Advancing Open Science

Emerging contaminants (ECs) and microplastics (MPs) are increasingly detected in surface waters, wastewaters, and drinking water, often as complex mixtures, transformation products, and particle-associated burdens that challenge routine monitoring. This critical review examines current analytical strategies for the detection and characterization of both molecular and particulate emerging contaminants in aquatic systems, with particular emphasis on their relevance to environmental and human health risk assessment. For molecular ECs, targeted LC–MS/MS and GC–MS and GC–MS/MS approaches are evaluated alongside high-resolution mass spectrometry (HRMS)-based suspect and non-target screening, retrospective data mining, and transformation-product elucidation. For MPs, particle-resolved vibrational spectroscopy including µ-FTIR and µ-Raman is critically assessed in comparison with complementary thermal analysis methods, such as pyrolysis–GC–MS and thermal extraction–desorption GC–MS (TED–GC–MS). Particular attention is given to the influence of sampling design, matrix-adapted sample preparation, analytical confidence, and method-dependent size and polymer coverage on data quality and interstudy comparability. The review further highlights the risks of ECs in relation to exposure pathways, mixture effects, and the potential carrier role of MPs for ECs, additives, and microorganisms. Finally, key priorities are identified for next-generation monitoring frameworks, including harmonized workflows, transparent confidence reporting, and stronger integration of analytical evidence with fate, exposure, and risk assessment.

Background: Artificial intelligence (AI) is becoming increasingly integrated into orthopaedic surgery for tasks such as implant positioning, dislocation risk prediction, and surgical decision-making. However, the current evidence varies widely across anatomical regions and applications. Methods: A structured narrative review was conducted using PubMed and Web of Science Core Collection to identify studies applying machine learning or deep learning in orthopaedic procedures, focusing on parameters such as the anatomical region addressed, data types used, primary AI tasks, evaluation designs, and validation strategies. Reviews and meta-analyses were excluded. Study selection was summarized using a PRISMA-style flow diagram, and included studies were narratively synthesized according to anatomical region, AI task, imaging modality, validation strategy, and clinical relevance. Results: We identified three main application areas: (1) AI in imaging-driven planning and implant positioning, often linked with navigation or robotic systems; (2) postoperative evaluation related to implants; and (3) prediction of clinically relevant outcomes such as dislocation risk. The strongest evidence is found in hip arthroplasty, where AI improves measurement accuracy and workflow efficiency, whereas applications in knee, shoulder, and spine surgery are less developed and often supported by smaller studies. Although existing risk prediction models demonstrate good performance, their generalizability is hindered by limited external validation and inconsistent reporting. Conclusions: Overall, while AI shows significant promise in enhancing various aspects of orthopaedic surgery, stronger links between technical advancements and patient outcomes are needed. Future research should prioritize extensive validations, workflow-aware evaluations, failure analysis, and adherence to AI-specific reporting guidelines to facilitate safe and effective clinical implementation.

Virtual reality heritage experiences can be understood as multisensory interaction systems, yet how auditory, haptic, and gestural cues combine at the system level to shape electronic word-of-mouth (eWOM) intention remains insufficiently understood. Addressing this problem from a configurational systems perspective, this study applies fuzzy-set qualitative comparative analysis (fsQCA) to five auditable interaction cues (acoustic clarity, rhythmic drive, vibrotactile actuation level, gesture complexity, and compound gesture frequency) across a set of widely used VR cultural heritage applications. The results identify two sufficient system-level pathways to high eWOM intention: a rhythm-driven, low-burden pathway and a coordination-driven pathway characterized by clearer audio, stronger rhythmic structure, and tighter haptic and gestural action closure. Low eWOM intention is most consistently associated with weak cue interpretability, limited temporal drive, or unbalanced stimulation patterns, suggesting that isolated enhancement of single channels does not reliably translate into downstream sharing intentions. These findings reposition VR heritage design as a problem of configuring coherent multisensory interaction systems rather than maximizing individual stimuli. The study contributes a bounded, case-comparative account of how auditable cue bundles shape eWOM intention and offers system design guidance for resource-sensitive multisensory coordination in VR heritage applications.

Hypersaline environments are unique ecosystems harboring specialized microbial communities with significant biotechnological potential. This study provides a comprehensive characterization of the taxonomic diversity and functional potential of two Tunisian salterns, Abbassia (Kerkennah) and Thyna (Sfax), using an integrated approach that combines 16S/18S rRNA gene amplicons (Illumina and full-length Nanopore) with shotgun metagenomics. Taxonomic profiling revealed a high species richness (S ≈ 1250 taxa); however, the Abbassia site was characterized by extreme taxonomic polarization, with over 95% of the community dominated by specialized halophilic Bacillota (Salinicoccus and Jeotgalicoccus). In contrast, Thyna exhibited a more even distribution dominated by Pseudomonadota and methanogenic Archaea. Beyond taxonomy, functional annotation via the HUMAnN 3.0 pipeline identified site-specific metabolic specializations. Abbassia was enriched in biosynthetic pathways and robust stress-response mechanisms, including ectoine biosynthesis and ppGpp-mediated stringent response, reflecting adaptation to stable hypersaline conditions. Conversely, Thyna’s microbiome prioritized energy extraction and nutrient recycling, with a high abundance of fermentation and glyoxylate cycle pathways. These findings demonstrate that environmental filtering shapes not only the microbial structure but also the metabolic landscape, highlighting the ecological plasticity of microbial life in extreme Tunisian salterns.

Remaining oil in high-water-cut reservoirs becomes increasingly dispersed during long-term waterflooding, while preferential flow paths cause severe ineffective water circulation and reduce the efficiency of further oil displacement. To improve the quantitative identification of remaining oil enrichment and water-flushed regions, this study proposes a displacement-unit-based classification and evaluation method for dominant remaining oil distribution. The method integrates dynamic allocation of injected water in multilayer reservoirs, time-varying characterization of reservoir physical properties, streamline-based delineation of displacement units, and saturation tracking using the φ-function. Two quantitative indicators, the remaining oil abundance index (I_so) and the water flushing intensity coefficient (C_f), were introduced to classify displacement units into strongly dominant, weakly dominant, and non-dominant types. The method was applied to a high-water-cut block of the W Oilfield, where 902 displacement units were identified from 65 oil and water wells and 36 sublayers. The results show that strongly dominant, weakly dominant, and non-dominant displacement units accounted for 37.9%, 33.7%, and 28.4% of the total, respectively. In 15 sublayers, the proportion of strongly dominant units exceeded 50%, indicating severe preferential water flow and limited remaining oil potential in these layers. Strongly dominant units were characterized by high water flushing intensity and low remaining oil abundance, whereas weakly dominant units showed remaining oil enrichment mainly at the margins of displacement units. The proposed method couples injection–production dynamics with seepage-field evolution and provides a quantitative basis for fine-scale adjustment of injection–production patterns in high-water-cut reservoirs.

Ischemic stroke remains a leading cause of global mortality and long-term neurological disability, where the “Time is Brain” paradigm dictates that rapid and accurate lesion assessment is fundamental for effective clinical intervention. While the nnU-Net v2 framework has established a new state of the art in medical image segmentation, its high computational demands and reliance on data-center-grade GPUs hinder its translation into real-time, point-of-care clinical workflows. This study presents a technical feasibility analysis of a Cloud-to-Edge optimization pipeline designed to transfer a 3D nnU-Net v2 model from a high-performance cloud environment to a resource-constrained embedded device. Experimental results showed that edge deployment was associated with a reduction in overlap-based segmentation metrics compared with the cloud reference, with Dice decreasing from approximately 0.78 to 0.67. However, TensorRT FP32 and FP16 inference produced nearly identical mean segmentation metrics, suggesting that reduced-precision inference did not introduce additional measurable degradation under the evaluated conditions. The optimized FP16 configuration achieved a processing time of 10.2 s per 3D volume, representing a 33% reduction compared with embedded FP32 inference, while operating within a low-power envelope of approximately 10–13 W. These findings support the preliminary technical feasibility of executing advanced 3D volumetric segmentation models on low-power edge hardware. Nevertheless, the evaluation was limited to an internal 25-case test subset and did not include external validation, prospective clinical assessment, or reader studies. Therefore, the proposed system should be interpreted as a preliminary deployment framework rather than a clinically validated tool for autonomous stroke imaging.

High-accuracy potential energy surface (PES) and rovibrational energy levels are essential for computational IR line lists used in (exo)planetary atmospheric spectroscopic analysis and modeling. We present a new ¹⁴N₂¹⁶O PES refinement achieving 0.001–0.002 cm⁻¹ statistical accuracy for E_vib ≤ 7000 cm⁻¹ and J_max = 88–100, relative to complete experiment-based rovibrational energy levels in RITZ, MARVEL, HITRAN2020, and NOSL-296 datasets. Building upon the high-quality ab initio Comp-I PES, the resulting D2n (and D2nB) PES outperform the Ames B1b PES, the UCL TYM PES, and the UCL 2025 PES series in both energy-resolved and J-resolved comparisons, exhibiting the smallest mean residuals and scatter below E_vib = 8000 cm⁻¹, as well as the highest fractions of |δ| < 0.0010 cm⁻¹ and |δ| < 0.0005 cm⁻¹. Robust analysis identified only seven outliers among the UCL-2025 reference level set; all remaining levels are retained to ensure resilient statistics. The D2n PES also shows stable IR intensities with the G10K dipole moment surface and reasonably consistent isotopologue accuracy. Analysis of J-resolved σ_rms highlights the critical role of reference-dataset accuracy and internal consistency. We discuss factors enabling (sub-)0.002 cm⁻¹ accuracy and prospects for extending similar accuracy to higher energies, additional isotopologues, and other molecules.

The explosive growth of electric vehicle (EV) charging infrastructure is increasingly straining power distribution networks, but the at-scale behavioral heterogeneity of charging stations remains poorly understood. In this study, we implement an unsupervised machine learning approach based on real data (encompassing 32,057 EV charging stations in the publicly available dataset of the Republic of Korea) to discover hidden load concentration patterns. We applied K-means clustering (k = 6) with the k-means++ initialization method to seven station-level features, which yielded six behavioral archetypes that were further evaluated using four supervised classifiers (Decision Tree, Logistic Regression, Random Forest, and XGBoost), all achieving an F1 macro ≥ 0.994 and ROC-AUC ≥ 0.999. The SHAP analysis revealed that geographic variables mainly explain the differentiation among low-use slow-charging sub-clusters, whereas operational variables such as session frequency, output capacity, charger type, and charging speed are decisive for the load-relevant C3 and C5 archetypes. We introduced three new grid load metrics: cluster load contribution, load imbalance coefficient of variation (CV = 1.1247), and the hidden load effect. Results indicate that the high-power fast cluster (C5) and high-use slow cluster (C3) combine to contribute 66.7% of the network station load score-based load while representing only 19.2% of stations. Under the station load score proxy assumption, C3 demonstrates 14.4% greater per-station utilization intensity than C5 (293.6 vs. 256.7), challenging the notion that fast chargers are the key source of infrastructure pressures. These insights provide actionable guidance for demand-side management approaches.