Multidisciplinary Digital Publishing Institute (Switzerland)
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Endoscopic Septoplasty—A Narrative Review of Outcomes, Complications and Patient-Reported Score
No full textBackground: The main cause of chronic nasal obstruction in ENT practice is represented by the deviated nasal septum. Septoplasty remains the gold standard treatment, performed using either conventional or endoscopic techniques. Methods: A narrative review of the literature was conducted using PubMed and Google Scholar for studies published between May 1999 and October 2024. Eligible studies included adult patients (≥16 years) undergoing conventional or endoscopic septoplasty, with at least one reported outcome measure: NOSE, VAS, or SNOT-22 scores; operative time; or complication rates. Results: Across multiple clinical studies, both conventional and endoscopic septoplasty provided significant improvements in nasal airflow and symptom relief. Endoscopic septoplasty was consistently associated with superior intraoperative visualization, more precise correction of posterior deformities and isolated septal spurs, and lower rates of intraoperative and postoperative complications. Complication rates were low overall for both approaches. Conclusions: Current evidence supports both conventional and endoscopic septoplasty as effective treatments for nasal obstruction due to septal deviation. However, endoscopic septoplasty offers distinct advantages in terms of visualization, operative efficiency, and safety, making it an increasingly preferred technique
Modern Quantum Chemistry Methodology for Predicting 31P Nuclear Magnetic Resonance Chemical Shifts
No full textPhosphorus-31 nuclear magnetic resonance (31P NMR) spectroscopy is a powerful analytical physical chemistry experimental technique that is widely used to study the structure and dynamics of phosphorus-containing compounds today. Accurate calculation of 31P NMR chemical shifts lies in the basis of the proper assignment of NMR signals, as they can be closely spaced to each other in the NMR spectra of systems that bear nuclei with subtly different electron environments, like complex organophosphorus compounds, nucleic acids, and phosphates, etc. The most advanced quantum chemistry (QC) methods allow us to reach the agreement between theoretical values of 31P NMR chemical shifts and experiments within a few ppm, which makes them a useful tool for studying chemical structure, reaction mechanisms, and catalyst design with the aid of the NMR method. This review surveys the application of both density functional and wave function methods of electron structure to the calculation of 31P NMR chemical shifts and proposes a thorough discussion of the latest findings related to the factors affecting the final accuracy of the 31P NMR chemical shifts prediction, including basis sets, the geometry factor effect, solvent, vibrational, and relativistic corrections
A Systematic Review and Meta-Analysis on the Clinical Performance and Longevity of Bioactive Composite Resin Restorations
No full textBackground: Bioactive composite resins combine the esthetic and mechanical properties of resin composites with therapeutic functions such as ion release, remineralization, and caries inhibition. While in vitro studies suggest promising bioactivity, their clinical performance in permanent teeth remains uncertain. Objective: This systematic review and meta-analysis critically appraised randomized controlled trials and prospective clinical studies to determine whether bioactive composites offer superior clinical performance compared to conventional resin composites and glass ionomer-based materials. Methods: Electronic databases (PubMed/MEDLINE, Scopus, Web of Science, Google Scholar) were searched for eligible studies (2018–2025). Clinical outcomes assessed restoration survival, marginal integrity, secondary caries, postoperative sensitivity, and esthetic outcomes (color match). Data were pooled using a random-effects model, and risk of bias was assessed with Cochrane criteria. Results: Twenty-two trials met the inclusion criteria. No significant differences were found between bioactive and control restorations for survival/retention (RD = 0.01; 95% CI, –0.01 to 0.03), marginal adaptation (RD = 0.02; 95% CI, –0.02 to 0.06), secondary caries (RD = 0.01; 95% CI, –0.01 to 0.03), or postoperative sensitivity (RD = 0.01; 95% CI, –0.02 to 0.04), with negligible heterogeneity (I2 = 0–4%). For color match, glass ionomer restorations showed significantly poorer outcomes (RD = –0.23; 95% CI, –0.31 to –0.14; p < 0.00001; I2 = 98%), while conventional resin composites had a slight but significant advantage over bioactive composites (RD = 0.07; 95% CI, 0.02 to 0.12; p = 0.003; I2 = 76%). Most studies presented moderate risk of bias and short-term follow-up (<36 months). Conclusions: Current evidence indicates that bioactive composites perform comparably, but not superior, to conventional restoratives in permanent teeth. The discrepancy between laboratory bioactivity and clinical effectiveness highlights the need for long-term, well-designed clinical trials with standardized outcome reporting
Fusing Multi-Source Data with Machine Learning for Ship Emission Calculation in Inland Waterways
No full textAccurate estimation of ship emissions is essential for the effective enforcement of emission control policies in inland waterways. However, existing “bottom-up” models face significant challenges owing to severe data scarcity for inland ships, particularly regarding ship static parameters. This study proposes a novel data fusion and machine learning framework to address this issue. The methodology integrates real-time SO2 and CO2 pollutant concentrations on the Nanjing Dashengguan Yangtze River Bridge, Automatic Identification System (AIS) data, and meteorological information. To address the scarcity of design data for inland ships, web scraping was used to extract basic parameters, which were then used to train five machine learning models. Among them, the XGBoost model demonstrated superior performance in predicting the main engine rated power. A refined activity-based emission model combines these predicted parameters, ship operational profiles, and specific emission factors to calculate real-time emission source strengths. Furthermore, the model was validated against field measurements by comparing the calculated and measured emission source strengths from ships, demonstrating high predictive accuracy with R2 values of 0.980 for SO2 and 0.977 for CO2, and MAPE below 13%. This framework provides a reliable and scalable approach for real-time emission monitoring and supports regulatory enforcement in inland waterways
A High-PSRR LDO with Low Noise and Ultra-Low Power Consumption
No full textHigh-performance low dropout regulator (LDO) chips are core components that provide clean power for high-precision sensors, radio frequency (RF) circuits, low noise amplifiers and other noise-sensitive circuits. In the reported literature, the designed LDO chip has advantages in certain parameters, but it cannot meet all the requirements of a high power supply rejection ratio (PSRR), low output noise and low standby current at the same time, which makes the high-end applications of LDOs greatly limited. In this paper, an LDO chip with high PSRR, low output noise and low standby current has been designed and fabricated. By increasing the loop gain, introducing an improved feedforward path, and adopting isolated power supply, the PSRR of the LDO at different frequency bands is greatly improved. By optimizing the design of the error amplifier (EA) and adding a low-pass filter to filter out the reference noise, the output voltage noise of the LDO is reduced. Within the depletion process and an optimized reference structure, the standby power consumption of the LDO is reduced without damaging the output voltage accuracy. The chip is taped out with SMIC’s 0.18 μm/5 V/BCD process. The measured PSRR of the chip is as high as 95dB at a frequency of 1 kHz, and the high-frequency (1 MHz) PSRR is above 45 dB. The amplitude of integrated output noise is below 5.4 μVrms within the frequency range of 10 Hz to 100 KHz. When the load current is zero, the measured standby current is less than 400 nA. The test results indicate that the chip has excellent performance in terms of PSRR, output noise and standby power consumption
Robust Trajectory Tracking for Omnidirectional Mobile Robots with Input Time Delay: An ADRC Approach
No full textIn this article, the problem of control of the kinematic model of an omnidirectional robot with time delay in the control input is tackled through an Active Disturbance Rejection Control (ADRC) with a disturbance predictor-based scheme, which consists in predicting the generalized forward disturbance input in order to cancel it and then using a feedforward linearization approach to control the system in trajectory tracking tasks. The novelties of the scheme are to demonstrate that using the proposed extended state disturbance estimation leads to a forward estimation following the Taylor series approximation, and, to avoid using additional pose predictions, a feedforward input as an exact linearization approach is used, in which the remaining dynamics can be lumped into the generalized disturbance input. Thus, the use of extended states in prediction improves the robustness of the predictor while increasing the prediction horizon for larger time delays. The stability of the proposal is demonstrated using the second method of Lyapunov, which shows the closed-loop estimation/tracking ultimate bound behavior. Additionally, numerical simulations and experimental tests validate the robustness of the approach in trajectory-tracking tasks
Patent-Based Prospective Life Cycle Assessment and Eco-Design of Lithium–Sulfur Batteries
No full textLithium–sulfur batteries (LSBs) are a promising emerging technology due to their high energy density, low-cost materials, and safety. However, their environmental sustainability is not yet well understood. This study conducted a prospective life cycle assessment (LCA) on two patented LSB models, using data from patents as the inventory: one with a standard sulfur cathode and another with a graphene–sulfur composite (GSC). The assessment is conducted for a functional unit of 1 Wh of produced electricity, adopting a cradle-to-gate system boundary and a prospective time horizon set to 2035. The LSB GSC model battery showed significantly better performance in terms of climate change and fossil depletion, with a 42% lower impact, mainly due to a reduction in the lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) content from 1205 mg Wh−1 to 250 mg Wh−1. However, the GSC model also had significant drawbacks, showing a 93% higher metal depletion and 49% higher water depletion than the standard sulfur battery. Building on an established patent-based prospective LCA approach, this work applies patent-derived quantitative inventories and patent-informed eco-design analysis to support environmentally informed design decisions for emerging LSB technologies prior to large-scale commercialization
Study on the Nonlinear Dynamic Behavior and Bifurcation of the Double-Rotor System Under the Coupling of Rubbing and Oil-Film Force
No full textSliding bearings–rotor systems are widely present in rotating machinery structures. The dynamic behavior triggered by friction and rub-impact faults is a key factor restricting the safe and stable operation of a rotor system. Existing studies mainly focus on analyzing dynamic characteristics but rarely explore the degree of friction and rub-impact in the system. This paper takes the sliding bearing–double-disk rotor system with friction and rub-impact as the research model, and defines the concept of the rubbing ratio. It analyzes the influence of relevant structural parameters on the system. The results reveal that the system exhibits rich nonlinear dynamics. Specifically, increasing either the rotor–stator clearance or the lubricant viscosity can drive the system into a broader regime of chaotic motion, while simultaneously reducing the extent of the rub-impact contact region. As the stator stiffness increases from 107 N/m to 9 × 107 N/m, the number of chaotic windows in the bifurcation diagram increases from one to three, while the maximum rubbing force rises by approximately 58% and the rubbing ratio increases from 50% to 56%. The phenomenon of coexisting attractors in the system is also revealed and analyzed. The above research results help to reveal the motion laws of this type of rotor system and have certain guiding significance for parameter matching and optimization design of the system dynamics
Plant-Forward Dietary Approaches to Reduce the Risk of Cardiometabolic Disease Among Hispanic/Latinx Adults Living in the United States: A Narrative Review
No full textBackground: Cardiometabolic risk (CMR), including obesity, dyslipidemia, hypertension, and impaired glucose regulation, disproportionately affects Hispanic/Latinx adults in the United States (U.S.). Although plant-forward dietary patterns are established as cardioprotective, less is known about how dietary patterns within Hispanic/Latinx subgroups relate to CMR. Methods: A narrative review was conducted of observational studies among U.S. Hispanic/Latinx adults (≥18 years) examining defined dietary patterns (a priori, a posteriori, or hybrid) in relation to CMR outcomes (e.g., BMI, waist circumference, blood pressure, glucose, lipids). Risk of bias was assessed using an adapted version of the Newcastle–Ottawa Scale. Results: Ten studies met the inclusion criteria, including Seventh-day Adventist Latinx, Puerto Rican adults, Mexican American adults, Hispanic women, and a national Hispanic cohort. Plant-forward dietary patterns were associated with lower BMI and waist circumference, lower triglycerides and fasting glucose, and higher HDL-C. In contrast, energy-dense patterns characterized by refined grains, added sugars, processed meats, fried foods, solid fats, and sugar-sweetened beverages were associated with greater adiposity, poorer lipid profiles, and higher blood pressure. Traditional rice-and-beans–based patterns observed in Puerto Rican and Mexican American groups were associated with central adiposity and higher metabolic syndrome prevalence, despite modestly higher intakes of fruits, vegetables, and fiber. Study quality ranged from good (n = 4) to very good (n = 6). Conclusions: Across Hispanic/Latinx subgroups, plant-forward dietary patterns were associated with favorable cardiometabolic profiles, whereas refined and animal-based patterns aligned with higher CMR. Given the predominance of cross-sectional evidence, these findings should be interpreted as associative rather than causal. Culturally grounded dietary counseling, along with additional longitudinal and intervention studies, is needed to support cardiometabolic health in these populations
Machine Learning-Based Prediction of Breakdown Voltage in High-Voltage Transmission Lines Under Ambient Conditions
No full textReliability and safety of high-voltage transmission lines are essential for stable and continuous operation of a power system. Environmental factors such as pressure, temperature, surface contamination, humidity, etc., significantly affect the dielectric strength of air, often causing unpredictable voltage breakdowns. This research presents a novel machine learning-based predictive framework that integrates Paschen’s Law with simulated and empirical data to estimate the breakdown voltage (Vbk) of transmission lines in various environmental conditions. The main contribution is to demonstrate that data-driven prediction of breakdown voltage (Vbk) using a hybrid machine learning model is in agreement with physical discharge theory. The model achieved root mean square error (RMSE) of 5.2% and mean absolute error (MAE) of 3.5% when validated against field data. Despite the randomness of avalanche breakdown, model predictions strongly match experimental measurements. This approach enables early detection of insulation stress, real-time monitoring, and optimises maintenance scheduling to reduce outages, costs, and safety risks. Its robustness is confirmed experimentally. Overall, this work advances the prediction of avalanche breakdown behaviour using machine learning