applied this approach to evaluate six heavy metal wastewater treatment technologies used in the lead–zinc smelting industry in the Yellow River Basin of China. A total of 4 primary and 16 secondary evaluation indicators were identified. The data were mainly composed of supervised monitoring data collected by local environmental protection authorities and self-monitoring operation data collected from factories; moreover, 10 relevant experts were invited to assess the scoring indicators. The results showed that technical performance had the greatest contribution to the overall efficacy of the treatment technology (62.31% weight), followed by environmental benefits (14.24% weight), economic costs (12.08% weight), and operational feasibility (11.36% weight). The final scores and rankings of the six technologies evaluated showed that a sulfurization precipitation with two-stage lime neutralization and sedimentation technology received the highest score due to its balanced technical performance, economic cost, environmental benefits, and operational feasibility. Conversely, lime neutralization with flocculation precipitation technology ranked lowest due to its non-compliance with the emission limits in China, despite its low economic cost and carbon emission intensity. This study provides a quantitative methodological framework for evaluating available technology, emphasizing the balance of the technical, economic, and environmental benefits of the pollutant treatment technologies chosen and the relevant policies made. Full article

This study investigates how neuromarketing practices intersect with consumer privacy regulation in California, with particular attention to the 2024 Senate Bill 1223 (SB 1223), which amends the CCPA/CPRA to explicitly define “neural data.” By examining corporate strategies and regulatory frameworks, the paper evaluates how firms navigate the tension between innovation, ethics, and consumer protection. A qualitative, multiple-case study approach was adopted, focusing on Coca-Cola, Frito-Lay, and Hyundai. Data were collected from corporate privacy policies, industry publications, and legislative documents, triangulated through doctrinal legal analysis and cross-case synthesis. The analysis reveals that, while companies comply with disclosure, consent, and oversight requirements under the CCPA/CPRA, such compliance remains largely procedural, with transparency often being technical rather than consumer-friendly, consent being insufficiently informed, and protections for vulnerable groups being inconsistently enforced. SB 1223’s recent definition of neural data directly encompasses techniques such as EEG, fMRI, eye-tracking, and biometrics, underscoring the urgent need for firms to treat neuromarketing as a category of regulated practice rather than discretionary innovation. The study is limited by its reliance on publicly available documentation and by the recency of SB 1223, which precludes observation of mature compliance patterns. Future research should explore consumer perceptions, track evolving regulatory responses, and extend the analysis across various sectors, including healthcare, education, and non-profits. This study contributes to theory by extending stakeholder theory to neural data governance and by conceptualizing neuromarketing as a governance-intensive strategic capability situated at the frontier of consumer rights and technological innovation. It contributes to practice by demonstrating how firms can transform compliance with emerging neural data regulations into a strategic capability that strengthens consumer trust, ethical legitimacy, and brand equity. Full article

To address the limitations of traditional single-point detection methods in monitoring the sulfur content of ship fuel (FSC), which are inadequate in meeting the regulatory demands of high-traffic ports, this study proposes an integrated analytical approach based on artificial intelligence. This approach synthesizes multi-source heterogeneous data, including historical fuel testing records, Automatic Identification System (AIS) trajectory data, ship and operator profiles, technical specifications, fuel supply chain documentation, fundamental ship attributes and so on. Following rigorous data cleaning and preprocessing procedures, a refined dataset comprising 3046 records collected between 2017 and 2024 from the Port of Ningbo was utilized. Initially, multiple linear regression analysis was con-ducted to identify key factors influencing sulfur emissions, resulting in an R² value of 0.67. Based on these findings, a deep neural network model was developed using TensorFlow to enable real-time estimation of FSC and classification of compliance risk levels. The results indicate that the proposed method exhibits high estimated accuracy and robustness. An AI-based intelligent monitoring module, developed based on this research, has been integrated into the ship exhaust gas detection system at the Port of Ningbo. This module enables real-time analysis of inbound ships and intelligent identification of potentially non-compliant ships, thereby significantly improving the precision and efficiency of port regulatory operations. This study not only contributes to the theoretical framework for ship fuel compliance monitoring but also provides a practical and scalable technical solution for intelligent port governance. Full article

Africa’s Critical Zones experience unprecedented environmental degradation but do not have effective governance modalities for policy implementation coordination across jurisdictional and stakeholder scales. This study addresses three specific scientific challenges: (1) How does policy discordance between national environmental policies and local implementation cultures undermine conservation effectiveness in Critical Zones? (2) What do power asymmetries among stakeholders contribute to governance failure? (3) To what extent do implementation gaps stem from the exclusion of Indigenous knowledge systems from mainstream policy-making processes? In this qualitative multi-case study, the research examines policy reports, technical reports, and interviews with important stakeholders in five African Critical Zones: Central Rift Valley (Ethiopia), Kilombero Valley (Tanzania), Maligunde Dam (Malawi), Lake Chivero (Zimbabwe), and Muizenberg East (South Africa). Evidence shows that shattered institutional imperatives create policy gaps exploited by industrial stakeholders, where policy design from the top down routinely leaves in place established community-based systems of governance that have historically maintained these ecosystems in equilibrium. Excess power held by government ministries compared to local communities results in 73% of environmental policy being enforced with ineffective stakeholder engagement, with non-compliance levels across examined locations exceeding 60%. The study attests to the fact that co-management incorporated governance systems that adopt traditional ecological knowledge systems register 40% greater compliance rates with policies. These findings are empirical evidence of adaptive governance models that can bridge Africa’s most vulnerable ecosystems’ policy–practice gap, and they guide direct implementation of the African Union Agenda 2063 environmental targets. Full article

Deforestation monitoring is critical to support compliance with regulatory frameworks such as the EU Deforestation Regulation (EUDR), which requires that products containing or derived from beef, cocoa, coffee, palm oil, rubber, soy, and timber are deforestation-free after 31 December 2020. Earth observation (EO) offers a means to assess deforestation, yet map-based verification remains technically limited and uncertain. This study addresses the lack of a systematic assessment of global Forest/Non-Forest (FNF), Tree Cover/Non-Tree Cover (TC/NTC) and Land Use/Land Cover (LULC) datasets by identifying and evaluating 21 publicly available global forest/tree cover reference maps for their alignment with EUDR criteria. This goes beyond merely treating these datasets as simply “fit” or “not fit” for the purpose of the EUDR, but rather aims to assess how well each dataset meets the needs compared to others, acknowledging strengths, weaknesses, and trade-offs. The 21 datasets are reviewed based on EUDR-related parameters (temporal proximity, spatial resolution, and forest definition) as well as accuracy metrics. From this broader review, eight datasets are shortlisted based on their alignment with key regulatory requirements. However, most datasets fail to fully meet all EUDR requirements, particularly forest definitions, with only two datasets satisfying all indicators. Notably, all datasets are unable to distinguish forests from other non-forest, tree-based systems. Reported accuracy metrics reveal a general overestimation of forest areas, while canopy height-based maps tend to underestimate tree cover, potentially excluding forested regions. Regional comparisons show more consistent estimates in South America, while Europe and North America display greater variability. These findings support informed decision-making by companies and policymakers for selecting suitable datasets, while also highlighting conflicts and challenges associated with the use of global forest/tree cover maps for regulatory compliance. Full article

Hand hygiene is a key measure to prevent healthcare-associated infections (HAIs), yet compliance remains low in Sub-Saharan Africa (SSA), due to limited resources, insufficient training, and behavioral challenges. Simulation-based education offers a promising approach to enhance technical and non-technical skills in safe learning environments, promoting behavioral change and patient safety. This study aimed to develop and pilot a contextually adapted hand hygiene simulation-based learning scenario for nursing students in SSA. Grounded in the Medical Research Council (MRC) Framework and Design-Based Research principles, a multidisciplinary team from European and African higher education institutions (HEIs) co-created this scenario, integrating international and regional hand hygiene guidelines. Two iterative pilot cycles were conducted with expert panels, educators, and students. Data from structured observation and post-simulation questionnaires were analyzed using descriptive statistics. The results confirm the scenario’s feasibility, relevance, and educational value. The participants rated highly the clarity of learning objectives (M = 5.0, SD = 0.0) and preparatory materials (M = 4.6, SD = 0.548), reporting increased knowledge/skills and confidence and emphasizing the importance of clear roles, structured facilitation, and real-time feedback. These findings suggest that integrating simulation in health curricula could strengthen HAI prevention and control in SSA. Further research is needed to evaluate long-term outcomes and the potential for wider implementation. Full article

The effective adoption of quality assurance and traceability systems is increasingly recognised as a critical enabler of circular economy (CE) outcomes in the plastics sector. This study examines the factors that influence the implementation of such systems within Australia’s recycled plastics industry, with a focus on how these factors vary by company size, supply chain role, and adoption of CE strategy. Recycled plastics are defined here as post-consumer or post-industrial polymers that have been reprocessed for reintegration into manufacturing applications. A mixed-methods survey was conducted with 65 stakeholders across the Australian plastics value chain, comprising recyclers, compounders, converters, and end-users. Respondents assessed a structured set of regulatory, technical, economic, and systemic factors, identifying whether each currently operates as an enabler or barrier in their organisational context. The analysis employed a comparative framework adapted from a 2022 European study, enabling a cross-regional interpretation of patterns and a comparison between CE-aligned and non-CE firms. The results show that firms with CE strategies report greater alignment with innovation-oriented enablers such as digital traceability, standardisation, and closed-loop models. However, these firms also express heightened sensitivity to systemic weaknesses, particularly in areas such as infrastructure limitations, inconsistent material quality, and data fragmentation. Small- and medium-sized enterprises (SMEs) highlighted compliance costs and operational uncertainty as primary barriers, while larger firms frequently cited frustration with regulatory inconsistency and infrastructure underperformance. These findings underscore the need for differentiated policy mechanisms that account for sectoral and organisational disparities in capacity, scale, and readiness for traceability. The study also cautions against the direct transfer of European circular economy models into the Australian context without consideration of local structural, regulatory, and geographic complexities. Full article

Carbon Capture and Storage (CCS) technology presents a practical solution for reducing industrial carbon dioxide (CO₂) emissions through underground anthropogenic CO₂ storage in depleted hydrocarbon reservoirs. The long-term storage efficiency faces several CO₂ leakage challenges that need to be addressed in the planning phase of the CCS project. Thus, effective risk assessment (RA) methodologies are crucial for ensuring safety, regulatory compliance, and public acceptance of CCS projects. This review examines RA parts and their corresponding technical and non-technical challenges. The analysis critically compares over 20 qualitative, semi-quantitative, quantitative, and hybrid RA techniques employed throughout GCS operations. Available quantitative RA tools do not deliver dependable results because they require technical data that become available late in the CCS project development process. Qualitative approaches work well for the initial screening of storage sites with limited data available, yet quantitative methods enable quantification of CO₂ leakage. For the first time, a comparative analysis of two integrated assessment tools is presented in this paper. The techniques achieve success based on high-quality data and analysis of existing technical and non-technical challenges which this paper examines. The comparative analysis outlines the limitations and advantages of every methodology studied and emphasizes the need for integrated hybrid frameworks to boost decision-making in the RA process. Future research should focus on creating or improving existing hybrid frameworks for late-stage RA while utilizing qualitative frameworks in the initial site screening stage to advance GSC’s safe and effective implementation. Full article

The hybridization of ADM-Type Rail Service Cars aims to enhance energy efficiency, environmental sustainability, and cost-effectiveness within Uzbekistan’s railway network. Diesel-powered service cars currently contribute to high fuel consumption, elevated emissions, and costly maintenance, necessitating a transition to hybrid technology. This study introduces an innovative “sequence of linear sets–torsion electric motor–wheel pairs” design, optimizing torque distribution and power efficiency for improved operational reliability. Through system modeling, performance simulations, and real-world field trials, the hybrid system demonstrates a 15% reduction in energy consumption, a 25% decrease in CO₂ emissions, and up to 30% lower maintenance costs compared to conventional diesel models. Additionally, the hybrid technology enhances operational flexibility, allowing seamless functionality on both electrified and non-electrified railway lines. From an economic perspective, retrofitting existing service cars instead of full fleet replacement provides a cost-effective alternative, offering an estimated 10-year return on investment (ROI) through fuel savings and reduced downtime. This initiative directly supports Uzbekistan’s Green Development Strategy and railway modernization plans while holding significant commercialization potential in Central Asia and other regions with aging railway infrastructure. By addressing technical scalability, regulatory compliance, and economic feasibility, this study proposes a practical and timely hybrid retrofit solution for sustainable railway operations, aligning current industry needs with long-term environmental and financial benefits. Full article

Large Language Models (LLMs) have revolutionized natural language processing but present significant technical and legal challenges when confronted with the General Data Protection Regulation (GDPR). This paper examines the complexities involved in reconciling the design and operation of LLMs with GDPR requirements. In particular, we analyze how key GDPR provisions—including the Right to Erasure, Right of Access, Right to Rectification, and restrictions on Automated Decision-Making—are challenged by the opaque and distributed nature of LLMs. We discuss issues such as the transformation of personal data into non-interpretable model parameters, difficulties in ensuring transparency and accountability, and the risks of bias and data over-collection. Moreover, the paper explores potential technical solutions such as machine unlearning, explainable AI (XAI), differential privacy, and federated learning, alongside strategies for embedding privacy-by-design principles and automated compliance tools into LLM development. The analysis is further enriched by considering the implications of emerging regulations like the EU’s Artificial Intelligence Act. In addition, we propose a four-layer governance framework that addresses data governance, technical privacy enhancements, continuous compliance monitoring, and explainability and oversight, thereby offering a practical roadmap for GDPR alignment in LLM systems. Through this comprehensive examination, we aim to bridge the gap between the technical capabilities of LLMs and the stringent data protection standards mandated by GDPR, ultimately contributing to more responsible and ethical AI practices. Full article

The Clean Label concept has gained significant traction in the cheese industry due to consumer preferences for minimally processed cheeses free from synthetic additives. This review explores different approaches for applying Clean Label principles to the cheese industry while maintaining food safety, sensory quality, and shelf life. Non-thermal technologies, such as high-pressure processing (HPP), pulsed electric fields (PEF), ultra-violet (UV), and visible light (VL), are among the most promising methods that effectively control microbial growth while preserving the nutritional and functional properties of cheese. Protective cultures, postbiotics, and bacteriophages represent microbiological strategies that are natural alternatives to conventional preservatives. Another efficient approach involves plant extracts, which contribute to microbial control, and enhance cheese functionality and potential health benefits. Edible coatings, either alone or combined with other methods, also show promising applications. Despite these advantages, several challenges persist: higher costs of production and technical limitations, possible shorter shelf-life, and regulatory challenges, such as the absence of standardized Clean Label definitions and compliance complexities. Further research is needed to develop and refine Clean Label formulations, especially regarding bioactive peptides, sustainable packaging, and advanced microbial control techniques. Addressing these challenges will be essential for expanding Clean Label cheese availability while ensuring product quality and maintaining consumer acceptance. Full article