Search Results (1,438)

The significant CO₂ emissions from cement manufacturing and overuse of natural aggregates, especially river sand mining, have been a global environmental concern for decades. This is a review study that aimed to evaluate the solution by reviewing past studies on the incorporation of supplementary cementitious materials (SCMs) and recycled aggregates (RAs) to produce sustainable concrete (SC). Regarding environmental consequences, the results highlighted that the cement industry accounts for a 5–8% carbon footprint. Concurrently, the demand for high-quality river sand has escalated, leading to widespread river degradation, altered channel morphology, and effects on river ecosystems. Past studies’ experimental results indicate that silica fume (SF), as an effective SCM, enhances the strength and durability of sustainable concrete to its optimal levels. However, the higher RA content resulted in reductions in engineering properties. The published studies also reported that lower percentages of SF combined with RAs had a positive effect on the strength and durability of design mix concrete, thereby further strengthening the findings of this review. This factor was found to be missing in most studies. A cost–benefit analysis for combined SCMs and RAs was introduced in this study. This review study evaluated the cost–benefit analysis of 1 m³ of sustainable concrete. The highest benefit was observed at 20.97% in a study when optimized 10%SF + 100 RAs were combined. It showed that the combined use of SCMs with RAs at optimal levels satisfied the strength and durability requirements. In addition, the benefits of sustainable concrete were achieved without any cost increase, a new outcome revealed by this review. Full article

Despite extensive research on social engineering victimisation on social networking sites (SNSs) across the Internet, user susceptibility continues to increase, indicating that existing explanatory models remain incomplete. Previous studies have predominantly examined susceptibility through isolated factors, including individual traits, message characteristics, or source attributes, while often overlooking how evolving Internet-based SNS environments interact with human and social factors. To address this gap, this study presents an interdisciplinary qualitative investigation into emerging determinants of user susceptibility to social engineering cyberattacks (SECAs) on Internet-enabled SNS platforms. Drawing on in-depth interviews with 18 experts from cybersecurity, psychology, sociology, criminology, and linguistics, the study captures perspectives that are rarely integrated within a single analytical framework. Using NVivo 14 and inductive thematic analysis, six core themes and seven sub-themes were identified, revealing previously underexplored cognitive-emotional, social-relational, and platform-mediated mechanisms of victimisation. The key contribution of this research is not the identification of entirely new susceptibility factors, but the development of an interdisciplinary framework that integrates these previously disconnected dimensions. By foregrounding the role of SNS design affordances within the broader Internet ecosystem and their interaction with human cognition and social dynamics, this study advances current understanding beyond fragmented models of user vulnerability. The findings provide a novel conceptual foundation for future empirical research and inform the design of more effective, context-aware mitigation and awareness strategies for SECAs on Internet-based SNSs. Full article

The Al-Ahsa Oasis region is an important agricultural area; however, continuous spatial–temporal monitoring is essential to assess and mitigate the impacts of climate change and land use change. The current study examines environmental and land cover changes in the Al-Ahsa Oasis region from 1990 to 2025 by utilizing spectral indices derived from multiple satellites. Multi-temporal Landsat imagery (Landsat 5, 8, and 9) was processed in Google Earth Engine (GEE) to derive key biophysical indicators, including the Normalized Difference Vegetation Index (NDVI), land surface temperature (LST), and bare soil index (BSI). Supervised classification techniques were employed to generate LULC maps for each time step, enabling the assessment of spatiotemporal land cover dynamics. In addition, a random forest (RF) machine learning algorithm was applied to accurately quantify and map the distribution of palm trees across the study area. The results showed that NDVI values fluctuated between −0.19 and 0.75 during the period from 1990 to 2025. Higher vegetation density was observed in central and eastern areas, with maximum values of −0.44–0.75 in 2025. The higher LST was observed in 2025, with a range of 34.7 to 54.6 °C, and the lower LST was observed in 1990 with a range 28.7 to 48.34 °C. BSI values decreased from −0.40 to 0.46 between 1990 and 2025 to a more variable range of −0.27 to 0.36, indicating reduced soil exposure. The classification of LULC numerical data shows a rapid rise in urban development of 67.19% and a 25% decrease in vegetation area. Furthermore, the results of the RF model indicate that palm tree area increased by 16.23% from 1990 to 2025, with overall accuracy of 98.15, and kappa coefficient of 0.962. This research highlights that urban expansion impacts environmental indicators such as LST, while the increasing trend of NDVI could support the palm trees expansion. This study finds valuable information for policymakers and land use planners to develop sustainable urban growth strategies, protect agricultural lands, and enhance oasis ecosystem resilience. Combined remote-sensing-based monitoring into regional planning frameworks can inform decision making for balancing urban development, environmental protection, and long-term agricultural sustainability in the Al-Ahsa Oasis. Full article

Serratia marcescens has emerged as an important opportunistic pathogen in intensive care units (ICUs), where critically ill patients, invasive devices, antimicrobial exposure, and complex environmental reservoirs create favorable conditions for colonization, infection, and recurrent outbreaks. This narrative review synthesizes evidence from the past decade regarding the clinical and molecular epidemiology, environmental persistence, device-associated transmission, biofilm-mediated resistance, and infection-control strategies of S. marcescens in ICU settings. The literature was reviewed using an integrative approach informed by Ferrari’s narrative review framework, with thematic synthesis across clinical, microbiological, environmental, and genomic domains. Recent evidence indicates that ICU-associated S. marcescens infections frequently involve respiratory tract colonization, ventilator-associated pneumonia, bloodstream infection, urinary tract infection, and device-related transmission. Hospital water systems, sink drains, wet surfaces, ventilator circuits, reusable equipment, and contaminated antiseptic or liquid products may serve as persistent reservoirs, particularly when biofilm formation supports long-term survival and recurrent dissemination. At the molecular level, S. marcescens demonstrates substantial genomic diversity, intrinsic and acquired antimicrobial resistance, inducible AmpC β-lactamase activity, efflux-mediated tolerance, and plasmid-associated resistance gene transfer. This review particularly emphasizes the molecular determinants that enable S. marcescens to persist in ICU ecosystems, including AmpC-mediated β-lactam resistance, efflux-associated tolerance, quorum-sensing-regulated biofilm formation, plasmid-mediated horizontal gene transfer, and WGS-defined clonal transmission. Whole-genome sequencing, rapid molecular diagnostics, active surveillance, environmental sampling, and integrated infection-control bundles have become increasingly important for distinguishing clonal outbreaks from endemic transmission and guiding timely interventions. Emerging perspectives emphasize the need to combine antimicrobial stewardship, environmental engineering, respiratory-care auditing, anti-biofilm strategies, and AI-assisted real-time surveillance into adaptive ICU infection-control frameworks. Overall, S. marcescens should be regarded not merely as an episodic outbreak organism, but as a highly adaptable ICU-associated pathogen requiring multidisciplinary prevention strategies. Full article

Sustainable water remediation requires catalytic strategies that remove contaminants efficiently while reducing chemical input, byproduct formation, and ecological disturbance. Conventional radical-dominated advanced oxidation processes can rapidly degrade pollutants, but their reliance on high oxidant dosages and freely diffusing reactive oxygen species often causes matrix quenching, non-selective oxidation, low oxidant utilization, and potential ecological risks. Mild interfacial catalysis provides a materials-chemistry strategy to regulate oxidative intensity and direct contaminant transformation under environmentally relevant conditions. In this review, mild catalysts are defined by pathway-selective, interfacially confined, and environmentally compatible oxidation rather than by low dosage alone. Representative non-radical or low-intensity pathways, including singlet oxygen generation, surface-mediated electron transfer, high-valent metal–oxo species, and direct oxidative transfer processes, are discussed in relation to active-site structure, oxidant utilization, matrix tolerance, and byproduct control. We further summarize how coordination environments, defect chemistry, heteroatom configurations, nanoconfinement, and immobilized interfaces regulate reactive-species formation and interfacial charge transfer. Key material platforms, including single-atom catalysts, heteroatom-doped carbons, defect-engineered oxides, catalytic membranes, hydrogels, and floating or immobilized composites, are evaluated from mechanistic and application-oriented perspectives. Finally, catalyst regeneration, cost, microbial community responses, algae–bacteria balance, ecotoxicity, and long-term safety are discussed to guide sustainable aquatic ecosystem restoration. Full article

The Baleysky gold deposit in Eastern Transbaikalia is a classic example of the long-term environmental legacy of gold mining. The cessation of industrial wastewater discharge in 1995 led to the accumulation of more than 3 million m³ of acidic water with high concentrations of heavy metals and metalloids. These waters contain concentrations many times higher than the maximum permissible levels for fishery waters (Mn up to 6594, Al—1473, Zn—486, and Cu—414), posing a significant threat to the ecosystem of the Unda River and the health of the local population. The aim of this study was to evaluate the effectiveness of the artificial geochemical barrier method for treating such waters under laboratory conditions. Column experiments were conducted using local soil and the commercial carbonate sorbent taurite at a sorbent-to-filtrate ratio of 1:5. Taurite demonstrated a significantly higher sorption capacity than soil, substantially reducing the concentrations of As, Cd, Pb, Al, Mn, Fe, Zn, and Cu and raising the pH from 2.90 to 7.96–8.03. Although health risks associated with both carcinogenic (CR) and non-carcinogenic effects (HI) decreased significantly after treatment with taurite, residual risk levels remained unacceptably high (CR ≈ 10⁻³, HI > 1). The results show that engineered geochemical barriers have great potential for reducing anthropogenic contamination at abandoned mining sites, although further optimization of this technology is necessary to achieve compliance with regulatory requirements. Full article

Urban mangroves are increasingly recognized for their important blue-carbon functions, yet their long-term aboveground carbon dynamics under climate extremes and human disturbances remain poorly understood. Here, we developed an integrated framework that combines multi-source satellite observations, field survey and LiDAR-constrained modeling to reconstruct annual species composition, canopy structure, and aboveground carbon dynamics from 1990 to 2022 in Shenzhen Bay, which is the only mangrove ecosystem within a megacity in China. Total aboveground carbon increased from 1820 (95% CI: 1386–2199) Mg C in 1990 to 6006 (95% CI: 5280–6618) Mg C in 2022, with habitat expansion accounting for most of the increase. Aboveground carbon accumulation was affected by coastal reclamation, estuarine engineering, and management-driven removal of introduced stands. Species composition emerged as a key determinant of ecosystem response to disturbance and long-term carbon dynamics. Native mangroves remained dominant and exhibited relatively stable canopy greenness during the 2008 extreme cold event. But the introduced Sonneratia apetala experienced a 42.9% drop in greenness and then took about five years to return to the level before the disturbance. By linking long-term changes in species composition, canopy structure, and aboveground carbon storage, this study provides a transferable foundation for monitoring urban blue-carbon ecosystems and evaluating the long-term consequences of disturbance, restoration, and management under accelerating urbanization and climate change. Full article

Autonomous ship maneuvering in harbor environments is increasingly supported by advances in model predictive control, reinforcement learning, digital twins, multi-sensor fusion, berth allocation, and multi-agent coordination. However, these developments are often studied as separate technological domains, while real harbor autonomy requires coordinated operation across vessels, port infrastructure, regulatory systems, cybersecurity mechanisms, and human supervisory processes. This study presents an architecture-oriented critical review of autonomous ship and harbor maneuvering research published between 2015 and May 2026. The review synthesizes literature from control engineering, maritime artificial intelligence, sensor fusion, digital twins, port logistics, cyber-physical systems, regulation, cybersecurity, and human–AI supervision. The analysis introduces two conceptual contributions: a layered cyber-physical taxonomy and an integration maturity model. The taxonomy organizes autonomous harbor maneuvering into seven interdependent layers: physical dynamics, perception and sensor fusion, prediction and state estimation, control, decision and coordination, digital twin federation, and regulatory–supervisory governance. The maturity model distinguishes isolated vessel autonomy, assisted coordination, shared digital synchronization, agent-based coordination, and fully federated maritime cyber-physical autonomy. The reviewed evidence shows substantial progress in individual layers, especially control, perception, digital twins, and berth allocation. However, major gaps remain in cross-layer synchronization, semantic interoperability, regulation-aware decision-making, cybersecurity integration, and validated ship–shore federation. To address these gaps, this study proposes a Federated Maritime Cyber-Physical Architecture for autonomous harbor maneuvering. The architecture integrates vessel autonomy cores, port intelligence cores, semantic federation middleware, agent-based negotiation, regulatory verification, cybersecurity safeguards, and human supervisory interfaces. This review argues that future progress in autonomous harbor operations depends not only on stronger algorithms, but on interoperable, explainable, regulation-aware, and cyber-resilient ship–shore ecosystems. Full article

Aquatic ecosystems are under severe stress from a diverse combination of contaminants, including heavy metals, pesticides, pharmaceuticals, and microplastics, driven by rapid industrialization, intensive agriculture, and urbanization. Globally, 80% of wastewater remains untreated, and conventional systems often fail to address emerging contaminants. Consequently, toxic heavy metals like lead and mercury can persist in water sources for decades. In response, phytoremediation has emerged as a scalable, eco-friendly, nature-based alternative. Among phytoremediation agents, duckweeds are increasingly recognized for their rapid growth, simple morphology, and continuous water-column contact. This review outlines the landscape of duckweed-based remediation, detailing molecular detoxification pathways and the synergistic role of associated microbiomes in enhancing environmental cleanup. Evidence indicates that contaminant removal is often supported by plant-microbe interactions. Despite extensive laboratory validation, field-scale implementation remains constrained by environmental complexity, pollutant mixtures, and variable climatic conditions. Furthermore, while duckweed systems hold promise within circular bioeconomy frameworks, converting wastewater into nutrient-rich biomass, contaminant accumulation in plant tissues raises concerns about biomass utilization and contaminant carryover. Addressing these challenges requires an integrative approach that links molecular detoxification, ecological interactions, and engineered system design to realize the full potential of duckweeds for sustainable aquatic pollution management. Full article

Per- and polyfluoroalkyl substances (PFASs) are persistent emerging contaminants characterized by high environmental stability and biotoxicity. Ubiquitous detection of these contaminants across aquatic environments poses severe threats to ecosystem stability and human health, while constructed wetlands (CWs) serve as a sustainable low-carbon alternative for the remediation of PFAS-laden wastewater. However, traditional mechanisms such as matrix adsorption, phytoaccumulation, and microbial transformation often suffer from low efficiency, rapid saturation, and incomplete degradation. To overcome the above drawbacks, the arbuscular mycorrhizal fungi (AMF)–plant–microbe synergistic consortium has become a promising remediation candidate, which facilitates PFAS immobilization and biodegradation via symbiotic crosstalk among three components. This paper reviews recent advancements in PFAS remediation within AMF-facilitated systems, examining fundamental synergistic mechanisms, treatment efficiencies, and key influencing factors. We propose several optimization strategies, including substrate modification, operational parameter refinement, and the integration of advanced technologies. Furthermore, we emphasize the necessity of elucidating the molecular pathways governing long-chain PFAS degradation and addressing current bottlenecks in engineering applications. Future research should prioritize molecular interaction level interaction mechanisms, the development of anti-interference systems, and field-scale validation. This review provides a theoretical foundation and technical framework for leveraging AMF–plant–microbe synergism to enhance PFAS removal in CWs. Full article

Organic vegetable cultivation requires soil management strategies that improve carbon balance and suppress soilborne diseases. This study evaluated the efficacy of acidified microbial biochar pellets (ABPM) in enhancing net ecosystem carbon balance (NECB) and suppressing clubroot disease (Plasmodiophora brassicae) during organic Chinese cabbage (Brassica rapa ssp. pekinensis) cultivation. In a field-scale evaluation, three treatments were compared: guano fertilizer (control), ABPM 27 (inoculated with Pseudomonas fluorescens 22BCO027), and ABPM 86 (inoculated with Bacillus megaterium 22BCO086). Soil incorporation of ABPM 27 and ABPM 86 significantly increased soil carbon sequestration by 29.1% and 22.4%, respectively, while simultaneously reducing cumulative greenhouse gas emissions under the experimental conditions. This resulted in positive NECB values of 2.63 and 2.94 t CO₂-eq ha⁻¹, suggesting enhanced carbon retention potential within the studied cultivation system. Beyond its impact on carbon dynamics, ABPM 27 increased marketable yield by 8.6% (77.4 t ha⁻¹) and reduced clubroot incidence by 46.2%. Rhizosphere microbial analysis revealed that ABPM 27 promoted late-season microbial diversity and the persistence of beneficial Bacillus spp. and Pseudomonas spp. populations. These findings suggest the potential multifunctional role of microbially engineered biochar pellets in improving crop production, carbon retention, and pathogen suppression under organic cultivation conditions. However, these findings are based on a single-season field experiment and NECB-based carbon balance estimates, and therefore require validation across multiple growing seasons and cultivation environments. Full article

Engineering education prepares graduates to face complex environmental and societal challenges. This involves the intersection of sustainability and innovation. Integrating these agendas is therefore necessary, and this involves identifying specific elements that have not yet been explored. To examine this relationship, a systematic literature review was conducted using an adapted PRISMA 2020 approach appropriate for a bibliometric and thematic systematic review, through four research questions related to knowledge production, pedagogical methods, innovation outcomes, and reported results. The PRISMA phases were adopted using the SCOPUS and ERIC databases. This yielded three clusters: innovation, sustainability, and engineering education. Student-centered pedagogies have also been identified as an explored opportunity to enhance innovation skills aligned with sustainability objectives. However, this incorporation involves many elements to explore, including the connection between innovation outcomes and sustainability impact. This context involves both development and the relationships among individuals, institutions, and ecosystems. This requires managing diverse visions, languages, and cultures, which highlights several challenges: long-term impacts, mindset development, contextual influences, pedagogical strategies, research–practice alignment, stakeholder communication, and faculty preparation. Overall, the findings show progress but reveal challenges across approaches and contexts. This is because sustainability-driven innovation in engineering education requires coordinated curricular, institutional, and ecosystem-oriented strategies to support learning and strengthen contributions to sustainable futures. Full article

Autonomous robotic systems are being deployed in commercial, healthcare, logistics, and mixed-use built environments at a rate that significantly outpaces the adaptive capacity of existing building design and management paradigms. Buildings have historically been conceived exclusively for human occupants, and the resulting absence of a structured, scalable framework for evaluating or designing robot-ready facilities constitutes a critical gap in both research and professional practice. This article introduces the Robot-Friendly Buildings Level of Service (RFB-LOS) framework: a five-tier hierarchical classification system that characterises the degree to which a built environment supports autonomous robotic operations across six evaluative dimensions—building intelligence, active infrastructure, architectural planning, accessibility, observability, and safety. The framework spans a continuum from Robot Excluded (RFB-LOS-1), in which a building has no awareness of its robotic occupants, to Physical AI Robot Optimised (RFB-LOS-5), in which a Physical AI middleware layer assumes the highest command authority within a coordinated human–robot–building ecosystem. Drawing structural inspiration from the SAE J3016 Levels of Driving Automation, the EU Smart Readiness Indicator, HIMSS EMRAM, and BREEAM/LEED sustainability certification, the RFB-LOS framework is positioned as a foundational standard for the built environment and systems engineering community. Five real-world case studies spanning retail, hospitality, healthcare, and corporate sectors across four countries validate the framework’s tier assignments against observed operational outcomes. Full article

Google Earth Engine (GEE) has become a central platform for planetary-scale geospatial analysis, but its rapid evolution in the last few years is not yet reflected in the existing review literature. Earlier reviews mainly describe the platform’s architecture and its initial application domains, whereas a structured bibliometric and thematic overview of the post-2022 phase of GEE is still lacking. In this more recent phase, the platform has introduced foundation models, satellite embeddings, and native links to cloud databases. Drawing on a structured bibliometric analysis of 5591 Scopus and Web of Science indexed documents published between 2011 and 2025, the results reveal sustained long-term growth, with annual publications increasing from 3 records in 2011 to 1371 records in 2025, corresponding to a compound annual growth rate (CAGR) of 54.88%, indicating a shift from exploratory testing of the platform to more operational use. Logistic growth modelling ($R^2=0.991$) suggests that GEE research is transitioning from rapid expansion towards a scientific maturity phase, where the platform increasingly functions as a normalized analytical infrastructure embedded within broader cloud-native geospatial ecosystems. The full 2011–2025 corpus is used to establish long-term bibliometric trajectories, whereas the thematic synthesis focuses on the post-2022 transition towards Geospatial Artificial Intelligence(GeoAI), satellite embeddings, and cloud-database interoperability. The review examines how new satellite embedding datasets and BigQuery integrations help close the gap between raster-centric Earth observation (EO) workflows and tabular data science. We summarise methodological changes from traditional pixel-based classifiers to multimodal fusion approaches that combine Synthetic Aperture Radar (SAR), Global Ecosystem Dynamics Investigation (GEDI), and optical sensors, and we discuss how GEE’s highly integrated ecosystem influences reproducibility and the risk of vendor lock-in. Finally, we propose a roadmap for the ongoing transition of GEE towards GeoAI, offering researchers and policymakers a transparent and reproducible framework for deploying the platform in high-impact environmental governance. Full article

The pervasive integration of artificial intelligence across e-commerce ecosystems has fundamentally transformed the competitive landscape, rendering systematic and reproducible platform evaluation frameworks an operational necessity rather than an academic exercise. Conventional multi-criteria decision analysis approaches for e-commerce evaluation remain structurally constrained by their dependency on human expert panels, which introduce recruitment costs, cognitive biases, limited reproducibility, and the practical infeasibility of assembling genuinely multidisciplinary panels spanning e-commerce strategy, machine learning engineering, and financial technology simultaneously. This study proposes a novel decision support framework that integrates Large Language Model (LLM) multi-agent simulation with the Best–Worst Method (BWM) to derive reproducible priority weights for AI-driven e-commerce platform evaluation within a rigorous business intelligence architecture. Twelve domain-differentiated LLM agents—organized into three expertise groups representing e-commerce management, AI and machine learning technology, and digital payment systems—were instantiated with structured system prompts encoding professional domain knowledge and deployed across three independent simulation rounds to perform BWM pairwise comparisons across a comprehensive six-dimensional, 30-sub-criterion evaluation hierarchy. Inter-agent consensus was synthesized through geometric mean aggregation, with consistency verification conducted via BWM’s xi* indicator and inter-round stability assessed through coefficient of variation analysis. Results reveal that Transaction Security and Trust achieves the highest dimension-level weight (w = 0.248), followed by AI Recommendation Effectiveness (w = 0.213), with Personal Data Protection (G = 0.0750), Recommendation Accuracy (G = 0.0607), and Transaction Transparency (G = 0.0549) emerging as the three highest globally ranked sub-criteria. The aggregated consistency indicator xi* = 0.062 confirms logical coherence of the multi-agent judgment consensus, and all dimension weights exhibit CV values below 2.8%, demonstrating exceptional inter-round stability. Spearman rank correlations among the three domain-expertise groups exceed 0.92, confirming strong inter-group convergence. Sensitivity analysis under perturbations of ±10% and ±20% demonstrates that the top-five priority indicators are structurally stable. This study establishes LLM multi-agent BWM simulation as a methodologically rigorous, institutionally accessible, and computationally reproducible alternative to traditional expert elicitation for complex platform evaluation tasks. Full article