Search Results (2,168)

The equitable allocation of carbon emission responsibility is fundamental to advancing China’s industrial decarbonization, achieving its dual-carbon goals, and realizing regional sustainable development. However, prevailing interprovincial carbon accounting frameworks often neglect the coupled dynamics of economic benefits, energy flows, and ecological capacity, leading to systematic misattribution of industrial carbon footprint transfers. Here, we develop an integrated analytical framework combining multi-regional input–output (MRIO) modeling and net primary productivity (NPP) assessment to comprehensively quantify industrial carbon footprints and their transfers across 30 Chinese provinces. By embedding both the benefit principle (aligning responsibility with trade-generated economic gains) and the energy flow principle (accounting for interprovincial energy trade), we construct a dual-adjustment mechanism that rectifies spatial and sectoral imbalances in traditional accounting. Our results reveal pronounced east-to-west industrial carbon footprint transfers, with resource-rich provinces (e.g., Inner Mongolia, Xinjiang) disproportionately burdened by external consumption, impacting the balance of sustainable development in these regions. Implementing benefit and energy flow adjustments redistributes responsibility more fairly: high-benefit, energy-importing provinces (e.g., Shanghai, Jiangsu, Beijing) assume greater carbon obligations, while energy-exporting, resource-dependent regions see reduced responsibilities. This approach narrows the gap between production- and consumption-based accounting, offering a scientifically robust, policy-relevant pathway to balance regional development and environmental accountability. The proposed framework provides actionable insights for designing carbon compensation mechanisms and formulating equitable decarbonization policies in China and other economies facing similar regional disparities. Full article

This study aimed to develop an environmentally friendly and relatively efficient method for extracting natural antioxidants from avocado by-products while investigating the antioxidant mechanisms of their core bioactive components on multiple dimensions. In vitro antioxidant assays (ABTS, FRAP, SAFR, SFR, ORAC, DPPH) demonstrated that flavonoid procyanidin was the primary antioxidant component in avocado seeds, exhibiting the strongest activity (DPPH EC₅₀ = 3.6 µg/mL). The Hill model indicated a positive synergistic effect (n = 3.1). Chemical and molecular mechanism analyses revealed that avocado seeds exert antioxidant activity predominantly through hydrogen atom transfer (HAT) and electron transfer (ET) pathways. The model predictions suggested procyanidins may stably bind to protein targets in the Keap1-Nrf2 pathway and NOX2 via hydrogen bonding, hydrophobic interactions, and π-cation interactions. Furthermore, response surface methodology (RSM) was employed to optimize the extraction process of avocado seed antioxidants in an ethanol-water system. This study underscores the considerable health benefits and antioxidant capacity of avocado by-products, supporting their promising application in functional foods formulations. Full article

Raising rural household income is central to narrowing the rural–urban gap and advancing common prosperity. Using data from the China Family Panel Studies (CFPS), this study examines the impact of digital skills, a key for human capital, on rural Chinese households’ income and uses a fixed-effects model and the instrumental variable method to address endogeneity. The study finds that digital skills raise total household income, and each additional skill is associated with an increase of CNY 1678. By skill type, online business skills have the largest effect, followed by work–study skills, while entertainment–social skills are negatively associated with income. Heterogeneity analyses indicate larger gains for households with lower educational attainment and lower income, showing that a stronger regional digital environment amplifies these effects. Mechanism tests point to factor reallocation toward the nonfarm sector, via higher probabilities of off-farm employment and entrepreneurship and improved access to formal credit, as the primary pathway. Consistent with these channels, digital skills increase wages and operating income and reduce inequality in these components, as well as benefitting total income, but they have no detectable effect on property or transfer income or their dispersion. These findings point to key implications for boosting rural income growth and reducing inequality, namely strengthening digital skill development and optimizing the digital environment to enhance rural households’ endogenous income-generating capacity. Full article

Production industries create high-quality products through effective machining precision, lead times, productivity, cost benefits, and implementing sustainable manufacturing practices. This study compares the effect of cryogenic CO₂ as a cutting fluid with a flooded conventional system and dry turning on the surface roughness, early-stage tool phenomena (including adhesion, material transfer, and built-up edge (BUE) formation), and the chip morphology of aluminum 7075-T6. Taguchi’s L9 orthogonal array is applied to identify the optimal cutting parameters that minimize surface roughness (Ra). Cutting speed (Vc), feed rate (f), depth of cut (ap), and the type of cutting fluid condition were defined at three levels. The surface roughness (Ra) was determined, and the built-up edge (BUE) and chip morphology were evaluated. Moreover, SEM and energy-dispersive X-ray spectroscopy (EDX) were employed to characterize the machined surface and the cutting tools. The optimal values for the cryogenic cooling and cutting parameters are as follows: 220 m/min (Vc), 0.05 mm/rev (f), and 0.5 mm (ap). These conditions yield a surface roughness mean (Ra) of 0.736 µm, improving the surface roughness by 10.57% compared with the lowest Ra value from all of the tests. In addition, ANOVA showed the feed rate to be the most significant cutting parameter over surface roughness under the given conditions. Regarding chip morphology, snarled chip shapes are associated with low surface roughness values. The results indicate that cryogenic cutting fluid enhances the machined surface quality and reduces the built-up edge compared with dry and flooded conditions. Full article

Electrified powertrains in the transportation sector have increased significantly in recent years, thanks to the need for decarbonization of the on-the-road transport means. However, management of powertrains still deserves particular attention to assess necessary improvements for reducing electric consumption and increasing the mileage of the vehicles. In this regard, electric motor cooling is essential for maintaining optimal performance and longevity. In fact, as electric motors operate, they generate heat due to electric and magnetic phenomena as well as mechanical friction. If not properly managed, this heat can lead to decreased efficiency, accelerated wear, or even failure of critical components. Effective cooling systems ensure that the motor runs within its ideal temperature range, reducing the occurrence of the mentioned concerns. This improves operational reliability and, at the same time, contributes to energy savings and reduced maintenance costs over the components’ life. In this study, the cooling of the rotor of a 130-kW electric motor via refrigerating fluid circulating inside the shaft has been investigated. Two configurations of fluid passages have been considered: a direct-through flow crossing the shaft along its axis and a hollow shaft with recirculating flow, with three types of rotating helical configurations at different pitches. The benefits when using nanofluids as a cooling medium have also been evaluated to enhance the heat transfer coefficient and decrease temperature values. Compared with the baseline configuration using standard fluids (water), the proposed solution employing nanofluids demonstrates effectiveness in terms of heat transfer coefficients (up to 28% higher than pure water), with limited impact on pressure losses, thus reducing rotor temperature by up to 30 K with respect to the baseline. This study opens the possibility of integrating the cooling of the rotor with whole electric motor cooling for electric and hybrid powertrains. Full article

The recent advancements in blockchain technology have also expanded its applications to smart agricultural fields, leading to increased research and studies in areas such as supply chain traceability systems and insurance systems. Policies and reward systems built on top of centralized systems face several problems and issues, including data integrity issues, modifications in data readings, third-party banking vulnerabilities, and central point failures. The current paper discusses how farming is becoming a leading cause of water and electricity wastage and introduces a novel idea called IncentiveChain. To keep a limit on the usage of resources in farming, we implemented an application for distributing cryptocurrency to the producers, as the farmers are responsible for the activities in farming fields. Launching incentive schemes can benefit farmers economically and attract more interest and attention. We provide a state-of-the-art architecture and design through distributed storage, which will include using edge points and various technologies affiliated with national agricultural departments and regional utility companies to make IncentiveChain practical. We successfully demonstrate the execution of the IncentiveChain application by transferring crypto-ether from utility company accounts to farmer accounts in a decentralized system application. With this system, the ether is distributed to the farmer more securely using the blockchain, which in turn removes third-party banking vulnerabilities and central, cloud, and blockchain constraints and adds data trust and authenticity. Full article

This study aims to explore whether the dual-scale management model, formed by integrating service-scale management with land-scale management, can further break through the benefit limits of single land-scale management and unlock additional profit potential in agricultural scale operations. This study used data from a 2024 questionnaire survey of 2166 farming households in Anhui Province and employed a coupling coordination degree model to measure the level of dual-scale management. Subsequently, we utilized OLS regression and mediation effect models to empirically examine the impact of dual-scale management on agricultural operational benefit and their underlying mechanisms. We find that dual-scale management significantly improves agricultural operational benefit. Our measurements show that dual-scale management not only breaks through the upper limit of the optimal operating area inherent in single land-scale management but also yields a greater improvement in agricultural operational benefit than single land-scale management. Heterogeneity analysis reveals that dual-scale management significantly enhances the agricultural operational benefit of farmers in plain areas and farmers with fully developed high-standard farmland. Mechanism analysis indicates that dual-scale management enhances agricultural operational benefit through an endogenous efficiency improvement mechanism and an exogenous risk-burden-sharing mechanism. These findings suggest that fostering a synergistic development system for land-scale management and service-scale management is conducive to improving the economic returns for land scale operators and unlocking new dividend spaces for agricultural scale operation in China’s post-land transfer era. Full article

Microbial electrolysis cells (MECs) present a viable platform for sustainable hydrogen generation from organic waste, but their scalability is limited by cathode performance, cost, and durability. This study evaluates three hybrid carbon nanotube (CNT) cathodes—acid-washed CNT (AW-CNT), thin layer non-acid-washed CNT (TN-NAW-CNT), and thick layer non-acid-washed CNT (TK-NAW-CNT)—each composed of stainless-steel-supported CNTs coated with molybdenum phosphide (MoP). These were benchmarked against woven carbon cloth (WCC) under varied operational conditions. A custom multi-channel reactor operated for 341 days, testing cathode performance across applied voltages (0.7–1.2 V), buffer types (phosphate vs. bicarbonate), pH (7.0 and 8.5), buffer concentrations (10–200 mM), and substrates including acetate, lactate, and treated acid whey. CNT-based cathodes consistently showed higher current densities than WCC across most conditions with significant difference found at higher applied voltages. TK-NAW-CNT achieved peak current densities of 259 A m⁻² at 1.2 V and maintained >41 A m⁻² in real-waste conditions with no added buffer. Long-term performance losses were minimal: 4.5% (TN-NAW-CNT), 0.1% (TK-NAW-CNT), 10.8% (AW-CNT), and 6.8% (WCC). CNT cathodes showed improved performance from reduced resistance and greater electrochemical stability, while proton transfer improvements benefited all materials due to buffer type and pH conditions. These results highlight CNT-based cathodes as promising, scalable alternatives to WCC for energy-positive wastewater treatment. Full article

Industrial park (IP) expansions in Mediterranean peri-urban areas can generate conflicts between economic development and agricultural heritage preservation. This paper develops a theoretically derived circular land use symbiosis model based on Hubs for Circularity (H4C) principles, using Fosso Imperatore IP in southern Italy as an illustrative case. This model proposes a transferable three-zone gradient design that enables the transformation of industrial–agricultural boundaries when combined with appropriate governance mechanisms and stakeholder engagement. Zone A concentrates vertical industrial development with rooftop agriculture; Zone B creates mixed agro-industrial interfaces; and Zone C enhances agricultural productivity through industrial resources. The model’s components (gradient zonation, temperature–cascade matching, and bidirectional resource flows) constitute generalizable design principles. When applied to Fosso Imperatore, where farmers oppose expansion that threatens culturally significant San Marzano tomato production, the model shows how 547 tons of organic waste could generate 87,520 m³ of methane, while industrial waste heat cascades from 150–200 °C to 25–40 °C of greenhouse heating across distances of 3 km. Implementation constraints include regulatory gaps and limited empirical data. This study operationalizes H4C through spatial design, showing how benefit-sharing mechanisms can transform stakeholder conflicts into collaboration. The model provides a replicable framework for Mediterranean contexts where industrial expansion encounters agricultural heritage. Full article

This study aims to investigate the role of Industry 4.0 (I4.0) technologies in facilitating the transition towards a circular economy (CE) in the manufacturing sector, exploring four key circular economy strategies—reuse, repair, refurbishment, and remanufacturing. This study combines a comprehensive literature review with case studies of ten manufacturing organisations from various sectors, including electronics, information and communication technologies, and the household and furniture industries. The research focuses on three main areas: the adoption of circular strategies, the challenges associated with implementing Industry 4.0 technologies, and the role of these technologies in enabling the transition to a circular economy. Data were collected through ten interviews with managers responsible for sustainability, corporate social responsibility, or circular economy projects and initiatives, as well as through documentary analysis of archival materials. The study found that organisations typically adopt multiple circular strategies, with repair being the most prevalent strategy across all sectors and adopted in every case analysed. However, the adoption of I4.0 technologies faces challenges such as scalability issues, digital expertise shortages, and outdated infrastructure. Advanced adopters of I4.0 technologies benefit from robust delivery systems supported by collaborative networks, which enhance knowledge transfer and development among stakeholders. Full article

Multi-Robot Task Assignment (MRTA) is a critical and inherently multi-objective problem in diverse real-world applications, demanding the simultaneous optimization of conflicting objectives such as minimizing total travel distance and balancing robot workload. Existing multi-objective evolutionary algorithms (MOEAs) often struggle with slow convergence and insufficient diversity when tackling the combinatorial complexity of large-scale MRTA instances. This paper introduces the Collaborative Swarm-Differential Evolution (CSDE) algorithm, a novel MOEA designed to overcome these limitations. CSDE’s core innovation lies in its deep, operator-level fusion of Differential Evolution’s (DE) robust global exploration capabilities with Particle Swarm Optimization’s (PSO) swift local exploitation prowess. This is achieved through a unique fused velocity update mechanism, enabling particles to dynamically benefit from their personal experience, collective swarm intelligence, and population diversity-driven knowledge transfer. Comprehensive experiments on various MRTA scenarios demonstrate that CSDE consistently achieves superior performance in terms of convergence, solution diversity, and Pareto front quality, significantly outperforming standard multi-objective algorithms like Multi-Objective Particle Swarm Optimization (MOPSO), Multi-Objective Differential Evolution (MODE), and Multi-Objective Genetic Algorithm (MOGA). This study highlights CSDE’s substantial contribution to the MRTA field and its potential for more effective and efficient multi-robot system deployment. Full article

The biomechanical performance of dental implants is affected by both abutment height and bone quality, which influence stress distribution around the implant and the preservation of surrounding bone. This study used three-dimensional finite element analysis (FEA) to assess the combined effects of these factors. Two implants with abutment heights of 3 mm and 6 mm were modeled and placed in mandibular bone blocks representing class II and class IV bone, according to Lekholm and Zarb’s classification. A static load of 150 N, inclined at 6° buccolingually, was applied during the analysis. The simulation results showed that increasing the abutment height raises stress on the implant, leading to greater stress transfer to the peri-implant bone. The von Mises stress levels were higher in the crestal cortical bone of the class IV model with a 6 mm abutment (126 MPa). Notably, peak stresses exceeding 300 MPa were localized at the implant-abutment connection. These findings suggest that abutment height is a critical factor that negatively affects the biomechanical response, especially in low-density bone, although longer abutments offer biological benefits. This highlights the importance of minimizing the crown-to-implant ratio to reduce overload, preserve bone, and prevent mechanical failure complications. Full article

Today, science and medicine are striving to develop novel techniques for treating deadly diseases, including a wide range of cancers. Efforts are being made to better understand the molecular and biochemical mechanisms of tumor cell functioning, but a particular emphasis has recently been given to investigating immune cells residing in the tumor microenvironment, which may lead to revolutionary benefits in the design of new immunotherapies. Among these cells, tumor-associated macrophages (TAMs) are highly abundant and act as critical regulators of ovarian cancer progression, metastasis, and resistance to therapy. Their dual nature—as drivers of malignancy and as potential therapeutic mediators—has positioned them at the forefront of research into next-generation immunotherapies. As therapeutic targets, approaches include blocking macrophage recruitment (e.g., CSF-1/CSF-1R inhibitors), selectively depleting subsets of TAMs (e.g., via Folate Receptor Beta), or reprogramming immunosuppressive M2-like macrophages toward an anti-tumor M1 phenotype. On the other hand, macrophages can also serve as a therapeutic tool—they may be engineered to enhance anti-tumor immunity, as exemplified by the development of Chimeric Antigen Receptor Macrophages (CAR-Ms), or leveraged as delivery vehicles for targeted drug transport into the tumor microenvironment. A particularly innovative strategy involves Macrophage–Drug Conjugates (MDCs), which employs the transfer of iron-binding proteins (TRAIN) mechanism for precise intracellular delivery of therapeutic agents, thereby enhancing drug efficacy while minimizing systemic toxicity. This review integrates current knowledge of TAM biology, highlights emerging therapeutic approaches, and underscores the promise of macrophage-based interventions in ovarian cancer. By integrating macrophage-targeting strategies with advanced immunotherapeutic platforms, novel treatment paradigms may be determined that could substantially improve outcomes for patients with ovarian cancer and other solid tumors. Our work highlights that macrophages should be a particular area of research interest in the context of cancer treatment. Full article

Honey is a valuable natural product prized for its nutritional and therapeutic properties, including antioxidant, antimicrobial, and anti-inflammatory effects. However, in addition to health-promoting compounds, honey may also contain plant-derived toxins, contaminants, and degradation products. Certain phytotoxins—such as pyrrolizidine alkaloids, grayanotoxins, triptolide, celastrol, gelsedine-type alkaloids, and tutin—can be transferred to honey from specific plant sources and pose health risks, particularly at high doses or with long-term exposure. Furthermore, compounds like 5-hydroxymethylfurfural, trace metals, pesticide residues, and Clostridium botulinum spores may present additional risks, especially to sensitive groups such as infants. Consumers often assume that natural products are inherently safe, which may lead to unintentional exposure to harmful substances. Adverse effects can range from chronic toxicity to, in extreme cases, death. Therefore, raising awareness among consumers and vendors is essential to reduce the intake of honey from unverified sources. Continuous monitoring of honey composition and further studies on the toxicodynamics of rare contaminants are crucial to ensuring safety while preserving the therapeutic benefits of this remarkable natural substance. Full article

This systematic literature review addresses the critical challenge of ensuring robustness and adaptability in AI-based machine health monitoring (MHM) systems. While the field has seen a surge in research, a significant gap exists in understanding how to effectively manage data scarcity, unknown fault types, and the integration of diverse data streams for real-world industrial applications. The problem is magnified by the rarity of failure events, which leads to imbalanced datasets and hampers the generalizability of predictive models. To synthesize the current state of research and identify key solutions, we followed a rigorous, modified PRISMA methodology. A comprehensive search across Scopus, IEEE Xplore, Web of Science, and Litmaps initially yielded 3235 records. After a multi-stage screening process, a final corpus of 85 peer-reviewed studies was selected. Data were extracted and synthesized based on a thematic framework of 13 core research questions. A bibliometric analysis was also conducted to quantify publication trends and research focus areas. The analysis reveals a rapid increase in research, with publications growing from 1 in 2018 to 35 in 2025. Key findings highlight the adoption of transfer learning and generative AI to combat data scarcity, with multimodal data fusion emerging as a crucial strategy for enhancing diagnostic accuracy. The most active research themes were found to be Predictive Maintenance and Edge Computing, with 12 and 10 references, respectively, while critical areas like standardization remain under-explored. Overall, this review shows that AI benefits machine health monitoring but still faces challenges in reproducibility, benchmarking, and large-scale validation. Its main limitation is the focus on English peer-reviewed studies, excluding industry reports and non-English work. Future research should develop standardized datasets, energy-efficient edge AI, and socio-technical frameworks for trust and transparency. The study offers a structured overview, a roadmap for future work, and underscores the importance of AI in Industry 4.0. Full article