Kangxian Xie, Jiancheng Yang, Nandor Pinter
et al.
Accurate 3D anatomical segmentation is essential for clinical diagnosis and surgical planning. However, automated models frequently generate suboptimal shape predictions due to factors such as limited and imbalanced training data, inadequate labeling quality, and distribution shifts between training and deployment settings. A natural solution is to iteratively refine the predicted shape based on the radiologists' verbal instructions. However, this is hindered by the scarcity of paired data that explicitly links erroneous shapes to corresponding corrective instructions. As an initial step toward addressing this limitation, we introduce CoWTalk, a benchmark comprising 3D arterial anatomies with controllable synthesized anatomical errors and their corresponding repairing instructions. Building on this benchmark, we further propose an iterative refinement model that represents 3D shapes as vector sets and interacts with textual instructions to progressively update the target shape. Experimental results demonstrate that our method achieves significant improvements over corrupted inputs and competitive baselines, highlighting the feasibility of language-driven clinician-in-the-loop refinement for 3D medical shapes modeling.
Nonylphenols (NPs), widely used as emulsifiers in petroleum production and refining, are compounds of environmental concern, with endocrine-disrupting effects. They can be released during oil extraction and processing, carried into petroleum products, and subsequently emitted during downstream applications such as combustion. Despite these potential pathways, information on their occurrence in petroleum streams remains limited, partly due to the lack of reliable methods for measuring NPs in complex petroleum matrices. In this study, we developed an analytical method combining normal-phase chromatography (NPC), solid-phase extraction (SPE), and liquid chromatography–Orbitrap high-resolution mass spectrometry (LC–Orbitrap-HRMS) for NP determination in crude oils and petroleum products. NPC was performed using alumina (5% water deactivation) as the stationary phase. The column was eluted sequentially with n-hexane, n-hexane/dichloromethane (4:1 and 1:1, v/v), dichloromethane, and dichloromethane/methanol (2:1, v/v). The first three fractions were discarded, and the remaining two fractions were combined and further purified using a C18 SPE cartridge to analysis. The method showed high recovery (82.8 ± 2.6%) and a low detection limit (1.0 ng/g) in crude oil. Application revealed widespread occurrence of NPs, with concentrations up to 784.4 ng/g in crude oils and up to 439.1 ng/g in refined fuels, indicating that these compounds can persist through refining and may be released during downstream use. These results demonstrate that the method is suitable for the routine monitoring of NPs in petroleum-related samples and provide a practical tool for supporting sustainable refining practices and improved environmental management in the upstream oil and gas sector.
Hamed Nejati, Ehsan Khamehchi, Ali Ashraf Derakhshan
et al.
Abstract In loose sandstone reservoirs, sand enters the wellbore along with the production fluid. Sand production causes numerous problems, such as the erosion of downhole, wellhead, and surface equipment, ultimately leading to a decline in production. In this paper, the authors present a new epoxy-based nanofluid for controlling sand production. To demonstrate its effectiveness, a near-wellbore laboratory simulator system (NeWSS) was developed, which takes into account all downhole conditions, such as the radial distribution of flow, temperature, and reservoir pressure. The epoxy/g-C3N4-NS nanofluid has two special properties. First, carbon nitride nanosheets were used as an active strengthening agent to increase the compressive strength of the epoxy resin after curing. Second, a bubbling agent was used to create micro- and macro-pores, facilitating the movement of the production fluid and ultimately increasing permeability. Laboratory results showed that the optimum concentrations of the bubbling agent and g-C3N4-NS are 3 wt% and 0.5 wt%, respectively. The preflush solution (3% KCl, 3% surface modifier, and 5% organic solvent) used to remove formation fluids (oil and brine) before nanofluid sand consolidation resulted in good adhesion of the nanofluid to the sand matrix and significantly increased the compressive strength of the porous area. Results from the near-wellbore laboratory simulation system under reservoir conditions (90 °C and 2800 psig) show that the regained permeability is above 90% when the epoxy/g-C3N4-NS nanofluid is used along with preflush and overflush solutions. Moreover, the presence of the optimum concentration of g-C3N4-NS in the epoxy-based nanofluid increased the failure stress of the hollow cylinder sample by 0.7 MPa.
Abstract The sedimentary mechanisms and depositional processes of deep-water gravity flows in lacustrine basins remain incompletely understood, posing challenges for accurate reservoir prediction in continental rift basins. This study investigates the Triassic Yanchang Formation in the Ordos Basin to elucidate the genetic processes and evolutionary mechanisms of deep-lake gravity flow deposits. Through systematic core observations, grain size analysis (including mean size, sorting, skewness and kurtosis), and sedimentary facies analysis, we identified four distinct gravity flow types: (1) sandy debris flows, (2) muddy debris flows, (3) turbidity currents, and (4) slumps. Quantitative grain size parameters reveal significant differences among these facies, with mean grain size ranging from 1.19Φ to 3.53Φ. Three primary triggering mechanisms are recognized: seismic events (seismites), volcanic events (tuffaceous layers), and anoxic events ("Zhangjiatan" shales), with seismic and volcanic events exerting particularly strong influences. A new depositional pattern is proposed, detailing the complete evolutionary sequence from slope failure initiation to final deposition, encompassing the sliding-slumping-debris flow-turbidity current transition. This study advances understanding by: (1) establishing quantitative discriminators for different gravity flow types, (2) clarifying the causal relationships between external triggers and flow deposition, and (3) providing a comprehensive evolutionary framework for lacustrine gravity flow systems. These findings significantly enhance the ability to predict deep-lacustrine sedimentary architectures and hydrocarbon reservoirs, offering valuable insights for exploration in analogous rift basins.
Despite over 40 years of exploration focusing on the deep to ultra-deep Ordovician carbonates as major hydrocarbon targets in the Tarim Basin, the identification of their source rocks remains elusive. Based on biomarkers, carbon and sulfur isotopes of hydrocarbons, the primary source for the Ordovician petroleum system has been attributed mainly to the lower Cambrian shales, although some oils were likely contributed from the Lower Ordovician source rocks. However, the current understanding of the evolution of the Ordovician petroleum system remains rudimentary, largely due to the complex interplay of multi-source (i.e., the widespread Precambrian shales) hydrocarbon inputs, diagenetic alterations, and tectonic processes over geological time. This study systematically investigates the molecular geochemistry of reservoir bitumen within the Ordovician carbonates from Tabei uplift, coupled with bitumen from the Sinian units at the western edge of the Tarim Basin. Our results indicate that the ion chromatography-mass spectrometry spectra and the saturated to aromatic hydrocarbons ratio of Ordovician reservoir bitumen closely resemble those of Sinian bitumen, as well as the published data of Sinian shales. This coupling linkage is revealed by cross-plots and ternary phase diagrams of various biomarker parameters, which can effectively distinguish the Sinian sources from other sources, i.e., the Lower Cambrian and Ordovician, for the Ordovician reservoir bitumen. Specifically, the ∑n-C21-/∑n-C22+, Pr/Ph, G/C31H22S, C23/C21TT ratios are effective indexes to differentiate these source rocks. By compiling the published organic geochemistry data of oils, it appears to infer that approximately 8.1% of the present-day oils produced from the Ordovician carbonates likely contain some proportion of Sinian-sourced oils. The recognition of Sinian sourced oils contributing to the paleo- and present-day Ordovician petroleum system offers valuable insights for the exploration of deep-ultra deep carbonates in the Tarim Basin, emphasizing the need to consider Precambrian shales as a significant hydrocarbon source.
Oils, fats, and waxes, Petroleum refining. Petroleum products
This paper defines Massey-type products for a homotopy inner product on an $A_\infty$ algebra, called Massey inner products. We include an explicit description of ordinary Massey products for $A_\infty$ algebras, and for $A_\infty$ modules, and show that Massey product sets can have interesting internal structure. We give non-trivial applications from lens spaces, links, and low dimensional manifolds, showing Massey inner products contain information beyond ordinary Massey products.
The fine-grained annotations in whole slide images (WSIs) show the boundaries of various pathological regions. However, generating such detailed annotation is often costly, whereas the coarse annotations are relatively simpler to produce. Existing methods for refining coarse annotations often rely on extensive training samples or clean datasets, and fail to capture both intra-slide and inter-slide latent sematic patterns, limiting their precision. In this paper, we propose a prototype-guided approach. Specifically, we introduce a local-to-global approach to construct non-redundant representative prototypes by jointly modeling intra-slide local semantics and inter-slide contextual relationships. Then a prototype-guided pseudo-labeling module is proposed for refining coarse annotations. Finally, we employ dynamic data sampling and re-finetuning strategy to train a patch classifier. Extensive experiments on three publicly available WSI datasets, covering lymph, liver, and colorectal cancers, demonstrate that our method significantly outperforms existing state-of-the-art (SOTA) methods. The code will be available.
Stefano Giani, Jeffrey Ovall, Gabriel Pinochet-Soto
We introduce an adaptive finite element scheme for the efficient approximation of a (large) collection of eigenpairs of selfadjoint elliptic operators in which the adaptive refinement is driven by the solution of a single source problem -- the so-called landscape problem for the operator -- instead of refining based on the computed eigenpairs. Some theoretical justification for the approach is provided, and extensive empirical results indicate that it can provide an attractive alternative to standard adaptive schemes, particularly in the hp-adaptive environment.
Depth refinement aims to infer high-resolution depth with fine-grained edges and details, refining low-resolution results of depth estimation models. The prevailing methods adopt tile-based manners by merging numerous patches, which lacks efficiency and produces inconsistency. Besides, prior arts suffer from fuzzy depth boundaries and limited generalizability. Analyzing the fundamental reasons for these limitations, we model depth refinement as a noisy Poisson fusion problem with local inconsistency and edge deformation noises. We propose the Self-distilled Depth Refinement (SDDR) framework to enforce robustness against the noises, which mainly consists of depth edge representation and edge-based guidance. With noisy depth predictions as input, SDDR generates low-noise depth edge representations as pseudo-labels by coarse-to-fine self-distillation. Edge-based guidance with edge-guided gradient loss and edge-based fusion loss serves as the optimization objective equivalent to Poisson fusion. When depth maps are better refined, the labels also become more noise-free. Our model can acquire strong robustness to the noises, achieving significant improvements in accuracy, edge quality, efficiency, and generalizability on five different benchmarks. Moreover, directly training another model with edge labels produced by SDDR brings improvements, suggesting that our method could help with training robust refinement models in future works.
Monocular metric depth estimation (MMDE) is a crucial task to solve for indoor scene reconstruction on edge devices. Despite this importance, existing models are sensitive to factors such as boundary frequency of objects in the scene and scene complexity, failing to fully capture many indoor scenes. In this work, we propose to close this gap through the task of monocular metric depth refinement (MMDR) by leveraging state-of-the-art MMDE models. MultiDepth proposes a solution by taking samples of the image along with the initial depth map prediction made by a pre-trained MMDE model. Compared to existing iterative depth refinement techniques, MultiDepth does not employ normal map prediction as part of its architecture, effectively lowering the model size and computation overhead while outputting impactful changes from refining iterations. MultiDepth implements a lightweight encoder-decoder architecture for the refinement network, processing multiple samples from the given image, including segmentation masking. We evaluate MultiDepth on four datasets and compare them to state-of-the-art methods to demonstrate its effective refinement with minimal overhead, displaying accuracy improvement upward of 45%.
Natural language explanations (NLEs) are vital for elucidating the reasoning behind large language model (LLM) decisions. Many techniques have been developed to generate NLEs using LLMs. However, like humans, LLMs might not always produce optimal NLEs on first attempt. Inspired by human learning processes, we introduce Cross-Refine, which employs role modeling by deploying two LLMs as generator and critic, respectively. The generator outputs a first NLE and then refines this initial explanation using feedback and suggestions provided by the critic. Cross-Refine does not require any supervised training data or additional training. We validate Cross-Refine across three NLP tasks using three state-of-the-art open-source LLMs through automatic and human evaluation. We select Self-Refine (Madaan et al., 2023) as the baseline, which only utilizes self-feedback to refine the explanations. Our findings from automatic evaluation and a user study indicate that Cross-Refine outperforms Self-Refine. Meanwhile, Cross-Refine can perform effectively with less powerful LLMs, whereas Self-Refine only yields strong results with ChatGPT. Additionally, we conduct an ablation study to assess the importance of feedback and suggestions. Both of them play an important role in refining explanations. We further evaluate Cross-Refine on a bilingual dataset in English and German.
In this paper we study the strict refinement property for connected partial ordersalso known as Hashimoto's Theorem. This property implies that any isomorphismbetween products of irreducible structures is determined is uniquely determinedas a product of isomorphisms between the factors. This refinement implies asort of smallest possible decomposition for such structures. After a brief recallof the necessary notion we prove that Hashimoto's theorem can be extendedto connected loop-free categories, i.e. categories with no non-trivial morphismsendomorphisms. A special case of such categories is the category of connectedcomponents, for concurrent programs without loops.
ABSTRACT The commercial shape-stabilized phase change material (PCM) products are obtained from synthetic raw materials. These materials are produced from carbon-intensive petroleum-refining processes. The carbon footprint can be minimized by using biobased products. Beeswax is a naturally available PCM with high phase transition enthalpy comparable to synthetic PCMs. In the previous literature studies, beeswax was shape-stabilized with synthetic materials. In the present study, beeswax was shape-stabilized with biopolymer ethyl cellulose by emulsion solvent evaporation method. The study aims to determine optimum microencapsulation process parameter levels for synthesizing microcapsules with high thermal energy storage (TES) capacity. The optimized process parameters for formulating microcapsules were 60:40 core/shell ratio, 2% PVA concentration, ethyl acetate solvent, and 40°C evaporation temperature. The microcapsules prepared with optimized process parameters were characterized using differential scanning calorimeter (DSC), T-history analysis, Fourier transform infrared (FTIR) spectroscope, and scanning electron microscope (SEM). FTIR and SEM analysis confirmed ethyl cellulose shell formation over the beeswax core. The optimized MPCM formulation possesses 115.8 J/g melting enthalpy with peak phase transition temperature of 58.2°C and thermal conductivity of 0.219 W/mK. The porous structure of the shell reduced melting enthalpy of optimized MPCM formulation to 84.6 J/g after 50 thermal cycles. The synthesized microcapsules comprise sustainable materials and have high TES capacity. The fabricated microcapsules can be used as TES additive in composite and coating formulations in food packaging.
Silicon cutting waste (SCW) mainly consists of Si (80 ~ 85 wt%), SiO2 (13 ~ 16 wt%) and other impurities (2 ~ 4 wt%). Nowadays, the Si in SCW is commercially recycled to produce Si ingots by a slag refining method, but the SiO2 in SCW is melted into silicon slag and discarded as waste. In this paper, a carbothermal reduction method has been proposed for recycling Si resources from both Si and SiO2 in SCW to prepare high-quality silicon in a submerged arc furnace. Petroleum coke was selected as the carbonaceous reducing agent. Firstly, the effects of carbon content on the equilibrium compositions of the Si-SiO2-C system were simulated. Secondly, SCW was mixed with petroleum coke under the guidance of thermodynamic analysis results. Finally, the mixtures were charged into furnace and smelted. Thermodynamic equilibrium analysis results showed that the value of n(Si):n(C):n(SiO2) should be controlled as 2.62:0.22:0.44 theoretically. Experimental results revealed that the recovery ratio of SCW was 50% and the purity of Si products was 99.40%. This proposed method provides an effective and industrialized applicable approach for recycling SCW.
Human cell-based test methods can be used to evaluate potential hazards of mixtures and products of petroleum refining (“unknown or variable composition, complex reaction products, or biological materials” substances, UVCBs). Analyses of bioactivity and detailed chemical characterization of petroleum UVCBs were used separately for grouping these substances; a combination of the approaches has not been undertaken. Therefore, we used a case example of representative high production volume categories of petroleum UVCBs, 25 lower olefin substances from low benzene naphtha and resin oils categories, to determine whether existing manufacturing-based category grouping can be supported. We collected two types of data: nontarget ion mobility spectrometry-mass spectrometry of both neat substances and their organic extracts and in vitro bioactivity of the organic extracts in five human cell types: umbilical vein endothelial cells and induced pluripotent stem cell-derived hepatocytes, endothelial cells, neurons, and cardiomyocytes. We found that while similarity in composition and bioactivity can be observed for some substances, existing categories are largely heterogeneous. Strong relationships between composition and bioactivity were observed, and individual constituents that determine these associations were identified. Overall, this study showed a promising approach that combines chemical composition and bioactivity data to better characterize the variability within manufacturing categories of petroleum UVCBs.
This paper discusses the synthesis of environmental sustainability framework for Modular Refinery (MR) in the Nigeria Oil and Gas Sector. Energy, and most importantly oil and gas plays an important role in the economic development. The downstream sector demonstrates a lack of adequate petroleum refining regulation in some countries such as Nigeria, which has resulted in environmental pollution and Green House Gas (GHG) emissions that have continued to impair environmental sustainability in the country. The wastes generated from petroleum refineries are in the form of gases, particles and liquid effluent, which becomes hazardous to the environment and to human health. The development of MR is one of the best, if not the only, option for making global south nations self-sufficient in domestic demand and net exporters of petroleum products. Literature in public domain have narrated various issues in respect of the theme of this paper as noted below. The oil and gas industry is essential to the global economy and the activities of the oil and gas companies have a huge effect on the world and the environment. Oil refineries in Nigeria are responsible for 4% of global CO2 emission and 24% in GHG emissions resulting in health and environmental issues. National Oil Spill Detection and Response Agency (NOSDRA) detects and responds to oil spill according to Government Regulations. Regulatory standing in Nigeria stipulates that environmental impact assessments (EIA) is a prerequisite before, during and after oil and gas refinery projects In order to cut GHG emissions that achieve global temperature rise of 1.5 -2 0 C, Global North are reducing reliance on oil. Global South (GS) countries including Nigeria plan to increase MR installation in order to increase both domestic availability of oil and the oil sector contribution to Gross Domestic Product (GDP). MR are viable option in inaccessible location or rural areas, especially where oil supply is inadequate to meet daily consumption demand. The methodology for this study uses interpretivism as an innovative research philosophy, allowing for the development of a sustainable environmental framework. The key considerations for developing an environmental sustainability framework for modular refinery in GS can be encapsulated under the indices of stringency, compliance and transparency. One limitation of the indices would be over-reliance of GS on oil revenue. This paper argues the need for urgent diversification in the GS. Summarily, all the three indices needs detailing when adopting the proposed framework in the oil and gas sector of the GS. Future work will include the collection of primary data from key stakeholders in the oil and gas sector to ascertain the effectiveness of the proposed framework for MR. Further study to include financial evaluations for MR framework in terms of its economic feasibility will also be desirable.
In order to solve the problem of the perforation gun not being centered in the casing due to the gravity of the perforation gun during unconventional horizontal well perforation operations in oil and gas reservoirs, and the uneven perforation of the casing hole after perforation. A 73type GH equal aperture perforating projectile with a conical top with equal wall thickness and a curved lower inner wall is designed. Through numerical simulation analysis, it is found that the jet diameter formed by the perforating bullet is thick and the gradient is small, which can achieve uniform and consistent casing aperture after perforation when the perforating gun is not in the center. Through ground target testing, the average penetration depth of the perforating bullet is 534.3mm, and the average casing aperture is 10.4mm, indicating that it has the characteristics of large aperture deep penetration. The consistency deviation of aperture is 4%, which verifies that the numerical simulation results are consistent with the actual results, achieving the effect of equal aperture. The results show that the type 73GH equal hole perforating charge can effectively reduce the rupture pressure when perforating horizontal wells.
The geochemical analysis and experimental simulation are comprehensively used to systematically study the hydrocarbon generation material, organic matter enrichment and hydrocarbon generation model of Paleogene source rock in the Western Qaidam Depression, Qaidam Basin, NW China. Three main factors result in low TOC values of saline lacustrine source rock of the Qaidam Basin: relatively poor nutrient supply inhibits the algal bloom, too fast deposition rate causes the dilution of organic matter, and high organic matter conversion efficiency causes the low residual organic carbon. For this type of hydrogen-rich organic matter, due to the reduction of organic carbon during hydrocarbon generation, TOC needs to be restored based on maturity before evaluating organic matter abundance. The hydrocarbon generation of saline lacustrine source rocks of the Qaidam Basin is from two parts: soluble organic matter and insoluble organic matter. The soluble organic matter is inherited from organisms and preserved in saline lacustrine basins. It generates hydrocarbons during low-maturity stage, and the formed hydrocarbons are rich in complex compounds such as NOS, and undergo secondary cracking to form light components in the later stage; the hydrocarbon generation model of insoluble organic matter conforms to the traditional “Tissot” model, with an oil generation peak corresponding to Ro of 1.0%.
The notion of a generalized product, refining that of a (symmetric and smooth) simplicial space is introduced and shown to imply the existence of an algebra of pseudodifferential operators. This encompasses many constructions of such algebras on manifolds with corners. The main examples discussed in detail here are related to the semiclassical (and adiabatic) calculus as used in the approach to a twisted form of the Atiyah-Singer index theorem in work with Is Singer and Mathai Varghese.