Search Results (1,054)

Addressing IMO 2020 compliance, this study investigates marine fuel oil production from hydrotreated residues, focusing on mitigating excessive total sediment potential (TSP) caused by over-hydrotreatment. This study systematically investigates the impact of blending ratios of Fluid Catalytic Cracking (FCC) slurry oil with Residue Desulfurization (RDS) heavy oil on TSP, colloidal stability, and asphaltene structure evolution. Techniques such as XRD, SEM, and XPS were employed to analyze the structural changes in asphaltenes during the TSP exceeding process. The results indicate that as the FCC slurry oil blending ratio increases, TSP in the blended oil initially rises and then decreases. The peak TSP value of 0.41% occurs at a 10% FCC slurry oil blending ratio, primarily due to high-saturation hydrocarbons in RDS heavy oil disrupting the colloidal stability of asphaltenes in FCC slurry oil. When the blending ratio reaches 25%, TSP significantly decreases to 0.09%, attributed to the solubilizing effect of high aromatic compounds in the FCC slurry oil on the asphaltenes. The ω(Asp)/ω(Res) ratio mirrors the TSP trend, and the colloidal solubilizing capacity of asphaltenes increases with the blending ratio. Asphaltenes in RDS heavy oil exhibit a spherical structure, whereas those in FCC slurry oil show a layered structure. The precipitated asphaltenes in the blends primarily result from the aggregation of asphaltenes in FCC slurry oil, with heteroatoms (N, S, O) mainly originating from RDS heavy oil asphaltenes. During the early stage of blending, TSP formation is dominated by FCC slurry oil asphaltenes, but increasing the aromatic content in the system can significantly reduce TSP. This work provides theoretical and technical support for optimizing marine fuel blending processes in petrochemical enterprises. Full article

This paper presents the design of a permanent-magnet-assisted synchronous reluctance motor (PMa-SynRM) for compressor applications using Sm-series injection-molded magnets that eliminate heavy rare-earth elements. The high shape flexibility of the injection-molded magnets enables the formation of a curved multi-layer flux-barrier rotor geometry based on the Joukowski airfoil potential, optimizing magnetic flux flow under typical compressor operating conditions. Furthermore, electromagnetic performance, irreversible demagnetization behavior, and rotor stress sensitivity were analyzed with respect to key design variables to derive a model that satisfies the target performance requirements. The validity of the proposed design was confirmed through finite element method (FEM) comparisons with a conventional IPMSM using sintered NdFeB magnets, demonstrating the feasibility of HRE-free PMa-SynRM for high-performance compressor drives. Full article

Weathered crust elution-deposited rare earth ores (WCE-REOs) are the primary global source of medium and heavy rare earth elements (M/HREEs). The recent discovery of high-altitude (1500–2500 m) WCE-REOs in southern Yunnan Province, China, presents new opportunities for the development of M/HREE resources. This study investigates the enrichment and fractionation mechanisms of rare earth elements (REEs) in these deposits through a systematic analysis of three representative weathering profiles associated with the Lincang granite batholith. The analytical results indicate that the profiles consist mainly of clay minerals (kaolinite, halloysite, illite, minor montmorillonite) and iron oxides, with high SiO₂ (64.10–74.40 wt.%) and Al₂O₃ (15.50–20.20 wt.%) and low CaO/MgO—typical of weathered REE deposits. The total REE contents (238.12–1545.53 ppm) show distinct fractionation: LREE-enriched upper layers and HREE-enriched deeper zones. Sequential extraction revealed that the REEs in the Lincang granite weathering profiles predominantly occur in ion-exchangeable, residual, and iron-manganese oxide-bound states (>95% total REEs). Ion-exchangeable REEs showed depth-dependent enrichment (peaking at 819.96 ppm), while iron-manganese oxides exhibited a strong REE affinity (up to 47% total REEs), with amorphous phases that were preferentially enriched in Ce (partitioning >80%). Fissure systems exerted critical control over the redistribution of elements, particularly REEs. Full article

This study conducts a detailed and systematic Life Cycle Assessment (LCA) of demineralized (DEMI) water production at the Al-Hilla Second Gas Power Plant in Iraq, employing the Open LCA-ReCiPe 8 Midpoint (H) method to evaluate potential environmental impacts across 18 midpoint categories. The analysis focuses on the production of 1 cubic meter of high-purity water, offering a comprehensive evaluation of the environmental burdens associated with chemical usage, energy consumption, and resource depletion. The results indicate that terrestrial ecotoxicity is the most dominant impact category (20.383 kg 1,4-DCB-eq), largely driven by the extensive use of treatment chemicals such as coagulants, disinfectants, and antiscalants. Climate change follows as the second highest impact category (3.496 kg CO₂-eq), primarily due to significant electricity consumption during energy-intensive stages, particularly reverse osmosis (RO) and electro-deionization (EDI). These stages also contribute notably to fossil resource depletion (1.097 kg oil-eq) and particulate matter formation, reflecting the heavy reliance on fossil fuel-based energy in the region. Additional environmental concerns identified include human toxicity (both carcinogenic and non-carcinogenic), freshwater and marine ecotoxicity, and metal/mineral resource depletion, all of which underscore the need for improved chemical and material management throughout the treatment process. While impacts from categories such as ozone layer depletion, ionizing radiation, and eutrophication are relatively low, their cumulative effect over time remains a concern for long-term sustainability. The energy assessment reveals that the RO and EDI units alone account for over 70% of the total energy consumption, estimated at 3.143 kWh/m³. This research provides insights into minimizing environmental burdens in water treatment systems, especially in regions facing energy and water stress. Full article

Despite significant progress, precipitation forecasting in West Africa remains challenging due to the complexity of atmospheric processes and the region’s climatic variability. This study aims to identify optimal configurations of the WRF model to improve precipitation forecasting. To evaluate the sensitivity of the model’s physical parameterizations, 15 configurations were tested by combining various cumulus parameterization schemes (CPSs) and planetary boundary layer (PBL) schemes. The analysis examines two contrasting rainfall events in Senegal: one characterized by widespread intense precipitation and another featuring localized moderate rainfall. Simulated rainfall, temperature, and humidity were validated against rain gauges, satellite products (ENACTS, ARC, CHIRPS, and IMERG), and ERA5 reanalysis data. The results show that the WRF configurations achieve correlation coefficients (r) ranging from 0.27 to 0.62 against ENACTS and from 0.15 to 0.41 against rain gauges. The sensitivity analysis reveals that PBL schemes primarily influence temperature and humidity, while CPSs significantly affect precipitation. For the heavy rainfall event, several configurations accurately captured the observed patterns, particularly those using Tiedtke or Grell–Devenyi CPSs coupled with the Mellor–Yamada–Janjic (MYJ) PBL. However, the model showed limited skill in simulating localized convection during the moderate rainfall event. These findings highlight the importance of selecting appropriate parameterizations to enhance WRF-based precipitation forecasting, especially for extreme weather events in West Africa. Full article

Epigenetic regulation, particularly DNA methylation, plays a crucial role in plant adaptation to environmental stresses by modulating gene expression without altering the underlying DNA sequence. In response to major abiotic stresses such as salinity, drought, heat, cold, and heavy metal toxicity, plants undergo dynamic changes in DNA methylation patterns. These modifications are orchestrated by DNA methyltransferases and demethylases with variations depending on plant species, genetic background, and ontogenic phase. DNA methylation affects the expression of key genes involved in cellular, physiological, and metabolic processes essential for stress tolerance. Furthermore, it contributes to the establishment of stress memory, which can be transmitted across generations, thereby enhancing long-term plant resilience. The interaction of DNA methylation with other epigenetic mechanisms, including histone modifications, small RNAs, and chromatin remodeling, adds layers of regulatory complexity. Recent discoveries concerning N6-methyladenine have opened new avenues for understanding the epigenetic landscape in plant responses to abiotic stress. Overall, this review addresses the central role of DNA methylation in regulating plant stress responses and emphasizes its potential for application in crop improvement through epigenetic and advanced biotechnological approaches. Full article

Legacy sulfur smelting has left behind complex contamination landscapes, yet the spatial structuring of microbial risks and adaptation strategies across soil profiles remains insufficiently understood. Microbial risk genes, including those conferring resistance to antibiotic resistance (ARGs), biocide and metal resistance (BRGs/MRGs), and virulence (VFGs), are increasingly recognized as co-selected under heavy metal stress, posing both ecological and public health concerns. In this study, we integrated geochemical analyses with metagenomic sequencing and functional annotation to jointly characterize the vertical (0–7 m) and horizontal (~2 km) distribution of heavy metals/metalloids, microbial communities, and functional risk genes at a historic smelting site in Zhenxiong, Yunnan. Heavy metals and metalloids such as arsenic (As), chromium (Cr), copper (Cu), and lead (Pb) showed clear accumulation with depth, while significantly lower concentrations were observed in both upstream and downstream locations, revealing persistent vertical and horizontal pollution gradients. Correspondingly, resistance and virulence genes were co-enriched at contaminated sites, suggesting potential co-selection under prolonged stress. LEfSe analysis revealed distinct ecological patterns: vertically, upper layers were dominated by nutrient-cycling and mildly stress-tolerant taxa, while deeper layers favored metal-resistant, oligotrophic, and potentially pathogenic microorganisms; horizontally, beneficial and diverse microbes characterized low-contamination zones, whereas heavily polluted areas were dominated by resistant and stress-adapted genera. These findings provide new insights into microbial resilience and ecological risk under long-term smelting stress. Full article

Taking a typical carburized alloy steel for heavy-duty gears as the research object, this work regulates carburizing–quenching and tempering processes to conduct a layer-by-layer analysis of gradient-distributed microstructures and mechanical properties in the carburized layer. The effects of tempering temperature on martensite evolution, mechanical properties, and wear resistance were specifically investigated. Results demonstrate that carburizing–quenching followed by cryogenic treatment generates high-carbon martensite at the surface, progressively transitioning to lath martensite towards the core. Low-temperature tempering promotes fine carbide precipitation, while elevated temperatures cause carbide coarsening. Specimens tempered at 175 °C achieve surface hardness of 800 HV and near-surface compressive yield strength of 2940 MPa. These samples exhibit 13% lower wear mass loss compared to 240 °C tempered counterparts, demonstrating superior wear resistance characterized by relatively flat wear surfaces, uniform contact stress distribution, and reduced cross-sectional plastic deformation zones. Key strengthening mechanisms at lower tempering temperatures involve solution strengthening, dislocation strengthening, and partial precipitation strengthening from carbides. Coherent carbides formed under these conditions impede fatigue dislocation motion via shearing mechanisms to suppress plastic deformation and fatigue crack initiation under contact fatigue stress, thereby enhancing wear performance. Full article

The excessive emission of cadmium (Cd²⁺) poses a serious threat to the aquatic environment due to its high toxicity and bioaccumulation potential. This study constructed three types of vertical-subsurface-flow constructed wetlands configured with iron–carbon–zeolite composite substrates, including an iron–carbon–zeolite constructed wetland (TF-CW), a zeolite–iron–carbon constructed wetland (FT-CW), and an iron–carbon–zeolite mixed constructed wetland (H-CW), to investigate the purification performance and mechanisms of constructed wetlands for cadmium-containing wastewater (0~6 mg/L). The results demonstrated that iron–carbon–zeolite composite substrates significantly enhanced Cd²⁺ removal efficiency (>99%) through synergistic redox-adsorption mechanisms, where the iron–carbon substrate layer dominated Fe-Cd co-precipitation, while the zeolite layer achieved short-term cadmium retention through ion-exchange adsorption. FT-CW exhibited superior NH₄⁺-N removal efficiency (77.66%~92.23%) compared with TF-CW (71.45%~88.05%), while iron–carbon micro-electrolysis effectively inhibited NO₃⁻-N accumulation (<0.1 mg/L). Under cadmium stress, Typha primarily accumulated cadmium through its root systems (>85%) and alleviated oxidative damage by dynamically regulating antioxidative enzyme activity, with the superoxide dismutase (SOD) peak occurring at 3 mg/L Cd²⁺ treatment. Microbial community analysis revealed that iron–carbon substrates promoted the relative abundance of Bacteroidota and Patescibacteria as well as the enrichment of Saccharimonadales, Thauera, and Rhodocyclaceae (genera), enhancing system stability. This study confirms that iron–carbon–zeolite CWs provide an efficient and sustainable technological pathway for heavy metal-contaminated water remediation through multidimensional mechanisms of “chemical immobilization–plant enrichment–microbial metabolism”. Full article

The Zigui Basin, located in the Three Gorges Reservoir Area, has developed numerous landslides due to its interlayering of sandstone and mudstone, geological structure, and reservoir operations. This study identifies a fourth type of landslide failure mode: an oblique-dip slope wedge (OdSW) landslide, based on the Wanshuitian landslide. Following four heavy rainfall events from 3 to 13 July 2024, this landslide exhibited significant deformation on the 17th and was completely destroyed within 40 min. The dimensions of the landslide were 350 m in length, 160 m in width, and 20 m in thickness, with a volume estimated at 8.0 × 10⁵ m³. The characteristics of landslide deformation and the changes in moisture content within the shallow slide body were ascertained using unmanned aerial vehicles, moisture meters, and mobile phone photography. The landslide was identified to have occurred within the weathered residual layer of mudstone, situated between two sandstone layers, with the eastern boundary defined by an inclined rock layer. Upon transitioning into the accelerated deformation stage, the landslide initially exhibited uniform overall sliding deformation, culminating in accelerated deformation destruction. The dip structure created terrain disparities, resulting in a step-like terrain on the left bank and gentler slopes on the right bank, with interbedded soil and rock in a shallow layer, because the interlayered soft and hard geological conditions caused varied weathering and erosion patterns on the riverbank slopes. The interbedded weak–hard stratum layer fostered the development of the oblique-dip slope wedge landslide. Based on the improved Green–Ampt model, we developed a stability prediction methodology for an oblique-dip slope wedge landslide and determined the rainfall infiltration depth threshold of the Wanshuitian landslide (9.8 m). This study aimed not merely to sharpen the evolution mechanism and stability prediction of the Wanshuitian landslide but also to formulate more effective landslide-monitoring strategies and emergency management measures. Full article

Urban estuarine environments face increasing water safety risks due to microbial contamination from combined sewer overflows (CSOs), particularly during heavy rainfall events. In megacities like Tokyo, where waterfronts are widely used for recreation, such contamination poses significant public health risks. The challenge is compounded by the variability in both intensity and spatial distribution of rainfall across the catchment, combined with complex tidal dynamics making effective water quality management difficult. To address this challenge, we conducted a series of hydrodynamic–microbial fate simulations to examine the spatial and vertical behavior of Escherichia coli (E. coli) under different rainfall–tide conditions. Focusing on the Sumida River estuary, rainfall data from eight drainage areas were classified into six event types using cluster analysis. Two contrasting events were selected for detailed analysis: a light rainfall (G2, 15 mm over 13 h) and an intense event (G6, 272 mm over 34 h). Vertical water quality profiling was performed along an 8.5 km transect from the Kanda–Sumida River confluence to the Tokyo Bay Tunnel, illustrating E. coli and salinity. The results showed that the rainfall intensity and tidal phase at the event onset are critical in shaping both the magnitude and vertical distribution of microbial contamination. The intense event (G6) led to deep microbial intrusion (up to 6–7 m) and major salinity disruption, while the lighter event (G2) showed surface-layer confinement. Salinity gradients were more strongly affected during G6, indicating freshwater intrusion. Tidal phase also influenced transport: the flood-high condition retained E. coli, whereas ebb-low tides facilitated downstream flushing. These findings highlight the influence of rainfall intensity and tidal timing on microbial distribution and support the use of vertical profiling in estuarine water quality management. They also support the development of dynamic, event-based water quality risk assessment tools. With appropriate local calibration, the modeling framework is transferable to other urban estuarine systems to support proactive and adaptive water quality management. Full article

Unmanned Aerial Vehicles (UAVs) are known to have limited payloads, which challenges their widespread use in transporting heavy goods. Meanwhile, collaboration between multiple UAVs in performing such a task may be a promising solution. To address the issues associated with the simultaneous use of UAVs, this paper presents a formation control system for transporting a payload suspended via a cable using two UAVs. The control structure is based on a layered scheme that combines a null-space-based kinematic controller with a PID controller associated with each UAV (quadcopters) with a neural correction system. The null-space supervisor controller is designed to generate the desired velocity for the UAV system to maintain formation. This proposal aims to avoid obstacles, balance the weight distribution across each vehicle, and also reduce the payload trajectory tracking error. The PID controller associated with the neural correction system receives these desired speeds and performs dynamic compensation, taking into account parametric uncertainties and dynamic disturbances caused by the movement of the payload coupled to the UAV systems. The stability analysis of the entire control system is performed using Lyapunov theory. Detailed dynamic models of each UAV in the system, the flexible cables, and the payload are presented in a realistic scenario. Finally, numerical simulations demonstrate the good performance of the UAV system control in formation. Full article

The rapid detection and confirmation of Suspicious Regions of Forest Fire (SRoFF) are critical for timely alerts and firefighting operations. In the early stages of forest fires, small flames and heavy occlusion lead to low accuracy, false detections, omissions, and slow inference in existing target-detection algorithms. We constructed the Suspicious Regions of Forest Fire Dataset (SRFFD), comprising publicly available datasets, relevant images collected from online searches, and images generated through various image enhancement techniques. The SRFFD contains a total of 64,584 images. In terms of effectiveness, the individual augmentation techniques rank as follows (in descending order): HSV (Hue Saturation and Value) random enhancement, copy-paste augmentation, and affine transformation. A detection model named SRoFF-Yolover is proposed for identifying suspicious regions of forest fire, based on the YOLOv8. An embedding layer that effectively integrates seasonal and temporal information into the image enhances the prediction accuracy of the SRoFF-Yolover. The SRoFF-Yolover enhances YOLOv8 by (1) adopting dilated convolutions in the Backbone to enlarge feature map receptive fields; (2) incorporating the Convolutional Block Attention Module (CBAM) prior to the Neck’s C2fLayer for small-target attention; and (3) reconfiguring the Backbone-Neck linkage via P2, P4, and SPPF. Compared with the baseline model (YOLOv8s), the SRoFF-Yolover achieves an 18.1% improvement in mAP@0.5, a 4.6% increase in Frames Per Second (FPS), a 2.6% reduction in Giga Floating-Point Operations (GFLOPs), and a 3.2% decrease in the total number of model parameters (#Params). The SRoFF-Yolover can effectively detect suspicious regions of forest fire, particularly during winter nights. Experiments demonstrated that the detection accuracy of the SRoFF-Yolover for suspicious regions of forest fire is higher at night than during daytime in the same season. Full article

Skid resistance, or friction, on a road surface is a critical parameter in functional highway assessments, given its direct relationships with safety and accident frequency. Therefore, road administrations must collect friction data across their road networks to ensure safe roads for users. In addition, having a predictive model of skid resistance for each road section is essential for an efficient pavement management system (PMS). Traditionally, road authorities disregard rural roads, since they are more focused on freeways and traffic-intense roads. This study develops a model for predicting minimum-available skid resistance, which occurs in summer, measured using the Sideway-force Coefficient Routine Investigation Machine (SCRIM), on bituminous pavements in the single-carriageway road network of the Province of Gipuzkoa, Spain. To this end, traffic volume data available in the PMS of the Provincial Council of Gipuzkoa, such as the annual average daily traffic (AADT) and the AADT of heavy vehicles (AADT.HV), were uniquely used to forecast skid-resistance values collected in summer. Additionally, a methodology for eliminating outliers is proposed. Despite the simplicity of the model, which does not include information about the materials at the surface layer, a coefficient of determination (R²) of 0.439 was achieved. This model can help road authorities identify the roads for which lower skid-resistance values are most likely to occur, allowing them to focus their attention and efforts on these roads, which are key infrastructure in rural areas. Full article

Public transport agencies must triage thousands of multilingual complaints every day, yet the cost of training and serving fine-grained sentiment analysis models limits real-time deployment. The proposed “one encoder, any facet” framework therefore offers a reproducible, resource-efficient alternative to heavy fine-tuning for domain-specific sentiment analysis or opinion mining tasks on digital service data. To the best of our knowledge, we are the first to test this paradigm on operational multilingual complaints, where public transport agencies must prioritize thousands of Russian- and Kazakh-language messages each day. A human-labelled corpus of 2400 complaints is embedded with five open-source universal models. Obtained embeddings are matched to semantic “anchor” queries that describe three distinct facets: service aspect (eight classes), implicit frustration, and explicit customer request. In the strict zero-shot setting, the best encoder reaches 77% accuracy for aspect detection, 74% for frustration, and 80% for request; taken together, these signals reproduce human four-level priority in 60% of cases. Attaching a single-layer logistic probe on top of the frozen embeddings boosts performance to 89% for aspect, 83–87% for the binary facets, and 72% for end-to-end triage. Compared with recent fine-tuned sentiment analysis systems, our pipeline cuts memory demands by two orders of magnitude and eliminates task-specific training yet narrows the accuracy gap to under five percentage points. These findings indicate that a single frozen encoder, guided by handcrafted anchors and an ultra-light head, can deliver near-human triage quality across multiple pragmatic dimensions, opening the door to low-cost, language-agnostic monitoring of digital-service feedback. Full article