Search Results (224)

This paper presents an efficient algorithm for Explicit Model-Following (EMF) control using an Output-derivative Feedback Control (OFC) scheme within the Reciprocal State Space (RSS) framework, aimed at overcoming the performance limitations associated with state-derivative dependence. For Lipschitz Nonlinear Systems (LNS), two approaches are proposed: a linear EMF (LEMF) strategy, which transforms the system into a Linear Parameter-Varying (LPV) representation via the Differential Mean Value Theorem (DMVT) to facilitate controller design, and a nonlinear EMF (NEMF) scheme, which enables the direct tracking of a nonlinear reference model. The stability of the closed-loop system is ensured by deriving control gains through Linear Quadratic Regulator (LQR) optimization. The proposed algorithms are validated through Real-Time Implementation (RTI) on an Arduino DUE platform, demonstrating their effectiveness and practical feasibility. Full article

Background: Pediatric road traffic injuries (RTIs) represent a significant public health concern, particularly in countries like Romania, where road infrastructure and safety remain challenges. Despite recent economic reclassification, Romania continues to report high rates of pediatric traffic-related injuries. Non-fatal RTIs often result in long-term physical and psychological harm. This study aims to assess age- and gender-specific injury patterns and mechanisms of non-fatal RTIs in children and adolescents, using data from “St. Mary’s” Emergency Clinical Hospital for Children in Iași over a ten-year period to inform targeted prevention strategies. Methods: This 10-year retrospective study (2015–2024) was conducted at “St. Mary’s” Emergency Clinical Hospital for Children in Iași, Romania, a regional referral center. Data from 1074 pediatric patients (aged 1 month–17 years, 11 months) with RTIs were analyzed using ICD-10 codes and verified manually. Variables included demographics, injury type, mechanism, and treatment. Patients were stratified into four age groups. Statistical analysis was performed using IBM SPSS Statistics 25, with significance set at p < 0.05. Results: The highest incidence was observed among boys (77.7%) and children aged 10–14 years. Car passengers and cyclists constituted the most frequently affected groups, with only 11% of passengers appropriately restrained and 78% of cyclists not wearing helmets. Common injuries included excoriations, thoracic contusions, and abdominal trauma, with notable variations by age and sex. Thoracic injuries were more frequent among girls, whereas younger children exhibited a higher incidence of abdominal trauma. Conclusions: The findings emphasize critical safety gaps in child restraint and helmet use and highlight the urgent need for targeted, age-specific road safety interventions and improved public health education. Full article

Objectives: This study aimed to identify differences in risk factors for carbapenem-resistant Enterobacteriaceae (CRE) infections across different anatomical sites and to explore risk factors associated with mortality in CRE-infected patients. Methods: Patients who underwent CRE screening and were subsequently diagnosed with CRE infections were included and categorized by infection site: respiratory tract (RTI), urinary tract (UTI), and bloodstream (BSI). Forty ICU patients without CRE infection were randomly selected as controls. Statistical comparisons were performed using the Mann–Whitney U or Chi-square test, as appropriate. Potential risk factors were evaluated via univariate and multivariate analyses, and a predictive model was constructed, with its performance assessed using ROC curve analysis. Results: CRE colonization was identified as a common independent risk factor across all three groups (RTI, UTI, and BSI). Infection-site-specific analyses revealed independent risk factors: RTI was associated with mechanical ventilation, UTI with trauma, and BSI with gastrointestinal injury. Predictive models for RTI, UTI, and BSI demonstrated good discrimination, with ROC AUCs of 0.94, 0.94, and 0.95, respectively. In the analysis of Survived versus Deceased patients, the BSI group had the highest mortality, though the difference was not statistically significant. Deceased patients exhibited significantly higher PCT levels than Survived patients (p = 0.005). Prior use of carbapenems and antifungal agents, as well as Ln(PCT), were independently associated with mortality in CRE-infected patients. Conclusions: Risk factors for CRE infections vary across anatomical sites, with CRE colonization, mechanical ventilation, trauma, and gastrointestinal injury playing key roles. Overuse of antibiotics and elevated inflammatory responses are associated with increased mortality. These findings provide evidence for early identification of high-risk patients and optimization of individualized treatment strategies. Full article

As global temperatures continue to rise, surpassing the +2.5 °C threshold under current emissions scenarios, the urgency for sustainable, effective carbon management strategies has intensified. Geological carbon storage (GCS) has been explored as a potential mitigation tool; however, its large-scale feasibility remains highly uncertain due to concerns over storage permanence, leakage risks, and economic viability. This study proposes three advanced deep learning models—DeepDropNet, GateSeqNet, and RecurChainNet—to predict the Residual Trapping Index (RTI) and Solubility Trapping Index (STI) with enhanced accuracy and computational efficiency. Using a dataset of over 2000 high-fidelity simulation records, the models capture complex nonlinear relationships between key reservoir properties. Results indicate that GateSeqNet achieved the highest predictive accuracy, with an R² of 0.95 for RTI and 0.93 for STI, outperforming both DeepDropNet and RecurChainNet. Ablation tests reveal that excluding post injection and injection rate significantly reduced model performance, decreasing R² by up to 90% in RTI predictions. The proposed models provide a computationally efficient alternative to traditional numerical simulations, which makes them viable for real-time CO₂ sequestration assessment. This work advances AI-driven carbon sequestration strategies, offering robust tools for optimizing long-term CO₂ storage performance in geological formations and for achieving global sustainability goals. Full article

This paper proposes a cascaded state estimation framework based on proprioception for robots. A generalized-momentum-based Kalman filter (GMKF) estimates the ground reaction forces at the feet through joint torques, which are then input into an error-state Kalman filter (ESKF) to obtain the robot’s prior state estimate. The system’s dynamic equations on the Lie group are parameterized using canonical coordinates of the first kind, and variations in the tangent space are mapped to the Lie algebra via the inverse of the right trivialization. The resulting parameterized system state equations, combined with the prior estimates and a sliding window, are formulated as a moving horizon estimation (MHE) problem, which is ultimately solved using a parallel real-time iteration (Para-RTI) technique. The proposed framework operates on manifolds, providing a tightly coupled estimation with higher accuracy and real-time performance, and is better suited to handle the impact noise during foot–ground contact in legged robots. Experiments were conducted on the BQR3 robot, and comparisons with measurements from a Vicon motion capture system validate the superiority and effectiveness of the proposed method. Full article

The Karnak Temples are considered one of Egypt’s most significant archaeological sites, dating back to the Middle Kingdom (c. 2000–1700 BC) and were continuously expanded until the Ptolemaic period (305–30 BC). As the second most visited UNESCO World Heritage archaeological site in Egypt after the Giza Pyramids, Karnak faces severe deterioration processes due to prolonged exposure to environmental impacts, mechanical damage, and historical interventions. This study employs a multidisciplinary approach integrating non-destructive testing (NDT) methods to assess the physical and mechanical condition and degradation mechanisms of scattered sandstone blocks at the site. Advanced documentation techniques, including Reflectance Transformation Imaging (RTI), photogrammetry, and Infrared Thermography (IRT), were used to analyze surface morphology, thermal stress effects, and weathering patterns. Ultrasonic Pulse Velocity (UPV) testing provided internal structural assessments, while spectral and gloss analysis quantified chromatic alterations and surface roughness. Additionally, the Karsten Tube test determined the water absorption behavior of the sandstone, highlighting variations in porosity and susceptibility to salt crystallization. In this sense, the results indicate that climatic factors such as extreme temperature fluctuations, wind erosion, and groundwater infiltration contributed to sandstone deterioration. Thermal cycling leads to microcracking and granular disintegration, while high capillary water absorption accelerates chemical weathering processes. UPV analyses showed substantial internal decay, with low-velocity zones correlating with fractures and differential cementation loss. Finally, an interventive conservation plan was proposed. Full article

Background: The COVID-19 pandemic has profoundly altered the clinical and microbiological landscape of respiratory tract infections (RTIs), potentially reshaping pathogen distribution and antimicrobial resistance (AMR) profiles across care settings. Objectives: The objective of this study was to assess temporal trends in respiratory bacterial pathogens, antimicrobial resistance, and polymicrobial infections across three pandemic phases—pre-COVID (2018–2019), COVID (2020–2022), and post-COVID (2022–2024)—in hospitalized and ambulatory patients. Methods: We retrospectively analyzed 1827 respiratory bacterial isolates (hospitalized patients, n = 1032; ambulatory patients, n = 795) collected at a tertiary care center in Northern Italy. Data were stratified by care setting, anatomical site, and pandemic phase. Species identification and susceptibility testing followed EUCAST guidelines. Statistical analysis included chi-square and Fisher’s exact tests. Results: In hospitalized patients, a significant increase in Pseudomonas aeruginosa (from 45.5% pre-COVID to 58.6% post-COVID, p < 0.0001) and Acinetobacter baumannii (from 1.2% to 11.1% during COVID, p < 0.0001) was observed, with 100% extensively drug-resistant (XDR) rates for A. baumannii during the pandemic. Conversely, Staphylococcus aureus significantly declined from 23.6% pre-COVID to 13.7% post-COVID (p = 0.0012). In ambulatory patients, polymicrobial infections peaked at 41.2% during COVID, frequently involving co-isolation of Candida spp. Notably, resistance to benzylpenicillin in Streptococcus pneumoniae reached 80% (4/5 isolates) in hospitalized patients during COVID, and carbapenem-resistant P. aeruginosa (CRPA) significantly increased post-pandemic in ambulatory patients (0% pre-COVID vs. 23.5% post-COVID, p = 0.0014). Conclusions: The pandemic markedly shifted respiratory pathogen dynamics and resistance profiles, with distinct trends observed in hospital and community settings. Persistent resistance phenotypes and frequent polymicrobial infections, particularly involving Candida spp. in outpatients, underscore the need for targeted surveillance and antimicrobial stewardship strategies. Full article

The great megalithic sites reveal an extended use of their monuments. In Late Prehistory, in Protohistory, and even in historical times, dolmens remained visible references on the landscape and were central for navigating it. The megaliths of Menga, Viera, and Romeral provide quality data to confirm their continued relevance. Our aim here is to understand whether menhirs also played that role, using the area of Tierras de Antequera, which is connected to the sea, as a case study. With that goal in mind, a research project has been initiated through intensive archaeological field surveying, combined with the collection of testimonies from oral tradition and other archaeological tools such as GIS, geophysical prospection, photogrammetry and RTI, for the detection of engravings and paintings on some of the located landmarks. We present in this paper the first geological analyses in the megalithic territory of Antequera to determine the raw material of the menhirs that are studied and the geological outcrops from which they come. Full article

Inertial confinement fusion (ICF) stands as one of the approaches to achieve controlled thermonuclear fusion, capable of supplying humans with abundant, economical, and safe energy. In this study, the high-order hybrid compact–WENO scheme is employed to simulate Rayleigh–Taylor instability (RTI) phenomena, one of the challenges hindering the realization of ICF, and to investigate the interaction of RTI phenomena in a multi-layer fluid system. To ensure a more reasonable comparison, the corresponding initial and boundary conditions for three-layer and four-layer fluids are derived based on the same Atwood number. Numerical results show that with the development of RTI phenomena, the interaction between interfaces can be gradually observed. The number of fluid layers exhibits an inhibitory effect on the development of RTI phenomena. When a pair of spike and bubble at two adjacent interfaces reach the same height, the evolution of the spike–bubble gap changes significantly. Full article

Cooper Hewitt, Smithsonian Design Museum, in partnership with the Museum Conservation Institute, investigated the use of Reflectance Transformation Imaging (RTI) with visible and blue light to assess micro-scale details on glass surfaces. The image sets were captured using a bespoke RTI dome, which greatly facilitates the repeatability of the process. Our tests suggest that if conducted with care, this technique allows tracking and comparison across time of the continued deterioration of crizzled glass. This technical note introduces the project, addresses challenges, and discusses results in order to provide guidance to others looking to replicate a similar protocol. Full article

Malaria, typhoid fever, respiratory tract infections, and urinary tract infections significantly impact women, especially in remote, resource-constrained settings, due to limited access to quality healthcare and certain risk factors. Most studies have focused on vector control measures, such as insecticide-treated nets and time series analysis, often neglecting emerging yet critical risk factors vital for effectively preventing febrile diseases. We address this gap by investigating the use of machine learning (ML) models, specifically extreme gradient boost and random forest, in predicting adult females’ susceptibility to these diseases based on biological, environmental, and socioeconomic factors. An explainable AI (XAI) technique, local interpretable model-agnostic explanations (LIME), was applied to enhance the transparency and interpretability of the predictive models. This approach provided insights into the models’ decision-making process and identified key risk factors, enabling healthcare professionals to personalize treatment services. Factors such as high cholesterol levels, poor personal hygiene, and exposure to air pollution emerged as significant contributors to disease susceptibility, revealing critical areas for public health intervention in remote and resource-constrained settings. This study demonstrates the effectiveness of integrating XAI with ML in directing health interventions, providing a clearer understanding of risk factors, and efficiently allocating resources for disease prevention and treatment. Full article

Background: Inclisiran is a novel lipid-lowering agent targeting PCSK9 via small interfering RNA (siRNA) technology. While clinical trials such as the ORION studies have demonstrated significant reductions in low-density lipoprotein cholesterol (c-LDL), real-world data (RWD) often differ due to variations in patient populations and clinical practices. Methods: This systematic review and meta-analysis adhered to PRISMA guidelines. A comprehensive search was conducted in MEDLINE for real-world studies evaluating inclisiran’s efficacy in reducing c-LDL. Articles meeting predefined inclusion criteria were assessed for quality and bias using RTI Item Bank. Data transformations were applied to harmonize median and IQR values to means and standard deviations for meta-analytic synthesis using RevMan 5.4. Results: A total of 3774 articles were identified, of which 7 studies comprising 1454 patients met the inclusion criteria. The meta-analysis revealed an average c-LDL reduction of 42.77% (95% CI: 37.42–48.12%). The subgroup analysis indicated greater reductions in patients receiving inclisiran alongside statins (45.67%; 95% CI: 36.64–54.71%) compared to monotherapy (37.53%; 95% CI: 29.91–45.15%). Discrepancies with clinical trials (e.g., 52% reduction in ORION studies) were attributed to baseline c-LDL differences and real-world adherence. Conclusions: Inclisiran demonstrates robust efficacy in real-world settings, achieving significant c-LDL reductions with a convenient dosing schedule. However, the observed discrepancies with clinical trials highlight the need for further RWD studies to bridge gaps in effectiveness and optimize therapeutic outcomes. Full article

Background/Objectives: Sudden sensorineural hearing loss (SSNHL) is an acute condition with unclear etiology, commonly hypothesized to be associated with viral infections. Acute respiratory tract infections (RTIs), particularly those of viral origin, have been implicated in SSNHL through proposed mechanisms such as cochlear invasion and immune-mediated damage. However, robust large-scale epidemiological evidence examining this association remains limited. This study aimed to investigate the potential association between acute RTIs and subsequent risk of developing SSNHL across diverse populations. Methods: We conducted a multinational retrospective cohort study using data from the TriNetX Global Collaborative Network. Adults diagnosed with acute RTIs between 1 January 2012 and 30 June 2023 were compared to matched controls without RTI exposure. Patients with predisposing conditions for SSNHL were excluded. Propensity score matching (1:1) was performed by age and sex. SSNHL diagnoses within 60 days post index were analyzed using Cox proportional hazards models. Subgroup and sensitivity analyses were conducted by race, sex, and age strata. Results: Among 37 million patients analyzed, individuals with acute RTIs had a lower incidence of SSNHL compared to matched controls. Hazard ratios (HRs) for SSNHL were significantly reduced across all racial groups: Whites (HR: 0.572), Blacks (HR: 0.563), and Asians (HR: 0.409). Subgroup analyses revealed stronger inverse associations in males and younger age groups, particularly those aged 18–25 years. Conclusions: Contrary to prior assumptions, acute RTIs were associated with a lower incidence of SSNHL in a large, diverse cohort. While the findings raise the possibility of immunological or physiological factors influencing this association, the results should be interpreted with caution due to unmeasured confounding and the observational nature of the study. Full article

This study explored innovative methods for teaching mathematics to seventh-grade students with persistently low performance by using an AI-driven neural network approach, specifically focusing on solving first-degree inequalities. Guided by the Response to Intervention (RTI) framework, the intervention aimed to reduce math anxiety and build academic resilience through the development of cognitive and metacognitive strategies. A rigorous pre- and post-test design was employed to evaluate changes in performance, anxiety levels, and resilience. Fifty-six students participated in the 12-week program, receiving personalized instruction tailored to their individual needs. The AI tool provided real-time feedback and adaptive problem-solving tasks, ensuring students worked at an appropriate level of challenge. Results indicated a marked decrease in math anxiety alongside significant gains in cognitive skills such as problem-solving and numerical reasoning. Students also demonstrated enhanced metacognitive abilities, including self-monitoring and goal setting. These improvements translated into higher academic performance, particularly in the area of inequalities, and greater resilience, highlighting the effectiveness of AI-based strategies in supporting learners who struggle persistently in mathematics. Overall, the findings underscore how AI-driven teaching approaches can address both the cognitive and emotional dimensions of mathematics learning. By offering targeted, adaptive support, educators can foster a learning environment that reduces stress, promotes engagement, and facilitates long-term academic success for students with persistently low performance in mathematics. Full article

Objectives: This study aimed to evaluate changes in the rate and spectrum of healthcare-associated infections (HCAIs) and to analyse the rate and spectrum of antimicrobial prescriptions in four long-term care facilities (LTCFs) in Graz, Austria, from 2018 to 2022 in a prospective cohort study. Methods: Nursing staff prospectively collected data on HCAIs and antimicrobial prescriptions once a week. Log-linear Poisson models for counts were applied mostly to evaluate the difference effects of the various calendar years compared to the reference year of 2018. Results: A total of 1684 infections were recorded in 720 residents during the study period. The overall annual incidence rate of HCAIs varied over time with a significant increase to 2.86/1000 resident days in 2019 and to 4.09/1000 resident days in 2022, both compared to 2018, p < 0.001. A large peak in respiratory tract infections (RTIs) occurred in winter 2021/2022 due to a large number of SARS-CoV-2 infections in all four LTCFs. Urinary tract infections (UTIs) were the most commonly recorded infections. Beta-lactams were the most frequently prescribed systemic anti-infectives. A statistically significant increase in the rate of beta-lactam prescriptions/1000 resident days occurred between 2018 and 2022 (p = 0.016), whereas a statistically significant decrease in quinolone prescriptions/1000 resident days occurred in the same time period (p < 0.001). Conclusions: The incidence rates of HCAIs varied over time with a significant increase during the COVID-19 pandemic in 2022 compared to 2018. Continued surveillance efforts are necessary to assess the effect of infection control efforts after the pandemic. Full article