Search Results (226)

Hypergolic propellants have long been central to spacecraft propulsion because of their storability, reliability and rapid ignition. Conventional systems such as hydrazine derivatives paired with oxidisers like nitrogen tetroxide deliver ignition delays in the order of a few milliseconds but pose serious risks due to extreme toxicity and handling hazards. The search for safer and environmentally friendlier alternatives has therefore become a priority in recent years. This review examines ignition delay times reported in the literature for both conventional and green propellants under ambient experimental conditions. Data were collected from published studies between 2000 and 2025 using major scientific databases, including Scopus, Web of Science, and Google Scholar, and are compared across three categories of propellants: traditional hydrazine-based systems, self-igniting ionic liquids and amines, and systems enhanced with catalytic or reactive promoters. The analysis shows that while conventional propellants remain benchmarks with ignition delays typically between 1 and 5 ms, some new formulations, particularly those containing reactive additives such as borohydrides or iodide salts, are achieving similar or improved performance in laboratory tests. The review also highlights that variability in reported ignition delays often stems from differences in test methods, droplet size, oxidiser concentration, and diagnostic approaches. Beyond performance considerations, attention is given to safety and environmental aspects since several green candidates reduce acute toxicity but introduce other challenges, such as instability or corrosive byproducts. By bringing together data in a comparative format and emphasising methodological limitations, this review aims to support the future design and evaluation of practical green hypergolic propellants. Full article

To address persistent challenges in tennis—such as inefficient ball retrieval, the high cost of serving equipment, and difficulties in scheduling matches—this study proposes the design of a shared tennis service robot aimed at improving user experience and validating design feasibility. Grounded in user experience theory, user requirements were collected through questionnaires and structured interviews. The Analytic Hierarchy Process (AHP) was adopted to construct a hierarchical model of requirements. Weighted calculations were then applied to quantify and rank user needs. Design solutions were then derived based on these rankings. To evaluate the solutions, the Fuzzy Comprehensive Evaluation (FCE) method was utilized for multidimensional assessment. The results show that AHP identified three core requirements: intelligent ball retrieval, intelligent serving, and personalized serving parameter customization. Guided by these priorities, the proposed design integrates a shared rental model with multisensory interactive feedback. The final evaluation yielded an FCE score of 87.83, confirming the effectiveness of the solution. The combined AHP-FCE method provides a systematic framework for quantifying user needs and objectively evaluating design alternatives. It also offers a methodological foundation for the development of sports service robots. The shared tennis robot effectively reduces labor and operational costs while enhancing the overall user experience. Full article

Background and Objectives: Periodontitis is a common chronic inflammatory disease and a leading cause of tooth loss worldwide. Traditional diagnostic methods, such as probing and radiographic assessment, are retrospective and fail to detect ongoing disease activity. In recent years, salivary biomarkers and oral microbiome profiling have emerged as promising tools for earlier detection and precision-based management. The aim of this review is to synthesize current evidence on salivary and microbiome-derived biomarkers in periodontitis and to evaluate their translational potential in diagnostics and therapy. Materials and Methods: A narrative review was performed using PubMed, Scopus, and Web of Science to identify studies published between 2020 and 2025. Search terms included periodontitis, salivary biomarkers, oral microbiome, dysbiosis, and precision therapy. Priority was given to systematic reviews, meta-analyses, and translational studies that addressed diagnostic or therapeutic applications. Eligible publications included English-language original studies and reviews reporting on the diagnostic or therapeutic relevance of salivary or microbiome biomarkers in periodontitis. Results: Salivary biomarkers such as cytokines, matrix metalloproteinases (MMPs), oxidative stress markers, microRNAs, and extracellular vesicles (EVs) show consistent associations with disease activity and treatment outcomes. Oral microbiome studies reveal that both classical pathogens and community-level dysbiosis contribute to disease risk. Translational advances include chairside immunoassays, biosensors, lab-on-a-chip devices, and artificial intelligence (AI)-driven analyses. Biomarker-guided therapies—such as microbiome modulation, natural bioactive compounds, host-response modulation, and smart biomaterials—are being evaluated with increasing frequency in translational studies. Conclusions: By integrating salivary and microbiome biomarkers with novel diagnostic technologies and emerging therapies, this review complements existing systematic evidence and offers a translational roadmap toward precision periodontology. Full article

Background/Objectives: Natural products exhibit significant immunomodulatory potential but face severe efficacy loss in three-dimensional (3D) tumor models. This review comprehensively examines the penetration–activity trade-off and proposes integrated strategies for developing effective natural product-based cancer immunotherapies. Methods: We analyzed formulation strategies across three natural product categories (hydrophobic, macromolecular, stability-sensitive), evaluating penetration enhancement versus activity preservation in spheroids, organoids, and advanced 3D platforms. Results: Tumor spheroids present formidable barriers: dense extracellular matrix (33-fold increased fibronectin), pH gradients (7.4 → 6.5), and extreme cell density (6 × 10⁷ cells/cm³). While nanoparticles, liposomes, and cyclodextrins achieve 3–20-fold penetration improvements, biological activity frequently declines through conformational changes, incomplete release (10–75%), and surface modification interference. Critically, immune cells remain peripheral (30–50 μm), questioning deep penetration pursuit. Patient-derived organoids display 68% predictive accuracy, while emerging vascularized models unveil additional complexity. Food and Drug Administration (FDA) Modernization Act 2.0 enables regulatory acceptance of these advanced models. Conclusions: Effective therapeutic outcomes depend on maintaining immunomodulatory activity in peripherally-located immune cell populations rather than achieving maximum tissue penetration depth. Our five-stage evaluation framework and standardization protocols guide development. Future priorities include artificial intelligence-driven optimization, personalized formulation strategies, and integration of multi-organ platforms to bridge the critical gap between enhanced delivery and therapeutic efficacy. Full article

A major challenge for modern transit systems relying on traditional fixed-route designs is providing broad accessibility to users. Flex-route transit can enhance accessibility in low-density areas, since it combines the directness of fixed-route transit with the coverage of on-demand mobility. Although deviating for optional pickups can increase ridership and transit accessibility, it also deteriorates the service performance for fixed-route riders. To balance this inherent trade-off, this paper proposes a reinforcement learning approach for deviation decisions. The proposed model is used in a case study of a proposed flex-route service in the city of Boston. The performance on competing objectives is evaluated for reward configurations that adapt to peak and off-peak scenarios. The analysis shows a significant improvement of our method compared to a heuristic derived from industry practice as a baseline. To evaluate robustness, we assess performance across scenarios with varying demand compositions (fixed and requested riders). The results show that the method achieves greater improvements than the baseline in scenarios with increased request ridership, i.e., where decision-making is more complex. Our approach improves service performance under dynamic demand conditions and varying priorities, offering a valuable tool for smart cities to operate flex-route services. Full article

Background and objectives: Antibiotic resistance in Cutibacterium acnes is undermining topical macrolides and clindamycin, prompting renewed interest in zinc oxide nanoparticles (ZnO-NPs) as non-antibiotic alternatives. We aimed to (i) determine the antimicrobial and anti-inflammatory performance of topical ZnO-NP formulations across in vitro, animal and early human models; (ii) identify physicochemical parameters that modulate potency and tolerance; and (iii) delineate translational gaps and priority design elements for randomised trials. Methods: We systematically searched PubMed, Scopus and Web of Science until 1 June 2025 for in vitro, animal and human studies that evaluated ≤100 nm ZnO-NPs applied topically to C. acnes cultures, extracting data on bacterial load, lesion counts, biophysical skin parameters and acute toxicity. Eight eligible investigations (five in vitro, two animal, one exploratory human) analysed particles 20–50 nm in diameter carrying mildly anionic zeta potentials. Results: Hyaluronic acid-coated ZnO-NPs achieved a sixteen-fold higher selective kill ratio over Staphylococcus epidermidis at 32 µg mL¹, while centrifugally spun polyvinyl alcohol dressings reduced C. acnes burden by 3.1 log₁₀ on porcine skin within 24 h, and plant-derived nanogels generated inhibition zones that were 11% wider than benzoyl-peroxide’s 5%. In human subjects, twice-daily 0.5% hyaluronic–ZnO nanogel cut inflammatory-lesion counts by 58% at week four and lowered transepidermal water loss without erythema. Preclinical safety was reassuring, zero mortality among animals at 100 µg mL¹ and no irritation among patients, although high-dose sunscreen-grade ZnO (20 nm) delayed rat wound closure by 38%, highlighting dose-dependent differences. Conclusions: Collectively, the evidence indicates that nanoscale reformulation markedly augments zinc’s antibacterial and anti-inflammatory performance while maintaining favourable acute tolerance, supporting progression to rigorously designed, adequately powered randomised trials that will benchmark ZnO-NPs against benzoyl peroxide and retinoids, optimise dosing for efficacy versus phototoxicity, and establish long-term dermatological safety. Full article

This study introduces a novel approach to correlation-based feature selection and dimensionality reduction in high-dimensional data structures. To this end, a customized scoring function is proposed, designed as a dual-objective structure that simultaneously maximizes the correlation with the target variable while penalizing redundant information among features. The method is built upon three main components: correlation-based preliminary assessment, feature selection via the tailored scoring function, and integration of the selection results into a t-SNE visualization guided by Rel/Red ratios. Initially, features are ranked according to their Pearson correlation with the target, and then redundancy is assessed through pairwise correlations among features. A priority scheme is defined using a scoring function composed of relevance and redundancy components. To enhance the selection process, an optimization framework based on stochastic differential equations (SDEs) is introduced. Throughout this process, feature weights are updated using both gradient information and diffusion dynamics, enabling the identification of subsets that maximize overall correlation. In the final stage, the t-SNE dimensionality reduction technique is applied with weights derived from the Rel/Red scores. In conclusion, this study redefines the feature selection process by integrating correlation-maximizing objectives with stochastic modeling. The proposed approach offers a more comprehensive and effective alternative to conventional methods, particularly in terms of explainability, interpretability, and generalizability. The method demonstrates strong potential for application in advanced machine learning systems, such as credit scoring, and in broader dimensionality reduction tasks. Full article

Digital transformation (DT) has become a strategic priority for public administrations, particularly due to the need to deliver more efficient and citizen-centered services and respond to societal expectations, ESG (Environmental, Social, and Governance) criteria, and the United Nations Sustainable Development Goals (UN SDGs). In this context, the main objective of this study is to propose an innovative methodology to automatically evaluate the level of digital transformation (DT) in public sector organizations. The proposed approach combines traditional assessment methods with Artificial Intelligence (AI) techniques. The methodology follows a dual approach: on the one hand, surveys are conducted using specialized staff from various public entities; on the other, AI-based models (including neural networks and transformer architectures) are used to estimate the DT level of the organizations automatically. Our approach has been applied to a real-world case study involving local public administrations in the Valencian Community (Spain) and shown effective performance in assessing DT. While the proposed methodology has been validated in a specific local context, its modular structure and dual-source data foundation support its international scalability, acknowledging that administrative, regulatory, and DT maturity factors may condition its broader applicability. The experiments carried out in this work include (i) the creation of a domain-specific corpus derived from the surveys and websites of several organizations, used to train the proposed models; (ii) the use and comparison of diverse AI methods; and (iii) the validation of our approach using real data. Based on the deficiencies identified, the study concludes that the integration of technologies such as the Internet of Things (IoT), sensor networks, and AI-based analytics can significantly support resilient, agile urban environments and the transition towards more effective and sustainable Smart City models. Full article

The theoretical reliability of complex equipment often significantly deviates from real-world performance due to the inherent influence of diverse environmental and operational factors, making scientific reliability evaluation particularly challenging. This study proposes a multi-attribute group decision-making (MAGDM) evaluation framework based on a strict intuitionistic fuzzy distance and an improved TOPSIS approach. First, an improved strict intuitionistic fuzzy distance measure (ISIFDisM) is rigorously developed to overcome the limitations of existing methods, exhibiting high robustness, monotonicity, and discriminability. Second, building upon ISIFDisM, a systematic MAGDM evaluation model is constructed, comprising three key steps: (1) data acquisition through structured questionnaire surveys; (2) attribute weights determined using the entropy weight method; and (3) alternative ranking through normalized priority coefficients derived from intuitionistic fuzzy distance calculations. Third, the proposed framework is applied to a practical case study focused on reliability assessment of ship equipment, enabling effective ranking of various marine engines. Finally, through static comparative analyses and dynamic scenario simulations, the feasibility, robustness, and methodological superiority of the proposed framework are thoroughly validated. Full article

Consistency indices quantify the degree of transitivity and proportionality violations in a pairwise comparison matrix (PCM), forming a cornerstone of the Analytic Hierarchy Process (AHP) and Analytic Network Process (ANP). Several methods have been proposed to compute consistency, including those based on the maximum eigenvalue, dot product, Jaccard index, and the Bose index. However, these methods often overlook two critical aspects: (i) vector projection or directional alignment, and (ii) the weight or importance of individual elements within a pointwise evaluative structure. The first limitation is particularly impactful. Adjustments made during the consistency improvement process affect the final priority vector disproportionately when heavily weighted elements are involved. Although consistency may improve numerically through such adjustments, the resulting priority vector can deviate significantly, especially when the true vector is known. This indicates that approaches neglecting projection and weighting considerations may yield internally consistent yet externally incompatible vectors, thereby compromising the validity of the analysis. This study builds on the idea that consistency and compatibility are intrinsically related; they are two sides of the same coin and should be considered complementary. To address these limitations, it introduces a novel metric, the Consistency Index G (CI-G) based on the compatibility index G. This measure evaluates how well the columns of a PCM align with its principal eigenvector, using CI-G as a diagnostic component. The proposed approach not only refines consistency measurement but also enhances the accuracy and reliability of derived priorities. Full article

Seismic retrofitting of reinforced concrete (RC) structures is essential for improving resilience and extending service life, particularly in regions with outdated building codes. However, selecting the optimal retrofitting strategy requires balancing multiple interdependent sustainability criteria—economic, environmental, and social—under expert-based uncertainty. This study presents a fuzzy hybrid multi-criteria decision-making (MCDM) approach that combines DEMATEL, DANP, and TOPSIS to represent causal interdependencies, derive interlinked priority weights, and rank retrofit alternatives. The assessment applies three complementary life cycle-based tools—cost-based, environmental, and social sustainability analyses following LCCA, LCA, and S-LCA frameworks, respectively—to evaluate three commonly used retrofitting strategies: RC jacketing, steel jacketing, and carbon fiber-reinforced polymer (CFRP) wrapping. The fuzzy-DANP methodology enables accurate modeling of feedback among sustainability dimensions and improves expert consensus through causal mapping. The findings identify CFRP as the top-ranked alternative, primarily attributed to its enhanced performance in both environmental and social aspects. The model’s robustness is confirmed via sensitivity analysis and cross-method validation. This mathematically grounded framework offers a reproducible and interpretable tool for decision-makers in civil infrastructure, enabling sustainability-oriented retrofitting under uncertainty. Full article

The rapid demographic transition in middle-income countries creates unprecedented challenges for age-friendly urban development, as cities experience compressed aging within severe resource constraints—a phenomenon termed “getting old before getting rich.” This study develops a preliminary Resource-Constrained Age-Friendly City (RC-AFC) framework through empirical analysis of Bangkok’s urban aging challenges, addressing the need for context-specific approaches in resource-constrained environments. Using convergent parallel mixed-methods design, the research analyzed data from 1000 older adults and 195 multi-sectoral stakeholders to examine age-friendly service gaps and collaboration potential within Bangkok’s rapidly aging context. Importance-Performance Analysis revealed significant service disparities (average gap: 1.34) with Communication and Information (2.03), Housing (1.93), and Outdoor Spaces (1.78) identified as priority areas in Bangkok’s setting. The study proposes three initial RC-AFC principles based on Bangkok findings: Priority Hierarchy Adaptation suggesting systematic resource allocation approaches; Multi-Sectoral Resource Optimization indicating collaboration as structural necessity; and Leapfrog Innovation Potential demonstrating potential for constraint-driven solutions. This proof-of-concept study provides initial conceptual foundation specifically developed from Bangkok’s context, though systematic validation across different urban environments remains essential before any broader consideration. The research offers a Bangkok-derived starting point for understanding resource-constrained age-friendly development that requires substantial further testing and adaptation for application in other contexts. Full article

Background: Spontaneous intracerebral hemorrhage (ICH) has the highest case fatality of all stroke types, yet recent epidemiological and outcome data from Central and Eastern Europe remain limited. Methods: We retrospectively analyzed prospectively collected data for 601 consecutive adults with primary ICH admitted to Sibiu County Clinical Emergency Hospital, Romania (2017–2023). Demographics, Glasgow Coma Scale (GCS), CT-derived hematoma volume (ABC/2), anatomical site, intraventricular extension (IVH), treatment, comorbidities, and in-hospital death were reported with exact counts and percentages; no imputation was performed. Results: Mean age was 68.4 ± 12.9 years, and 59.7% were male. Mean hematoma volume was 30.4 mL, and 23.0% exceeded 30 mL. IVH occurred in 40.1% and doubled mortality (50.6% vs. 16.7%). Overall case fatality was 29.6% and climbed to 74.5% for brain-stem bleeds. Men, although younger than women (66.0 vs. 71.9 years), died more often (35.4% vs. 21.1%; risk ratio 1.67, 95% CI 1.26–2.21). Systemic hazards amplified death risk: Oral anticoagulation, 44.2%; chronic alcohol misuse, 51.4%; thrombocytopenia, 41.0%; chronic kidney disease, 42.3%. Conservative management (74.9%) yielded 27.8% mortality overall and ≤15 for small-to-mid lobar or capsulo-lenticular bleeds; lobar surgery matched this (13.4%) only in large clots. Thalamic evacuation was futile (82.3% mortality), and cerebellar decompression performed late still carried 54.5% mortality versus 16.6% medically. Multivariable analysis confirmed that low GCS, IVH, large hematoma volume, thrombocytopenia, and chronic alcohol use independently predicted in-hospital mortality. Limitations: This retrospective study lacked post-discharge functional outcome data (e.g., mRS at 90 days). Conclusions: This study presents the largest Romanian single-center ICH cohort, establishing national benchmarks and underscoring modifiable risk factors. Early ICH lethality aligns with Western data but is amplified by exposures such as alcohol misuse, anticoagulation, thrombocytopenia, and CKD. Priorities include preventive strategies, timely surgical access, wider adoption of minimally invasive techniques, and development of a prospective regional registry. Full article

Background: Artificial intelligence (AI) and machine learning (ML) have been reshaping maternal, fetal, neonatal, and reproductive healthcare by enhancing risk prediction, diagnostic accuracy, and operational efficiency across the perinatal continuum. However, no comprehensive synthesis has yet been published. Objective: To conduct a scoping review of reviews of AI/ML applications spanning reproductive, prenatal, postpartum, neonatal, and early child-development care. Methods: We searched PubMed, Embase, the Cochrane Library, Web of Science, and Scopus through April 2025. Two reviewers independently screened records, extracted data, and assessed methodological quality using AMSTAR 2 for systematic reviews, ROBIS for bias assessment, SANRA for narrative reviews, and JBI guidance for scoping reviews. Results: Thirty-nine reviews met our inclusion criteria. In preconception and fertility treatment, convolutional neural network-based platforms can identify viable embryos and key sperm parameters with over 90 percent accuracy, and machine-learning models can personalize follicle-stimulating hormone regimens to boost mature oocyte yield while reducing overall medication use. Digital sexual-health chatbots have enhanced patient education, pre-exposure prophylaxis adherence, and safer sexual behaviors, although data-privacy safeguards and bias mitigation remain priorities. During pregnancy, advanced deep-learning models can segment fetal anatomy on ultrasound images with more than 90 percent overlap compared to expert annotations and can detect anomalies with sensitivity exceeding 93 percent. Predictive biometric tools can estimate gestational age within one week with accuracy and fetal weight within approximately 190 g. In the postpartum period, AI-driven decision-support systems and conversational agents can facilitate early screening for depression and can guide follow-up care. Wearable sensors enable remote monitoring of maternal blood pressure and heart rate to support timely clinical intervention. Within neonatal care, the Heart Rate Observation (HeRO) system has reduced mortality among very low-birth-weight infants by roughly 20 percent, and additional AI models can predict neonatal sepsis, retinopathy of prematurity, and necrotizing enterocolitis with area-under-the-curve values above 0.80. From an operational standpoint, automated ultrasound workflows deliver biometric measurements at about 14 milliseconds per frame, and dynamic scheduling in IVF laboratories lowers staff workload and per-cycle costs. Home-monitoring platforms for pregnant women are associated with 7–11 percent reductions in maternal mortality and preeclampsia incidence. Despite these advances, most evidence derives from retrospective, single-center studies with limited external validation. Low-resource settings, especially in Sub-Saharan Africa, remain under-represented, and few AI solutions are fully embedded in electronic health records. Conclusions: AI holds transformative promise for perinatal care but will require prospective multicenter validation, equity-centered design, robust governance, transparent fairness audits, and seamless electronic health record integration to translate these innovations into routine practice and improve maternal and neonatal outcomes. Full article

Background/Objectives: Leishmaniasis is a zoonotic and anthropozoonotic disease with significant public health impact worldwide and is classified as a neglected tropical disease. The search for new affordable treatments, particularly oral and/or topical ones that are easy to administer and have fewer side effects, remains a priority for the scientific community in this field of research. In previous investigations, 3-alkoxy-1-benzyl-5-nitroindazole derivatives showed remarkable in vitro results against Leishmania species, and predictions of absorption, distribution, metabolism, excretion, and toxicity properties, as well as pharmacological scores, of the compounds classified as active were superior to those of amphotericin B, indicating their potential as candidates for in vivo studies. Therefore, the aim of the present study was to evaluate the in vivo antileishmanial activity of the indazole derivatives NV6 and NV16. Methods: The compounds were administered intralesionally at concentrations of 10 and 5 mg/kg in a BALB/c mouse model of cutaneous leishmaniasis caused by Leishmania amazonensis. To evaluate the efficacy of the compounds, indicators such as lesion size, ulcer area, lesion weight, and parasitic load were determined. Amphotericin B was used as a positive control. Results: The compound NV6 showed leishmanicidal activity comparable to that observed with amphotericin B, with a significant reduction in lesion development and parasite load, while NV16 caused a reduction in ulcer area. Conclusions: These results provide strong evidence for the antileishmanial activity of NV6 and support future studies to improve its pharmacokinetic profile, as well as the investigation of combination therapies with other chemotherapeutic agents currently in use. Full article