Job–candidate matching is pivotal in recruitment, yet traditional manual or keyword-based methods can be laborious and prone to missing qualified candidates. In this study, we introduce the first Siamese framework that systematically contrasts GRU, LSTM, and Transformer sequential heads on top of a multilingual Sentence Transformer backbone, which is trained end-to-end with triplet loss on real-world recruitment data. This combination captures both long-range dependencies across document segments and global semantics, representing a substantial advance over approaches that rely solely on static embeddings. We compare the three heads using ranking metrics such as Top-K accuracy and Mean Reciprocal Rank (MRR). The Transformer-based model yields the best overall performance, with an MRR of 0.979 and a Top-100 accuracy of 87.20% on the test set. Visualization of learned embeddings (t-SNE) shows that self-attention more effectively clusters matching texts and separates them from irrelevant ones. These findings underscore the potential of combining multilingual base embeddings with specialized sequential layers to reduce manual screening efforts and improve recruitment efficiency.

Understanding and monitoring the phenological phases of trees is essential for ecological research and climate change studies. In this work, we present a comprehensive evaluation of state-of-the-art convolutional neural networks (CNNs) and transformer architectures for the automated classification of the flowering phase of Tilia cordata Mill. (small-leaved lime) based on a large set of real-world images acquired under natural field conditions. The study introduces a novel, automated image quality filtering approach using an XGBoost classifier trained on diverse exposure and sharpness features to ensure robust input data for subsequent deep learning models. Seven modern neural network architectures, including VGG16, ResNet50, EfficientNetB3, MobileNetV3 Large, ConvNeXt Tiny, Vision Transformer (ViT-B/16), and Swin Transformer Tiny, were fine-tuned and evaluated under a rigorous cross-validation protocol. All models achieved excellent performance, with cross-validated F1-scores exceeding 0.97 and balanced accuracy up to 0.993. The best results were obtained for ResNet50 and ConvNeXt Tiny (F1-score: 0.9879 ± 0.0077 and 0.9860 ± 0.0073, balanced accuracy: 0.9922 ± 0.0054 and 0.9927 ± 0.0042, respectively), indicating outstanding sensitivity and specificity for both flowering and non-flowering classes. Classical CNNs (VGG16, ResNet50, and ConvNeXt Tiny) demonstrated slightly superior robustness compared to transformer-based models, though all architectures maintained high generalization and minimal variance across folds. The integrated quality assessment and classification pipeline enables scalable, high-throughput monitoring of flowering phases in natural environments. The proposed methodology is adaptable to other plant species and locations, supporting future ecological monitoring and climate studies. Our key contributions are as follows: (i) introducing an automated exposure-quality filtering stage for field imagery; (ii) publishing a curated, season-long dataset of Tilia cordata images; and (iii) providing the first systematic cross-validated benchmark that contrasts classical CNNs with transformer architectures for phenological phase recognition.

Large online marketplaces increasingly rely on multi-stage ranking pipelines where a learned relevance model is complemented by business-aware constraints such as contractual pacing, exposure caps and commercial alignment objectives. This paper develops a second-stage, contract-aware re-ranking layer for real-estate recommendation that explicitly balances user–item relevance with plan fulfillment, lead value and operational guardrails. The proposed multi-objective re-ranker (PMOR) combines a calibrated base relevance score with multiplicative business adjustments and subtractive penalties for approaching contractual caps and for within-slate similarity. The method supports heterogeneous settlement models, including pay-per-action and fixed-fee contracts, via contract-specific weights. Because the scoring function is deterministic and structured, it admits exact component-wise contribution analysis and counterfactual ablations without relying on surrogate explainability methods. Offline evaluation on production logs from an anonymized marketplace covers 4219 recommendation requests and 184,147 candidate items, joined with daily business snapshots using an as-of strategy to prevent look-ahead bias. Under a profit proxy based on effective lead value, position discounting and billability, PMOR achieves an indexed expected-revenue proxy of 487.4 (baseline = 100), corresponding to a lift of 387.4% over a model-only baseline and 48.4% over a legacy production re-ranker (LPR). The gain is primarily associated with improved billable exposure, increasing the share of billable positions in TOP-3 to 78.22% compared with 37.85% for LPR. We discuss parameter sensitivity, operational considerations and limitations of offline proxy objectives for deployment.

In industry a drill is judged only as fit or worn; the three-class scheme with an intermediate warning state, introduced in earlier studies, smooths monitoring and lets tool replacement be planned ahead, yet hides the gradual nature of wear. This feasibility study proposes a continuous drill-wear index on a 0–100 scale, estimated from images of holes drilled in melamine-faced chipboard without per-hole wear measurements. The index is learned from two weak supervision signals: the drilling order within each drill’s life, through a pairwise ranking loss, and the wear classes, as anchors tied to the thresholds $W=0.2$ mm and $W=0.35$ mm. Vision foundation models (DINOv2, ConvNeXt V2) are fine-tuned on 8526 hole images from five drills and evaluated by leave-one-drill-out validation. On held-out drills the index follows the drilling order (profile-level Spearman correlation 0.97) and recovers the classes with 66.7–69.9% accuracy, similar to cross-entropy classifiers; its errors fall mainly towards the more worn class, and worn drills are rarely judged sharp. Conformal prediction sets reach a coverage near 90% on average but not on every drill, and an exploratory remaining-life extrapolation is at best comparable to a lifetime baseline. Larger tool batches are needed before industrial use.