Advancing Open Science

The emergence of surgical robots has revolutionized complex operations, improving precision, lowering operating risks, and shortening recovery periods. Given the merits, an eight degrees of freedom (DOF) hybrid surgical robot (HSR) has been proposed, which leverages the benefits of both serial and parallel manipulators. However, its performance is hindered by the constrained range of motion of its parallel platform. To address the issue, this research presents a systematic approach for designing and optimizing the proposed HSR. The first step is the design of the HSR, followed by a multi-stage design analysis of its parallel platform, concentrating on kinematic, geometrical, and singularity analysis. Higher values of the condition number indicate singular configurations in the platform’s workspace, highlighting the need for an optimized design. For optimization of the platform, performance parameters like global condition number (GCN), actuator forces, and stiffness are identified. Initially, the design is optimized by targeting GCN only through a genetic algorithm (GA). This approach compromised the other parameters and raised the need for simultaneous optimization employing a non-dominated sorting genetic algorithm (NSGA II). It offered a better trade-off between performance parameters. To further assess the working of the optimized parallel platform, workspace analysis and motion planning of a predefined trajectory have been performed.

Background/Aim: Furcation lesions in primary molars are critical in pediatric dentistry, often guiding treatment decisions between root canal therapy and extraction. This study introduces a deep learning-based clinical decision-support system that directly maps radiographic lesion characteristics to corresponding treatment recommendations—a novel contribution in the context of pediatric dental imaging, also represents the first integration of panoramic radiographic classification of primary molar furcation lesions with treatment planning in pediatric dentistry. Materials and Methods: A total of 387 anonymized panoramic radiographs from children aged 3–13 was labeled into five distinct bone lesion categories. Three object detection models (YOLOv12x, RT-DETR-L, and RT-DETR-X) were trained and evaluated using stratified train-validation-test splits. Diagnostic performance was assessed using precision, recall, mAP@0.5, and mAP@0.5–0.95. Additionally, qualitative accuracy was evaluated with expert-annotated samples. Results: Among the models, RT-DETR-X achieved the highest performance (mAP@0.5 = 0.434), representing modest but clinically promising diagnostic capability, despite the limitations of a relatively small, single-center dataset. Specifically, RT-DETR-X achieved the highest diagnostic accuracy (mAP@0.5 = 0.434, Recall = 0.483, Precision = 0.440), followed by YOLOv12x (mAP@0.5 = 0.397, Precision = 0.442) and RT-DETR-L (mAP@0.5 = 0.326). All models successfully identified lesion types and supported corresponding clinical decisions. The system reduced diagnostic ambiguity and showed promise in supporting clinicians with varying levels of experience. Conclusions: The proposed models have potential for standardizing diagnostic outcomes, especially in resource-limited settings and mobile clinical environments.

Elevated CO₂ (eCO₂) influences crop nutrition, but the impact of its interaction with soil amendments on selenium (Se) bioavailability is unclear. This study investigated how eCO₂ (+200 ppm), biochar (BC, 1% w/w), and phosphate fertilizer (PF, 1 g kg⁻¹) affect Se uptake in garlic—a model crop chosen for its efficiency in accumulating and transforming Se into bioactive forms. The results showed that eCO₂ significantly enhanced garlic biomass by 19.1–34.2% and decreased soil pH by 0.05–0.13 units. Concurrently, eCO₂ increased Se concentration in garlic tissues by 2.9–13.3% compared to ambient CO₂ (aCO₂). Biochar amendment reduced soil Se bioavailability, leading to a 15.2–22.8% decrease in garlic Se concentration under eCO₂. In contrast, phosphate fertilizer enhanced Se bioavailability via competitive ligand exchange, increasing Se uptake by 18.7–31.4%. These findings demonstrate that PF can be strategically co-managed with eCO₂ to optimize Se biofortification in garlic, providing a practical strategy to safeguard nutritional security under future climate scenarios.

Olive pomace (OP), an olive mill byproduct, poses environmental risks if mismanaged due to its high phenolic content, acidic pH, organic load, and electrical conductivity. This study evaluated the impact of olive pomace filtrate (OPF) at varying doses (OP-5, OP-10, OP-15) on broad bean (Vicia faba L.) growth, secondary metabolites, and nutrient accumulation. The highest OPF dose (OP-15) exhibited a clear negative, dose-dependent phytotoxic effect, causing stem discoloration, reduced root growth, necrosis, and chlorosis, while untreated controls showed vigorous growth. This significantly (p < 0.05) reduced leaf development, average number of leaves, and total leaf area, even at the lowest concentration (5%). Consequently, OP-15 reduced dry and fresh biomass by over 50% and shoot/root lengths by up to 61.55% compared to the control. Liquid chromatography mass spectrometry (LC-MS/MS) analysis revealed a positive dose-dependent effect of OPF on beneficial phenol and flavonoid accumulation, with significantly higher amounts of ferulic, isoferulic, caffeic, chlorogenic, and 4-hydroxybenzoic acids, as well as luteolin-4′-rutinoside and 4,7-dihydroxyflavone. OP application significantly (p < 0.05) decreased relative water content and increased electrolyte leakage and malondialdehyde, indicating stress. Furthermore, OP decreased the uptake of K, P, Fe, S, Zn, and Cu. Therefore, the intrinsic phytotoxicity of OPF suggests that mitigation measures are essential before considering environmental application to prevent potential adverse effects on sensitive crops and the wider ecosystem.

In this paper, a 3D-printed S-Band corrugated horn antenna with X-Band radar cross section (RCS) reduction is investigated. This work demonstrates effective RCS reduction at the X-band through the application of the phase cancellation principle. Specifically, the corrugated horn antenna is partitioned into eight identical sections, with three discrete height offsets introduced between them. The reflection phase cancellation, which can be attained through the path difference introduced by a designed height step among different regions, leads directly to a consequent suppression of scattered waves. The proposed low-RCS corrugated horn antenna is monolithically fabricated using stereolithography appearance (SLA) 3D printing technology, followed by a surface metallization process. The measured results demonstrate that the proposed antenna operates over the frequency band of 2.34–3.3 GHz in the S-band with good impedance matching, exhibiting a peak gain of 11.7 dB. Furthermore, the monostatic RCS of the antenna under normal incidence for both x- and y-polarizations exhibits a significant reduction of over 10 dB within the frequency range of 8.7–12.0 GHz and 8.2–12.0 GHz, respectively. This indicates that effective stealth performance is achieved across the majority of the X-band. The proposed design integrates exceptional out-of-band RCS reduction, low cost, light weight, and high efficiency, making it a promising candidate for radar stealth system applications.

Acrylic solid surface composites made of poly (methyl methacrylate) (PMMA) and aluminum trihydrate, Al(OH)₃ (ATH) are widely used in furniture and interior applications. However, independent brand comparative data, especially on density-normalized (“specific”) properties, remain limited. This study quantifies the flexural response of 11 commercial sheets (6, 8, and 12 mm, including one translucent) under ISO 178 three-point bending and evaluates the effects of heating and cooling relevant to thermoforming. The density is concentrated in the range 1680–1748 kg/m³ (weighted mean of 1712 kg/m³). The flexural strength ranged between 51 and 79 MPa, divided into three groups—high (76–79 MPa), medium (63–67 MPa), and low (51–56 MPa) levels, while the modulus ranged between 7700 and 9400 MPa with a narrow dispersion. The strength showed no significant correlation with density, while the modulus increased with density, indicating that stiffness is composition-dominated, while strength is influenced by factors related to microstructural defects/particle boundaries. Heating at 160 °C and subsequent cooling have a significant influence on flexural strength and strain. Flexural strength increased by an average of approximately 7%, and flexural strain increased by approximately 12%, while the modulus remained virtually unchanged (within ±0.5%); additionally, shock cooling did not bring any benefits. The density-normalized parameters (σ/ρ, E/ρ) reflected these trends, allowing for a more accurate comparison when limited by mass or deformation. Overall, the results are broadly consistent with manufacturers’ declarations and demonstrate that thermoforming-relevant heating at 160 °C, followed by cooling, can be used not only to improve formability but also to modestly increase flexural strength and strain without compromising stiffness.

Objectives: Determining individuals’ addiction levels plays a crucial role in facilitating more effective smoking cessation. For this purpose, the Fagerstrom Test for Nicotine Dependence (FTND) is used all over the World as a traditional testing method. It can be subjective and may influence the evaluation results. This study’s key innovation is the use of physiological signals to provide an objective classification of addiction levels, addressing the limitations of the inherently subjective Fagerström Test for Nicotine Dependence (FTND). Methods: Physiological parameters were recorded from 123 voluntary participants (both male and female) aged between 18 and 60 for 120 s using the Masimo Rad-G pulse oximeter and the Hartman–Veroval blood pressure monitor. All participants were categorized into four addiction groups: healthy, lightly addicted, moderately addicted, or heavily addicted with the help of FTND. The recorded data were classified using Decision Tree, KNN, and SVM methods. SMOTE and class-weighting techniques were used to eliminate class imbalance. Also, the PCA technique was applied for dimensionality reduction, and the k-fold cross-validation method was employed to enhance the reliability of the machine learning algorithms. Results: Machine learning methods, when evaluated using the SMOTE with a (7380×7) sample of physiological signals recorded every 2 s from 123 participants, showed a high recall of 98.74%, specificity of 99.58%, precision of 98.79%, F-score of 98.74%, and accuracy of 98.75%. Also, it is extracted that there is a direct relationship between physiological parameters and smoking addiction levels. Conclusions: The study’s core novelty lies in leveraging non-invasive physiological signals to objectively classify addiction levels, addressing the subjectivity of the Fagerström Test for Nicotine Dependence (FTND). This study provides a proof-of-concept for the feasibility of using machine learning and physiological signals to assess addiction levels. The results indicate that the approach is promising.

The multi-factor coupling mechanism of droplet impact dynamics remains unclear due to insufficient analysis of leaf structure–droplet interaction and inadequate integration of simulations and experiments, limiting precision pesticide application. To address this, we developed a droplet impact model using the Volume of Fluid (VOF) method combined with high-speed camera experiments and systematically analyzed the effects of impact velocity, angle, and droplet size on slip behavior via response surface methodology. Methodologically, we innovatively integrated 3D reverse modeling technology to reconstruct soybean leaf microstructures, overcoming the limitations of traditional planar models that ignore topological features. This approach, coupled with the VOF method, enabled precise tracking of droplet spreading, retraction, and slip processes. Scientifically, our study advances beyond previous single-factor analyses by revealing the synergistic mechanisms of impact parameters through response surface methodology, identifying impact angle as the most critical factor (42.3% contribution), followed by velocity (28.7%) and droplet size (19.5%). Model validation demonstrated high consistency between simulation predictions and experimental observations, confirming its reliability. Practically, the optimized parameter combination (90° impact angle, 1.5 m/s velocity, and 300 μm droplet size) reduced slip displacement by over 50% compared to non-optimized conditions, providing a quantitative tool for spray parameter control. This work enhances the understanding of droplet–leaf interaction mechanisms and offers technical guidance for improving pesticide deposition efficiency in agricultural production.