From macro-, through meso- to micro-scale: Densification behavior, deformation response and microstructural evolution of selective laser melted Mg-RE alloy

To clarify the densification behavior, deformation response and strengthening mechanisms of selective laser melted (SLM) Mg-RE alloys, this study systematically investigates a representative WE43 alloy via advanced material characterization techniques. A suitable laser output mode fell into the transition mode, allowing for the fabrication of nearly full-density samples (porosity = 0.85 ± 0.021 %) with favorable mechanical properties (yield strength=351 MPa, ultimate tensile strength = 417 MPa, the elongation at break = 6.5 % and microhardness = 137.9 ± 6.15 HV0.1) using optimal processing parameters (P = 80 W, v = 250 mm/s and d = 50 µm). Viscoplastic self-consistent analysis and transmission electron microscopy observations reveal that the plastic deformation response of the SLM Mg-RE alloys is primarily driven by basal and prismatic slips. Starting from a random texture before deformation (maximum multiple of ultimate density, Max. MUD = 3.95), plastic stretching led the grains to align with the Z-axis, finally resulting in a {0001} texture orientation after fracture (Max. MUD = 8.755). Main phases of the SLM state are mainly composed of α-Mg, Mg24Y5 and β’-Mg41Nd5, with an average grain size of only 4.27 µm (about a quarter of that in the extruded state), resulting in a favorable strength-toughness ratio. Except for the nano-β’ phase and semi-coherent Mg24Y5 phase (mismatch = 16.12 %) around the grain boundaries, a small amount of nano-ZrO2 and Y2O3 particles also play a role in dispersion strengthening. The high mechanical properties of the SLM state are chiefly attributed to precipitation hardening (44.41 %), solid solution strengthening (34.06 %) and grain boundary strengthening (21.53 %), with precipitation hardening being predominantly driven by dislocation strengthening (67.77 %). High-performance SLM Mg-RE alloy components were manufactured and showcased at TCT Asia 2024, receiving favorable attention. This work underscores the significant application potential of SLM Mg-RE alloys and establishes a strong foundation for advancing their use in the biomedical fields. © 202

Nanoindentation test for the determination of interfacial deterioration in GFRP composites in alkaline environment

The mechanical properties of glass fiber reinforced polymer (GFRP) composites can be significantly affected by the fiber-matrix interface due to its large area per volume and its vital role in load transfer. The bonding at such interface, however, may be vulnerable when exposed to alkali environment mainly because of the accelerated water/ion penetration caused by the matrix hydrolyzation. In this study, a novel approach using nanoindentation is brought forward to quantitatively investigate the interfacial deterioration in alkaline environment. Cylindrical GFRP bars are fully immersed in either NaOH solutions with different pH values or distilled water (DW) under 60 °C for 360 days, followed by nanoindentation test on fibers at different distances to the edge to determine interfacial shear strength (τs) and interfacial friction (τf). Results indicate that long-term immersion in alkaline solutions can cause interfacial deterioration. Such detrimental effect of alkaline environment is more apparent (e.g., ∼ 30 % drop in τs of edge fibers with immersion in pH13 solution) than mere moist condition (e.g., ∼ 13 % drop in the counterpart immersed in DW). Results also reveal that for aged specimens, fibers with no observable interfacial damage under the microscope can also suffer from interfacial degradation, which mainly occurs at regions very close (within 1 mm) to the member surface. This study demonstrates the nanoindentation method as an applicable and sensitive approach for quantifying changes at the fiber-matrix interface. With this method, in-situ interfacial degradation of fibers inside GFRP can be determined, providing input for modeling durability performance under various environmental conditions. © 2024 Elsevier Lt

High Throughput Shortest Distance Query Processing on Large Dynamic Road Networks

Shortest path (SP) computation is the building block for many location-based services, and achieving high throughput SP query processing with real-time response is crucial for those services. However, existing solutions can hardly handle high throughput queries on large dynamic road networks due to either slow query efficiency or poor dynamic adaption. In this paper, we leverage graph partitioning and propose novel Partitioned Shortest Path (PSP) indexes to address this problem. Specifically, we first put forward a cross-boundary strategy to accelerate the query processing of PSP index and analyze its efficiency upper bound theoretically. After that, we propose a non-trivial Partitioned Multi-stage Hub Labeling (PMHL) that subtly aggregates multiple PSP strategies to achieve fast index maintenance and consecutive query efficiency improvement during index update. Lastly, to further optimize throughput, we design tree decomposition-based graph partitioning and propose Post-partitioned MHL (PostMHL) with faster query processing and index update. Experiments on real-world road networks show that our methods outperform state-of-the-art baselines in query throughput, yielding up to 2 orders of magnitude improvement

The interplay between histone modifications and nuclear lamina in genome regulation

Gene expression is regulated by chromatin architecture and epigenetic remodeling in cell homeostasis and pathologies. Histone modifications act as the key factors to modulate the chromatin accessibility. Different histone modifications are strongly associated with the localization of chromatin. Heterochromatin primarily localizes at the nuclear periphery, where it interacts with lamina proteins to suppress gene expression. In this review, we summarize the potential bridges that have regulatory functions of histone modifications in chromatin organization and transcriptional regulation at the nuclear periphery. We use lamina-associated domains (LADs) as examples to elucidate the biological roles of the interactions between histone modifications and nuclear lamina in cell differentiation and development. In the end, we highlight the technologies that are currently used to identify and visualize histone modifications and LADs, which could provide spatiotemporal information for understanding their regulatory functions in gene expression and discovering new targets for diseases. © 2024 Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, and Genetics Society of Chin

Confusion Matrix Design for Downstream Decision-Making

We initiate the study of confusion matrix design. In this problem, an algorithm designer needs to generate a machine learning model (for a classification task from contexts to labels) which makes predictions for a population of downstream decision makers. The prediction accuracy of the machine learning model is characterized by its confusion matrix, which is a stochastic matrix where each entry encodes the probability of predicting the true label to another label. Each downstream decision maker faces a separate optimization task and will decide his binary action based on his own context, realized prediction given his context, and the confusion matrix selected by the algorithm designer. Decision makers are heterogeneous, as they may hold different contexts. Both the decision makers and the algorithm designer will obtain utilities that are determined by the actions the decision makers take, and their true labels. The goal of the algorithm designer is to design a public confusion matrix that is used for all decision makers subject to some feasibility constraints in order to maximize her net utility. We consider a general class of net utility functions, which could be a combination of both decision makers' utilities and the algorithm designer’s utility. Classic outcome-independent utility and utilitarian/Nash/egalitarian social welfare are all special cases of our net utility formulation. We study the above problem through an information design framework, where we view training machine learning model as designing an information structure (signaling scheme) subject to some specific constraints motivated by the machine learning literature. By building the connection to the public persuasion with heterogeneous priors, we design convex programming-based algorithms that compute the optimal confusion matrix subject to (i) post-processing constraints and (ii) receiver operating characteristic (ROC) constraints in polynomial time, respectively. Besides the computational results, we also obtain analytical structural results and numerical results for the special cases of outcome-independent utility and social-aware utility, by utilizing the convex programming-based characterization of the optimal confusion matrix

Well-being of Ukrainian and Polish college students during the Russo-Ukrainian war and coping strategies as predictors of mental health disorders

The aim of the study was to compare mental health outcomes, coping strategies, and well-being between Ukrainian and Polish college students during the Russo-Ukrainian War. The sample included a total of 1,286 Ukrainian and Polish college students. An online survey was conducted using the DASS-21, PERMA-Profiler, and Brief-COPE questionnaires were collected. Pearson correlation and SEM analyses were performed to assess the relationships between the variables. Polish college students reported significantly higher levels of depression (p < 0.001) and stress (p < 0.001) compared to Ukrainian college students. Avoidant coping was positively linked to psychological distress in both groups (p < 0.001). Problem-focused coping was a protective factor for depression (p < 0.05), anxiety (p < 0.05), and stress (p < 0.005). Socio-demographic factors, such as female gender (Polish: p < 0.05 for depression, p < 0.001 for anxiety and stress; Ukrainian: p < 0.001 for anxiety and stress), poor financial status (Polish: p < 0.001 for depression, anxiety, and stress; Ukrainian: p < 0.05 for depression), and young age (Polish: p < 0.05 for anxiety; Ukrainian: p < 0.001 for anxiety, p < 0.005 for stress) were associated with poor mental health among Polish and Ukrainian college students. The study underscores the need for targeted, gender-sensitive, and financially supportive interventions to improve the mental health and well-being of college students affected by the Russo-Ukrainian War. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group

Importance Weighting Can Help Large Language Models Self-Improve

Large language models (LLMs) have shown remarkable capability in numerous tasks and applications. However, fine-tuning LLMs using high-quality datasets under external supervision remains prohibitively expensive. In response, LLM self-improvement approaches have been vibrantly developed recently. The typical paradigm of LLM self-improvement involves training LLM on self-generated data, part of which may be detrimental and should be filtered out due to the unstable data quality. While current works primarily employs filtering strategies based on answer correctness, in this paper, we demonstrate that filtering out correct but with high distribution shift extent (DSE) samples could also benefit the results of self-improvement. Given that the actual sample distribution is usually inaccessible, we propose a new metric called DS weight to approximate DSE, inspired by the Importance Weighting methods. Consequently, we integrate DS weight with self-consistency to comprehensively filter the self-generated samples and fine-tune the language model. Experiments show that with only a tiny valid set (up to 5% size of the training set) to compute DS weight, our approach can notably promote the reasoning ability of current LLM self-improvement methods. The resulting performance is on par with methods that rely on external supervision from pre-trained reward models

Carbonation of natural fibers reinforced MgO-SiO₂ (NFs-MS) composites

The heavy reliance of reactive magnesia cement (RMC) on CO2 sources to gain sufficient mechanical strength limits its productivity. The present work developed natural fibers reinforced MgO-SiO2 (NFs-MS) composites, in which the formation of magnesium-silicate-hydrate (M-S-H) yielded sufficient early strength (e.g., >30 MPa), and subsequent carbonation of residual brucite enabled continuous strength development (e.g., >70 MPa). The presence of NFs in MS composites not only accelerates the strength gain under moisture curing and subsequent carbonation curing, but also effectively improves the volume stability and CO2 sequestration. Moreover, carbonation curing densified the fiber-matrix interface zone, leading to improved fiber-matrix interfacial properties and tensile performance. The results from aqueous carbonation test show that the synthetic M-S-H has greater chemical stability compared to MgO/brucite. However, partial leaching of Mg2+ from M-S-H was also observed, implying the carbonation potential of M-S-H phase. These findings suggest that NFs-MS composites hold great potential to be directly applied in load-bearing structures without requirement for special CO2 pre-curing