Advancing Open Science

The rapid development of small unmanned aerial vehicles (UAVs) has introduced significant challenges in both civilian and defense security domains, such as public safety and protecting critical infrastructure. Detecting and classifying misused drones intruding into low-altitude, cluttered environments remains particularly challenging because of limitations in robust fusion across multiple sensing modalities. Although acoustic and visual fusion has been widely studied, its use for detecting intruding drones has not been sufficiently investigated. This paper proposes DroneAVFusion, a lightweight acoustic–visual fusion pipeline that integrates acoustic and visual sensing via a novel Late Attention Fusion (LAF) mechanism. The proposed system combines a Mel-spectrogram-based convolutional neural network for acoustic detection and optimized YOLO-based visual detection, followed by a computationally efficient fusion module that dynamically weights modalities using confidence-aware gating and temperature-scaled attention. A custom acoustic–visual dataset comprising three commercial drones and non-drone objects is collected and used to evaluate performance under realistic conditions. Experimental results show that DroneAVFusion achieves a $99.6$% F1 score on the validation set and perfect performance on the test set while running at an average of $30.40$ FPS with only $72,868$ parameters in the fusion module. Compared with single-modality baselines and existing fusion approaches, the proposed method demonstrates excellent robustness, making it well-suited for security applications.

Yogurt adhesion to plastic packaging surfaces contributes significantly to product wastage, poor emptiability, and consumer dissatisfaction, which highlights the need for advanced non-stick food packaging solutions. In this study, commercially used yogurt containers made of polypropylene (PP) and low-density polyethylene (LDPE) sourced from Ugandan and Greek markets were engineered into non-stick surfaces through a combination of hot embossing and subsequent hydrophobic coating. Hot embossing successfully transferred hierarchical micro/nano-scale textures onto the polymer surfaces, increasing average surface roughness from 0.79–1.65 µm to 2.27–5.12 µm (p < 0.05), with LDPE exhibiting the highest texture replication. These morphological changes resulted in a marked transition in wetting behavior, with water contact angles (WCAs) increasing from 69.6–100.3° on untreated surfaces to 143.1–160.5° after embossing, accompanied by a significant reduction in contact angle hysteresis (CAH) from 23.9–64.6° to 4.5–23.3°. Subsequent hydrophobic coating further reduced surface free energy, yielding superhydrophobic surfaces with contact angles up to 171.6° and ultra-low hysteresis values as low as 3.6°, consistent with a superhydrophobic wetting state. The combined physical (hierarchical roughness) and chemical (low surface energy) modifications significantly reduced yogurt adhesion and enhanced surface cleanability across all polymers. Bacterial recovery results did not demonstrate antibacterial activity; rather, Teflon™ AF 1600-treated surfaces generally showed higher recoverable viable bacterial counts than untreated surfaces. Principal component analysis further showed associations between surface roughness, water contact angle (WCA), contact angle hysteresis (CAH), yogurt angle (YCA), and recovered bacterial counts. Overall, this work demonstrates a scalable and effective strategy for transforming conventional yogurt packaging into superhydrophobic and non-stick systems, offering a practical pathway toward smart and sustainable dairy packaging with reduced food waste and an improved consumer experience.

European policy ambitions and recent legislation have created new opportunities for energy communities (ECs) to expand their role in the energy transition. Such developments are often associated with acceleration processes. As ECs become more established, questions arise as to whether further development can be achieved by initiatives alone or whether it requires stronger forms of collective development. However, ECs often emphasize their local embeddedness. This study examines ECs’ perceived willingness and ability to contribute to collective development by exploring their sense of collectiveness, strategic visions, upscaling ambitions and perceived barriers. Based on interviews with 44 board members of 43 ECs and three intermediary representatives in Groningen, the Netherlands, four types of ECs were identified. Participating ECs differed substantially in their ambitions, orientations and involvement in collective development. Differences in ambitions, local embeddedness, organizational capacity, and future visions appear to complicate collective development and the emergence of a shared strategic vision. The findings indicate multiple pathways through which ECs may respond to conditions associated with acceleration. The study contributes to sustainability transition research by proposing collective development as an extension of network formation and highlighting how differences between ECs may shape their capacity and willingness to contribute to broader societal change.

This work was conducted to investigate the activity of nanosized zinc oxides in carboxylated nitrile elastomer (XNBR) and natural rubber (NR) under the vulcanization process. Zinc oxide (ZnO) nanoparticles were synthesized by using the sol-gel method and calcination method. Nano-ZnO-substituted NR and XNBR vulcanizates were prepared by varying the loading level of nano-ZnO from 10 phr to 20 phr, 30 phr, 40 phr and 50 phr added to NR and XNBR. The testing specimens of the prepared nano-ZnO-added NR and XNBR vulcanizates were produced through the dipping and drying process. The samples were cut to the required size and standard before being subjected to the tensile test, scanning electron microscopy analysis, and colloidal stability test. ZnO nanoparticles, acting as a curing activator in both NR and XNBR, were found to be more uniformly dispersed in the rubber matrix in comparison with the conventional rubber grade ZnO, as evident from scanning electron microscopy. The tensile strength of NR was observed to be significantly improved as the amount of ZnO nanoparticles was gradually increased up to 50 phr. This indicates that the ZnO nanoparticles can be used as curing activator in NR. For the samples of XNBR, it was also found that further increasing of nano-ZnO amount up to 30 phr significantly improved the tensile strength of XNBR samples. However, subsequent increment of the nano-ZnO amount up to 50 phr did not improve the tensile properties. There was an increase in elongation at break of XNBR vulcanizate with ZnO nanoparticles added. The XNBR vulcanizate showed an optimum performance at 30 phr, which gave the vulcanizates the optimum tensile strength and elongation at break. Meanwhile, for the NR vulcanizate, the addition of 50 phr nano-ZnO was found to provide the optimum effect in term of tensile strength and elongation at break.

Ground penetrating radar (GPR) and electrical resistivity tomography (ERT) are increasingly combined for the condition assessment of reinforced concrete, yet published integration schemes generally rely on empirical curve-fitting or qualitative visual overlay rather than on an explicit physical model for the anomaly thresholds applied. This paper presents a forward-modelling programme used to propose provisional, model-informed GPR–ERT screening multipliers and embeds them within a four-stage preliminary risk-screening framework. The multipliers are bracketed by the modelled responses but are not derived from a closed mathematical procedure; the framework is a physics-informed preliminary screening approach, not a validated diagnostic method, and the reported sensitivity results do not constitute statistically validated diagnostic accuracy. A linearised, sensitivity-kernel-weighted (Born-type) formulation was used to compute synthetic Wenner–Schlumberger ERT pseudosections, and a semi-analytical impulse-response superposition (800 MHz Ricker source) was used to compute synthetic GPR radargrams, for four scenarios: an intact reference, a water-filled crack, an air void, and a combined crack–void–corrosion zone. The GPR model reproduces reflection polarities prescribed from electromagnetic theory (R = −0.48 for the water-filled crack mouth; R = +0.52 then −0.52 for the top and bottom interfaces of an air void), which demonstrates implementation consistency rather than independent validation. For the air void, the inverted ERT peak (250–275 Ω·m against a 180 Ω·m background) lies 39–53% above background, which is under 1% of the true resistivity contrast (about 10,000 Ω·m), illustrating the strong under-recovery of high-contrast targets by smoothed, linearised inversion. The modelled responses were used to bracket Stage 2 screening criteria—a GPR rebar-amplitude criterion (A < 0.70 × A₀) and ERT conductive/resistive criteria (ρ < 0.60 × ρ₀ or ρ > 1.25 × ρ₀)—within a four-stage procedure comprising baseline establishment, anomaly screening, spatial cross-validation and indicative risk categorisation. The procedure was applied to previously reported classifications of four masonry field sites, for which no destructive ground truth is available and to which rebar-specific quantities do not directly apply; a preliminary one-at-a-time multiplier sweep of these classifications is under audit and supports no robustness claim. All results derive from a single reference concrete mix, electrode spacing and antenna frequency, and their transfer to other mixes, cover depths and survey geometries has not been tested.

Background: Severe class imbalance hampers mortality prediction in pediatric oncology, where clinical-improvement discharges greatly outnumber deaths and bias standard models toward the majority class. Objectives: We aimed to develop and evaluate a Cost-Sensitive Hybrid Resampling Framework (CSHRF) across recurrent deep-learning architectures to mitigate this bias and improve discharge-outcome classification. Methods: A retrospective cross-sectional study was conducted using electronic health record data from a National Institute of Health in Mexico City during 2023. We analyzed 1607 hospital-discharge records, comprising 1554 clinical improvements and 53 deaths. The CSHRF combines cost-sensitive learning, which amplified the mortality class by a factor of 2.5, with a fixed-seed hybrid resampling protocol embedded in a regularized funnel-shaped architecture (N → N/2 → N/4) trained with gradient clipping (clipnorm = 1.0). Simple Recurrent Neural Network (SimpleRNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models were compared with a naïve recurrent baseline and standard tabular classifiers (Weighted Logistic Regression, Balanced Random Forest, and XGBoost) using stratified cross-validation. Performance was evaluated using the area under the receiver operating characteristic curve (AUC), G-mean, sensitivity, specificity, precision, F1-score, mean absolute error (MAE), and root mean squared error (RMSE). Results: The naïve baseline showed limited mortality detection (sensitivity 0.150 ± 0.147). The proposed framework substantially improved minority-class detection, with CSHRF-GRU achieving sensitivity of 0.740 ± 0.145, specificity of 0.700 ± 0.076, G-mean of 0.714 ± 0.045, and AUC of 0.761 ± 0.075. Ablation analysis demonstrated that cost-sensitive weighting contributed most substantially to mortality detection, whereas hybrid resampling alone produced more modest gains. Among tabular baselines, Weighted Logistic Regression achieved competitive performance, with sensitivity of 0.684 ± 0.130 and G-mean of 0.767 ± 0.073. Precision remained low (0.079 ± 0.015) and probabilistic calibration errors were substantial (MAE 0.448; RMSE 0.488), reflecting the trade-off required to prioritize mortality detection under severe imbalance. Conclusions: The CSHRF improved discharge-outcome classification in a highly imbalanced pediatric oncology dataset and provides a methodological framework for data-constrained hospital environments. Given its high false-positive rate and probability overestimation, the approach should be interpreted as a discharge-related outcome classification model rather than a standalone clinical decision-support system, and future work should incorporate richer clinical and disease-specific variables to improve predictive performance and calibration.

Breast reconstruction is an integral component of comprehensive breast cancer care rather than a merely aesthetic adjunct. For many women, reconstruction is associated with meaningful improvements in body image, psychosocial well-being, and quality of life after mastectomy. Nevertheless, it is not a mandatory part of treatment and represents a preference-sensitive decision. This article presents a clinical perspective, informed by selected literature, clinical guidelines, and institutional practice, on why patients with non-metastatic breast cancer in whom mastectomy is a realistic possibility should be offered reconstructive consultation before surgery. At that stage, reconstructive options remain open, patients have time to reflect, and expectations can be discussed realistically. Counseling should present all legitimate pathways, including implant-based and autologous reconstruction, immediate and delayed timing, the implications of postmastectomy radiotherapy, and no reconstruction with an appropriately planned primary closure. The final plan should be individualized according to oncologic safety, anticipated adjuvant treatment, anatomy, comorbidities, and the surgical burden acceptable to the patient, while also incorporating the patient’s expectations and priorities. Reconstructive consultation should therefore support shared decision-making rather than promote reconstruction, with the final informed choice remaining the patient’s own. Psychological and quality-of-life benefits vary among individuals. The success of early reconstructive involvement should not be measured by reconstruction rates, but by whether patients are able to make informed, realistic, and preference-concordant decisions. We propose a consultation framework addressing oncologic context, reconstructive feasibility, patient priorities, expectation management, treatment decisions, and alternative planning, explicitly including no reconstruction with appropriately planned primary closure when preferred. The framework has not yet undergone formal evaluation.

Background/Objectives: Major depressive disorder (MDD) is a prevalent mental health disorder for which objective and reliable diagnostic approaches remain an active area of research. Electroencephalography (EEG) has emerged as a promising non-invasive modality for identifying neural activity patterns associated with depression, while advances in deep learning have enabled effective analysis of complex EEG signals. Methods: In this study, we propose a hybrid deep learning framework that integrates convolutional neural networks (CNNs) for spatial feature extraction and gated recurrent units (GRUs) for temporal modeling of EEG signals. The spatial and temporal features extracted by the CNN and GRU components were concatenated into a 120-dimensional representation, after which minimum redundancy maximum relevance (mRMR) feature selection was applied to reduce redundancy and retain the 30 most informative features before classification. The proposed framework was evaluated using the publicly available MODMA EEG dataset, and its performance was assessed using accuracy, precision, recall, and F1-score. Results: Across 30 independent simulation runs, the CNN–GRU–mRMR framework achieved a mean classification accuracy of 96.74%, exceeding the performance of the comparison methods evaluated in this study. The model also demonstrated high precision, recall, and F1-score, indicating strong classification performance within the evaluated dataset. Conclusions: These findings suggest that integrating spatial and temporal feature learning with mRMR-based feature selection can improve EEG-based depression classification. Further validation using larger, more diverse, and independent clinical datasets is needed to establish the generalizability of the framework and evaluate its potential utility for objective EEG-based depression assessment.