Search Results (1,152)

Small-scale wind turbines (SWTs) represent a promising solution for the energy transition and the decentralization of electricity generation in non-interconnected areas. Conventional strategies to improve SWT performance often rely on active pitch control, which, while effective at rated conditions, is too costly and complex for small systems. An alternative is passive pitch control through bend–twist coupling in the blade structure, which enables self-regulation and improved power generation. This work proposes a novel blade design methodology for a 5 kW SWT that integrates passive bend–twist coupling with conventional pitch adjustment, thereby creating a hybrid passive–active control strategy. The methodology encompasses the definition of aerodynamic blade geometry, laminate optimization via genetic algorithms combined with finite element analysis, and experimental characterization of composite materials. Aerodynamic–structural interactions are studied using one-way fluid–structure simulations, with responses analyzed through the blade element momentum method to assess turbine performance. The results indicate that the proposed design enhances power generation by about 4%. The study’s originality lies in integrating optimization, structural tailoring, and material testing, offering one of the first demonstrations of combined passive–active pitch control in SWTs, and providing a cost-effective route to improve efficiency and reliability in decentralized renewable energy systems. Full article

Small-scale thermal treatment systems can provide environmentally improved sewage sludge treatment due to processing sludge locally, which lowers transport costs and emissions. However, the combined effect of confined volume and complex sludge properties makes achieving strict regulations on flue gas emissions and end-ash composition challenging. System development thus requires the use of advanced, 3D CFD simulation supported studies. An important step forward regarding these is the application of combustion models which introduce tailored surrogate fuels and apply detailed chemical kinetics to achieve a high-fidelity combustion description in confined volumes. In relation to this, the paper presents an innovative computationally efficient sewage sludge surrogate-based combustion model capable of defining surrogates, tailored to sewage sludge, and capable of providing detailed insight into reaction zone evolution in small-scale sludge incineration systems. The validity of the proposed model and surrogates is confirmed via simulated temperatures differing from measurements in the small-scale system for less than 30 K. The validated model of a small-scale system is used in the parametric analysis of variable air–fuel ratios, higher fuel moisture presence, varying bed temperature, and varying thermal power to enable unprecedentedly accurate and efficient definition of design features of small-scale systems and to provide key guidelines for operation optimization. Full article

Electricity price spikes are the most important characteristic of the electricity price time series. Operationally, they result from various stresses in the power system or the strategic bidding behavior of market participants. These high prices are important as they represent economic opportunities in the form of profits and savings. Theoretically, price spikes are defined as prices that exceed a threshold over a typically short duration. This definition serves as the basis for several established modeling approaches in the literature. In general, the threshold component determines the design of a price spike model, often overlooking the duration aspect. Therefore, this paper presents a simple yet informative model to quantify the duration of electricity price spikes using historical price data from different market jurisdictions. We approach the problem through the lens of survival analysis, a widely used technique for evaluating time-to-event data. Specifically, we use the Kaplan–Meier (KM) estimator, which enables a nonparametric evaluation of the survival (duration) of price spikes over time. We refer to this as the price spike duration model. Full article

Mineral prospecting from vast geological text corpora is impeded by challenges in domain-specific semantic interpretation and knowledge synthesis. General-purpose Large Language Models (LLMs) struggle to parse the complex lexicon and relational semantics of geological texts, limiting their utility for constructing precise knowledge graphs (KGs). Our novel framework addresses this gap by integrating a domain-specific LLM, GeoGPT, with a lightweight retrieval-augmented generation architecture, LightRAG. Within this framework, GeoGPT automates the construction of a high-quality mineral-prospecting KG by performing ontology definition, entity recognition, and relation extraction. The LightRAG component then leverages this KG to power a specialized geological question-answering (Q&A) system featuring a dual-layer retrieval mechanism for enhanced precision and an incremental update capability for dynamic knowledge incorporation. The results indicate that the proposed method achieves a mean F1-score of 0.835 for entity extraction, representing a 17% to 25% performance improvement over general-purpose large models using generic prompts. Furthermore, the geological Q&A model, built upon the LightRAG framework with GeoGPT as its core, demonstrates a superior win rate against the DeepSeek-V3 and Qwen2.5-72B general-purpose large models by 8–29% in the geochemistry domain and 53–78% in the remote sensing geology domain. This study establishes an effective and scalable methodology for intelligent geological text analysis, enabling lightweight, high-performance Q&A systems that accelerate knowledge discovery in mineral exploration. Full article

Ozone (O₃) has re-emerged in periodontology for its antimicrobial, oxygenating, and immunomodulatory actions, yet its role in regeneration remains contentious. This narrative review synthesizes current evidence on adjunctive ozone use in periodontal therapy, delineates cellular constraints—especially in periodontal ligament fibroblasts (PDLFs)—and explores mitigation strategies using bioactive compounds and advanced delivery platforms. Two recent meta-analyses indicate that adjunctive ozone with scaling and root planing yields statistically significant reductions in probing depth and gingival inflammation, with no significant effects on bleeding on probing, plaque control, or clinical attachment level; interpretation is limited by heterogeneity of formulations, concentrations, and delivery methods. Mechanistically, ozone imposes a dose-dependent oxidative burden that depletes glutathione and inhibits glutathione peroxidase and superoxide dismutase, precipitating lipid peroxidation, mitochondrial dysfunction, ATP depletion, and PDLF apoptosis. Concurrent activation of NF-κB and upregulation of IL-6/TNF-α, together with matrix metalloproteinase-mediated extracellular matrix degradation and tissue dehydration (notably with gaseous applications), further impairs fibroblast migration, adhesion, and ECM remodeling, constraining regenerative potential. Emerging countermeasures include co-administration of polyphenols (epigallocatechin-3-gallate, resveratrol, curcumin, quercetin), coenzyme Q10, vitamin C, and hyaluronic acid to restore redox balance, stabilize mitochondria, down-modulate inflammatory cascades, and preserve ECM integrity. Nanocarrier-based platforms (nanoemulsions, polymeric nanoparticles, liposomes, hydrogels, bioadhesive films) offer controlled ozone release and co-delivery of protectants, potentially widening the therapeutic window while minimizing cytotoxicity. Overall, current evidence supports ozone as an experimental adjunct rather than a routine regenerative modality. Priority research needs include protocol standardization, dose–response definition, long-term safety, and rigorously powered randomized trials evaluating bioactive-ozone combinations and nanocarrier systems in clinically relevant periodontal endpoints. Full article

At present, artificial intelligence (AI) has shown significant potential in digestive endoscopy image analysis, serving as a powerful auxiliary tool for the accurate diagnosis and treatment of gastrointestinal diseases. However, mainstream models represented by deep learning are often characterized as complex “black boxes,” with decision-making processes that are difficult for humans to interpret. The lack of interpretability undermines physicians’ trust in model results and hinders the broader use of models in clinical practice. To address this core challenge, Explainable AI (XAI) has emerged to enhance the transparency of decision-making, thereby establishing a foundation of trust for human–machine collaboration. The review systematically reviews 34 articles (7 articles in esophagogastroduodenoscopy, 13 articles in colonoscopy, 9 articles in endoscopic ultrasonography, and 5 articles in wireless capsule endoscopy), focusing on the research progress and applications of XAI in the field of digestive endoscopic image analysis, with particular emphasis on the visual explanation-based methods. We first clarify the definition and mainstream classification of XAI, then introduce the principles and characteristics of key XAI methods based on visual explanation. Subsequently, we review the applications of these methods in digestive endoscopy image analysis. Lastly, we explore the obstacles presently faced in this domain and the future directions. This study provides a theoretical basis for constructing a trustworthy and transparent AI-assisted digestive endoscopy diagnosis and treatment system and promotes the implementation and application of XAI in clinical practice. Full article

In this paper, a full-domain stochastic response analysis is performed based on the meshless method to reveal the time–frequency dynamic characteristics, including the power spectral density (PSD) responses in the frequency domain and the evolving PSD distribution in the time domain for a sandwich trapezoidal plate–shell coupled system. The general governing equations are derived based on the first-order shear deformation theory (FSDT), linear piston theory and Hamilton’s principle, and the stochastic excitation is integrated into the meshless framework based on the pseudo-excitation method (PEM). By constructing the meshless shape function covering the entire structural domain from Chebyshev polynomials and discretizing the continuous domain into a series of nodes within a square definition domain, the points are assembled according to the sequence number and the equilibrium relationship on the coupling edge to obtain the overall vibration equations. The validity is demonstrated by matching the mode shapes, PSD responses, time history displacement and critical flutter boundaries with FEM simulation and reported data. Finally, the time–frequency characteristics of each substructure under global and single stochastic excitation, and the effect of aerodynamic pressure on full-domain stochastic vibration, are revealed. Full article

Background: Esophageal strictures lacking typical malignant endoscopic features present a significant diagnostic challenge, often mimicking malignancy on imaging while concealing their true nature under regular white-light endoscopy. This study evaluated the utility of EUS-FNA in the differential diagnosis of such indeterminate strictures. Methods: We retrospectively analyzed 38 patients with suspicious malignant esophageal strictures indicated by CT but lacking definite malignant features on initial white-light gastroscopy. All patients underwent EUS-FNA for definitive pathological diagnosis. Clinicopathological data, imaging reports, endoscopic mucosal features, and procedural outcomes were assessed. Results: Among all 38 patients suspected of esophageal cancer by CT scan, 30 of them had malignant cytology results, including ESCC, EAC, metastatic cancer, and esophageal lymphoma. A total of 8 patients had benign findings, including esophageal tuberculosis, fungal esophagitis, eosinophilic esophagitis, and esophageal varices. Critically, EUS-FNA identified benign entities, such as eosinophilic esophagitis and esophageal tuberculosis masquerading as malignancy. CT features and mucosal features are also summarized and analyzed. Conclusions: EUS-FNA is a powerful tool for diagnosing esophageal strictures lacking typical malignant features. It reliably differentiates malignancy from challenging benign mimics, preventing misdiagnosis and guiding appropriate therapy. Clinicians should maintain a high suspicion for both occult malignancy and rare benign conditions in such stenotic lesions. Full article

Emotion recognition from electroencephalography (EEG) often relies on Euclidean features that ignore the curved geometry of covariance matrices. We introduce a Riemannian-manifold pipeline which, combined with the Fisher Geodesic Minimum Distance to Mean (FgMDM) classifier, leverages the full geometry of symmetric positive-definite (SPD) EEG covariances. The approach applies an additional geodesic-mean contraction that stabilizes trial covariances before tangent space projection. Experiments on the five-class SEED-V dataset show high accuracy, robustness to session-to-session variability and improved interpretability relative to baselines. These results highlight Riemannian geometry as a powerful framework for emotion recognition with high-dimensional, non-stationary EEG. Full article

It is well known that power converters have the highest failure rate in the energy conversion chain in different industrial applications. This could definitely affect the reliability of the system. The reliability of converters in power conversion systems is crucial, as failures can lead to critical consequences and damage other system components. Therefore, it is important to predict and detect failures and take corrective actions to prevent them. One of the most common types of failure in power converters is semiconductor failure, which can manifest as an open circuit or a short circuit. This paper focuses on single and double open-circuit switch failures in a 5-level cascaded H-bridge inverter, for which a fast, precise method is required. A data-driven approach is employed here, using the output voltage and voltages across each H-bridge as diagnostic signals. A 1D-CNN LSTM neural network is trained to accurately detect and localize open-circuit faults, providing a reliable, practical solution. Full article

The concept of mechanical power (MP) has emerged as a comprehensive indicator of ventilator-induced lung injury (VILI). It integrates the effects of tidal volume, airway pressures, respiratory rate, and flow. However, applying MP as a universal threshold (e.g., 12 J/min) across heterogeneous patients with acute respiratory distress syndrome (ARDS) may be inadequate. This review introduces the rheological model, which conceptualizes the lung as a viscoelastic body (i.e., one that exhibits both elastic and viscous properties), and applies it to ARDS ventilation. The rheological model may offer individualized MP thresholds. The potential benefits of adjusting MP based on ideal body weight (J/min/kg) are discussed and, more accurately, on static compliance (J/min/L). Static compliance could better reflect functional lung size, though clinical validation remains needed. Preliminary clinical and modeling evidence suggests that normalized MP correlates more closely with mortality than absolute MP and aligns with pulmonary stress–strain behavior. This normalization provides a more precise risk stratification and facilitates the easier setting of ventilation targets, particularly in patients with low compliance or abnormal body composition. This review clarifies definitions and consolidates evidence, highlights the clinical implications of rheology for lung-protective strategies. MP normalization within a lung-protective strategy could enhance the safety and efficacy of mechanical ventilation; however, clinical validation is still required. This review summarizes the theoretical foundations, supporting evidence, and clinical implications of this approach within the broader context of rheological modeling in ARDS. Full article

Osteoarthritis (OA) is an inflammatory disorder characterized by metabolic changes in the bone tissue, including the degeneration of hyaline cartilage (articular cartilage) and fibrocartilage (including the meniscus and labrum), sclerosis of the subchondral bone, and osteophyte formation. OA poses a major challenge for adults of all ages, leading to increased morbidity and decreased quality of life. The current conventional therapies mainly focus on pain control, with no definitive or regenerative therapies to reverse OA progression available. Lasers consist of electromagnetic waves generated by radiation emitted by an excited material. In medicine and dentistry, photobiomodulation by low-power laser therapy (photobiomodulation therapy [PBMT]) has been widely applied clinically to promote healing, regenerate tissue, modulate inflammation, and relieve pain. Basic studies have explored the regulation of OA manifestations and joint inflammation using PBMT, as well as the mechanisms of action involved, and clinical research has validated the beneficial effects of PBMT for patients with OA. However, the effects of PBM on OA and its mechanisms of action remain unknown. Herein, we review basic research that has examined the effects of PBMT on OA using in vitro and in vivo testing and discuss future challenges and prospects. Full article

Exergy efficiency is viewed as the degree of approaching reversible operation, with a value of 100 percent for a reversible process characterized by zero entropy generation or equivalently zero exergy destruction since X_destroyed = T₀S_gen. As such, exergy efficiency becomes a measure of thermodynamic perfection. There are different conceptual definitions of exergy efficiency, the most common ones being (1) the ratio of exergy output to exergy input η_ex = X_output/X_input = 1 − (X_destroyed + X_loss)/X_input, (2) the ratio of the product exergy to fuel exergy η_ex = X_product/X_fuel = 1 − (X_destroyed + X_loss)/X_fuel, and (3) the ratio of exergy recovered to exergy expended η_ex = X_recovered/X_expended = 1 − X_destroyed/X_expended. Most exergy efficiency definitions are formulated with steady-flow systems in mind, and they are generally applied to systems in steady operation such as power plants and refrigeration systems whose exergy content remains constant. If these definitions are to be used for closed and unsteady-flow systems, the terms need to be interpreted broadly to account for the exergy change of the systems as exergy input or output, as appropriate. In this paper, general exergy efficiency relations are developed for closed and unsteady-flow systems and their use is demonstrated with applications. Also, the practicality of the use of the term exergy loss X_loss is questioned, and limitations on the definition η_ex = W_act,out/W_rev,out are discussed. Full article

Background. Oncological diseases are among the leading causes of death in people living with HIV (PLWH). With the introduction of antiretroviral therapy and the consequent reduction in AIDS-defining cancers (ADC), there has been a growing incidence of non-AIDS-defining cancers (NADC). Methods. A retrospective observational study (cross sectional prevalence analysis) was conducted to investigate the prevalence and spectrum of oncological diseases in patients attending the HIV/AIDS Division at the Ferrara Hospital. The sample included 534 patients evaluated between January 2023 and November 2024 (534/682 met eligibility). Demographic, clinical, and serological data were extracted from medical records. The CDC’s 2014 definition has been adopted for the ADC/NADC classification. Statistical analysis was performed using SPSS version 29 and G*Power 3.1 software. Results. The data analysis revealed 62.8% NADC vs. 37.2% ADC (44 NADCs vs. 26 ADCs). Male individuals and those aged 50 and older were more represented. Patients with ADC more often fell into C2–C3 groups, indicative of severe immunodeficiency, while NADCs were more prevalent in clinical groups A1–B3. Statistical analysis showed that viral load was more frequently under 50 copies/mL in the NADC group, while it tended to be higher in the ADC group. Conclusions. These results align with current scientific evidence regarding the global prevalence of ADCs and NADCs. The findings highlight the need to implement targeted oncological screening strategies for HIV-positive patients to promote early diagnosis and improve prognosis. Full article

Patients with bowel diseases (inflammatory bowel disease (IBD) in general) tend to seek medical, nursing, and/or physiotherapeutic consultations. Physiotherapists specialized in gastrointestinal (visceral) therapy can help reduce inflammation in patients with ulcerative colitis (UC). In this study, we divided UC patients into three groups according to their age: the youngest (18–35 years old), middle-aged (36–49 years old), and oldest (50–70 years old). Our hypothesis was that gut inflammatory markers (zonulin and fecal calprotectin levels) and microbiota strains would exhibit age-dependent variations in UC patients. We compared differences in zonulin, calprotectin, and vitamin D levels, together with a plethora of microbiota strains, based on age. Calprotectin is a marker of intestinal inflammation and zonulin identifies gut permeability; as IBD is characterized by gastrointestinal inflammation, these are useful markers for diagnosing and monitoring treatment/s in IBD patients, including ulcerative colitis (UC). Dysbiosis can alter the normal balance of intestinal function, and thus, several microbiota strains were compared between different age ranges in UC patients. The results indicated that the middle-aged UC (36–49) patients had the highest endogenous vitamin D levels, as well as lower zonulin and calprotectin levels than the youngest (18–35) and oldest (50–70) UC participants, respectively. The middle-aged group also had lower Enterococcus, E. Coli biovare, and Pseudomonas spp. levels than the youngest UC participants. Meanwhile, the most LPS microbiota producers were found in middle-aged patients. Finally, a higher number of Candida albicans and elevated LPS were found in the oldest UC participants than in the middle-aged (36–49) group. This study was, however, limited by uneven age-group sizes, which may have may limited the power in the youngest cohort. Although altered gut microbiota levels can increase gut inflammation in rodent models of UC, a definitive cause–effect relationship between UC and intestinal microbiota alteration is difficult to demonstrate in humans. Full article