Rank-Based Learning and Local Model Based Evolutionary Algorithm for High-Dimensional Expensive Multi-Objective Problems

Surrogate-assisted evolutionary algorithms have been widely developed to solve complex and computationally expensive multi-objective optimization problems in recent years. However, when dealing with high-dimensional optimization problems, the performance of these surrogate-assisted multi-objective evolutionary algorithms deteriorate drastically. In this work, a novel Classifier-assisted rank-based learning and Local Model based multi-objective Evolutionary Algorithm (CLMEA) is proposed for high-dimensional expensive multi-objective optimization problems. The proposed algorithm consists of three parts: classifier-assisted rank-based learning, hypervolume-based non-dominated search, and local search in the relatively sparse objective space. Specifically, a probabilistic neural network is built as classifier to divide the offspring into a number of ranks. The offspring in different ranks uses rank-based learning strategy to generate more promising and informative candidates for real function evaluations. Then, radial basis function networks are built as surrogates to approximate the objective functions. After searching non-dominated solutions assisted by the surrogate model, the candidates with higher hypervolume improvement are selected for real evaluations. Subsequently, in order to maintain the diversity of solutions, the most uncertain sample point from the non-dominated solutions measured by the crowding distance is selected as the guided parent to further infill in the uncertain region of the front. The experimental results of benchmark problems and a real-world application on geothermal reservoir heat extraction optimization demonstrate that the proposed algorithm shows superior performance compared with the state-of-the-art surrogate-assisted multi-objective evolutionary algorithms. The source code for this work is available at https://github.com/JellyChen7/CLMEA

Elucidating STEM Concepts through Generative AI: A Multi-modal Exploration of Analogical Reasoning

This study explores the integration of generative artificial intelligence (AI), specifically large language models, with multi-modal analogical reasoning as an innovative approach to enhance science, technology, engineering, and mathematics (STEM) education. We have developed a novel system that utilizes the capacities of generative AI to transform intricate principles in mathematics, physics, and programming into comprehensible metaphors. To further augment the educational experience, these metaphors are subsequently converted into visual form. Our study aims to enhance the learners' understanding of STEM concepts and their learning engagement by using the visual metaphors. We examine the efficacy of our system via a randomized A/B/C test, assessing learning gains and motivation shifts among the learners. Our study demonstrates the potential of applying large language models to educational practice on STEM subjects. The results will shed light on the design of educational system in terms of harnessing AI's potential to empower educational stakeholders

Oscillation of mineral compositions in Core SG-1b, western Qaidam Basin, NE Tibetan Plateau

Uplift of the Tibetan Plateau since the Late Miocene has greatly affected the nature of sediments deposited in the Qaidam Basin. However, due to the scarcity of continuously dated sediment records, we know little about how minerals responded to this uplift. In order to understand this response, we here present results from the high-resolution mineral profile from a borehole (7.3–1.6 Ma) in the Basin, which shows systematic oscillations of various evaporite and clay minerals that can be linked to the variation of regional climate and tectonic history. In particular, x-ray diffraction (XRD) analyses show that carbonate minerals consist mainly of calcite and aragonite, with minor ankerite and dolomite. Evaporates consist of gypsum, celesite and halite. Clay minerals are principally Fe-Mg illite, mixed layers of illite/smectite and chlorite, with minor kaolinite and smectite. Following implications can be drawn from the oscillations of these minerals phases: (a) the paleolake was brackish with high salinity after 7.3 Ma, while an abrupt change in the chemical composition of paleolake water (e.g. Mg/Ca ratio, SO4 2− concentration, salinity) occurred at 3.3 Ma; (b) the three changes at ~6.0 Ma, 4.5–4.1 Ma and 3.3 Ma were in response to rapid erosions/uplift of the basin; (c) pore water or fluid was Fe/Mg-rich in 7.3–6.0 Ma, Mg-rich in 6.0–4.5 Ma, and K-rich in 4.1–1.6 Ma; and (d) evaporation rates were high, but weaker than today’s

Outcomes of surgery for patients with Behcet’s disease causing aortic pseudoaneurysm: a shift from open surgery to endovascular repair

OBJECTIVES: Behcet’s disease is a form of systematic vasculitis that affects vessels of various sizes. Aortic pseudoaneurysm is one of the most important causes of death among patients with Behcet’s disease due to its high risk of rupture and associated mortality. Our study aimed to investigate the outcomes of Behcet’s disease patients with aortic pseudoaneurysms undergoing open surgery and endovascular aortic repair. METHODS: From January 2003 to September 2014, ten consecutive patients undergoing surgery for aortic pseudoaneurysm met the diagnostic criteria for Behcet’s disease. Endovascular repair was the preferred modality and open surgery was performed as an alternative. Systemic immunosuppressive medication was administered after Behcet’s disease was definitively diagnosed. RESULTS: Eight patients initially underwent endovascular repair and two patients initially underwent open surgery. The overall success rate was 90% and the only failed case involved the use of the chimney technique to reach a suprarenal location. The median follow-up duration was 23 months. There were 7 recurrences in 5 patients. The median interval between operation and recurrence was 13 months. No significant risk factors for recurrence were identified, but a difference in recurrence between treatment and non-treatment with preoperative immunosuppressive medication preoperatively was notable. Four aneurysm-related deaths occurred within the follow-up period. The overall 1-year, 3-year and 5-year survival rates were 80%, 64% and 48%, respectively. CONCLUSIONS: Both open surgery and endovascular repair are safe and effective for treating aortic pseudoaneurysm in Behcet’s disease patients. The results from our retrospective study indicated that immunosuppressive medication was essential to defer the occurrence and development of recurrent aneurysms

Type I interferons protect neonates from acute inflammation through interleukin 10–producing B cells

Newborns and infants are highly susceptible to viral and bacterial infections, but the underlying mechanism remains poorly understood. We show that neonatal B cells effectively control the production of proinflammatory cytokines by both neonatal plasmacytoid and conventional dendritic cells, in an interleukin (IL) 10–dependent manner, after Toll-like receptor (TLR) 9 triggering. This antiinflammatory property of neonatal B cells may extend to other TLR agonists (Pam3CSK4, lipopolysaccharide, and R848) and viruses. In the absence of B cells or of CD5+ B cell subsets, neonatal mice developed stronger inflammatory responses and became lethally susceptible to CpG challenge after galactosamine sensitization, whereas wild-type (WT) mice were resistant. Paradoxically, interferon (IFN)-α/β enhanced the inflammatory response to CpG challenge in adult mice, whereas they helped to control neonatal acute inflammation by stimulating the secretion of IL-10 by neonatal B cells. Finally, WT neonatal B cells rescued IL-10−/− neonates from a lethal CpG challenge, whereas IFN-α/β receptor–deficient B cells did not. Our results show that type I IFNs support a negative regulatory role of neonatal B cells on TLR-mediated inflammation, with important implications for neonatal inflammation and infection

Enriching Phrases with Coupled Pixel and Object Contexts for Panoptic Narrative Grounding

Panoptic narrative grounding (PNG) aims to segment things and stuff objects in an image described by noun phrases of a narrative caption. As a multimodal task, an essential aspect of PNG is the visual-linguistic interaction between image and caption. The previous two-stage method aggregates visual contexts from offline-generated mask proposals to phrase features, which tend to be noisy and fragmentary. The recent one-stage method aggregates only pixel contexts from image features to phrase features, which may incur semantic misalignment due to lacking object priors. To realize more comprehensive visual-linguistic interaction, we propose to enrich phrases with coupled pixel and object contexts by designing a Phrase-Pixel-Object Transformer Decoder (PPO-TD), where both fine-grained part details and coarse-grained entity clues are aggregated to phrase features. In addition, we also propose a PhraseObject Contrastive Loss (POCL) to pull closer the matched phrase-object pairs and push away unmatched ones for aggregating more precise object contexts from more phrase-relevant object tokens. Extensive experiments on the PNG benchmark show our method achieves new state-of-the-art performance with large margins.Comment: Accepted by IJCAI 202

Effects of oil-in-water based nanolubricant containing TiO2 nanoparticles in hot rolling of 304 stainless steel

Energy saving and improvement of product quality are of crucial importance in hot rolling of 304 stainless steel. In this paper, oil-in-water (O/W) based nanolubricants containing TiO2 nanoparticles were developed to reduce the rolling force and improve the surface quality of rolled 304 stainless steel product. Practical hot rolling tests with and without application of lubricant were conducted to systematically investigate the effects of the developed O/W based nanolubricants on the rolling force, surface roughness, oxide scale thickness and tribological behaviour. The obtained results indicate that the nanoparticles can enter the deform zone with oil droplets to take a lubrication effect. The optimal lubrication effect can be achieved when the O/W (1% oil mass fraction) based nanolubricant with a TiO2 mass fraction of 1.5% was applied. The novel nanolubricant has a great potential to be applied in the hot steel rolling, to realise the cost-effective and environmental-friendly manufacturing process