Search Results (4,295)

The severe degradation of thin-layer black soil in the Southern Songnen Plain threatens both regional agricultural sustainability and national food security. While various fertile topsoil restoration technologies have been proposed, a systematic evaluation of their comprehensive benefits is lacking, hindering effective policy and technology promotion. This study addresses this gap by employing an entropy weight–fuzzy comprehensive evaluation method to assess the economic, social, and ecological performance of four predominant restoration models—no-tillage, strip-tillage, deep-tillage, and indirect return—using survey data from 263 farmers. Results identify strip-tillage as the optimal model, achieving the highest integrated benefit score (8.153) by successfully balancing superior economic profitability and social acceptance with robust ecological performance. Although no-tillage excels in ecological benefits like moisture conservation (8.901) and pesticide reduction (8.524), its economic potential is constrained by higher management costs. Deep-tillage rapidly enhances soil fertility (8.628) but is limited by high operational costs, whereas the indirect model, despite high ecological sustainability (7.781), faces adoption barriers due to technical complexity and cost. The findings underscore the necessity of moving beyond one-size-fits-all approaches. We propose a targeted promotion system based on “categorized guidance and precision adaptation”, offering a practical framework for optimizing technology deployment to support both black soil conservation and sustainable agricultural development. Full article

This study conceptualizes how artificial intelligence (AI)-based customer engagement strategies can shape consumers’ green purchasing intentions, focusing on the theorized roles of attitude and perceived risk toward green products as articulated in prior literature. Building on contemporary research in sustainable marketing and consumer psychology, the article proposes a conceptual framework in which AI-enabled engagement influences green purchase intention via attitudes, with perceived risk operating as a boundary condition that moderates these effects. To qualitatively substantiate the salience and practical relevance of these constructs, an exploratory sentiment analysis of Amazon reviews for green products was conducted to surface emotional responses, perceived value drivers, and behavioral cues. The review corpus predominantly reflects positive sentiment alongside mixed subjectivity and factual commentary, highlighting recurring decision factors such as product quality, packaging, sustainability claims, and price sensitivity. Consistent with literature, the evidence aligns with the view that personalization and transparency can bolster trust and more favorable attitudes, while perceived risks—spanning greenwashing concerns, cost, and performance doubts—remain obstacles to adoption. Crucially, the sentiment analysis is presented as illustrative and does not statistically test the proposed mediation or moderation pathways; rather, it offers qualitative support that complements the literature-based conceptual model. The study contributes by integrating insights from digital technologies, consumer psychology, and sustainable marketing to guide authentic, strategic engagement practices that can encourage eco-conscious behavior. Full article

Early intervention is pivotal for optimizing neurodevelopmental outcomes in children with language delay, where increased language stimulation can optimize therapeutic outcomes. Extending speech–language therapy from clinical settings to the home is a promising strategy; however, practical barriers and a lack of scalable, customizable home-based models limit the implementation of this approach. The integration of AI-powered digital interactive tools could bridge this gap. This pilot feasibility study adopted a single-arm pre–post (before–after) design within a two-phase, mixed-methods framework to evaluate a generative AI-powered interactive platform supporting home-based language therapy in children with either idiopathic language delay or autism spectrum disorder (ASD)-related language impairment: two conditions known to involve heterogeneous developmental profiles. The participants received clinical language assessments and engaged in home-based training using AI-enhanced tablet software, and 2000 audio recordings were collected and analyzed to assess pre- and postintervention language abilities. A total of 22 children aged 2–12 years were recruited, with 19 completing both phases. Based on 6-week cumulative usage, participants were stratified with respect to hours of AI usage into Groups A (≤5 h, n = 5), B (5 < h ≤ 10, n = 5), C (10 < h ≤ 15, n = 4), and D (>15 h, n = 5). A threshold effect was observed: only Group D showed significant gains between baseline and postintervention, with total words (58→110, p = 0.043), characters (98→192, p = 0.043), type–token ratio (0.59→0.78, p = 0.043), nouns (34→56, p = 0.043), verbs (12→34, p = 0.043), and mean length of utterance (1.83→3.24, p = 0.043) all improving. No significant changes were found in Groups A to C. These findings indicate the positive impact of extended use on the development of language. Generative AI-powered digital interactive tools, when they are integrated into home-based language therapy programs, can significantly improve language outcomes in children who have language delay and ASD. This approach offers a scalable, cost-effective extension of clinical care to the home, demonstrating the potential to enhance therapy accessibility and long-term outcomes. Full article

Unmanned Aerial Vehicles (UAVs) are increasingly integral to construction site management, supporting monitoring, inspection, and data collection tasks. Effective UAV path planning is essential for maximizing operational efficiency, particularly in complex and dynamic construction environments. While previous BIM-based approaches have explored representation models such as space graphs, grid patterns, and voxel models, each has limitations. Space graphs, though common, rely on predefined spatial spaces, making them less suitable for projects still under construction. Voxel-based methods, considered well-suited for 3D indoor navigation, suffer from three key challenges: (1) a disconnect between the BIM and voxel models, limiting data integration; (2) the computational cost and time required for voxelization, hindering real-time application; and (3) inadequate support for 4D BIM integration during active construction phases. This research introduces a novel framework that bridges the BIM–voxel gap via an enriched voxel map, eliminates the need for repeated voxelization, and incorporates 4D BIM and additional model data such as defined workspaces and safety buffers around fragile components. The framework’s effectiveness is demonstrated through path planning simulations on BIM models from two real-world construction projects under varying scenarios. Results indicate that the enriched voxel map successfully creates a connection between BIM model and voxel model, while covering every timestamp of the project and element attributes during path planning without requiring additional voxel map creation. Full article

Anaerobic digestion of manures, crop residues, food waste, and sludge frequently yields biogas with elevated hydrogen sulfide concentrations, which accelerate corrosion and reduce biogas quality. Microaeration, defined as the controlled addition of oxygen at 1 to 5% of the biogas production rate, has been investigated as a low-cost desulfurization strategy. This review synthesizes studies from 2015 to 2025 spanning laboratory, pilot, and full-scale anaerobic digester systems. Continuous sludge digesters supplied with ambient air at 0.28–14 m³ h⁻¹ routinely achieved 90 to 99% H₂S removal, while a full-scale dairy manure system reported a 68% reduction at 20 m³ air d⁻¹. Pure oxygen dosing at 0.2–0.25 m³ O₂ (standard conditions) per m³ reactor volume resulted in greater than 99% removal. Reported methane yield improvements ranged from 5 to 20%, depending on substrate characteristics, operating temperature, and aeration control. Excessive oxygen, however, reduced methane yields in some cases by inhibiting methanogens or diverting carbon to CO₂. Documented benefits of microaeration include accelerated hydrolysis of lignocellulosic substrates, mitigation of sulfide inhibition, and stimulation of sulfur-oxidizing bacteria that convert sulfide to elemental sulfur or sulfate. Optimal redox conditions were generally maintained between −300 and −150 mV, though monitoring was limited by low-resolution oxygen sensors. Recent extensions of the Anaerobic Digestion Model No. 1 (ADM1), a mathematical framework developed by the International Water Association, incorporate oxygen transfer and sulfur pathways, enhancing its ability to predict gas quality and process stability under microaeration. Economic analyses estimate microaeration costs at 0.0015–0.0045 USD m⁻³ biogas, substantially lower than chemical scrubbing. Future research should focus on refining oxygen transfer models, quantifying microbial shifts under long-term operation, assessing effects on digestate quality and nitrogen emissions, and developing adaptive control strategies that enable reliable application across diverse substrates and reactor configurations. Full article

Wire Arc Additive Manufacturing (WAAM) has been recognized as an efficient and cost-effective metal additive manufacturing technique due to its high deposition rate and scalability for large components. However, the quality and repeatability of WAAM parts are highly sensitive to process parameters such as arc voltage, current, wire feed rate, and torch travel speed, requiring advanced monitoring and adaptive control strategies. In this study, a vision-based monitoring system integrated with a reinforcement learning framework was developed to enable intelligent in situ control of WAAM. A custom optical assembly employing mirrors and a bandpass filter allowed simultaneous top and side views of the melt pool, enabling real-time measurement of layer height and width. These geometric features provide feedback to a tabular Q-learning algorithm, which adaptively adjusts voltage and wire feed rate through direct hardware-level control of stepper motors. Experimental validation across multiple builds with varying initial conditions demonstrated that the RL controller stabilized layer geometry, autonomously recovered from process disturbances, and maintained bounded oscillations around target values. While systematic offsets between digital measurements and physical dimensions highlight calibration challenges inherent to vision-based systems, the controller consistently prevented uncontrolled drift and corrected large deviations in deposition quality. The computational efficiency of tabular Q-learning enabled real-time operation on standard hardware without specialized equipment, demonstrating an accessible approach to intelligent process control. These results establish the feasibility of reinforcement learning as a robust, data-efficient control technique for WAAM, capable of real-time adaptation with minimal prior process knowledge. With improved calibration methods and expanded multi-physics sensing, this framework can advance toward precise geometric accuracy and support broader adoption of machine learning-based process monitoring and control in metal additive manufacturing. Full article

Residual oil zones (ROZ) arise under the oil–water contact of main pay zones due to diverse geological conditions. Historically, these zones were considered economically unviable for development with conventional recovery methods because of the immobile nature of the oil. However, they represent a substantial subsurface volume with strong potential for CO₂ sequestration and storage. Despite this potential, effective techniques for assessing CO₂-EOR performance coupled with CCUS in ROZs remain limited. To address this gap, this study introduces a machine learning framework that employs artificial neural network (ANN) models trained on data generated from a large number of reservoir simulations (300 cases produced using Latin Hypercube Sampling across nine geological and operational parameters). The dataset was divided into training and testing subsets to ensure generalization, with key input variables including reservoir properties (thickness, permeability, porosity, Sorg, salinity) and operational parameters (producer BHP and CO₂ injection rate). The objective was to forecast CO₂ storage capacity and oil recovery potential, thereby reducing reliance on time-consuming and costly reservoir simulations. The developed ANN models achieved high predictive accuracy, with R² values ranging from 0.90 to 0.98 and mean absolute percentage error (MAPRE) consistently below 10%. Validation against real ROZ field data demonstrated strong agreement, confirming model reliability. Beyond prediction, the workflow also provided insights for reservoir management: optimization results indicated that maintaining a producer BHP of approximately 1250 psi and a CO₂ injection rate of 14–16 MMSCF/D offered the best balance between enhanced oil recovery and stable storage efficiency. In summary, the integrated combination of reservoir simulation and machine learning provides a fast, technically robust, and cost-effective tool for evaluating CO₂-EOR and CCUS performance in ROZs. The demonstrated accuracy, scalability, and optimization capability make the proposed ANN workflow well-suited for both rapid screening and field-scale applications. Full article

The civil construction sector is crucial to global economic development, influencing GDP and driving innovation with Industry 4.0 technologies such as BIM and IoT. However, how these technologies can be effectively aligned with the principles of Sustainability 4.0 within the framework of Construction 4.0 remains unclear. This paper aims to identify the barriers and drivers related to the impact of adopting Industry 4.0 enabling technologies on Sustainability 4.0 in the construction sector. To achieve this, we conducted a Systematic Literature Review (SLR) using articles from the Web of Science and Scopus databases, focusing on the period from 2021 to 2025. The methodology applied enabled a comprehensive analysis of 50 articles, highlighting challenges, barriers, and potential facilitators in the adoption of Sustainability 4.0 practices. Among the key findings, advanced technologies such as BIM and IoT have shown positive impacts on sustainability dimensions, like reducing energy consumption; yet, practical implementation still encounters significant barriers, including high costs and insufficient public policies. Only 30% of the reviewed articles discuss adoption in less developed regions, indicating geographical disparity in the application of these technologies. The paper provides valuable insights for managers and policymakers on overcoming existing barriers, emphasizing the importance of innovative business models and the need for cultural and educational adaptation. The study suggests that, with a collaborative approach and adequate support policies, Industry 4.0 technologies can transform sustainable practices in civil construction, fostering a more balanced and environmentally responsible economy. Full article

Background: African American (AA)/Black and Latinx communities have limited access to evidence-based fall prevention programs such as Tai Chi Prime (TCP). Community-led interventions that incorporate peer support are cost-effective and sustainable. Using the Treatment Fidelity Framework (TFF) and a mixed-methods research approach, we evaluated the training and support given to trainees during the TCP leader training pathway process and their subsequent fidelity in delivering six culturally tailored community courses. Methods: Trainees completed feedback forms after each TCP leader training pathway course. Using a fidelity checklist, a TCP master trainer rated six community TCP classes led by race- and language-concordant leaders. Trainees were invited to participate in virtual one-on-one semi-structured interviews to share their perspectives on the appropriateness and relevance of the TCP leader training pathway and their experience leading community TCP classes. Quantitative data was analyzed using descriptive statistics on Microsoft Excel. Three study team members coded qualitative data using directed content analysis approach. Results: Twenty-five candidates enrolled in the TCP leader training. Forty-eight percent identified as AA/Black while 52% identified as Latinx. Eleven trainees (six AA/Black and five Latinx) completed the entire TCP leader training pathway to become certified TCP leaders. Trainees rated the training process as highly satisfactory and appropriate. Leaders from both communities received high fidelity scores for community course delivery. Conclusions: Findings contribute to the existing literature, particularly regarding how to effectively disseminate and evaluate a culturally tailored TCP leader training and certification process for culturally diverse communities while maintaining fidelity to the curriculum. Full article

High-definition maps (HDMaps) are critical for safe autonomy on structured roads. Yet traditional production—relying on dedicated mapping fleets and manual quality control—is costly and slow, impeding large-scale, frequent updates. Recently, standard-definition maps (SDMaps) derived from remote sensing have been adopted as priors to support HDMap perception, lowering cost but struggling with subtle urban changes and localization drift. We propose Loop-MapNet, a self-evolving, multimodal, closed-loop mapping framework. Loop-MapNet effectively leverages surround-view images, LiDAR point clouds, and SDMaps; it fuses multi-scale vision via a weighted BiFPN, and couples PointPillars BEV and SDMap topology encoders for cross-modal sensing. A Transformer-based bidirectional adaptive cross-attention aligns SDMap with online perception, enabling robust fusion under heterogeneity. We further introduce a confidence-guided masked autoencoder (CG-MAE) that leverages confidence and probabilistic distillation to both capture implicit SDMap priors and enhance the detailed representation of low-confidence HDMap regions. With spatiotemporal consistency checks, Loop-MapNet incrementally updates SDMaps to form a perception–mapping–update loop, compensating remote-sensing latency and enabling online map optimization. On nuScenes, within 120 m, Loop-MapNet attains 61.05% mIoU, surpassing the best baseline by 0.77%. Under extreme localization errors, it maintains 60.46% mIoU, improving robustness by 2.77%; CG-MAE pre-training raises accuracy in low-confidence regions by 1.72%. These results demonstrate advantages in fusion and robustness, moving beyond one-way prior injection and enabling HDMap–SDMap co-evolution for closed-loop autonomy and rapid SDMap refresh from remote sensing. Full article

Airports are among the most energy-intensive infrastructures, and the decarbonization of ground operations is essential to achieving sustainable aviation goals. Vehicle-integrated photovoltaic (VIPV) offers a promising strategy to complement electrification by enabling on-board renewable generation. While previous studies have mainly focused on fixed PV installations such as rooftops or carports, the potential of VIPV in airports has largely been overlooked, and no structured methodology has been established to investigate it. This study addresses this gap by proposing a two-scenario framework for assessing VIPV performance. The first scenario, named the Generalized Approach, estimates annual energy production based on irradiance data, vehicle surface area, and driving-to-standby ratios. The second scenario, named the Data-Driven Approach, incorporates detailed GPS-based driving data to capture the dynamic effects of orientation, speed, and operating conditions. Applied to European and Middle Eastern airports, the framework showed that VIPV could cover 1700–5500 $\mathrm{k}\mathrm{m}/\mathrm{y}\mathrm{e}\mathrm{a}\mathrm{r}$ for buses, 650–5000 $\mathrm{k}\mathrm{m}/\mathrm{y}\mathrm{e}\mathrm{a}\mathrm{r}$ for minibuses, and 840–6180 $\mathrm{k}\mathrm{m}/\mathrm{y}\mathrm{e}\mathrm{a}\mathrm{r}$ for luggage tractors, with avoided emissions strongly influenced by local grid intensity. Grid parity analysis indicated favorable conditions in sunny, high-cost electricity markets. The framework is transferable to other VIPV applications and provides a practical tool for evaluating their technical, environmental, and economic potential. Full article

Supervised learning models in healthcare and other domains heavily depend on high-quality, labeled data. However, acquiring expert-verified labels (i.e., the gold standard) is often impractical due to cost, time, and subjectivity. Crowdsourcing offers a scalable alternative by collecting labels from multiple non-expert annotators; however, it introduces label noise due to the heterogeneity of annotators. In this work, we propose CrowdAttention, a novel end-to-end deep learning framework that jointly models classification and annotator reliability using a cross-attention mechanism. The architecture consists of two coupled networks: a classification network that estimates the latent true label, and a crowd network that assigns instance-dependent reliability scores to each annotator’s label based on its alignment with the model’s current prediction. We demonstrate the effectiveness of our approach on both synthetic and real-world datasets, showing improved accuracy and robustness compared to state-of-the-art multi-annotator learning methods. Full article

This study uses the case of substituting natural river sand with molybdenum tailings in concrete production in China to propose a methodological framework for evaluating the life cycle environmental impact of concrete materials. This approach addresses the mechanical performance adaptability and environmental friendliness, as well as the resource utilization of solid waste. The resource consumption, environmental impact, and economic costs are systematically analyzed using a life cycle assessment (LCA) approach, and the circular economy potential of tailings-based concrete is explored. A three-dimensional evaluation framework is constructed, encompassing raw material production, transportation, and construction stages. The environmental impacts of concrete with different molybdenum tailings replacement rates and strength grades are quantified using a willingness-to-pay (WTP) model. The results indicate that increasing the dosage of molybdenum tailings can significantly reduce environmental indicators such as global warming potential and acidification potential value. Specifically, C30 concrete with a 100% replacement rate shows an 8.5% reduction in total WTP compared to ordinary concrete, with a 2.85% reduction in energy consumption during the production stage. High-strength concrete further optimizes the environmental cost per unit strength through the “strength dilution effect,” with a 44.9% reduction in carbon footprint for 60 MPa concrete compared to 30 MPa concrete. Regional analysis reveals that the environmental contribution of the production stage dominates in short-distance transportation scenarios, while logistics optimization has a significant emission reduction effect in long-distance transportation scenarios. The study demonstrates that the proposed LCA methodology provides a scientific approach for the development of green building materials and the sustainable resource utilization of solid waste through case-informed generalization. Full article

Monkeypox (Mpox) has re-emerged as a global public health threat, with recent outbreaks linked to novel mutations that enhance viral transmissibility and immune evasion. The Mpox virus (MPXV), a double-stranded deoxyribonucleic acid (DNA) orthopoxvirus, shares high structural and enzymatic similarity with the variola virus, underscoring the need for urgent therapeutic interventions. While conventional antiviral development is time-intensive and costly, drug repurposing offers a rapid and cost-effective strategy by leveraging the established safety and pharmacological profiles of existing medications. This is a narrative integrative review synthesizing published evidence on drug repurposing strategies against MPXV. To address these issues, this review explores MPXV molecular targets critical for genome replication, transcription, and viral assembly, highlighting how the Food and Drug Administration (FDA)-approved antivirals (cidofovir, tecovirimat), antibiotics (minocycline, nitroxoline), antimalarials (atovaquone, mefloquine), immunomodulators (infliximab, adalimumab), and chemotherapeutics (doxorubicin) have demonstrated inhibitory activity against the virus using computational or experimental approaches. This review further evaluates advances in computational methodologies that have accelerated the identification of host-directed and viral-directed therapeutic candidates. Nonetheless, translational challenges persist, including pharmacokinetic limitations, toxicity concerns, and the limited efficacy of current antivirals such as tecovirimat in severe Mpox cases. Future research should integrate computational predictions with high-throughput screening, organ-on-chip technologies, and clinical pipelines, while using real-time genomic surveillance to track viral evolution. These strategies establish a scalable and sustainable framework for the MPXV drug discovery. Full article

Traditional multi-modal dynamic transit assignment (DTA) models predominantly focus on bus and rail systems, overlooking the role of bike-sharing in passenger flow distribution. To bridge this gap, a multi-modal dynamic transit assignment model incorporating bike-sharing (MMDTA-BS) is proposed. This model integrates bike-sharing, buses, rail services, and walking into a unified framework. Represented by the variational inequality (VI), the MMDTA-BS model is proven to satisfy the multi-modal dynamic transit user equilibrium conditions. To solve the VI formulation, a projection-based approach with dynamic path costing (PA-DPC) is developed. This approach dynamically updates path costs to accelerate convergence. Experiments conducted on real-world networks demonstrate that the PA-DPC approach achieves rapid convergence and outperforms all compared algorithms. The results also reveal that bike-sharing can serve as an effective means for transferring passengers to rail modes and attracting short-haul passengers. Moreover, the model can quantify bike-sharing demand imbalances and offer actionable insights for optimizing bike deployment and urban transit planning. Full article