Estimating soil nutrient components is a key analytical methodology for creating fertilization maps for agricultural fields. To develop a compact laser-induced breakdown spectroscopy (LIBS) system for fertilizer map creation, this study examined the sensitivity enhancement of phosphorus (P) and potassium (K) in soil by adding magnetic field assistance using Halbach-arrayed neodymium magnets. The LIBS signal intensity was compared with the plant-available content determined by chemical analysis. The addition of a magnetic field ( 0.5 T) increased the spectral intensity by 1.3 times, owing to densification of the laser-induced plasma. Linearity in the calibration curve of P at 213.6 nm in soil was maintained up to 0.5 wt
Selecting a source language for zero-shot cross-lingual transfer is typically done by intuition or by defaulting to English, despite large performance differences across language pairs. We study whether linguistic similarity can predict transfer performance and support principled source-language selection. We introduce quantified WALS (qWALS), a typology-based similarity metric derived from features in the World Atlas of Language Structures, and evaluate it against existing similarity baselines. Validation uses three complementary signals: computational similarity scores, zero-shot transfer performance of multilingual transformers (mBERT and XLM-R) on four NLP tasks (dependency parsing, named entity recognition, sentiment analysis, and abusive language identification) across eight languages, and an expert-linguist similarity survey. Across tasks and models, higher linguistic similarity is associated with better transfer, and the survey provides independent support for the computational metrics.
This paper presents a multi-stage framework for detecting reclaimed slurs in multilingual social media discourse. It addresses the challenge of identifying reclamatory versus non-reclamatory usage of LGBTQ+-related slurs across English, Spanish, and Italian tweets. The framework handles three intertwined methodological challenges like data scarcity, class imbalance, and cross-linguistic variation in sentiment expression. It integrates data-driven model selection via cross-validation, semantic-preserving augmentation through back-translation, inductive transfer learning with dynamic epoch-level undersampling, and domain-specific knowledge injection via masked language modeling. Eight multilingual embedding models were evaluated systematically, with XLM-RoBERTa selected as the foundation model based on macro-averaged F1 score. Data augmentation via GPT-4o-mini back-translation to alternate languages effectively tripled the training corpus while preserving semantic content and class distribution ratios. The framework produces four final runs for the evaluation purposes where RUN 1 is inductive transfer learning with augmentation and undersampling, RUN 2 with masked language modeling pre-training, RUN 3 and RUN 4 are previous predictions refined via language-specific decision thresholds optimized via ROC analysis. Language-specific threshold refinement reveals that optimal decision boundaries vary significantly across languages. This reflects distributional differences in model confidence scores and linguistic variation in reclamatory language usage. The threshold-based optimization yields 2-5
Abstract Large Forbush decreases (FDs) in the Galactic cosmic ray flux were observed by the paired neutron monitor (SNM) and the vertical channel of the muon detector (SMDV) at the Antarctic Syowa Station in May and October, 2024. The maximum count rate depressions in SNM and SMDV are $$\sim$$ ∼ 10% and $$\sim$$ ∼ 5% in the May FD, respectively. In the October FD, the observed count rates decreased in two steps following two successive interplanetary shocks. The maximum count rate depression recorded by SNM (SMDV) is $$\sim$$ ∼ 7% ( $$\sim$$ ∼ 3%) in the first step, while it is $$\sim$$ ∼ 8% ( $$\sim$$ ∼ 5%) in the second step. It is demonstrated that the SNM count rate fraction relative to the total count rate in SNM and SMDV ( $$F_n$$ F n ) is a good real-time indicator of the temporal variation of the cosmic ray rigidity spectrum ( $$\Delta \Gamma (P,t)$$ Δ Γ ( P , t ) ), which is an important parameter for identifying the physical processes responsible for FD effects. It is verified that the variation of $$F_n$$ F n is consistent with $$\Delta \Gamma (P,t)$$ Δ Γ ( P , t ) calculated from the Global Fitting Analysis (GFA) at the Syowa Station. $$F_n$$ F n also shows a significant increase of $$\Delta \Gamma (P,t)$$ Δ Γ ( P , t ) during the ground-level enhancement (GLE) due to solar energetic particles on May 11 superposed on the FD recovery phase, implying that the total (Galactic + solar) cosmic ray spectrum is softened due to the contribution from GLE intensity steeply increasing with decreasing rigidity. This implies that $$F_n$$ F n can be also a good indicator of small GLEs which are sometimes difficult to identify in the count rate variation when masked by an FD and the associated diurnal anisotropy. Graphic Abstract
