Search Results (266)

Provincial power systems must decarbonize while sustaining rapid demand growth and improving air quality; yet, few integrated assessments separate the contributions of renewable expansion and carbon capture, utilization, and storage (CCUS) for China’s less-developed regions. We apply the Low Emissions Analysis Platform (LEAP) to model Guangxi’s power system to 2060 under four scenarios—Reference (BAS), Renewable-driven (RES), CCUS-intensive (CCS), and an Integrated comprehensive-policy scenario (ICS). Under ICS, solar photovoltaic generation rises from 38 TWh in 2025 to 408 TWh in 2060 (close to half of all generations), non-fossil capacity grows by about 730%, and power-sector CO₂ falls by roughly 95% relative to BAS. A counterfactual decomposition shows that CCUS provides about 75% of the CO₂ reduction along the coal-retaining CCS pathway but only about 5% along the renewables-led ICS pathway and reduces neither SO₂ nor NO_x. The air-quality co-benefits—up to 82%, 78%, and 73% lower SO₂, NO_x, and PM_2.5 than BAS—arise mainly from renewable substitution, which also abates carbon more cheaply (about 120–190 versus 300 CNY t⁻¹ CO₂) while sharply raising flexibility needs. Once avoided fuel and carbon-market costs are included, the renewables-led pathways become net cost-saving on a full-system basis and yield a monetized air-quality health co-benefit roughly twice that of the CCUS-intensive route. The findings give policy-relevant guidance for sustainable low-carbon transitions in Guangxi and comparable emerging regions. Full article

In this article, machine learning (ML) is proposed as a solution to detect and classify false message injection attacks in autonomous ground navigation. First, multiple trajectories are designed and simulated to collect authentic feature samples offered by the odometry and inertial measurement unit (IMU) of an autonomous ground vehicle (UGV). Then, a dataset comprising these samples and other injected samples that simulate two cyberattacks, namely path modification (PM) and velocity drift (VD), is created to train, validate, and benchmark various ML classification models. These include decision tree (DT), k-nearest neighbors (KNN), multi-layer perceptron (MLP), random forest (RF), and support vector machine (SVM). The optimum classification model is experimentally evaluated using a UGV platform, and results suggest that the proposed solution allows the detection of authentic and attacked messages with more than 98% average accuracy and sub-millisecond prediction time. Thus, this solution is ideal for real-time classification, especially in fixed-route applications, e.g., public transportation. Full article

Heavy-duty methanol engines are regarded as a promising low-carbon solution for commercial vehicle decarbonization, yet the comprehensive coupled characteristics of fuel consumption, multi-dimensional exhaust emissions, and the corresponding aftertreatment adaptability between stoichiometric and lean-burn technical routes remain insufficiently quantified, restricting the optimized application of methanol powertrains for China-VI emission compliance. To address this research gap, this study systematically investigates two China-VI compliant heavy-duty methanol engines with stoichiometric and lean-burn combustion strategies under cold-start and hot-start Worldwide Harmonized Transient Cycle. And a comparative analysis is conducted to clarify the differences in the fuel consumption, raw exhaust emission (including regulated pollutants, particulate matters, greenhouse gases, and unregulated pollutants), and the catalytic performance of aftertreatment systems between two engines with stoichiometric and lean-burn strategy. Results demonstrate that the lean-burn strategy achieves a 6% reduction in methanol fuel consumption compared with stoichiometric combustion, delivering superior fuel economy. In terms of regulated gaseous pollutants, both combustion strategies satisfy China-VI emission limits for CO and NO, while lean-burn combustion effectively lowers raw CO and NO emissions and reduces the purification pressure of aftertreatment systems. Non-methane Hydrocarbon emission under cold-start condition is identified as the primary compliance challenge, requiring a minimum aftertreatment conversion efficiency of 95%. Although lean-burn increases raw exhaust NMHC emission under hot-start condition, the post-catalyst emission could still meet the regulation limits. For particulate pollutants, lean-burn strategy realizes substantial reductions in both PM and PN emissions, which can meet emission standards without the corresponding aftertreatment system. In contrast, the stoichiometric combustion faces a risk of PN emission exceeding the regulation limit under cold-start conditions even with aftertreatment system. Additionally, lean-burn strategy optimizes greenhouse gas emission performance by cutting CO and CH₄ emissions. Regarding unregulated pollutants, lean-burn strategy increases raw exhaust unburned methanol and formaldehyde emissions, particularly under cold-start condition, but significantly inhibits NH₃ emission. This study quantitatively clarifies the performance trade-offs and adaptation advantages of lean-burn and stoichiometric strategy for heavy-duty methanol engines, providing fundamental data support and technical guidance for the low-carbon and low-pollution optimization of heavy-duty methanol vehicles. Full article

Energy and power management systems (EMS/PMS) are essential for electric-propulsion ships, affecting propulsion performance, fuel consumption, emissions, and component lifetime. As shipboard power systems integrate heterogeneous energy resources and face nonlinearity, uncertain load demand, and multi-source interactions, deep reinforcement learning (DRL) has emerged as a promising adaptive, sequential decision-making tool in shipboard EMS/PMS. This perspective reviews DRL studies through a hierarchical decision-making framework comprising power dispatch, energy coordination, and operational strategy. Most research focuses on real-time power dispatch, while emerging research addresses energy coordination via multi-source cooperation, multi-objective operation, degradation awareness, and uncertainty handling. However, operational strategy remains underexplored, despite its role in speed control, route-aware planning, predictive operation, and voyage scheduling. This paper argues that future shipboard EMS/PMS adopt integrated hierarchical DRL frameworks across all three decision layers, leveraging DRL’s strengths in sequential policy learning in dynamic environments and supporting multi-time-scale decision-making. This paper clarifies current research trends, identifies gaps, and outlines future directions toward adaptive, reliable, and autonomous shipboard EMS/PMS in next-generation electric-propulsion ships. Full article

This research investigates the home health care routing and scheduling problem with soft time windows and perishable medicines (HHCRSP-STW-PM), where care crews must serve patients over multiple periods while accounting for skill-based assignments, medicine perishability, and flexible service times. The objective minimizes total travel and penalty costs for early or late services. We formulate a mixed integer linear program (MILP) model to optimally solve small instances and develop an effective greedy randomized adaptive search procedure (GRASP) to address large instances. GRASP includes a tailored construction heuristic with problem-specific local search operators in the local search phase. Numerical experiments conducted on newly generated instances demonstrate that while the MILP model provides optimal solutions for small instances, only GRASP is able to handle large-scale instances within a reasonable computational time. Sensitivity analyses allow us to examine the impact of the approach’s parameters, perishability of medicine, soft time windows, and care crew resources. Full article

Advanced biofuels represent a key option for transport decarbonization, particularly in sectors where electrification is constrained by technical and economic barriers. Their compatibility with existing vehicle fleets and fuel distribution infrastructure enables rapid deployment without the need for major capital investments. In local public transport, biodiesel (FAME), hydrotreated vegetable oil (HVO), and biomethane are mature solutions capable of delivering greenhouse gas emission reductions of 60–90% compared with fossil fuels. Among these, HVO is particularly promising, as an extensive body of literature has consistently shown its potential to significantly reduce engine-out emissions, especially particulate matter (PM) and nitrogen oxides (NO_x). This study reports the results of an experimental campaign carried out on a diesel-powered local public transport bus equipped with a Euro III engine and lacking particulate matter and NO_x after-treatment systems. Emissions were measured using a portable emissions measurement system (PEMS) under real driving conditions, operating the vehicle with neat diesel, a 15% HVO blend, and a 70% HVO blend. Tests were conducted over urban and extra-urban routes. The results show that NO_x emissions decrease proportionally with increasing HVO content, with high-blend ratios (HVO70) yielding estimated reductions of approximately 13–18%, and up to 23% under carefully controlled and comparable urban driving conditions. Based on these findings and the existing literature, HVO proves to be a useful instrument to meet 2025–2030 climate and air quality targets (particularly NO_x and PM emission reductions), alongside electrification and modal shift measures, if used in public transport fleets. Full article

For decades, peritoneal metastases (PM) have been regarded as a terminal manifestation of advanced malignancies and managed primarily with palliative intent because of limited sensitivity to systemic therapies. Accumulating clinical, molecular, and immunological evidence now supports the view that PM is not merely an anatomic pattern of spread but a distinct metastatic niche with characteristic biological, microenvironmental, and therapeutic features. This review summarizes the major routes of PM development—transcoelomic, lymphatic, and hematologic dissemination—and emphasizes how these pathways converge through shared biological programs. Core mechanisms include epithelial–mesenchymal transition (EMT), adhesion signaling, extracellular matrix remodeling, and tumor–immune cell interactions. A central focus is the peritoneal tumor microenvironment: mesothelial-to-mesenchymal transition, cancer-associated fibroblast activity, adipocyte-derived metabolic support, macrophage polarization, and regulatory T-cell enrichment collectively shape an immunotolerant and treatment-resistant niche on the peritoneal surface. In addition, evidence from pre-metastatic niche biology suggests that primary tumor-derived exosomes and epitranscriptomic regulation can prime the peritoneal environment before overt implantation. These features provide a biological rationale for locoregional strategies such as cytoreductive surgery and hyperthermic intraperitoneal chemotherapy, as well as emerging intraperitoneal modalities and microenvironment-targeted approaches. Finally, organoid platforms, liquid biopsy-based minimal residual disease monitoring, and theranostic technologies may enable more personalized, biology-driven management of PM. Full article

Oil and gas pipelines are critical infrastructures that require continuous and reliable monitoring to detect leaks, pressure anomalies, corrosion, and unauthorized activities. Wireless sensor networks (WSNs) have emerged as an effective solution for large-scale pipeline monitoring due to their low deployment cost and real-time sensing capabilities. However, the resource-constrained nature of sensor nodes and the open wireless communication environment expose pipeline monitoring systems to various routing attacks, for example, blackhole, sinkhole, selective forwarding, and false data injection attacks, while simultaneously demanding strict energy efficiency to prolong network lifetime. In this paper, we propose SEER-PM (Secure and Energy-Efficient Routing for Pipeline Monitoring): a novel protocol that integrates an Artificial neural network (ANN)-based trust mechanism with energy-aware routing metrics. SEER-PM dynamically evaluates node trustworthiness based on packet forwarding behavior, residual energy, and signal consistency. By training the ANN on historical behavioral data, the system accurately detects malicious nodes with high precision. Simulation results demonstrate that SEER-PM outperforms existing secure routing protocols (Sec-AODV and T-LEACH) in terms of packet delivery ratio (PDR) by 14%, detection rate by 9.5%, and network lifetime by 12% under heavy attack scenarios. The proposed protocol enhances the reliability, security, and sustainability of pipeline monitoring WSNs operating in harsh and remote environments. Full article

The catalytic removal of refractory VOCs in gas–solid reactions usually suffers from the formation of toxic byproducts and catalyst deactivation. The advanced oxidation process (AOP) wet scrubber has recently attracted interest in VOCs purification due to its high efficiency and inhibited gaseous byproducts emission. MOFs/metal sulfides (termed M50C50) were designed to activate peroxymonosulfate (PMS) for toluene removal in a wet scrubber. The heterojunction interface synergistically couples MIL-100(Fe) and CoS for dual functions, the M50C50 enabled the rapid transfer the toluene from the gas phase to the aqueous phase, where they were subsequently mineralized by SO₄^•− and •OH radicals. The primary active sites responsible for PMS activation were identified as reducing sulfur species, along with low-valence cobalt and iron species. Over 90% of toluene were removed with a wide pH range, while •OH and SO₄^•− were involved in the mineralization of intermediates. The process showed high mineralization efficiency (75% CO₂ evolution) and effectively reduced the formation of toxic byproducts, underscoring its potential for minimizing secondary pollution risks. This work provides a novel route to designing composite catalysts for deep VOC oxidation via AOP wet scrubbers, greatly facilitating their use in environmental remediation. Full article

This study examines whether digital–intelligent technology innovation supports sustainable urban transition by improving urban pollution–carbon synergy in China. Using panel data for 278 prefecture-level cities from 2012 to 2023, we measure digital–intelligent technology innovation by the per capita intensity of patent applications in key digital–intelligent technology fields and construct an urban pollution–carbon synergy index based on a global non-radial directional distance function combined with data envelopment analysis. The results show that digital–intelligent technology innovation is positively associated with urban pollution–carbon synergy, and this finding remains robust to alternative variable definitions, sample adjustments, alternative frontier settings, and supplementary identification strategies. Further analyses suggest that the relationship is stage-dependent rather than purely linear, with stronger sustainability gains emerging after critical development thresholds are crossed. Channel analyses indicate that green technological innovation, digital inclusive finance, and AI firm agglomeration are important routes through which digital–intelligent innovation is translated into environmental governance capacity. Additional analyses show that the effect is stronger on the carbon mitigation dimension than on the pollution reduction dimension, is more pronounced in cities with higher human capital and more developed financial technology, and exhibits both temporal persistence and spatial spillover effects. In addition, digital–intelligent technology innovation is associated with higher energy efficiency, lower total energy consumption, and lower PM2.5, SO₂, total CO₂ emissions, and CO₂ intensity. Overall, these findings contribute to the sustainability literature by showing that digital–intelligent innovation can facilitate sustainable urban transition when it is effectively transformed through green innovation, financial support, and local application scenarios. Full article

Fine particulate matter (PM_2.5) exposure in public transport microenvironments has important implications for commuter health, yet concentration-based assessments may not adequately reflect the dose actually inhaled by passengers. This study quantified dynamic PM_2.5 inhaled doses in Taiyuan, China, using 1 Hz portable monitoring and matched travel surveys across 19 bus and metro routes during summer and winter 2025. After data screening, 1103 valid commuter samples were retained. We combined dose estimation with DML, XGBoost-SHAP, SEM, and Random Forest analysis to examine adjusted associations, explore potential nonlinear patterns, and characterize behavioral responses. Trip-averaged PM_2.5 concentrations exceeded the WHO 24 h guideline on most monitored routes when interpreted as a health-based reference benchmark for short commuting exposures rather than as a direct regulatory exceedance metric. More importantly, a clear concentration-dose decoupling pattern was observed: 6.6% of trips fell into a low-concentration but high-dose category, indicating that prolonged in-vehicle exposure could substantially elevate inhaled dose even when PM_2.5 concentrations remained below the sample median. The mean inhaled dose in the longer observed-duration group (top 20% by observed in-vehicle duration) reached 612.26 ± 412.21 μg, which was 7.2 times that of the remaining trips (84.87 ± 115.71 μg). DML results showed that inhaled dose, rather than PM_2.5 concentration alone, was significantly associated with psychological distress. SHAP analysis suggested an exploratory threshold-like pattern at approximately 300 μg per trip, above which health-risk attribution increased rapidly. SEM results indicated that inhaled dose was associated with higher self-reported somatic burden, whereas PM_2.5 concentration mainly influenced health indirectly through risk perception. These findings suggest that public transport exposure assessment should move beyond static concentration metrics and incorporate dynamic inhaled dose to better identify high-risk commuting scenarios and support more targeted health-oriented transit management. Full article

Bird migration is the regular, long-distance movement of birds between breeding and wintering grounds, influenced by climate change and human activities. The East Asia–Australasia Flyway (EAAF) is one of the largest migratory routes in the world, covering various species such as waders and waterfowl, with the eastern coastal areas of China serving as important stopover and wintering grounds. This paper selects the Qingdao area as the research object, and based on weather radar and meteorological data, explores the spatiotemporal characteristics of bird migration patterns in this region, discusses changes in regional bird activity and their causes, and investigates the influence of weather factors on migration altitude. By analyzing weather radar data from spring 2023, the peak migration period was found to occur mainly from mid-April to mid-May, with multiple large-scale migrations in late April exhibiting alternating peaks and troughs. Migration activity peaked between 8 p.m. and midnight, with altitudes below 600 m serving as the primary migration height range. Using correlation analysis, linear regression, and generalized additive models, the study further analyzed the contribution of various weather factors to birds’ altitude selection. Results showed that wind conditions, temperature, and humidity had significant effects on migration altitude. Full article

The powder metallurgy route (PM route) for producing aluminum closed-cell foams has recently attracted significant scientific and industrial interest. The process involves mixing a blowing agent powder (e.g., TiH₂) with aluminum powder, then compacting the mixture to produce a high-density bulk foamable precursor (BFP). The BFP is then heated above the melting point of aluminum, where the hydrogen released from TiH₂ particles forms bubbles in the molten aluminum, which become closed pores (cells) upon solidification. Despite metal powder agglomeration being an important factor in powder metallurgy research that can significantly influence processing, it has surprisingly received little to no attention in the powder-based foaming of metals. To the best of our knowledge, this paper is the first to address aluminum powder agglomeration within the context of powder-based metallic foams. Results show that significant aluminum powder agglomeration not only leads to an inhomogeneous distribution of the TiH₂ particles within the BFP, but also to the formation of locally higher than nominal concentrations of TiH₂ particle-rich regions, which greatly influence foaming characteristics. The work, for the first time, highlights the need to seriously consider metal-powder agglomeration (even partial agglomeration) in future foaming research via the PM route, and its effect on foaming characteristics. Full article

NanoArduSiPM represents a paradigm shift in the ArduSiPM (Architected Detection Unit for Silicon Photomultipliers) roadmap, evolving from a standalone instrument into a high-density modular building block (36 mm × 42 mm × 3 mm, 7 g). This revision does not merely pursue miniaturization; it re-engineers the signal-processing chain to maintain high performance within a scaled-down footprint, enabling the transition from single-unit detection to scalable, distributed multi-detector systems. NanoArduSiPM is based on a three-layer architecture comprising an external scintillator and Silicon Photomultiplier (SiPM) detection module, a dedicated high-speed discrete analog front-end, and a System-on-Chip (SoC) for embedded acquisition and processing. The physical implementation adopts high-integrity PCB routing and rigorous isolation techniques designed to suppress digital–analog coupling, a critical requirement in such a compact form factor. This deterministic layout strategy provides the architectural foundation for time-tagging capabilities, currently under quantitative characterization, by addressing the fundamental sources of signal interference at the hardware level. Beyond hardware integration, NanoArduSiPM introduces the capability for extended firmware functionality, including event tagging via external inputs and the implementation of coincidence and veto logic. This framework supports the acquisition of multiple correlated histograms and allows multiple units to be interconnected on a shared SPI bus. By shifting from standalone operation to a coordinated, hierarchical architecture, NanoArduSiPM enables distributed detection schemes where event selection and correlation are handled natively within the system, reducing the dependency on external data acquisition electronics. The compact modular architecture, together with the high-performance discrete analog front-end and embedded data handling, makes NanoArduSiPM suitable for applications where low mass and low power consumption are critical, targeting applications such as space-based payloads, laboratory instrumentation, remote sensing, and large-scale distributed multi-channel detection systems. While no radiation-tolerance qualification of the complete system has been performed in this work, the microcontroller family used in the design is also available in radiation-tolerant variants, which may support future implementations targeting more demanding radiation environments. Full article

Background: Pleural mesothelioma (PM) is characterised by often rapid therapeutic failure and chemotherapy resistance. While terminal resistance is well studied, the initial transition into a drug-tolerant phenotype remains poorly understood. Methods: We established patient-derived organoids (PDOs) from malignant pleural effusions to model this transition. Cisplatin-tolerant lines were generated via repeated incremental exposure to cisplatin and compared to time-matched treatment-naive controls using RNA sequencing and Seahorse XFe96 metabolic flux analysis. Results: Integrated profiling suggested that the route to tolerance may be influenced by the underlying mutational profile. In this cohort, all BAP1-retained models (including those with KRAS mutations or MTAP loss) adopted an elevated basal metabolic hybrid phenotype, significantly upregulating baseline oxidative phosphorylation and glycolysis to fuel survival mechanisms. Conversely, BAP1-deficient models entered a hypometabolic state of dormancy, characterised by baseline bioenergetic suppression and reduced Ki-67 proliferation. Transcriptomic analysis identified a vesicular transport signature (SYNGR3, VPS52, PROM2) in plastic models, suggesting altered membrane trafficking as a potential survival strategy. Conclusions: Our findings demonstrate that mesothelioma therapeutic escape is not a uniform process. Identifying these patient-specific metabolic and transcriptomic trajectories via 3D PDOs provides a hypothesis-generating framework to explore potential avenues for future personalised therapy. Full article