Search Results (1,382)

The building renovation sector is under growing pressure to balance environmental responsibility, economic efficiency, and occupant well-being simultaneously. Existing evaluation approaches are predominantly finance-driven, marginalising ecological and social dimensions. This study develops and validates a parametric multi-criteria assessment framework for building renovation elements, structured around the three pillars of sustainability: environmental, economic, and social. A dataset of 33 renovation elements—encompassing green façade systems, extensive and intensive green roofs, interior wall, floor, and ceiling solutions, and exterior envelope and site components—was compiled and digitized as BIM objects in ArchiCAD 26, enriched with non-graphic parameters including cost, lifespan, recyclability, eco-index, maintenance effort, and qualitative social descriptors. Parameters were aggregated using type-specific logic: additive summation for economic indicators, minimum-value selection for lifespan, arithmetic mean for environmental indicators, and descriptive consolidation for social attributes. Five renovation scenarios (A–E), each composed of nine elements, were evaluated to demonstrate how the sustainability profile changes with selection priorities. Scenarios A, B, and C confirmed single-dimension dominance (environmental, economic, and social, respectively), Scenario D achieved a balanced three-pillar profile, and Scenario E revealed a latent economic bias in an apparently random element selection. The framework is scalable and extensible, and its data structure may provide a basis for future exploration of integration with BIM environments. Full article

Two crucial yet opposing design criteria govern the performance of packed bed latent thermal energy storage systems (PBLTESS): energy storage capacity and thermal power. While the former depends on the packing density of the phase change material (PCM) capsules forming the packed bed, the latter is influenced by the surface-area-to-volume ratio (SVR) of these capsules. This study introduces novel superellipsoidal geometries for PCM capsules to address both these factors and quantifies the impact of design parameters on both mentioned performance criteria. First, by using discrete element method (DEM) simulations, we performed virtual bed filling experiments and generated packed beds from 116 superellipsoidal designs with similar volume. These simulations revealed a maximum packing density of 65.2%—significantly higher than conventional spherical capsule designs. Validation through bed filling experiments using 3D-printed superellipsoids confirmed the results of the DEM simulations, with an average deviation of less than 5%. Additionally, the SVR of each superellipsoidal design was determined through CAD analyses. Subsequently, six superellipsoidal designs as well as a spherical design were selected for further investigation using a 1D PBLTESS model to simulate charging and discharging. With up to 85% higher storage capacity (due to increased packing density) and up to 50% higher thermal power (resulting from enhanced heat transfer), the superellipsoidal geometries clearly outperformed the spherical design. Full article

The rapid advancement of machine learning and artificial intelligence (AI) has created new opportunities to enhance diagnostic accuracy in dermatology, particularly within primary care settings. Computer-aided diagnosis (CAD) systems have demonstrated potential to support General Practitioners (GPs) by enabling earlier and more consistent identification of skin diseases. This review critically examines the literature on explainable artificial intelligence (XAI) for skin disease classification, with a specific focus on the evolution of explainability frameworks and the methodological implications of dataset selection. A comprehensive review of studies published between 2020 and 2025 was conducted across multiple academic databases, encompassing research on skin lesion detection, classification, and monitoring. The analysis reveals that deep learning architectures, particularly those leveraging transfer learning with models such as EfficientNet, ResNet, and Xception, frequently report high classification accuracies—often exceeding 90% when evaluated on single benchmark datasets. However, studies employing multiple datasets consistently demonstrate more stable and generalisable performance, albeit with modest reductions in reported accuracy, highlighting a critical trade-off between performance optimisation and real-world robustness. The review further identifies a clear temporal progression in the adoption of XAI techniques. Early studies relied on a broader range of post hoc explainability while later work increasingly consolidated around Grad-CAM, SHAP, and related attribution techniques, followed by gradual diversification into more specialised frameworks such as TCAVs (Testing with Concept Activation Vectors) and Prototype-based Networks. Despite these advances, the lack of clinically grounded explanations, limited integration of ethical considerations, and reliance on non-clinical imagery continue to constrain clinical applicability which we have explored using a GRADE-style narrative. Notably, evidence suggests that CAD systems can improve GP diagnostic accuracy for conditions such as melanoma and seborrhoeic keratosis; however, sustained clinical adoption remains contingent on transparent, reliable, and context-aware explainability mechanisms. Full article

Early identification of anatomically significant coronary artery disease (CAD) remains a major clinical challenge despite advances in cardiovascular diagnostics. Novel circulating biomarkers may improve risk stratification and diagnostic discrimination beyond conventional parameters. We investigated the diagnostic utility of four emerging biomarkers—Phoenixin-14, Syntenin-1, Alamandine, and Cerebellin-1—for the assessment of CAD severity. In this prospective observational study, 90 participants undergoing coronary angiography were categorized into three groups: severe CAD (≥70% stenosis; n = 30), non-obstructive/non-critical CAD (<70% stenosis; n = 30), and angiographically normal controls (n = 30). Patients with acute coronary syndrome, diabetes mellitus, prior coronary revascularization, cardiomyopathy, or significant systemic disease were excluded. Circulating biomarker concentrations were quantified using the enzyme-linked immunosorbent assay. Comparative analyses, correlation testing, and receiver operating characteristic (ROC) analyses were performed to evaluate discriminatory performance. Circulating Phoenixin-14 concentrations progressively declined across the control, non-critical CAD, and severe CAD groups [40.1 (29.0–49.7) vs. 24.4 (18.5–30.1) vs. 16.7 (13.4–19.0) pg/mL, respectively; p < 0.001]. Phoenixin-14 demonstrated outstanding discrimination for severe CAD, achieving an area under the ROC curve (AUC) of 0.969 (95% CI, 0.888–0.997), with 86.7% sensitivity and 96.7% specificity at a threshold of ≤20.2 pg/mL. Diagnostic performance was substantially lower for Syntenin-1 (AUC, 0.795), Alamandine (AUC, 0.661), and Cerebellin-1 (AUC, 0.597). Phoenixin-14 also showed robust discrimination for non-critical CAD (AUC, 0.832). Biomarker concentrations exhibited correlations with metabolic indices while remaining largely independent of traditional cardiovascular risk factors. Among the evaluated novel circulating biomarkers, Phoenixin-14 demonstrated superior diagnostic performance for both obstructive and non-obstructive CAD, markedly outperforming Syntenin-1, Alamandine, and Cerebellin-1. These findings identify Phoenixin-14 as a promising candidate biomarker for CAD severity assessment and clinical risk stratification. Larger multicenter studies are warranted to validate these exploratory findings and determine their incremental value in contemporary cardiovascular practice. Full article

Deep margin elevation (DME) is a conservative technique used to relocate subgingival proximal margins to a more favorable supragingival position, facilitating adhesive procedures and impression taking. This in vitro study evaluated the influence of two DME materials—a universal flowable resin composite and a self-adhesive flowable resin composite—on the cervical interfacial sealing ability of lithium disilicate glass–ceramic CAD/CAM inlay restorations. Twenty extracted maxillary premolars were randomly allocated into two groups (n = 10). Group A received DME using a universal flowable resin composite (3M™ Filtek™ Z350 XT) preceded by a conventional adhesive system, while Group B received DME using a self-adhesive flowable resin composite (Vertise™ Flow). All teeth were restored with lithium disilicate CAD/CAM inlays (CEREC Tessera) cemented with a self-adhesive resin cement (Breeze^®). Specimens underwent thermocycling (10,000 cycles; 5–55 °C). Marginal gaps were assessed at the DME interface using high-resolution micro-computed tomography (micro-CT) in both coronal and sagittal cross-sections, before and after thermocycling. Statistically significant differences were found between groups in both sections before and after thermocycling (p < 0.05). The self-adhesive composite (Group B) demonstrated significantly lower gap values compared to the universal flowable composite (Group A) in both coronal and sagittal assessments. Thermocycling increased the gap in both groups; however, Group B maintained considerably lower leakage. The self-adhesive resin composite showed superior sealing ability at the DME interface compared to the universal flowable composite when used under lithium disilicate glass–ceramic inlay restorations. Further clinical studies are recommended to validate these findings. Full article

Staphylococcus aureus is a leading cause of bacterial keratitis and antimicrobial resistance-associated death globally. This study aimed to evaluate the efficacy of CaD23, a human-derived hybrid antimicrobial peptide (AMP), in combination with antibiotics in treating S. aureus infections. The efficacy of CaD23 and six medically important antibiotics (amikacin, cefuroxime, chloramphenicol, fosfomycin, vancomycin and levofloxacin) was examined against six strains of methicillin-sensitive and methicillin-resistant S. aureus using a minimum inhibitory concentration (MIC) assay. CaD23–antibiotic interactions were evaluated using checkerboard and time–kill kinetics assays. 3,3′-dipropylthiadicarbocyanine iodide (DiSC_3,5) cytoplasmic membrane depolarisation assay was performed to examine the mechanism of action. Overall, CaD23 exhibited good efficacy against all MSSA and MRSA (MIC = 16–32 μg/mL [6.7–13.3 μM]). Of 20 peptide–antibiotic–organism combinations, 19 (95%) combinations demonstrated positive interactions, with six (31.6%) and 13 (68.4%) exhibiting synergistic (FICI = 0.293–0.412) and additive effects (FICI = 0.521–0.890), respectively. CaD23 was able to achieve complete bacterial eradication significantly faster than cefuroxime and levofloxacin (15 min vs. 8–24 h). When used at a sub-MIC concentration, CaD23 could accelerate the killing of S. aureus of cefuroxime from 8–24 h to within 1 h and enhance the activity of levofloxacin by 90%. CaD23 was shown to rapidly depolarise the inner membrane of S. aureus within seconds of the treatment. In conclusion, CaD23–antibiotic combination therapy serves as a useful strategy for tackling drug-resistant ocular and systemic S. aureus infections. Full article

Modern prosthetic dentistry has been significantly reshaped by adhesive dentistry, CAD/CAM technologies, and advanced ceramic materials, leading to the development of minimally invasive all-ceramic restorative approaches. However, the longevity of the adhesive interface is fundamental to the long-term effectiveness of these restorations. With a focus on bond durability and clinical performance, this narrative review aims to evaluate modern adhesive strategies, tooth preparation requirements, and cementation techniques in all-ceramic minimally invasive restorations. Methods: A narrative review of the literature was performed using Google Scholar, Web of Science, and PubMed/MEDLINE databases. Publications from 2000 to 2026 were analysed. In vitro research, narrative reviews, and systematic reviews related to adhesive systems, resin cements, CAD/CAM materials, and minimally invasive prosthodontic principles were the core subjects of the research. Results: The findings indicate that material selection, surface conditioning techniques, and cementation methods have a significant impact on the clinical effectiveness of all-ceramic restorations. Retention and marginal sealing are greatly enhanced by resin-based adhesive systems. Nevertheless, hydrolytic degradation, procedure sensitivity, and substrate-related factors remain a challenge to the adhesive interface. Advances in CAD/CAM and ultra-conservative designs, like occlusal veneers and partial-coverage restorations, have increased treatment alternatives while ensuring acceptable functional and aesthetic results. Conclusions: Minimally invasive all-ceramic restorations represent a conservative and clinically effective treatment approach in modern prosthodontics. Their long-term performance is primarily dependent on adhesive interface stability and adherence to evidence-based clinical protocols. Continued developments in adhesive materials and ceramic systems are expected to improve bond durability and broaden clinical indications. Full article

Background/Objectives: Hysteroscopy is the gold standard for visualization and treatment of intrauterine pathology. Because hysteroscopic interpretation remains operator-dependent, artificial intelligence (AI) has been evaluated as a tool to improve consistency, lesion recognition, and decision support. We aimed to systematically review AI, machine learning (ML), deep learning (DL), or computer-aided diagnosis (CAD) applications in hysteroscopy. Methods: A systematic search of PubMed/MEDLINE and EBSCOhost was performed from database inception to 8 March 2026, supplemented by targeted searches. Risk of bias was assessed using QUADAS-2 (diagnostic), PROBAST (prognostic), RoB2, and structured technical quality domains. Results: Nineteen primary studies were included, covering five areas: diagnostic classification and object detection (n = 8), real-time lesion detection and localization (n = 4), segmentation and visual-field support (n = 3), operative guidance (n = 1), and prognostic or decision-support applications (n = 3). Performance was highest in narrowly defined binary tasks and in large multicenter systems (e.g., ECCADx: AUC 0.979 internal, 0.975 external) and in prognostic fertility-prediction models after hysteroscopic adhesiolysis (AUC up to 0.992). Broader multiclass classification of heterogeneous lesions showed uneven and lower performance. Most studies were single-center, retrospective, and lacked external validation. Only one randomized study linked AI support to measurable procedural outcomes. Conclusions: The available studies indicate good technical performance in selected hysteroscopic tasks, particularly binary classification, focal lesion detection, and postoperative fertility stratification. Current evidence, however, remains limited by retrospective design, operator-dependent image acquisition, inconsistent validation, and scarce outcome-based clinical testing. In the short term, the most likely role of these systems is to support image interpretation, improve visual quality control, highlight suspicious lesions, and integrate hysteroscopic findings with complementary clinical data. Full article

Multilayer 4Y/5Y-PSZ zirconia materials have been developed to combine strength and translucency in monolithic “all-in-one” dental restorations. This study evaluated the flexural strength of different layers (incisal, transition, and dentin) in four commercially available multilayer zirconia systems using three-point bending tests in accordance with ISO 6872. A total of 360 CAD/CAM-fabricated bar-shaped specimens were prepared from the materials CE (Cercon yo ML, DentsplySirona), KA (Katana YML, Kuraray Noritake), PZ (3D ProZir, Aidite), PE (IPS e.max ZirCAD Prime esthetic), and assigned to layer-specific groups based on their position within the discs. After sintering and standardized surface finishing, specimens were tested under three-point bending conditions. Fracture strength was calculated and statistically analysed. Microstructural and fractographic analyses were performed to assess grain structure and to identify fracture origins. The results demonstrated significant differences in flexural strength both among materials and between layers. In general, dentin layers exhibited the highest strength, reaching mean values up to 1143 MPa, while incisal layers showed significantly lower values, with minima around 572 MPa. Only one material (CE) maintained flexural strength above the ISO threshold of 800 MPa across all layers, qualifying for unrestricted (class 5) clinical use. Other materials showed limitations, particularly in the more translucent incisal regions (KA, PE). One material fell below the ISO threshold (PZ). Weibull moduli revealed differences in reliability, with moduli ranging from 4.7 to 16.5. Fractographic evaluation identified typical fracture patterns such as surface grinding defects and internal porosity, but no abnormal fracture origins. The strength gradient corresponds to microstructural differences, particularly grain size and phase composition, influenced by yttria content. Increased translucency in incisal layers is associated with reduced mechanical performance. These findings emphasize that, despite aesthetic advantages, layer-dependent strength variations must be considered when selecting multilayer zirconia for clinical applications, especially in long-span restorations. Full article

The development of accessible and experimentally validated robotic systems for engineering education is a challenge, especially in academic environments where industrial manipulators are economically inaccessible. This paper presents the design, mechanical validation, and experimental evaluation of a robotic arm-based didactic module developed for the classification of objects according to color and morphology. The proposed system integrates a five-degree-of-freedom articulated configuration, a servomotor drive, motion planning with a trapezoidal speed profile, and a web-based control interface, enabling local and remote operation within an educational environment aligned with Industry 4.0 principles. The mechanical structure was designed using CAD modeling and validated through static structural analysis to ensure mechanical integrity and adequate safety factors. The selection of actuators was made considering the torque, angular velocity, and load requirements. A trapezoidal speed profile was implemented in order to ensure smooth trajectories and minimize positioning errors. Experimental validation was carried out through repetitive tests under controlled laboratory conditions, evaluating the accuracy and repeatability metrics. Statistical indicators such as mean error, standard deviation, and root mean square error (RMSE) were calculated. The results show the stable performance of the system, with low variability in multiple test cycles, confirming the viability of the proposed architecture for its implementation in automation and educational robotics laboratories. The integration of structural validation, motion control strategy, and experimental quantitative evaluation contributes to bridging the gap between theoretical teaching of robotics and its practical application, offering a scalable, low-cost platform for engineering training. Full article

Coronary artery disease (CAD) remains a leading cause of global morbidity and mortality. Accurate diagnosis of ischemia-causing coronary stenoses is essential for guiding revascularization and improving outcomes. Although invasive fractional flow reserve (FFR) remains the gold standard for functional lesion assessment, its use is limited by procedural invasiveness, cost, and complexity. CT-derived FFR (FFRct), based on computational fluid dynamics (CFD), was the first major advance in noninvasive physiological assessment, but its adoption has been hindered by intensive off-site computation and dependence on high-quality imaging. This review summarizes the evolution from invasive FFR to AI-driven functional assessment of coronary lesions. We examine the principles and validation of CFD-based FFRct and then focus on the shift toward artificial intelligence, including both machine learning (ML) and deep learning (DL) approaches. These methods range from models using engineered geometric and plaque features trained on large synthetic datasets to end-to-end systems that learn directly from imaging data. We discuss key validation studies evaluating diagnostic accuracy, prognostic value, and clinical utility, with attention to performance in challenging settings such as intermediate stenoses, heavy calcification, and patients with comorbidities. We also highlight major barriers to widespread adoption, including dependence on input data quality, limited explainability, regulatory hurdles, and integration into clinical workflows. Finally, we outline future directions, including AI-enabled virtual PCI planning, multimodal risk stratification, and broader access to functional cardiac assessment. AI has the potential to transform noninvasive coronary imaging by enabling a single CCTA scan to provide rapid, integrated evaluation of anatomy, plaque characteristics, and physiological significance, supporting more personalized care and better clinical outcomes. Full article

Background: Coronary bifurcation lesions represent one of the most technically demanding scenarios in coronary artery disease (CAD), associated with higher procedural complexity, restenosis, and periprocedural complications. Recent advances in coronary computed tomography angiography (CCTA) have markedly improved its ability to visualize complex coronary anatomy, assess plaque morphology, and guide revascularization. Objectives: This review summarizes (1) technological advances in CCTA over the last decade, (2) its role in evaluating bifurcation stenosis, (3) assessment of plaque morphology and distribution, (4) quantification of bifurcation geometry, and (5) emerging evidence supporting its application in revascularization planning and guidance. Findings: Modern wide-detector and dual-source CT systems, iterative and deep-learning reconstruction algorithms, and photon-counting CT (PCCT) have significantly improved temporal and spatial resolution, reduced blooming artifacts, and lowered radiation dose. CCTA now reliably quantifies bifurcation stenosis and plaque distribution, characterizes high-risk plaque features, and accurately measures bifurcation angles. The integration of CT-derived fractional flow reserve (FFR-CT) and artificial intelligence (AI)-based plaque quantification further strengthens its diagnostic and prognostic performance. CCTA-derived bifurcation scores and 3D modelling support procedural strategy selection, stent sizing, and side-branch (SB) protection. Conclusions: CCTA has evolved into a comprehensive tool for non-invasive diagnosis, physiological assessment, and pre-procedural planning of bifurcation disease. With the advent of PCCT and AI-enhanced quantitative tools, CCTA is poised to become a central component of revascularization decision-making in complex coronary bifurcations. Full article

Implant-Supported Fixed Prostheses (ISFPs) have become a common option for the rehabilitation of fully edentulous arches and have traditionally incorporated metallic substructures with ceramic or acrylic veneering. The rapid expansion of CAD/CAM technologies has introduced not only a range of polymer-based materials as alternatives to conventional metallic frameworks but also the possibility of the fabrication of monolithic rehabilitations. However, the evidence regarding the mechanical behavior of monolithic polymer-based full-arch rehabilitations remains limited. This study aimed to evaluate and compare the mechanical performance of monolithic polymer-based complete prostheses under static loading using Digital Image Correlation (DIC). A total of 12 specimens (3 per group) simulating an FP3 maxillary full-arch ISFP supported by four implants were milled from four materials: poly(ether ether ketone) (G1-PEEK), poly(ether ketone ketone) (G2-PEKK), poly(methyl methacrylate) (G3-PMMA), and fiber-reinforced composite (G4-FRC). All specimens were subjected to static loading up to 200 N at the incisors region, corresponding to the anterior unsupported span, and at the occlusal surface of the molars, corresponding to the most distal portion of the cantilever, using a universal testing machine. Full-field vertical displacement and strain distributions (principal tensile, compressive, and von Mises) were acquired through a stereo DIC system and analyzed using a Linear Mixed-Effects Model with Tukey’s HSD post hoc comparisons (α = 0.05). All prostheses withstood the applied load without macroscopic failure. G3-PMMA exhibited the highest vertical displacement, exceeding 1000 µm in the anterior span and 1500 µm in the cantilever region, along with the greatest strain concentrations, particularly at the interproximal embrasures distal to the terminal abutment. G1-PEEK provided the lowest displacement in the anterior span. G4-FRC presented displacements similar to G1-PEEK and G2-PEKK at the distal cantilever, but the lowest tensile strains and the most homogeneous strain dissipation in both loading at the anterior unsupported span and distal cantilever. This indicated that the biomechanical performance of full-arch ISFPs is highly influenced by the polymer used. PEEK, PEKK, and FRC appear as promising alternatives to PMMA for monolithic full-arch rehabilitations. Full article

(1) Background: Coronary artery disease (CAD) frequently coexists with thoracic aortic disease and may increase the risk of adverse outcomes after thoracic endovascular aortic repair (TEVAR). Whether routine preoperative coronary angiography (CAG) improves outcomes remains unclear. (2) Methods: We retrospectively analyzed 177 patients undergoing elective TEVAR between 2015 and 2025 with a median follow-up of 4.9 years. Two analyses were performed: patients who underwent preoperative CAG versus those who did not, and patients with versus without CAD. Survival was assessed using Kaplan–Meier analysis and overlap-weighted Cox regression. (3) Results: Preoperative CAG was performed in 94 patients (53.1%) and identified newly diagnosed or progressive CAD in 42 (44.7%). Overall, 24 patients (13.6%) underwent coronary revascularization before TEVAR. Patients with CAD were older and had a greater comorbidity burden. Despite these differences, preoperative CAG was not associated with differences in in-hospital mortality (2.1% vs. 6.0%, p = 0.159), major adverse cardiovascular events (11.3% vs. 9.0%, p = 0.754), or long-term survival (log-rank p = 0.10). Patients with CAD showed higher unadjusted long-term mortality than those without CAD (31.7% vs. 17.5%; log-rank p = 0.003). However, after overlap weighting, CAD was no longer significantly associated with mortality (adjusted HR 1.4, 95% CI 0.71–2.8). Among patients with angiographically verified coronary disease, preoperative revascularization before TEVAR was not associated with improved long-term survival (HR 2.20, 95% CI 0.69–6.98). (4) Conclusions: Preoperative CAG detects clinically relevant, often unrecognized CAD in a substantial proportion of TEVAR candidates and enables revascularization before surgery. Despite a higher coronary burden, patients who underwent CAG had outcomes comparable to those who did not, and the crude long-term survival disadvantage of CAD was largely explained by the accompanying systemic atherosclerotic burden. Routine preoperative coronary assessment appears justified in elective TEVAR. Full article

Single-view RGB-D observations are often affected by occlusion and restricted viewpoints, leading to incomplete object geometry and underestimated obstacle extents in indoor robot perception. This paper proposes a single-view scene completion framework that integrates candidate model retrieval and scale-aware registration. The framework first generates local RGB crops and partial point clouds through automatic instance segmentation; then retrieves complete candidate models by matching the local crops with multi-view rendered CAD images; and finally estimates candidate-to-observation rotation, translation, and scale to insert the selected aligned model into the original scene coordinate system. Experiments show that the retrieval module achieves Recall@1/Recall@5 of 80%/89%. The registration module reaches a success rate of 56.61%, outperforming the second-best method by 12.28 percentage points. More importantly, scene-level evaluation shows that the proposed method improves occupancy F1 from 0.445 to 0.523 and reduces boundary error from 0.202 m to 0.146 m compared with DiffCAD. These results indicate that the proposed framework improves navigation-oriented occupancy and obstacle-boundary recovery under CAD-library-based and segmentation-dependent single-view scene completion settings. Full article