Search Results (287)

Artificial intelligence (AI) and machine learning (ML) algorithms possess the capability to accelerate the design of novel materials; however, their advancement in materials science is severely hindered by a fundamental deficit of experimental data, commonly referred to as data starvation. Unlike solution-based chemistry, where high-throughput (HT) technologies are a well-established standard, the automated synthesis of solid materials—particularly polymers and multicomponent composites—poses an extreme engineering challenge. Furthermore, the traditional, manual research model is inherently flawed by human bias, notably the systematic non-publication of negative results, which deprives AI models of critical boundary information regarding the design space. This paper is the first in a three-part review series defining the architecture of a fully automated, unbiased “data factory” for closed-loop discovery. This section focuses on the physical foundations of the HT workflow: experimental planning, automated synthesis, and material management. Emphasis is placed on the paradigm shift from classical, discrete Design of Experiments (DoE) to the novel concept of Continuous Gradient DoE. It reviews how robotic platforms utilizing precise gravimetric and volumetric feeders, integrated with extruders and in-line capillary rheology, enable the seamless, high-throughput manufacturing of thermoplastics and composites. Moreover, an innovative approach to sample logistics is presented, redefining classical storage patterns through the implementation of Continuous Material Management. This encompasses direct physical tagging (e.g., inkjet marking on continuous filaments or films), spool-based transport systems, and precise, real-time metadata mapping. As demonstrated, the integration of these systems yields an order-of-magnitude increase in productivity (generating tens of thousands of novel material variants annually), a radical reduction in unit costs, and the production of terabytes of standardized, machine-readable data. Establishing this reliable hardware and analytical infrastructure represents the essential first step toward unlocking the full potential of artificial intelligence in advanced materials engineering. Full article

Acid mine drainage (AMD) pollution from closed coal mines in karst regions represents a major environmental challenge in the global mining industry. The complexity of hydrogeological conditions in such regions leads to significant challenges in both predictability and controllability of pollution. Taking the Yudong River Basin in Guizhou Province, Southwest China, as the study area, and based on six years (2017–2023) of systematic remediation practices and monitoring data, this study systematically evaluates the effectiveness and applicable conditions of three types of treatment technologies: centralized treatment stations, source control combined with end-of-pipe treatment, and water-sealing ecological plugging. On this basis, governance models applicable to karst regions are distilled. The results show that after six years of remediation, the number of pollution points in the Yudong River Basin decreased from 27 to 12. At the outflow section, the total Fe reduction rate reached 88.3%, the total Mn reduction rate reached 62.3%, and the proportion of contaminated river length was reduced by 78.5%. Each of the three technologies has its own applicable conditions. Centralized treatment stations, characterized by mature technology but high operational costs, are suitable for emergency transition periods. Source control combined with end-of-pipe treatment addresses both symptoms and root causes, making it applicable to complex pollution points. Water-sealing ecological plugging, although cost-controllable, carries a risk of secondary pollution in karst-developed areas. The failure of water-sealing ecological plugging technology is mainly attributed to two mechanisms: bypass flow through karst conduits and overflow induced by water level rise. Based on the six-year remediation practice, this study proposes a source control model for karst conduits centered on the core concepts of “filling, isolating, plugging, intercepting, draining, and controlling”. The implementation process consists of four stages: detailed investigation, graded optimization, stepwise implementation, and long-term monitoring. The core innovation lies in the cross-disciplinary application of coal mine water control techniques to environmental remediation, achieving a shift from passive end-of-pipe treatment to active source control. This model can provide theoretical reference and practical guidance for karst mining areas in Southwest China and other regions with similar geological conditions. Full article

Optoelectronic devices are emerging as a cornerstone of advanced renewable energy technologies, offering innovative routes for energy harvesting, conversion, and management with high efficiency and versatility. This review summarizes recent advances in the semiconductor materials engineering field, device configurations, and light–matter interaction mechanisms that underpin advanced optoelectronic systems for solar energy harvesting, solar-driven chemical conversion, and smart grid integration, among others. Emphasis is placed on the breakthroughs achieved in the perovskite and hybrid photovoltaics, photoelectrochemical energy conversion, and nanostructured optoelectronic platforms that enable much-increased light absorption, reduced recombination losses, and scalable large-scale fabrications. Moreover, the challenges closely linked with long-term stability, environmental durability and benevolence, and worldwide deployment are critically addressed, together with the emerging opportunities in AI design, tandem device technological solutions, integrated energy systems, and machine learning approaches for optimizing device performance, thermal management, and energy storage capabilities. Finally, the present review concludes by outlining the future research directions that could accelerate the transition toward high-performance, cost-effective, and sustainable optoelectronic solutions responsive to global renewable energy requirements. Full article

The safe disposal of sharps, particularly acupuncture and dry needling needles, remains a challenge in clinical and therapeutic environments, where inadequate management increases the risk of occupational injuries and infections. Commercial needle disposal devices are often costly, non-portable, and closed-source, limiting their adoption in small clinics and low-resource contexts. This work presents the design, construction, and validation of an open-source electric needle incinerator developed as a low-cost, safe, and reproducible alternative for biomedical waste management. The device was designed using accessible materials, 3D-printed components, and standard electronic parts, ensuring ease of replication. Detailed build and operating instructions are provided, to facilitate reproduction and future development of the system. Validation tests confirmed that the prototype incinerates individual needles in 3–5 s, processing typical sessions of 5–20 needles without performance degradation. Safety was ensured through thermal insulation, protective casing, and compliance with international standards. The fabrication cost of approximately 199 USD represents a reduction of over 65% compared to commercial devices priced at 600–1500 USD. By openly releasing the design, this contribution supports the hardware community with a replicable solution that enhances occupational safety, reduces costs, and fosters innovation in therapeutic and educational contexts. Full article

Driven by artificial intelligence, multi-source sensing, agricultural robots and big data technologies, global agriculture is rapidly upgrading from precision agriculture and agriculture 4.0 to agriculture 5.0. Artificial intelligence has evolved from a single diagnostic tool to an intelligent system that integrates the “perception-decision-execution” process throughout. It is widely applied in crop phenotype analysis, remote sensing monitoring, yield prediction, and autonomous operation of intelligent equipment, etc. This article takes the framework of “intelligent perception-cognitive decision-autonomous execution” to systematically review the core technologies, typical applications, and frontier directions of agricultural artificial intelligence. It focuses on introducing the progress of key technologies such as three-dimensional phenotype, hyperspectral remote sensing, multimodal fusion, and causal machine learning, as well as their value in improving resource utilization efficiency, enhancing climate resilience, and supporting field precision management. At the same time, it points out that current agricultural AI still faces practical bottlenecks such as insufficient generalization ability of models, scarce data and high annotation costs, difficulties in edge deployment, barriers in multi-source data integration, and weak interpretability and engineering reliability. Future research will focus on the construction of closed-loop autonomous farms, the collaboration of agricultural large models and intelligent agents, the construction of data centers and AI and data infrastructure, and the development of green and low-cost AI research. This will provide support for the technological innovation and industrialization implementation of agricultural artificial intelligence. Full article

The growing concern over greenhouse gas emissions has prompted the need for efficient carbon dioxide (CO₂) capture technologies. This study focuses on simulating CO₂ adsorption using a zinc-based metal–organic framework (Zn-MOF-5). The primary aim is to develop and refine a robust MATLAB-based approach for equilibrium and kinetic modelling using the Linear Driving Force (LDF) model and Langmuir isotherm, capable of accurately predicting CO₂ adsorption performance under varying operational conditions. By employing advanced computational methods, this research seeks to streamline the process design and enhance the feasibility of sustainable CO₂ capture solutions. Excel was used for statistical analysis and validation, while MATLAB R2025a was utilised for equilibrium and kinetic modelling using the LDF model and the Langmuir isotherm. The independent effects of temperature, pressure, and flow rate were evaluated using the variable effect method. The study found a significant negative association between temperature and CO₂ uptake, consistent with the exothermic nature of the adsorption process. Pressure had a significant impact on adsorption, whereas flow rate had little effect within the investigated range. The simulated CO₂ uptake (21.196 mmol/g) closely matched the experimental data (21.07 mmol/g) with a 0.59% variance, validating the model’s trustworthiness. The research shows that Zn-MOF-5 has a strong adsorption potential and that simulation tools can significantly minimise experimental costs and time. Furthermore, it underscores the potential of simulation tools to significantly reduce experimental costs and time, paving the way for more efficient and effective carbon capture solutions. This initiative not only contributes to optimising process design but also promotes sustainable practices in addressing global CO₂ emissions. By contributing to process optimisation, this study aligns with the United Nations Sustainable Development Goal (SDG) 13: Climate Action, which emphasises the urgent need for innovative solutions to combat climate change and its impacts. Furthermore, it promotes sustainable practices to address global CO₂ emissions, thereby supporting broader efforts for environmental sustainability. Full article

With the steady progress of the global energy transition and the pursuit of “dual carbon” goals, Offshore Carbon Sequestration (OCS) has emerged as a pivotal strategic pathway within Carbon Capture and Storage (CCS) initiatives aimed at mitigating climate warming. Nevertheless, the drilling of OCS injection wells faces severe challenges, including narrow geological pressure windows, high risks of shallow geohazards, stringent environmental protection standards, and prohibitive construction costs. Riserless Mud Recovery (RMR) technology, as a novel and eco-friendly deepwater drilling technique, provides innovative technical support for OCS by establishing a closed-loop seafloor circulation system that achieves dual-gradient pressure control and “near-zero discharge” of drilling fluids. This paper systematically reviews the development history and technical principles of RMR. By integrating the specific requirements of OCS injection well drilling—such as wellbore integrity, environmental protection, and shallow hazard mitigation—the study provides an in-depth analysis of the application potential of RMR in drilling CO₂ injection wells within shallow formations. Furthermore, it demonstrates the engineering feasibility of RMR across technical, environmental, and economic dimensions. Building on this analysis, the paper discusses current technical challenges regarding key equipment research and development, adaptability to complex operating conditions, enhancement of intelligent control systems, and the establishment of technical standards. It also outlines the prospects for the integrated development of RMR with emerging fields, including hydrate-based carbon sequestration, intelligent drilling and completion, and carbon sequestration in far-reaching deep-sea areas. The research indicates that RMR technology can effectively resolve the dual constraints of cost control and environmental protection in OCS drilling. With breakthroughs in critical hardware, such as high-displacement subsea lift pumps, and the deepening of cross-disciplinary integration, RMR is poised to become an essential technical pillar in the field of offshore carbon sequestration. Full article

This paper examines the integration of three-dimensional (3D) printing and robotic fabrication in contemporary architectural design, with a focus on overcoming the technical limitations that constrain large-scale adoption. While additive manufacturing enables the production of complex geometries and customized structures, its standalone application remains limited by fixed build volumes, planar deposition, lack of tensile reinforcement, open-loop process control, and single-process extrusion. To address these constraints, the paper proposes a functional integration framework that systematically maps robotic fabrication capabilities onto these five critical limitations. Evidence from recent studies demonstrates that such integration has already led to measurable advances, including up to a 90-fold increase in printable volume through mobile robotic systems, robotically fabricated reinforcement systems (e.g., Mesh Mold) achieving post-crack behavior comparable to conventional reinforced concrete, and the implementation of closed-loop sensor-based process control to enhance interlayer bonding. Despite these achievements, interdisciplinary collaboration across architecture, structural engineering, materials science, and robotics remains largely fragmented and is predominantly confined to academic and pilot-scale projects, such as the ETH Zurich DFAB House. Regulatory progress is also limited, with only isolated code-compliant implementations under frameworks such as ICC-ES AC509 and ISO/ASTM 52939. Persistent barriers including high capital costs, loss of information in BIM-to-fabrication workflows, anisotropic material behavior, and the absence of long-term durability standards continue to restrict widespread adoption. These findings suggest that advancing robotic additive manufacturing in architecture requires not only technological innovation but also coordinated cross-disciplinary integration, standardized testing protocols, and harmonized regulatory frameworks. Full article

Pile foundation is one of the most widely used deep foundation solutions, and the intelligent informatization of its design process holds significant theoretical and practical value. The innovation of this study lies in the construction of a systematic integrated framework for intelligent pile foundation design and 3D visualization. Unlike previous machine learning studies that primarily focus on the predictive accuracy of individual parameters, this framework establishes a “coordinate-driven” parameter generation mechanism, enabling a fully automated workflow from geological feature extraction and implicit design parameter computation to real-time 3D model mapping. We clarified the basic principles of intelligent informatization and constructed a database to conduct a correlation analysis to identify key algorithms. An intelligent calculation model based on random forest was established for predicting core design indicators such as pile length and diameter. By transforming complex nonlinear design logics into data-driven predictive models, the framework significantly reduces the reliance on empirical assumptions and the costs associated with frequent human–computer interaction in traditional design processes. Furthermore, a 3D mapping method is developed using Three.js to achieve real-time coupling of design data and spatial geometric models. The proposed method was applied to a practical engineering case in southern China for verification. The results demonstrate that the framework allows for the rapid formation of data models with reduced manual input while optimizing workflow efficiency and maintaining objective accuracy. This approach provides a closed-loop solution for geotechnical engineering transitioning from digital decision-making to visual presentation, offering high potential for adaptation to other engineering scenarios. Full article

Reducing the ecological footprint of aviation is a key objective in the development of future aircraft. This is particularly relevant in the emerging field of Urban Air Mobility, which demands sustainable yet industrially feasible solutions due to expected high production rates. As part of the cooperative research project KONKAV, innovative materials and manufacturing methods are being explored to meet these demands. One such approach is the partial consolidation of nonwovens made from recycled carbon fibers, aimed at producing multifunctional, recyclable components for Urban Air Mobility cabin linings for high bending stiffness requirements. This study presents the experimental characterization of various nonwoven architectures, focusing on how different levels of consolidation affect their specific mechanical properties. The partially consolidated structure enables tailored stiffness profiles, making it possible to optimize structural performance while integrating functions such as thermal insulation and acoustic damping directly into the lining. An analytical material model has been developed by analyzing the experimental results. The findings demonstrate that partially consolidated nonwovens can achieve a competitive stiffness-to-weight ratio, with advantages over conventional glass-fiber-reinforced composites in terms of eco-efficiency and circularity. The proposed construction method offers potential for cost-effective, lightweight solutions that support closed-loop material use in aviation interiors. Full article

Manufacturing SMEs in Ecuador operate under macroeconomic volatility and limited financing; improvements in processes and management are key mechanisms for sustaining productivity and competitiveness. In contexts where conventional innovation indicators are unavailable, financial ratios constitute replicable signals that close a measurement gap in emerging economies. This study constructs the Structure-Based Innovation Index (SBII) as the mean of within-sample percentile ranks of capital efficiency (EBIT/Assets) and sales per employee, using financial statements from the SCVS, sectoral indicators from ENESEM, and size classification from REEM. The sample includes 58 formal manufacturing SMEs in Ecuador in 2023, stratified by province and size. Performance is measured through labor productivity and operating profitability (EBIT/Sales). Tercile comparisons reveal clear performance differentiation: the high-SBII group exhibits substantially higher median sales per employee (USD 129,552 vs. USD 40,176 in the low group) and higher operating profitability. Signals are more strongly reflected in productivity than in margins, indicating that operational gains materialize earlier. A robustness check using SBIIalt confirms that gradients are not index artifacts. High-performing SMEs are distinguished by institutionalized operational discipline: asset utilization, throughput stability, and cost control. The SBII is a replicable proxy for structure-based innovation in data-constrained environments. The findings align with SDGs 8, 9, and 12. Full article

The transition toward sustainable food systems requires innovative approaches to managing perishable products, where inefficient inventory practices contribute significantly to global food loss and environmental degradation. This study develops a circular-economy-oriented inventory optimisation framework for dairy supply chains that integrates environmental externalities and waste valorisation pathways into operational decision-making. Departing from traditional linear “produce–consume–dispose” models, this study embeds three core sustainability mechanisms into a stochastic dynamic-programming framework: (1) progressive environmental cost internalisation aligned with EU Emissions-Trading System carbon pricing, capturing both waste-related emissions and cold-chain energy footprints; (2) circular-economy value-recovery channels that redirect near-expiry products to secondary applications (animal feed, biogas production, industrial processing) rather than disposal; and (3) deterioration-aware demand management that minimises resource throughput while maintaining service levels. Empirical calibration using Ukrainian dairy industry data demonstrates that sustainability-integrated inventory policies reduce waste generation by 4.8–10% relative to conventional approaches, with high-deterioration products showing the greatest potential for improvement. The authors identify a critical threshold in the circular economy: when salvage recovery rates exceed 35%, waste becomes an economic and ecological asset, fundamentally altering the sustainability calculus of inventory decisions. Environmental costs account for 4.6% of total operating expenses at current carbon prices, a share projected to increase substantially as climate regulations tighten. The findings provide actionable guidance for dairy supply chain stakeholders pursuing the Sustainable Development Goals (SDGs 2, 12, 13): processors should establish circular-economy partnerships that achieve salvage rates above 35%, implement product-specific policies for high-deterioration items, and proactively integrate carbon pricing into inventory optimisation. The framework bridges sustainable operations theory and circular economy practice, offering a replicable model for transitioning perishable food supply chains toward closed-loop, low-waste configurations that simultaneously reduce environmental impact and enhance economic performance. Full article

In recent years, scientific and technological development has made trade-in programs for innovative electronic products more and more popular. Many of these innovative companies that continue to launch new products offer trade-in and cash-out sales strategies to stimulate purchase. This paper studies when a company launches these two sales strategies and how to ensure optimal pricing that maximizes profits, while taking into account the degree of the consumer’s strategy, the degree of the new product’s innovation, the residual value of the old products, and the cost. We construct a two-period dynamic pricing joint optimization model with four core decision variables and derive the closed-form optimal solution through strict mathematical derivation including Hessian matrix analysis and KKT condition verification. We have adopted a dynamic pricing strategy that conforms to the actual market. The results show that this study provides new mathematical insights for dynamic pricing research, and reveals the substantive rule that companies are more likely to gain greater benefits when the degree of product innovation is not high and the consumer’s strategy degree is moderate. Statistics show that companies are more likely to gain greater benefits when the degree of product innovation is not high and the consumer’s strategy degree is moderate. Full article

This study investigates the shear behavior of glass fiber-reinforced polymer (GFRP)-reinforced concrete (RC) beams to address challenges associated with their low elastic modulus, absence of yielding, and reduced stirrup efficiency in bending regions. GFRP bars are increasingly adopted as an alternative to steel due to their superior corrosion resistance, durability, and cost-effectiveness. This study focuses on the effects of stirrup type, stirrup spacing, and shear span-to-effective depth ratio on the structural performance of GFRP RC beams. Twelve full-scale beams were tested under four-point bending, incorporating three GFRP shear reinforcement configurations: fabricated closed stirrups, integrated straight bar systems, and discrete vertical bars. Experimental observations were analyzed in terms of failure modes, load-carrying capacity, energy absorption, and deformation characteristics. Results indicate that fabricated F-type stirrups provide the highest shear performance, though their effectiveness is limited by premature rupture at bending points. Site-integrated S- and T-type configurations offer practical alternatives, maintaining structural integrity while mitigating bend-related stress concentrations, but with slightly lower energy absorption and load capacity. Increasing stirrup spacing significantly reduces shear resistance and shifts failure from flexural to shear-dominated modes. Comparisons with widely used design codes and analytical models show that CSA S806-12 provisions offer the most reliable predictions, while other guidelines tend to over- or underestimate shear capacity depending on configuration and a/d ratio. The study highlights the importance of optimizing stirrup type and spacing to enhance the shear performance of GFRP RC beams. Findings provide valuable insights for improving current design methodologies, offering guidance for engineers seeking durable, corrosion-resistant alternatives to steel reinforcement in aggressive environments. This research demonstrates that innovative site-integrated stirrup configurations can bridge practical fabrication constraints without compromising overall shear performance, promoting more efficient and resilient GFRP RC structures. Full article

Against the dual backdrop of worsening air pollution and industrial intelligent transformation, industrial robot technology has become an important means to promote air pollution synergistic governance. This study innovatively incorporates public environmental satisfaction and industrial robot application as dynamic mechanism variables, constructing an evolutionary game model involving the government, industrial enterprises, and the public. Through theoretical analysis and numerical simulation, the study reveals the influence mechanism of key cost–benefit parameters on stakeholders’ strategic interaction and the system’s evolution path. The conclusions are as follows: (1) The government’s environmental supervision directly affects enterprises’ green transformation willingness, and enterprises’ behavior reversely impacts public satisfaction and supervision effectiveness, forming a “supervision–response–feedback” closed-loop. (2) The cost and benefit parameters related to industrial robots are crucial for the evolution of the game system, and there is significant heterogeneity in their impact on the strategic choices of the three parties. The robot adaptation transformation of enterprise industrial depends on the comprehensive consideration of the transformation cost and the green benefits. Public supervision is regulated by both the supervision cost and the incentive benefit. The government regulation takes into account both the regulatory cost and the loss of social reputation. Various parameters dynamically regulate the system’s equilibrium by altering the party’s cost–benefit structure. (3) The application of industrial robots and the feedback of public environmental satisfaction form a coupling effect, jointly determining the long-term evolution direction of the game system. When the cost benefit and supervision incentives are well-matched, enterprises will actively promote the green transformation of industrial robots in order to achieve intelligent pollution control. The effectiveness of public supervision has also been fully realized. The dynamic adaptation of the two components can lead the system towards an efficient and stable equilibrium in air pollution governance. Full article