Search Results (1,215)

This paper examines the contrasting yet complementary approaches of the Constrained Disorder Principle (CDP) and Stefan Hell’s deterministic optical nanoscopy for managing noise in complex systems. The CDP suggests that controlled disorder within dynamic boundaries is crucial for optimal system function, particularly in biological contexts, where variability acts as an adaptive mechanism rather than being merely a measurement error. In contrast, Hell’s recent breakthrough in nanoscopy demonstrates that engineered diffraction minima can achieve sub-nanometer resolution without relying on stochastic (random) molecular switching, thereby replacing randomness with deterministic measurement precision. Philosophically, these two approaches are distinct: the CDP views noise as functionally necessary, while Hell’s method seeks to overcome noise limitations. However, both frameworks address complementary aspects of information extraction. The primary goal of microscopy is to provide information about structures, thereby facilitating a better understanding of their functionality. Noise is inherent to biological structures and functions and is part of the information in complex systems. This manuscript achieves integration through three specific contributions: (1) a mathematical framework combining CDP variability bounds with Hell’s precision measurements, validated through Monte Carlo simulations showing 15–30% precision improvements; (2) computational demonstrations with N = 10,000 trials quantifying performance under varying biological noise regimes; and (3) practical protocols for experimental implementation, including calibration procedures and real-time parameter optimization. The CDP provides a theoretical understanding of variability patterns at the system level, while Hell’s technique offers precision tools at the molecular level for validation. Integrating these approaches enables multi-scale analysis, allowing for deterministic measurements to accurately quantify the functional variability that the CDP theory predicts is vital for system health. This synthesis opens up new possibilities for adaptive imaging systems that maintain biologically meaningful noise while achieving unprecedented measurement precision. Specific applications include cancer diagnostics through chromosomal organization variability, neurodegenerative disease monitoring via protein aggregation disorder patterns, and drug screening by assessing cellular response heterogeneity. The framework comprises machine learning integration pathways for automated recognition of variability patterns and adaptive acquisition strategies. Full article

Long COVID (LC) may involve endocrine dysfunction; however, the underlying mechanism remains unclear. To examine hypothalamic–pituitary responses in patients with LC, we conducted a single-center retrospective study of patients with refractory LC referred to our University Hospital who underwent anterior pituitary stimulation tests. Between February 2021 and November 2025, 1251 patients with long COVID were evaluated, of whom 207 (19%) had relatively low random ACTH or cortisol levels. Ultimately, 16 underwent anterior pituitary stimulation tests and were included. All tests were performed in an inpatient setting without exogenous steroids. Fifteen patients (six women, mean age 35.6 years) underwent corticotropin-releasing hormone (CRH), thyrotropin-releasing hormone (TRH), and gonadotropin-releasing hormone (GnRH) tests. All patients had mild acute COVID-19, eight had ≥2 vaccinations, and the mean interval from infection was 343 days. Frequent symptoms included fatigue (100%), insomnia (66.7%), headache (60.0%), anorexia/nausea (40.0%), and brain fog (40.0%). Mean early-morning cortisol and 24 h urinary free cortisol were 7.5 μg/dL and 41.0 μg/day, respectively. MRI showed an empty sella in one case. Peak hormonal responses were preserved (ΔACTH 247%, ΔTSH 918%, ΔPRL 820%, ΔFSH 187%, ΔLH 1150%); however, peaks were delayed beyond 60 min in ACTH (13%), LH (33%), and FSH (87%). Notably, significantly delayed elevations remained at 120 min in the responses of TSH (4.1-fold), PRL (1.8-fold), LH (9.3-fold), and FSH (2.8-fold), suggesting possible hypothalamic involvement, particularly in the gonadotropin responses. Additionally, serum IGF-I was lowered (−0.70 SD), while GH response (mean peak 35.5 ng/mL) was preserved by growth hormone-releasing peptide (GHRP)-2 stimulation. Low-dose hydrocortisone and testosterone were initiated for three patients. Although direct viral effects and secondary suppression have been proposed, our findings may suggest that, at least in part, the observed response characteristics are consistent with functional secondary hypothalamic dysfunction rather than irreversible primary injury. These findings highlight the need for objective endocrine evaluation before initiating hormone replacements. Full article

Background & Objectives: Periodontal disease (PD) is a common oral disease that affects the supporting structures of the teeth and is a leading cause of tooth loss worldwide. This study aimed to estimate the prevalence of PD among 9th-grade teachers in the northern West Bank and examine its association with key behavioral and socioeconomic factors. Methods: A cross-sectional study was conducted among 920 teachers selected through proportional stratified random sampling from governmental and private schools. Periodontal health was assessed using the WHO Community Periodontal Index for Treatment Needs (CPITN), and oral hygiene status was measured with the Simplified Oral Hygiene Index (S-OHI). A structured questionnaire was administered to collect data on socioeconomic status, oral hygiene practices, dietary habits, and smoking behaviours. Data was analysed using descriptive statistics, bivariate and multivariate logistic regression. Results: Only 11.8% of participants exhibited completely healthy gingiva, with the mean condition ranging between calculus and shallow pockets. Oral hygiene practices were the strongest predictors of periodontal outcomes: frequent tooth brushing (Adjusted Odds Ratio: AOR = 0.015), morning brushing (AOR = 0.015), and regular toothbrush replacement (AOR = 2.514) were protective. Higher red meat intake was negatively associated with periodontal health (AOR = 0.032), while frequent nut consumption was protective (AOR = 0.227). The number of cigarettes smoked per week was positively associated with PD (AOR = 1.085). Conclusions: PD is highly prevalent among Palestinian adults, with significant behavioural and lifestyle-related determinants. Targeted oral health interventions are urgently needed to improve adults’ oral health. Full article

Background: Peri-implantitis, a condition characterized by inflammation and progressive bone loss around dental implants, presents a significant challenge in contemporary dentistry. Conventional non-surgical treatments often fail to fully eliminate bacterial biofilms, particularly on complex implant surfaces. Laser therapies have emerged as potential adjuncts due to their antimicrobial and bio-modulatory properties. However, their microbiological effectiveness and suitability for individualized patient treatment planning remain unclear. Objective: Τhis study aims to systematically assess and synthesize the microbiological effects of various laser-assisted non-surgical treatments for peri-implantitis compared to conventional mechanical debridement. Methods: This systematic review and meta-analysis followed PRISMA guidelines and was registered in PROSPERO (CRD420251035354). Randomized controlled trials (RCTs) evaluating microbiological changes following laser-assisted non-surgical treatment of peri-implantitis, with a minimum follow-up of one month, were identified through searches in multiple databases and registries up to February 2025. The ncluded studies used lasers such as diode, Er: YAG, and photodynamic therapy (PDT) either alone or as adjuncts to mechanical debridement. Outcomes of interest included bacterial counts. Risk of bias was assessed using the RoB2 tool, and certainty of evidence was evaluated via GRADE. Quantitative synthesis used random-effects meta-analysis, with standardized mean differences (SMDs) calculated. Results: Eight RCTs involving 266 patients and 335 implants were included in the systematic review. Quantitative synthesis of three pathogens (counts of Fusobacterium nucleatum, P. gingivalis, T. denticola) across three studies displayed no statistically significant differences between laser and control groups at 3 and 6 months (p > 0.05 for all comparisons). When examining individual study findings, PDT, particularly in patients with diabetes or acute abscess, showed short-term reductions in red complex bacteria (e.g., Porphyromonas gingivalis and Treponema denticola). In contrast, diode and Er: YAG lasers demonstrated inconsistent or transient effects. The quality of evidence was rated as very low according to GRADE. Conclusions: Laser-assisted therapies, especially PDT, may provide targeted microbiological benefit in selected patient groups, supporting their adjunctive use within personalized treatment planning rather than as replacements for mechanical debridement, which remains the gold standard. Further high-quality RCTs incorporating well-defined patient risk profiles, such as systemic conditions and behavioral factors, and precision treatment algorithms are needed. Full article

Peri-urban areas in Eastern Europe are undergoing rapid land transformation driven by suburban housing expansion and infrastructure development, yet the processes through which vegetation is progressively degraded and built-up areas intensify remain insufficiently documented. This study analyses vegetation loss and urban expansion in the peri-urban belt of Timișoara, Western Romania, between 2020 and 2025 using Sentinel-2 multispectral imagery, two key spectral indices (NDVI and NDBI), and a Random Forest (RF) classifier. The results reveal a gradual, multi-stage transformation trajectory, where dense vegetation transitions first into sparse vegetation and bare soil before consolidating into built-up surfaces, rather than being replaced abruptly. Substantial vegetation decline is accompanied by notable increases in built-up land, with strong spatial differences between communes depending on development pressure. The integration of RF classification with spectral index analysis allows these transitions to be validated and interpreted more reliably, helping distinguish structural suburbanisation from short-term spectral variability. Overall, the study demonstrates the value of combining NDVI, NDBI and AI-supported land-cover classification to capture nuanced peri-urban transformation dynamics and provides actionable insights for spatial planning and sustainable land management in rapidly growing metropolitan regions. Full article

Rainfall infiltration is one of the main factors inducing slope instability, while the spatial heterogeneity and uncertainty of soil parameters have profound impacts on slope response characteristics and stability evolution. Traditional deterministic analysis methods struggle to reveal the dynamic risk evolution process of the system under heavy rainfall. Therefore, this paper proposes an uncertainty analysis framework combining Karhunen–Loève Expansion (KLE) random field theory, Polynomial Chaos Kriging (PCK) surrogate modeling, and Monte Carlo simulation to efficiently quantify the probabilistic characteristics and spatial risks of rainfall-induced slope instability. First, for key strength parameters such as cohesion and internal friction angle, a two-dimensional random field with spatial correlation is constructed to realistically depict the regional variability of soil mechanical properties. Second, a PCK surrogate model optimized by the LARS algorithm is developed to achieve high-precision replacement of finite element calculation results. Then, large-scale Monte Carlo simulations are conducted based on the surrogate model to obtain the probability distribution characteristics of slope safety factors and potential instability areas at different times. The research results show that the slope enters the most unstable stage during the middle of rainfall (36–54 h), with severe system response fluctuations and highly concentrated instability risks. Deterministic analysis generally overestimates slope safety and ignores extreme responses in tail samples. The proposed method can effectively identify the multi-source uncertainty effects of slope systems, providing theoretical support and technical pathways for risk early warning, zoning design, and protection optimization of slope engineering during rainfall periods. Full article

The exponential growth of data-driven applications has intensified performance demands on database systems, where cache management represents a critical bottleneck. Traditional cache replacement policies such as Least Recently Used (LRU) and Least Frequently Used (LFU) rely on simple heuristics that fail to capture complex temporal and frequency patterns in modern workloads. This research presents a modular machine learning-enhanced cache management framework that leverages pattern recognition to optimize database performance through intelligent replacement decisions. Our approach integrates multiple machine learning models—Random Forest classifiers, Long Short-Term Memory (LSTM) networks, Support Vector Machines (SVM), and Gradient Boosting methods—within a modular architecture enabling seamless integration with existing database systems. The framework incorporates sophisticated feature engineering pipelines extracting temporal, frequency, and contextual characteristics from query access patterns. Comprehensive experimental evaluation across synthetic workloads, real-world production datasets, and standard benchmarks (TPC-C, TPC-H, YCSB, and LinkBench) demonstrates consistent performance improvements. Machine learning-enhanced approaches achieve $8.4\%$ to $19.2\%$ improvement in cache hit rates, $15.3\%$ to $28.7\%$ reduction in query latency, and $18.9\%$ to $31.4\%$ increase in system throughput compared to traditional policies and advanced adaptive methods including ARC, LIRS, Clock-Pro, TinyLFU, and LECAR. Random Forest emerges as the most practical solution, providing $18.7\%$ performance improvement with only $3.1\%$ computational overhead. Case study analysis across e-commerce, financial services, and content management applications demonstrates measurable business impact, including $8.3\%$ conversion rate improvements and USD 127,000 annual revenue increases. Statistical validation ($p<0.001$, Cohen’s $d>0.8$) confirms both statistical and practical significance. Full article

Background/Objectives: Evaluating the feasibility of randomized controlled trials (RCTs) represents a critical next step for advancing human–animal interaction (HAI) science and rigorously exploring the role of animal-assisted interventions (AAIs) in psychiatric acute care. This study presents strategies for conducting a pilot RCT comparing an animal-assisted intervention involving dogs (AAI) with an active conversational control (CC), which incorporated conversation with a human volunteer, and treatment as usual (TU) for improving mental health outcomes in psychiatrically hospitalized adults. Methods: We recruited participants from an acute-care psychiatric unit at an academic medical center. AAI and CC were delivered by volunteer handlers with and without their registered therapy dogs. Feasibility data included number of recruitment contacts, recruitment rate, and reasons for non-enrollment. We describe recruitment challenges encountered and mitigating strategies for successful study completion. Results: Recruitment occurred over 23 months with a goal of 60 participants participating in at least one intervention day. A total of 264 patients were referred to the study and 72 enrolled. The additional 12 people were recruited to replace participants who did not complete any intervention days and did not provide any intervention data. Study recruitment goals were met with a recruitment rate of 27.30%. Conclusions: Research to improve the lives of patients in acute psychiatric care is a vital public health goal, yet RCTs are difficult to conduct in acute care settings. Studies like this strengthen HAI and psychiatric science by providing a roadmap for implementing successful AAI RCT designs. Full article

The FDA plans to gradually replace animal testing with organoid and organ-on-a-chip technologies for drug safety assessment, driving surging demand for gut-on-a-chip in food and drug safety evaluation and highlighting the need for efficient, precise chip designs. Oxygen gradients are central to these devices because they shape epithelial metabolism, microbial co-culture, and overall gut homeostasis. We coupled machine learning with finite element analysis to build a parametric COMSOL Multiphysics model linking channel geometry, transport coefficients, and cellular oxygen uptake to the resulting oxygen field. For numerical prediction, three models—Random Forest (RF), XGBoost, and MLP—were employed, with XGBoost achieving the highest accuracy (RMSE = 1.68%). SHAP analysis revealed that medium flow rate (39.7%), external flux (26.9%), and cellular oxygen consumption rate (24.8%) contributed most importantly to the prediction. For oxygen distribution mapping, an innovative Boundary-Guided Generative Network (BG-Net) model was employed, yielding an average concentration error of 0.012 mol/m³ (~4.8%), PSNR of 33.71 dB, and SSIM of 0.9220, demonstrating excellent image quality. Ablation experiment verified the necessity of each architectural component of BG-Net. This pipeline offers quantitative, data-driven guidance for tuning oxygen gradients in gut-on-a-chip. Future work will explore extensions including real experimental data integration, real-time prediction, and multi-task scenarios. Full article

Background: Osteoarthritisof the first trapeziometacarpal (TMC) joint (rhizarthrosis) is a degenerative condition causing pain, reduced mobility, and functional limitations, particularly in older adults and postmenopausal women. Though conservative treatments offer symptomatic relief, advanced cases often require trapeziectomy or total joint replacement. The choice of prosthesis is tailored to patient-specific factors such as age, functional demands, and comorbidities. Despite the benefits of TMC joint replacements, prosthetic infections remain underexplored. Materials and Methods: This systematic review (covering 2000–2024) adhered to PRISMA guidelines, searching Medline, Cochrane, and Google Scholar for randomized controlled trials and case series. Data on demographics, prosthesis types, infection rates, and management strategies were extracted and analyzed. Results: Among 4165 TMC joint procedures reported in 63 studies, 15 cases (0.36%) involved superficial or deep infections, with Staphylococcus aureus identified in two instances. Management ranged from antibiotic therapy and debridement to prosthesis removal with or without reimplantation. Conclusions: Variability in diagnostic criteria and reporting limited uniform conclusions. Although infections are infrequent, they pose significant management challenges due to inconsistent diagnostic criteria and treatments. Early identification and tailored interventions remain critical. This review underscores the need for standardized protocols and highlights gaps in current research. Future studies should focus on multicenter trials and robust methodologies to improve outcomes and advance infection management in TMC prosthesis surgery. Full article

Debit and credit card payments have become the preferred method of payment for consumers, replacing paper checks and cash. However, this shift has also led to an increase in concerns regarding identity theft and payment security. To address these challenges, it is crucial to develop an effective, secure, and reliable payment system. This research presents a comprehensive study on payment card fraud detection using deep learning techniques. The introduction highlights the significance of a strong financial system supported by a quick and secure payment system. It emphasizes the need for advanced methods to detect fraudulent activities in card transactions. The proposed methodology focuses on the conversion of a comma-separated values (CSV) dataset into quick response (QR) code images, enabling the application of deep neural networks and transfer learning. This representation allows leveraging pre-trained image-based architectures to provide a layer of privacy by encoding numeric transaction attributes into visual patterns. The feature extraction process involves the use of a convolutional neural network, specifically a residual network architecture. The results obtained through the under-sampling dataset balancing method revealed promising performance in terms of precision, accuracy, recall, and F1 score for the traditional models such as K-nearest neighbors (KNN), Decision tree, Random Forest, AdaBoost, Bagging, and Gaussian Naive Bayes. Furthermore, the proposed deep neural network model achieved high precision, indicating its effectiveness in detecting card fraud. The model also achieved high accuracy, recall, and F1 score, showcasing its superior performance compared to traditional machine learning models. In summary, this research contributes to the field of payment card fraud detection by leveraging deep learning techniques. The proposed methodology offers a sophisticated approach to detecting fraudulent activities in card payment systems, addressing the growing concerns of identity theft and payment security. By deploying the trained model in an Android application, real-time fraud detection becomes possible, further enhancing the security of card transactions. The findings of this study provide insights and avenues for future advancements in the field of payment card fraud detection. Full article

Breslow thickness (BT) is the most powerful prognostic indicator in cutaneous melanoma, yet histopathological measurement exhibits some limitations such as interobserver variability and diagnostic delays. Preoperative clinical assessment demonstrates 30% misclassification rates. This narrative review synthesizes evidence on deep learning models for non-invasive BT estimation from dermoscopic images. Convolutional neural networks (ResNet, EfficientNet, Vision Transformers) with transfer learning from ImageNet achieve up to 75–79% accuracy and AUC 0.76–0.85 on single-center datasets. Preprocessing techniques (hair removal, color normalization, data augmentation) and interpretability methods (Grad-CAM, LIME) enhance clinical applicability. However, external validation reveals performance degradation. The clinically critical thickness range (0.4–1.0 mm) demonstrates poor discrimination. Significant dataset bias exists: most training data represents lighter skin phototypes, resulting in an underrepresentation of darker skin types. AI models function as complementary decision-support tools rather than replacements for histopathology. Prospective clinical trials validating clinical utility are lacking, and regulatory approval pathways are undefined. Research priorities include diverse public datasets with balanced skin tone representation, the adoption of threshold-weighted loss functions to prioritize accuracy at the 0.8 mm surgical cut-off, multi-institutional external validation, prospective randomized trials, federated learning frameworks, and regulatory engagement. Only rigorous, equitable research can translate AI from proof-of-concept to clinically reliable tools benefiting all melanoma patients. Full article

Failures in clean water service lines can disrupt supply, increase operational costs, and reduce customer satisfaction. This study develops a machine learning framework to predict such failures, providing a proactive tool for utility asset management. A case study was conducted on a water distribution network in Tehran, serving approximately 205,000 customers, with 11 years of service line data and over 88,000 recorded failures. Service line attributes, including length, diameter, material, age, demand, and pressure, were combined with historical failure data to train Random Forest, Extreme Gradient Boosting, and Long Short-Term Memory models. Model performance was assessed using F1-score, AUC-ROC, and AUC-PRC. A novel metric was introduced to quantify failure reduction when prioritising replacements. The results demonstrate that machine learning can effectively capture complex relationships between service line features and failures, offering significant benefits for tactical maintenance planning. This research underscores the potential of predictive approaches to improve reliability and reduce costs. Full article

Over the past three decades, cardiac surgery has undergone a deep transformation, shifting from full median sternotomy to minimally invasive (MICS) and micro-invasive techniques. These approaches aim to achieve equivalent therapeutic outcomes while reducing surgical trauma, postoperative pain, hospitalization time, and healthcare costs. Minimally invasive strategies are now widely applied to aortic and mitral valve surgery, coronary artery bypass grafting, atrial fibrillation ablation, and combined procedures. Key advancements such as sutureless prostheses, video- and robotic-assisted systems, and enhanced imaging technologies have improved surgical precision and clinical outcomes while promoting faster recovery and superior cosmetic results. Evidence from randomized trials and observational studies demonstrates that MICS provides mortality and morbidity rates comparable to conventional surgery, with additional benefits in high-risk, elderly, and frail patients. Micro-invasive transcatheter interventions, particularly transcatheter aortic valve implantation (TAVI) and transcatheter mitral repair or replacement, have further expanded therapeutic options for patients unsuitable for open-heart surgery. Their success has fostered debate not between conventional and minimally invasive surgery, but between minimally invasive and micro-invasive approaches. Hybrid procedures—combining surgical and percutaneous techniques—exemplify a multidisciplinary evolution aimed at tailoring treatment to patient-specific anatomy, comorbidities, and risk profiles. Despite clear advantages, these techniques present challenges, including a steep learning curve, increased procedural costs, and the requirement for specialized equipment and institutional expertise. Optimal patient selection based on clinical risk assessment and advanced imaging remains essential. Future directions include refinement of robotic platforms, artificial intelligence-based decision support, miniaturization of instruments, and broader validation of emerging technologies in younger and low-risk populations. Minimally and micro-invasive cardiac surgery represent a paradigm shift toward patient-centered care, offering reduced physiological burden, improved functional recovery, and long-term outcomes comparable to conventional techniques. As innovation continues, these approaches are poised to become integral to modern cardiac surgical practice. Full article

Timely tool wear detection has been an important target for the metal cutting industry for decades because of its significance for part quality and production cost control. With the shift toward intelligent and sustainable manufacturing, reliable tool-condition monitoring has become even more critical. One of the main challenges in sound-based tool wear monitoring is the presence of noise interference, instability and the highly volatile nature of machining acoustics, which complicates the extraction of meaningful features. In this study, a Convolutional Neural Network (CNN) model is proposed to classify tool wear conditions in milling operations using acoustic signals. Sound recordings were collected from tools at different wear stages under two cutting speeds, and Mel-Frequency Cepstral Coefficients (MFCCs) were extracted to obtain a compact representation of the short-term power spectrum. These MFCC matrices enabled the CNN to learn discriminative spectral patterns associated with wear. To evaluate model stability and reduce the effects of algorithmic randomness, training was repeated three times for each cutting speed. For the 520 rpm dataset, the model achieved an average validation accuracy of 96.85 ± 2.07%, while for the 635 rpm dataset it achieved 93.69 ± 2.07%. The results demonstrate the feasibility of using acoustic signals, despite inherent noise challenges, as a complementary approach for identifying suitable tool replacement intervals in milling. Full article