Search Results (6,570)

Background: Artificial intelligence (AI) is increasingly applied in pulmonary endoscopy, including diagnostic bronchoscopy, interventional pulmonology and endobronchial imaging. Advances in computer vision, machine learning and robotic systems have expanded the potential for automated lesion detection, navigation to peripheral pulmonary lesions, and real-time procedural support. However, the current evidence base remains heterogeneous, and translational challenges persist. Methods: This review summarizes current applications and developments of AI across white-light bronchoscopy (WLB), image-enhanced bronchoscopy (e.g., narrow-band imaging and autofluorescence imaging), endobronchial ultrasound (EBUS), virtual and robotic bronchoscopies, and workflow optimization and training. The authors also examine the methodological limitations, regulatory considerations, and implementation barriers that affect translation into routine practice. Results: Reported developments include deep learning-based models for mucosal abnormality detection, lymph-node characterization during EBUS-guided transbronchial needle aspiration (EBUS-TBNA), improved lesion localization, and reduction in operator-dependent variability. Additionally, AI-assisted simulation platforms and decision-support tools are reshaping training paradigms. Nevertheless, most studies remain retrospective or single-center, with limited external validation, dataset heterogeneity, unclear model explainability, and incomplete integration into clinical workflows. Conclusions: AI has the potential to support lesion detection, navigation, and training in pulmonary endoscopy. However, robust prospective validation, standardized datasets, transparent model reporting, robust data governance, multidisciplinary collaboration, and careful integration into clinical practice are required before widespread adoption. Full article

Industrialization has led to the substantial release of heavy metals and organic pollutants into soil and groundwater, resulting in severe contaminated site issues that pose significant threats to ecosystems and human health. This review aims to systematically review the current development status and challenges of contaminated site remediation technologies, and explore the potential of artificial intelligence (AI) applications in site remediation, to provide a theoretical reference for advancing intelligent remediation. Conventional remediation technologies mainly include physical methods (e.g., solidification/stabilization (S/S), soil vapor extraction (SVE), thermal desorption, pump and treat (P&T), groundwater circulation wells (GCWs)), chemical methods (e.g., chemical oxidation/reduction, electrokinetic remediation (EKR), soil washing), and biological methods (phytoremediation, microbial remediation), along with combined strategies that integrate multiple approaches. Although these technologies have achieved certain successes in engineering practice, they still face common challenges such as risks of secondary pollution, long remediation periods, high costs, poor adaptability to complex hydrogeological conditions, and insufficient long-term stability, making it difficult to fully meet the remediation demands of complex contaminated sites. Subsequently, the potential of emerging technologies—including nanomaterial-based remediation, bioelectrochemical systems, and molecular biology-assisted remediation—is introduced. On this basis, the forefront applications of AI in contaminated site remediation are discussed, covering site monitoring and characterization, risk assessment, remedial strategy selection, process prediction and parameter optimization, material design, and post-remediation intelligent stewardship. Machine learning (ML), explainable AI (XAI), and hybrid modeling approaches have markedly improved remediation efficiency and decision-making. Looking forward, with advancements in XAI, mechanism-data fusion models, and environmental foundation models, AI is poised to drive a paradigm shift toward intelligent and precision remediation. However, challenges related to data quality, model interpretability, and interdisciplinary expertise remain key barriers to overcome. Full article

This review critically evaluates current PA6 recycling technologies, with a specific focus on caprolactam-oriented chemical recycling pathways, including hydrolysis, pyrolysis, glycolysis, ammonolysis, hydrothermal treatment, ionic-liquid-assisted depolymerization, and microwave-assisted processes. Reported caprolactam yields vary significantly depending on reaction conditions and catalyst systems, ranging from below 60 wt% in conventional hydrolysis to above 90 wt% under optimized catalytic, hydrothermal, or microwave-assisted conditions. Among these approaches, microwave-assisted hydrolysis and catalytic depolymerization have emerged as particularly promising, offering substantially reduced reaction times (minutes rather than hours), improved energy efficiency, and high monomer selectivity at moderate temperatures (typically 200–350 °C). This review integrates kinetic modeling approaches, analytical methods for monitoring depolymerization, and downstream separation considerations that govern monomer purity and recyclability. Key challenges, including energy demand, feedstock contamination, scalability, and economic competitiveness, are critically discussed in relation to industrial implementation. Overall, hydrolysis-based and microwave-assisted chemical recycling routes are the most viable pathways for closed-loop recycling of PA6. Future progress will rely on integrated reaction–separation–repolymerization designs, catalyst optimization, and process intensification to enable sustainable and industrially relevant PA6 circularity. Full article

Automatic Identification System (AIS) data provide valuable insights into ship behavior, supporting maritime safety, situational awareness, and operational efficiency capabilities that are increasingly required for autonomous ship functions and harbor maneuvering assistance. This review synthesizes recent research on AIS-based ship behavior analysis and modeling published between 2022 and 2024 using a structured literature search and screening process informed by PRISMA principles. The review presents a five-stage workflow, spanning data processing, data analysis, knowledge extraction, modeling, and runtime applications with emphasis on how these stages contribute to perception, prediction, and decision support in automated navigation. Four dimensions are considered in data analysis, including statistical analysis, safety indicators, situational awareness, and anomaly detection. The modeling approaches are categorized into classification, regression, and optimization, highlighting current limitations such as data quality, algorithmic transparency, and real-time performance, while also assessing runtime feasibility for onboard or edge deployment. Three runtime application directions are identified: autonomous vessel functions, remote monitoring and control operations, and onboard decision-support tools, with numerous studies focusing on constrained waterways and port-approach scenarios. Future directions suggest integrating multi-source data and advancing machine learning models to improve robustness in complex traffic and harbor environments. By linking theoretical insights with practical onboard needs, this study provides guidance for developing intelligent, adaptive, and safety-enhancing maritime systems. Full article

Mild traumatic brain injury (mTBI) contributes substantially to years lived with disability (YLD), decreases health-related quality of life, and imposes significant costs on healthcare systems and society. Millions of people experience mTBI each year, and healthcare costs for mTBI in just the first year after injury exceed $44 billion USD. Despite the common occurrence of mTBI, estimates of incidence, prevalence, related disability, and costs vary widely. This variance is attributed to the underreporting of head impacts, inconsistent definitions of mTBI, and a lack of objective biomarkers. Currently available clinical blood biomarkers primarily assist in ruling out CT-detectable intracranial injury rather than definitively diagnosing mTBI itself, underscoring the continued need for objective, portable, and clinically specific biomarkers. Numerous imaging findings, blood proteins, and physiological measures are under investigation for these purposes, and some may have multiple uses. Specific biomarkers for acute diagnosis are needed urgently. Although many systematic reviews have been published, most focus on a single biomarker or class of biomarkers. Given the breadth of potential biomarker categories, conducting a comprehensive, systematic review across modalities is challenging. Here, we provide a narrative review summarizing the extant literature across major biomarker domains studied in adolescents and adults. We emphasize candidates supported by the most robust evidence to guide continued research and clinical translation. Full article

This study develops a reduced-order predictive model of the Sit-To-Stand (STS) task to examine whether a simplified biomechanical representation can reproduce key STS patterns reported in the literature and to investigate the role played in movement by a flexible trunk. The model represents the human body as a planar multibody system and formulates STS as an optimization problem within a discrete mechanics framework. This formulation combines reduced model complexity, explicit torso flexibility, and a structure-preserving numerical approach for trajectory generation. Simulations were used to evaluate the effects of movement duration, reduced joint strength, and seat height on joint torques, kinematics, trunk motion, and ground reaction forces (GRFs). The results reproduced several qualitative trends reported in previous experimental studies, including increased peak joint torques and GRFs with shorter movement duration, lower joint strength, and reduced seat height, as well as greater compensatory trunk motion under more demanding conditions. These findings suggest that the proposed framework captures key adaptive features of STS mechanics and may provide useful insights for rehabilitation analysis and the design of assistive technologies such as lower-limb exoskeletons and rehabilitation devices. At the same time, the present work should be regarded as an initial methodological study, since validation is currently qualitative and further experimental calibration, quantitative validation, and sensitivity analysis remain part of ongoing work. Full article

Rolling bearing simulations are often too computationally expensive for early design decisions, because many simulations are required in a large design of experiments. Therefore, the aim of this systematic literature review is to provide an overview of how machine learning (ML) is used to integrate engineering knowledge in advance when simulations are the primary data source for supervised learning. In the 11 included studies, ML is mainly applied as regression models trained on simulation data to replace repeated solver calls. The applications can be classified into three domains—contact mechanics, lubrication, and dynamics—mostly linked to their domain specific outputs. In most cases, ML models replace the simulation once the model is trained and validated, followed by optimization, which is often performed on the surrogate using evolutionary algorithms. Surrogates have the potential to enable design-space exploration, sensitivity analysis, and uncertainty propagation, but this capability is not yet fully exploited in current practice. The purpose of this review article is to provide a summary of methodological building blocks and practical guidelines to assist researchers and engineers in selecting appropriate ML workflows for simulation-based analysis of rolling bearings in the areas of tribology, dynamics, service life, load capacity, and system-level investigations. Full article

Patellar maltracking is among the most common causes of anterior knee pain after total knee arthroplasty (TKA), underscoring the need for accurate prevention and treatment. Therefore, the purpose of this narrative review is to provide a comprehensive overview of current evidence on post-TKA tracking, focusing on component alignment, preoperative patient assessment, and revision treatment options. A PubMed database search was performed, leveraging the literature from the last 20 years, and the results were qualitatively synthesized. According to current studies, several precautions should be taken to prevent patellofemoral stress and, consequently, patellar maltracking, such as avoiding internal rotation, valgus alignment, and excessive flexion of the femoral component and internal rotation of the tibial component. Regarding alignment strategies, kinematic alignment appears to offer potential benefits over mechanical alignment in certain functional outcomes and patient satisfaction scores. However, these differences should be interpreted cautiously as they may not always exceed the minimal clinically important difference. Furthermore, recent evidence indicates that quadriceps biomechanics influence TKA outcomes, potentially suggesting that conventional surgical approaches may need to be individualized, though these preliminary findings require prospective validation. Currently, robotic-assisted surgery represents a developmental direction for patient-tailored interventions and offers great promise for better prosthesis customization to the individual patient. Integration of imaging data with dynamic soft-tissue assessment enables more predictable reconstruction of joint kinematics. Regarding surgical treatment, the selection of specific methods requires a prior clinical and radiographic assessment. Indications range from patellar maltracking direction and component malrotation to patient preferences and rehabilitation potential. Ultimately, the future of TKA relies on personalized interventions to prevent complications and improve patient outcomes. This evolution is driven by the shift from mechanical alignment to kinematic alignment, alongside quadriceps tendon assessment and intraoperative robotic-assisted measurement, all aimed at optimizing the accuracy of implant positioning. Full article

Nurses are healthcare professionals who can play a leading role in preventing antimicrobial resistance, given their direct assistance to patients. For this reason, in-depth university training is desirable. This study was conducted to detect possible weak points in the university training about an important public health topic represented by general knowledge about antibiotics and antibiotic resistance. We carried out a survey on Knowledge, Attitudes, and Practices of students attending the Nursing Sciences course at the University of Messina, Italy, by administering an online standardised questionnaire that included general and specific questions about antibiotics and antibiotic resistance. General and specific scores were calculated. Some gaps were found about the knowledge of antibiotics (mean score: 3.6/4) and, especially, antibiotic resistance (mean score: 3.2/5). As expected, most of the incorrect answers to both antibiotic and antibiotic-resistance knowledge were given by students in the first year, but some gaps were also found among students in the last year. Given the growing role nurses can play in combating antibiotic resistance, these findings point to a possible information gap in the study course and highlight the need to enhance the current university training programmes with activities designed to increase knowledge on these important public health issues. Full article

Diet plays a fundamental role in maintaining human health, which has intensified scientific interest in bioactive food constituents and contributed to the development of functional foods. Polyphenols, one of the most important groups of plant secondary metabolites, are valued for their strong antioxidant properties and potential health benefits. Species belonging to the Rosaceae family, including Rosa, Crataegus, and Pyracantha, are recognized as promising sources of phenolic compounds, although their chemical profiles and antioxidant potential remain insufficiently characterized. The aim of this study was to quantitatively assess selected phenolic compounds in extracts obtained from ripe fruits of selected Rosaceae species and cultivars. The extracts were prepared using ultrasound-assisted solvent extraction, pressurized liquid extraction, and matrix solid-phase dispersion. The resulting samples were subsequently subjected to comprehensive analyses of their chemical composition and antioxidant capacity. These extraction techniques differ substantially in their operational principles and process parameters; notably, ultrasound-assisted solvent extraction and pressurized liquid extraction require more complex and tightly controlled conditions, whereas matrix solid-phase dispersion constitutes a comparatively simpler and less parameter-dependent approach. The results revealed distinct phenolic profiles among the examined species and confirmed the presence of compounds exhibiting strong antioxidant activity. Collectively, these findings broaden current knowledge of the phytochemical diversity present in Rosaceae fruits and underscore their potential as natural sources of bioactive constituents relevant to the development and formulation of functional food products. Full article

This study develops a new concept of computer-assisted exercises (CAX) on constructive simulation systems and how the proposed concept affects the strategy and teaching methods. The current state of affairs in the field of defense and security, both in Europe and in the world, requires the acquisition of competencies (European Qualifications Framework—EQF: knowledge, skills, independence, and responsibility), i.e., the education and training of a significantly larger number of personnel in the field of defense and security than has been the case in the last 70 years. In addition, an important specificity of today is that students need to acquire some competencies that were almost unknown until recently. Most of these competencies are the result of the rapid development of technology, which has significantly changed human life in all areas. In order to respond to the modern requirements of conducting operations, where the transfer of information both horizontally and vertically is exponentially accelerated, current concepts of preparation and implementation of education and training, of which exercises are often the most important part, need to be replaced with new concepts, and one such concept is developed in this paper. New information introduced is mostly related to the new weapons that are being introduced (unmanned systems, hypersonic missiles, weapons based on microwaves and lasers, etc.), which all result in necessary changes to the traditional approach to conducting war, i.e., tactics, techniques, and procedures (TTP). This novel exercise concept allows for the simultaneous implementation of training for up to three or four hierarchical levels (e.g., TF Div, brigade, battalion, and company) in one exercise, while in most countries, including the NATO alliance, it is still common for such exercises to be conducted according to a concept that is over 20 years old and, as a rule, is focused on the implementation of exercises for one or two hierarchical levels. This approach allows key personnel from the headquarters of units from four hierarchical levels to be simulated in real time, which is not provided by current concepts for preparing and conducting exercises. The new concept was applied as a multi-level, computer-assisted exercise (CAX) on constructive simulation systems. In addition, significant advantages of the new concept relate to the flexibility and adaptability of the proposed concept to be applied in addition to operational units and in training institutions such as academies and higher education institutions. In addition to the above, the new concept requires a shorter planning period as well as fewer total resources needed for the preparation and implementation of the exercise. The management, organizational, and technological components of the proposed exercise concept are implemented in the CAX model. The hypotheses in this paper will be tested in an applied study, which was evaluated through an external evaluation body. The implemented CAX model was tested in Croatia on the example of using exercises at the Croatian Defense Academy. Full article

Therapeutic endoscopic ultrasound (t-EUS) has transformed the management of biliopancreatic diseases by enabling minimally invasive access and intervention through the gastrointestinal wall. This narrative review summarizes current indications and evolving roles of t-EUS in benign and malignant biliary disease, with a focus on the different modalities of transmural drainage, EUS-guided gastroenterostomy (EUS-GE), and EUS-guided radiofrequency ablation (EUS-RFA). In benign settings, EUS-gallbladder drainage (EUS-GBD) has emerged as a minimally invasive alternative to percutaneous cholecystostomy for high-risk patients with acute cholecystitis, offering internal drainage with fewer tube-related adverse events. In malignant biliary obstruction, transmural drainages are consolidated alternatives of endoscopic retrograde cholangiopancreatography (ERCP) as first-line or rescue strategies, providing durable internal biliary drainage, avoiding post-ERCP pancreatitis without deteriorating quality of life. In surgically altered anatomy, t-EUS overcomes the limitations of enteroscopy-assisted ERCP by creating direct access routes to the biliary tree or pancreatic duct. EUS-guided pancreatic duct drainage offers a rescue or primary approach in benign strictures, anastomotic stenosis, and disconnected duct syndrome. EUS-GE has rapidly become a preferred modality for palliation of gastric outlet obstruction in pancreatic cancer, while EUS-RFA provides a platform for locoregional therapy in selected cases of pancreatic neuroendocrine tumors, adenocarcinoma, and pancreatic cystic neoplasms. Collectively, these applications position t-EUS as a central tool in the multidisciplinary management of complex biliopancreatic disease, with ongoing innovations expected to further expand its indications and safety and to refine patient selection and training pathways. Full article

Background and Objectives: Shortages of dermatologists create significant barriers to care, particularly for inflammatory and history-dependent conditions where image-only artificial intelligence (AI) classifiers have limited applicability. Current teledermatology solutions largely focus on single-task, morphology-based neoplasm classifiers, leaving the vast majority of dermatologic presentations underserved. This study evaluated the diagnostic accuracy and management plan quality of Dermflow (Prava Medical, Delaware, USA), a proprietary dermatology-focused Multimodal Image-and-Data Assistant (MIDA) that autonomously gathers dermatology-specific history, integrates data with patient-submitted images, and outputs structured differential diagnoses and management summaries. Materials and Methods: Two AI systems, Dermflow and Claude Sonnet 4 (Claude, a leading vision–language model), analyzed 87 clinical images from the Skin Condition Image Network and Diverse Dermatology Images databases, representing 10 inflammatory dermatoses and 9 neoplastic conditions stratified across Fitzpatrick Skin Tone (FST) categories (I–II, III–IV, V–VI). For the diagnostic comparison, Dermflow received images and autonomously gathered clinical history, while Claude received identical images without history. For the management plan comparison, both systems received the correct diagnosis and the clinical histories gathered by Dermflow. The primary outcome was diagnostic accuracy. The secondary outcome was management plan quality, assessed by two blinded dermatologists across eight clinical dimensions using 5-point Likert scales. Chi-square tests compared diagnostic accuracy between models; t-tests and ANOVA compared management quality scores. Results: Dermflow achieved markedly superior diagnostic accuracy compared to Claude (86.2% vs. 24.1%, p < 0.001). Both models maintained consistent diagnostic performance across FST categories without significant within-model differences (Dermflow p = 0.924; Claude p = 0.828). Management plan quality showed no significant overall differences between models. However, composite management quality scores declined significantly for darker skin tones across both systems: Dermflow scored 4.20 (FST I–II), 3.99 (FST III–IV), and 3.47 (FST V–VI); Claude scored 4.35, 3.97, and 3.44, respectively (p < 0.001 for most pairwise FST comparisons within each model). Conclusions: Multimodal AI integrating targeted history with image analysis achieves substantially higher diagnostic accuracy than image-only approaches across both inflammatory and neoplastic dermatologic conditions. Autonomous history gathering addresses fundamental limitations of morphology-only classifiers and enables scalable, patient-facing triage across the full spectrum of dermatologic disease. However, both models demonstrated reduced management plan quality for darker skin tones despite receiving the correct diagnosis, suggesting persistent training data limitations that require targeted bias-mitigation strategies beyond domain-specific instruction. Full article

Additive manufacturing (AM) enables a level of design flexibility that is difficult to achieve with conventional techniques, yet it inherently yields materials marked by significant variability, anisotropy, and sensitivity to defects that challenge classical mechanics-of-materials assumptions. Process-driven microstructural heterogeneity, stochastic defect populations, and residual stresses strongly influence deformation, fatigue, and fracture behavior, often outweighing nominal material properties and constraining the predictive capability of traditional constitutive and fracture mechanics models. Machine learning (ML) has emerged as a powerful means of handling the complexity of AM data; however, many current approaches depend on black-box models that lack physical transparency, extrapolate poorly, and treat uncertainty inadequately. This review contends that ML should augment—rather than replace—mechanics-based modeling, and that dependable prediction of AM material behavior requires mechanics-informed ML frameworks. We critically analyze the central mechanics challenges in AM and evaluate established modeling strategies alongside emerging ML methods relevant to deformation, damage, fatigue, and fracture. Particular emphasis is given to physics-informed and hybrid ML approaches that explicitly incorporate anisotropy, defect sensitivity, residual stress effects, and uncertainty quantification within learning architectures. Recent progress in ML-assisted constitutive modeling, fatigue and fracture prediction, and digital twin development is synthesized, and the implications for qualification, certification, and structural deployment of AM components are discussed. Full article

Dark current represents a fundamental limiting factor in photodiode performance, establishing the noise floor and constraining detectivity in low-light applications. This comprehensive literature review examines publications covering the physical mechanisms underlying dark current generation and diverse techniques employed for its reduction. Covered mechanisms include diffusion current, Shockley–Read–Hall (SRH) generation–recombination, trap-assisted tunneling, band-to-band tunneling, and surface leakage, each examined with respect to its physical origin and characteristic signatures. Reduction strategies are categorized into thermal management approaches, surface passivation techniques including atomic-layer-deposited aluminum oxide (ALD Al₂O₃), guard ring architectures (attached, floating, and combined configurations), gettering and defect engineering methods, doping profile optimization, bias voltage management, and advanced device architectures such as pinned photodiodes and black silicon structures. A classification table organizes all the reviewed literature by material system, reduction technique, and key findings. Special emphasis is placed on silicon, germanium, III–V compounds, and emerging material photodiodes relevant to near-infrared detection, CMOS imaging, single-photon avalanche diodes (SPADs), and Time-of-Flight (ToF) applications. Full article