Search Results (10,066)

Background/Objectives: Oral focal mucinosis (OFM) and solitary cutaneous focal mucinosis (SCFM) are rare, benign lesions characterized by localized mucin deposition in the stromal connective tissue. While both share similar histological features, they occur in distinct anatomical sites and clinical contexts and have not been directly compared in the literature. Method: This study presents a case series of 39 OFM cases diagnosed over 25 years, supplemented by a literature review of previously reported OFM cases, and compares the combined data with published cases of SCFM. The literature-based analysis included 116 OFM cases published in four articles and 138 cases of SCFM published in five articles. Demographic and clinical data were extracted and analyzed, including age, sex, lesion location, size, duration, symptoms, clinical impression, treatment, and recurrence. Results: The mean age of OFM patients was 41 years, with a slight female predominance, most commonly affecting the gingiva. SCFM cases were more common in males, with a higher mean age of 52 years and frequent occurrence on the extremities and trunk. Both lesions were predominantly asymptomatic and managed by conservative excision. Due to its rare occurrence and nonspecific clinical presentation, both entities were frequently clinically misdiagnosed. Conclusions: In conclusion, this is the first study to directly compare OFM with SCFM and represents the largest series of OFM reported to date. The study provides new comparative insights into SCFM and OFM, highlighting differences in age, gender, lesion site, size, and symptomatology. SCFM predominantly affects older males on the extremities, whereas OFM occurs in younger females, mainly in the gingiva, with larger, sometimes symptomatic lesions, and with a very low recurrence rate. Full article

In the current scenario of universal accessibility of data, organisations face highly complex challenges related to integrating and processing diverse sets of data in order to meet their analytical needs. This review paper analyses traditional and innovative methods used for data storage and integration, with particular focus on their implications for scalability, consistency, and interoperability within an analytical ecosystem. In particular, it contributes a cross-layer taxonomy linking integration mechanisms (schema matching, entity resolution, and semantic enrichment) to storage/query substrates (row/column stores, NoSQL, lakehouse, and federation), together with comparative tables and figures that synthesise trade-offs and performance/governance levers. Through schema mapping solutions addressing the challenges brought about by structural heterogeneity, storage architectures varying from traditional storage solutions all the way to cloud storage solutions, and ETL pipeline integration using federated query processors, the research provides specific attention for the application of metadata management, with a focus on semantic enrichment using ontologies and lineage management to enable end-to-end traceability and governance. It also covers performance hotspots and caching techniques, along with consistency trade-offs arising out of distributed systems. Empirical case studies from real applications in enterprise lakehouses, scientific exploration activities, and public governance applications serve to invoke this review. Following this work is the possibility of future directions in convergent analytical platforms with support for multiple workloads, along with metadata-centric orchestration with provisions for AI-based integration. Combining technological advancement with practical considerations results in an enabling resource for researchers and practitioners seeking the creation of fault-tolerant, reliable, and future-ready data infrastructure. This review is primarily aimed at researchers, system architects, and advanced practitioners who design and evaluate heterogeneous analytical platforms. It also offers value to graduate students by serving as a structured overview of contemporary methods, thereby bridging academic knowledge with industrial practice. Full article

As artificial intelligence continues to evolve, reinforcement learning (RL) has shown remarkable potential for solving complex sequential decision problems and is now applied in diverse areas, including robotics, autonomous vehicles, and financial analytics. Among the various RL paradigms, multi-agent reinforcement learning (MARL) stands out for its ability to manage cooperative and competitive interactions within multi-entity systems. However, mainstream MARL algorithms still face critical challenges in training stability and policy generalization due to factors such as environmental non-stationarity, policy coupling, and inefficient sample utilization. To mitigate these limitations, this study introduces an enhanced algorithm named MATD3_AHD, developed by extending the MATD3 framework, which integrates TD3 and MADDPG principles. The goal is to improve the learning efficiency and overall policy effectiveness of agents operating in complex environments. The proposed method incorporates three key mechanisms: (1) an Adaptive Exploration Policy (AEP), which dynamically adjusts the perturbation magnitude based on TD error to improve both exploration capability and training stability; (2) a Hierarchical Sampling Policy (HSP), which enhances experience utilization through sample clustering and prioritized replay; and (3) a Dynamic Delayed Update (DDU), which adaptively modulates the actor update frequency based on critic network errors, thereby accelerating convergence and improving policy stability. Experiments conducted on multiple benchmark tasks within the Multi-Agent Particle Environment (MPE) demonstrate the superior performance of MATD3_AHD compared to baseline methods such as MADDPG and MATD3. The proposed MATD3_AHD algorithm outperforms baseline methods—by an average of 5% over MATD3 and 20% over MADDPG—achieving faster convergence, higher rewards, and more stable policy learning, thereby confirming its robustness and generalization capability. Full article

Metastatic breast carcinoma (BC) cells are prone to spreading in the bone microenvironment, leading to a vicious cycle between local osteoclast-mediated osteolysis and tumor progression. Therefore, the targeted pharmacological down-modulation of BC cell proliferation as well as osteoclast differentiation and hyperactivity might represent a promising treatment option. We developed a multifunctional peptide nanocarrier combining bioactive EPI-X4 peptides and the Rho-inhibiting C3bot enzyme from Clostridium botulinum. C3bot is preferentially internalized into the cytosol of monocytic cells, including osteoclasts, where it inhibits Rho-mediated signal transduction. However, Rho-mediated cellular processes like migration and cell division can also be inhibited in non-monocytic cells if C3bot is delivered into their cytosol by a nanocarrier. To accomplish this, we designed a supramolecular transporter where one molecule of biotinylated C3bot and three biotinylated entities of the human EPI-X4 peptide-derived CXCR4 antagonist JM173 are assembled on avidin as a central platform. This modular transport system (JM173)₃-Avi-C3 down-modulated osteoclast formation and hyperactivity and delivered the therapeutic cargo C3bot successfully into the cytosol of breast cancer cells, where it inhibited Rho. Full article

Public service media (PSM) are undergoing essential digital transformations to compete in an audiovisual ecosystem dominated by new technological players that have reshaped traditional media consumption habits. This article examines how the digital developments of Spanish public media, within platformization processes, particularly in the field of digital audio and podcasts, integrate public service values based on a framework which identifies twelve key dimensions: universality, quality, independence, diversity, responsibility, innovation, social commitment, civic participation, media literacy, territorial cohesion, social justice, and cooperation. Using a qualitative multiple-case study methodology, these values are compared with the strategies of Radiotelevisión Española (RTVE), the media organizations grouped in the Federation of Regional Radio and Television Entities (FORTA), and Canal Extremadura. The results indicate that PSM, to varying degrees, incorporate public service values in their platformization processes. However, the findings also reveal significant challenges that, if addressed, could maximize the impact of their digital strategies. Full article

This paper explores the intersection of H.P. Lovecraft’s cosmicism and contemporary artificial intelligence (AI), proposing a philosophical shift from anthropocentric AI development to a “cosmicist” approach. Cosmicism, with its emphasis on humanity’s insignificance in a vast, indifferent universe, offers a provocative lens through which to reassess AI’s purpose, trajectory, and ethical grounding. As AI systems grow in complexity and autonomy, current human-centered frameworks, rooted in utility, alignment, and value-conformity, may prove inadequate for grappling with the emergence of intelligence that is non-human in origin and indifferent in operation. Drawing on Lovecraftian themes of fear, the unknown, and cognitive dissonance in the face of incomprehensible entities, this paper parallels AI with the “Great Old Ones”: systems so alien in logic and scale that they challenge the coherence of human-centric epistemology. We argue that a cosmicist perspective does not dismiss the real risks of AI (environmental, existential, or systemic), but reframes them within a broader ontology, one that accepts our limited place in a vast techno-cosmic continuum. By embracing cosmic humility, we propose an expanded AI ethics: one that centers not on domination or full control, but on coexistence, containment, and stewardship. This cosmicist reframing invites a deeper rethinking of intelligence, ethics, and the future: not just of humanity, but of all possible minds. Full article

Objectives: To evaluate the loss of quality of life (QoL) in patients with chronic obstructive sialadenitis (COS) using the Chronic Obstructive Sialadenitis Questionnaire (COSQ). Methods: The COSQ was administered to patients diagnosed with COS, with the diagnosis confirmed by sialendoscopy. Epidemiological data, obstructive causes and potentially obstructive entities were collected. QoL was assessed using the COSQ. Results: A total of 344 glands in 278 patients with COS were analyzed. Most patients were women (71.94%), and the main obstructive cause was stenosis (47.96%), followed by lithiasis, lack of papilla distensibility (LPD), and mucus plug. Stenosis was significantly more frequent in the parotid gland and in women, whereas lithiasis predominated in the submandibular gland and in men. The mean COSQ score was 30.55 and it was significantly higher in women (p < 0.005), parotid gland (p < 0.005), and in long-standing cases (p < 0.05). Stenosis and LPD were the obstructive causes with the greatest impact on QoL (p < 0.005), while lithiasis had the least impact. Potentially Obstructive Entities (POEs), such as eosinophilic sialodochitis, Sjögren’s syndrome, or radioiodine-induced sialadenitis, were associated with a notable loss of QoL. Likewise, patients without associated POEs presented significantly lower COSQ values (p < 0.05). Conclusions: COS significantly affects QoL, particularly in women and in cases of parotid gland, stenosis, and LPD. Lithiasis has the least impact on QoL. It is important to standardize a thorough evaluation of COS using validated tools such as the COSQ, which are fundamental for understanding the disease and predicting the outcomes of therapeutic interventions. Full article

Prefabricated stations utilizing digital modeling techniques demonstrate significant advantages over traditional cast-in-place methods, including improved dimensional accuracy, reduced environmental impact, and minimized material waste. To maximize these benefits, this study develops a digital twin platform for prefabricated assembled superimposed stations through the integration of Digital Twin Scene–Entity–Relationship–Incident–Control (SERIC) modeling with IoT technology. The platform adopts a “1+5+N” architecture that implements model-data separation, lightweight processing, and model-data association for SERIC model management, while IoT-enabled data acquisition facilitates lifecycle data sharing. By integrating BIM models, engineering data, and IoT sensor inputs, the platform employs multi-source analytics to monitor construction progress, enhance safety surveillance, ensure quality control, and optimize designs. Implementation at Jinan Metro Line 8’s prefabricated underground station confirms the SERIC-IoT digital twin’s efficacy in advancing sustainable, high-quality rail transit development. Results demonstrate the platform’s capacity to improve construction efficiency and operational management, aligning with urban rail objectives prioritizing sustainability and technological innovation. This study establishes that integrating SERIC modeling with IoT in digital twin frameworks offers a robust approach to modernizing prefabricated station construction, with scalable applications for future smart transit infrastructure. Full article

Smart manufacturing demands adaptive, scalable, and human-centric solutions for predictive maintenance. This paper introduces the concept of Agentic AI, a paradigm that extends beyond traditional multi-agent systems and collaborative AI by emphasizing agency: the ability of AI entities to act autonomously, coordinate proactively, and remain accountable under human oversight. Through federated learning, edge computing, and distributed intelligence, the proposed framework enables intentional, goal-oriented monitoring agents to form self-organizing predictive maintenance ecosystems. Validated in a ceramic manufacturing facility, the system achieved 94% predictive accuracy, a 67% reduction in false positives, and a 43% decrease in unplanned downtime. Economic analysis confirmed financial viability with a 1.6-year payback period and a €447,300 NPV over five years. The framework also embeds explainable AI and trust calibration mechanisms, ensuring transparency and safe human–machine collaboration. These results demonstrate that Agentic AI provides both conceptual and practical pathways for transitioning from reactive monitoring to resilient, autonomous, and human-centered industrial intelligence. Full article

This paper represents initial research with the aim to establishes a baseline for subsequent research into AI-based construction monitoring, building upon the NORMENG project in Croatia, which previously integrated photogrammetry, laser scanning, and BIM-based methods. The study tests general purpose AI’s ability to detect materials and estimate quantities, aiming to assess whether a broad, context-aware AI system can match the precision of specialized, domain-specific tools or even human work needed for productivity estimations. While the AI demonstrated potential for basic entity detection and preliminary quantity estimations, it showed significant limitations in delivering fine-grained, temporally accurate breakdowns without targeted adaptation. The findings underscore the need for domain-specific fine-tuning and human-in-the-loop validation to transform AI into a reliable tool for construction management. This initial contribution provides empirical insights and actionable recommendations for advancing automated progress monitoring in the construction sector. Full article

Network traffic analysis is crucial for understanding network behavior and identifying underlying applications, protocols, and service groups. The increasing complexity of network environments, driven by the evolution of the Internet, poses significant challenges to traditional analytical approaches. Graph Neural Networks (GNNs) have recently garnered considerable attention in network traffic analysis due to their ability to model complex relationships within network flows and between communicating entities. This scoping review systematically surveys major academic databases, employing predefined eligibility criteria to identify and synthesize key research in the field, following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) methodology. We present a comprehensive overview of a generalized architecture for GNN-based traffic analysis and categorize recent methods into three primary types: node prediction, edge prediction, and graph prediction. We discuss challenges in network traffic analysis, summarize solutions from various methods, and provide practical recommendations for model selection. This review also compiles publicly available datasets and open-source code, serving as valuable resources for further research. Finally, we outline future research directions to advance this field. This work offers an updated understanding of GNN applications in network traffic analysis and provides practical guidance for researchers and practitioners. Full article

Pavement condition assessment is a fundamental aspect of airport pavement management systems (APMS) for ensuring safe and efficient airport operations. However, conventional methods, which rely on extensive on-site inspections and complex calculations, are often time-consuming and resource-intensive. In response, Industry 4.0 has introduced machine learning (ML) as a powerful tool to streamline these processes. This study explores five ML algorithms (Linear Regression (LR), Decision Tree (DT), Random Forest (RF), Artificial Neural Network (ANN), and Support Vector Machine (SVM)) for predicting the Pavement Condition Index (PCI). Using basic alphanumeric distress data from three international airports, this study predicts both numerical PCI values (on a 0–100 scale) and categorical PCI values (3 and 7 condition classes). To address data imbalance, random oversampling (SMOTE—Synthetic Minority Oversampling Technique) and undersampling (RUS) were used. This study fills a critical knowledge gap by identifying the most effective algorithms for both numerical and categorical PCI determination, with a particular focus on validating class-based predictions using relatively small data samples. The results demonstrate that ML algorithms, particularly Random Forest, are highly effective at predicting both the numerical and the three-class PCI for the original database. However, accurate prediction of the seven-class PCI required the application of oversampling techniques, indicating that a larger, more balanced database is necessary for this detailed classification. Using 10-fold cross-validation, the successful models achieved excellent performance, yielding Kappa statistics between 0.88 and 0.93, an error rate of less than 7.17%, and an area under the ROC curve greater than 0.93. The approach not only significantly reduces the complexity and time required for PCI calculation, but it also makes the technology accessible, enabling resource-limited airports and smaller management entities to adopt advanced pavement management practices. Full article

Background and Clinical Significance: Vesicovaginal leiomyomas are an exceedingly rare form of extrauterine fibroids. They represent less than 1% of all leiomyomas and have been reported in less than 300 cases worldwide since 1733. These benign smooth muscle tumors typically occur in perimenopausal women aged 35–50 years, presenting in young adults extraordinarily uncommonly. The rarity in younger patients creates significant diagnostic challenges, as clinical presentation often mimics malignant entities, particularly embryonal rhabdomyosarcoma. Case Presentation: This paper presents a 20-year-old nulliparous female who developed progressive dyspareunia and urinary dysfunction over 12 months due to a large vesicovaginal mass. Physical examination revealed a 6–7 cm smooth, firm mass obstructing the vaginal canal. Transvaginal ultrasound demonstrated a well-circumscribed, hypoechoic solid lesion measuring 6.9 cm in the vesicovaginal space. Magnetic resonance imaging showed a characteristic T2-hypointense signal with restricted diffusion consistent with leiomyoma, revealing an incidental septate uterus. Ultrasound-guided core needle biopsy confirmed benign leiomyoma with bland spindle cells, absent atypia, and minimal mitotic activity. The patient underwent successful transvaginal enucleation with complete symptom resolution. Conclusion: This case highlights diagnostic challenges posed by benign leiomyomas in young women presenting with solid pelvic masses. Systematic diagnostic approaches incorporating multimodal imaging and guided tissue sampling are essential to avoid misdiagnosis and unnecessary radical surgery. When malignancy is confidently excluded, management should prioritize fertility preservation in young patients. Full article

Cardiovascular (CV) disease, chronic kidney disease, obesity, and diabetes mellitus have reached epidemic proportions over the past few decades. Accumulating evidence highlights the strong interconnection between these conditions, leading to the definition of a broader disease entity known as cardio-renal-metabolic (CRM) syndrome. This newly recognized clinical entity presents important challenges in identifying the optimal treatment strategy within a holistic, patient-centered framework. In line with this, sodium glucose cotransporter 2 inhibitors (SGLT2is), owing to their multifaceted pharmacological effects, have been suggested as possible treatment options in the management of CRM. SGLT2is exert their antihyperglycemic effects by impeding the renal reabsorption of sodium and glucose, causing glycosuria and natriuresis. Research has confirmed that their unique beneficial effects extend beyond glycemic control, reducing CV death and hospitalizations in patients with heart failure, and the incidence of kidney failure in dedicated kidney outcome studies—regardless of diabetes status. Furthermore, these agents contribute to weight loss and blood pressure reduction. Their benefits appear to stem from a combination of factors, which include reduced oxidative stress, lower levels of inflammation, regulated neurohormonal activation, improved endothelial function, and enhanced metabolic efficiency. This review aims to provide a comprehensive analysis of the pathophysiological mechanisms underlying the effects of SGLT2is in CRM syndrome, synthesize evidence from landmark clinical trials, evaluate current experimental and diagnostic approaches, and provide the emerging role of SGLT2is in the treatment of this new clinical entity. Full article

The forecasting of Drug-Drug Interactions (DDIs) is essential in pharmacology and clinical practice to prevent adverse drug reactions. Existing approaches, often based on neural networks and knowledge graph embedding, face limitations in modeling correlations among drug features and in handling complex BioKG relations, such as one-to-many, hierarchical, and composite interactions. To address these issues, we propose Rot4Cap, a novel framework that embeds drug entity pairs and BioKG relationships into a four-dimensional vector space, enabling effective modeling of diverse mapping properties and hierarchical structures. In addition, our method integrates molecular structures and drug descriptions with BioKG entities, and it employs capsule network–based attention routing to capture feature correlations. Experiments on three benchmark BioKG datasets demonstrate that Rot4Cap outperforms state-of-the-art baselines, highlighting its effectiveness and robustness. Full article