Search Results (29,663)

This study focuses on the nonlinear departure dynamics of spacecraft from the Near Rectilinear Halo Orbit (NRHO) to the outer regions of Selenocentric Space. By carefully selecting the combination of orbital parameters and the order of the evaluation process, it becomes possible to precisely identify the divergence moment and to reliably classify the subsequent dynamical space. An empirical divergence detection algorithm is proposed by integrating multiple parameters derived from multi-body dynamical models, including gravitational potentials and related quantities. In an applied analysis using this method, it is found that the majority of perturbed trajectories diverge into the outer Earth–Moon Vicinity, while transfers into the inner Earth–Moon Vicinity are relatively limited. Furthermore, transfers to Heliocentric Space are found to be dependent not on the magnitude of the initial perturbation but on the geometric configuration of the Sun, Earth, and Moon during the transfer phase. The investigation of the Sun’s initial phase reveals a rotationally symmetric structure in the perturbation distribution within the Sun–Earth–Moon system, as well as localized conditions under which the destination space varies significantly depending on the initial state. Identifying the divergence moment allows for comparative evaluation of the spacecraft’s nonlinear dynamical state, providing valuable insights for the development of safe and efficient transfer strategies from selenocentric orbits, including those originating from the NRHO. Full article

Background/Objectives: The COVID-19 pandemic has significantly intensified global concerns surrounding antimicrobial resistance (AMR), particularly in relation to tuberculosis (TB). In the European Union (EU), the reallocation of healthcare resources towards managing COVID-19 led to a de-prioritization of TB surveillance and control. This shift contributed to delays in TB diagnosis and treatment, creating conditions favorable for the emergence and spread of drug-resistant TB strains. This meta-analysis aims to assess epidemiological trends of drug-resistant TB across EU countries before, during, and after the pandemic and quantify the impact of COVID-19 on Mycobacterium tuberculosis resistance patterns. Methods: Data were obtained from the European Centre for Disease Prevention and Control (ECDC) covering 2015 to 2022. Following the TB incidence, the multidrug-resistant TB (MDR-TB) and rifampicin-resistant/MDR-TB (RR/MDR-TB) cases, as well as treatment success rates over 12- and 24-month periods, were analyzed. The analysis included 31 EU countries across three-time frames: pre-pandemic (2015–2019), pandemic onset (2020), and post-pandemic transition (2020–2022). Results: The pandemic was associated with a decrease in reported TB cases but a simultaneous increase in the proportion of MDR and RR/MDR cases. Treatment success rates showed a modest rise for 24-month regimens, while outcomes declined for 12-month therapies. Conclusions: These findings underscore the pandemic’s disruptive impact on TB control and highlight the need for renewed investment in diagnostic capacity, treatment access, and antimicrobial stewardship, in order to reduce antimicrobial resistance occurrence. Continued monitoring beyond 2022 is essential to fully understand long-term effects and inform future public health strategies. Full article

Bayesian optimization routines may have theoretical convergence results, but determining whether a run has converged in practice can be a subjective task. This paper provides a framework inspired by statistical process control for monitoring an optimization run for convergence. The maximum Expected Improvement (EI) tends to decrease during an optimization run, but decreasing EI is not sufficient for convergence. We consider both a decrease in EI as well as local stability of the variance in order to assess for convergence. The EI process is made more numerically stable through an expected log-normal approximation. An Exponentially Weighted Moving Average control chart is adapted for automated convergence analysis, which allows assessment of stability of both the EI and its variance. The success of the methodology is demonstrated on several examples. Full article

Compared to the L-filter, the LCL-filter provides superior high-frequency harmonic attenuation for a given inductance. However, it also introduces resonance issues that can compromise system stability. Consequently, the bandwidth of the inner current loop must be maintained well below the resonant frequency of the filter. This paper proposes a robust controller design strategy for LCL-filtered converters to extend the harmonic control range under wide variations in grid impedance. An analysis of the resonant controller phase-frequency characteristics reveals its capability to provide phase compensation up to 2π. Building on this finding, the damping ratio and phase leading angle are systematically optimized through a joint analysis of the phase characteristics introduced by the resonant controller and active damping, thereby enhancing system robustness. With these optimized parameters, the center frequency of the resonant controller can be tuned above the LCL-filter resonance frequency without inducing instability. In contrast to conventional methods, the proposed approach allows the LCL-filter to be designed with a lower resonance frequency. This enables improved attenuation of switching-frequency harmonics without compromising the tracking performance for higher-order harmonics. Such a capability is particularly beneficial in high-power and weak-grid scenarios, where the filter resonance frequency may fall to just a few hundred hertz. Experimental results validate the effectiveness of the proposed design strategy. Full article

In recent years, there has been growing recognition that reducing environmental pollution, particularly from building emissions, is essential for improving residents’ well-being. Buildings contribute substantially to worldwide greenhouse gas and pollutant emissions, making effective mitigation strategies a priority in achieving Sustainable Development Goals (SDGs). Using data from the 2021 China General Social Survey (CGSS), this study examines the relationship between perceived building environmental pollution and residents’ well-being, as well as the mechanism underlying this relationship, through an ordered probit model. The results indicate that higher levels of building environmental pollution significantly reduce residents’ well-being. To explore heterogeneity, the sample was further divided by urban–rural differences, local environmental protection expenditure level, and geographic region. The research found that residents with lower environmental protection expenditures, residents in rural areas and those in the central region are more likely to be negatively affected by building environmental pollution, with the correlation coefficients being −0.111, −0.104 and −0.101 respectively. Furthermore, the analysis indicates that annual income, the number of children, and type of work have moderating effects on this relationship, with correlation coefficients of 0.047, −0.054, and −0.095 respectively. Overall, this study provides empirical evidence for perceiving the social impact of building pollution in the context of building-related emissions and offers policy-related insights for strengthening environmental protection measures in the construction industry to enhance residents’ well-being. Full article

Accurate prediction of geopolymer concrete compressive strength is vital for sustainable construction. Traditional experiments are time-consuming and costly; therefore, computer-aided systems enable rapid and accurate estimation. This study evaluates three ensemble learning algorithms (Extreme Gradient Boosting (XGB), Random Forest (RF), and Light Gradient Boosting Machine (LightGBM)), as well as two baseline models (Support Vector Regression (SVR) and Artificial Neural Network (ANN)), for this task. To improve performance, hyperparameter tuning was conducted using Bayesian Optimization (BO). Model accuracy was measured using R², RMSE, MAE, and MAPE. The results demonstrate that the XGB model outperforms others under both default and optimized settings. In particular, the XGB-BO model achieved high accuracy, with RMSE of 0.3100 ± 0.0616 and R² of 0.9997 ± 0.0001. Furthermore, Shapley Additive Explanations (SHAP) analysis was used to interpret the decision-making of the XGB model. SHAP results revealed the most influential features for compressive strength of geopolymer concrete were, in order, coarse aggregate, curing time, and NaOH molar concentration. The graphical user interface (GUI) developed for compressive strength prediction demonstrates the practical potential of this research. It contributes to integrating the approach into construction practices. This study highlights the effectiveness of explainable machine learning in understanding complex material behaviors and emphasizes the importance of model optimization for making sustainable and accurate engineering predictions. Full article

The Atlantic blue crab (Callinectes sapidus) is an opportunistic invasive species in the Mediterranean that is negatively affecting biodiversity, fisheries, and tourism. In Italy, it is appreciated for its good meat quality, but the processing yield is low (21.87 ± 2.38%), generating a significant amount of by-products (72.45 ± 4.08%), which are underutilized. Valorizing this biomass is in line with circular economy principles and can improve both environmental and economic sustainability. This study aimed to valorize Atlantic blue crab by-products (BCBP), producing protein hydrolysates and assessing their in vitro bioactivities, in order to plan applications in animal food and related sectors. BCBP hydrolysates were obtained by enzymatic hydrolysis using Alcalase and Protamex enzymes. The treatment with Alcalase resulted in a higher degree of hydrolysis (DH = 23% in 205 min) compared to Protamex (DH = 14% in 175 min). Antioxidant activity of the hydrolisates was evaluated through DPPH, ABTS, reducing power and FRAP assays, as well as in vitro test in fibroblasts (HS-68). At 10 mg/mL, hydrolysates from both enzymes exhibited the maximum radical scavenging activity in DPPH and ABTS assays. In HS-68 cells, 0.5 mg/mL hydrolysates protected against H₂O₂-induced oxidative stress, showing a cell viability comparable to cells treated with 0.5 mM N-acetyl cysteine (NAC), as an antioxidant. Statistical analyses were performed using one-way ANOVA followed by Student–Newman–Keuls (SNK) or Games–Howell post hoc tests, with significance set at p < 0.05. Overall, both enzymes efficiently hydrolyzed BCBP proteins, generating hydrolysates with significant antioxidant activity and cytoprotective effects. These results demonstrate the potential to produce high-quality bioactive compounds from BCBPs, suitable for food, nutraceutical, and health applications. Scaling up this valorization process represents a viable strategy to improve sustainability and add economic value to the management of this invasive species, turning a problem in a resource. Full article

Ocular surface disorders such as dry eye disease are an increasingly encountered ophthalmic disorder, in which signs and symptoms can vary significantly from one patient to the next. Severe dry eye can be a challenge for the ophthalmic practitioner to manage. Contemporary management options are wide-ranging and include topical treatments, contact lenses, and surgical options. More recently, newer stem cell-based therapies have emerged, and early reports have shown promising outcomes. Meanwhile, other novel approaches, such as the eggshell membrane, are currently in development, and while no studies have yet reported on its use in ophthalmic applications, further developments in this area are expected. However, longer-term studies are needed in order to fully assess the safety and efficacy of these newer treatments. There are an increasing number of treatment options available for ocular surface disorders. This article provides an overview of some of the current treatment options that are available for severe ocular surface disorders, including dry eye disease, as well as insight into applications that are currently in development, which may show potential in the future. Full article

The term heart disease refers to a wide range of conditions that impact the heart and blood vessels. It continues to be a major global cause of morbidity and mortality. The narrowing or blockage of blood vessels, which can result in major medical events like heart attacks, angina (chest pain) or strokes, is a common issue linked to heart disease. In order to lower the risk of serious complications and facilitate prompt medical intervention, early diagnosis and prediction are essential. This study developed predictive models that can precisely identify people at risk by applying a variety of machine learning algorithms to a structured dataset on heart disease. Blood pressure, cholesterol, age, gender, and other health-related indicators are among the 13 essential characteristics that make up the dataset. Numerous machine learning models such as Naïve Bayes, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree, Random Forest, and others were trained using these features. Using the RapidMiner platform, which offered a visual environment for data preprocessing, model training, and performance analysis, all models were created and assessed. The best-performing model was the Naïve Bayes classifier which achieved an impressive accuracy rate of 90% after extensive testing and comparison of performance metrics like accuracy precision and recall. This outcome shows how well the model can predict heart disease in actual clinical settings. By supporting individualized health recommendations, enabling early diagnosis, and facilitating timely treatment, the effective application of such models can significantly benefit patients and healthcare professionals. Furthermore, heart disease incidence can be considerably decreased by identifying and addressing modifiable risk factors such as high blood pressure, elevated cholesterol, smoking, diabetes, and physical inactivity. In summary, machine learning has the potential to improve the identification and treatment of heart-related disorders. This study highlights the value of data-driven methods in healthcare and indicates that incorporating predictive models into standard medical procedures may enhance patient outcomes, lower healthcare expenses, and improve public health administration. Full article

Recent trends in research on rotating machinery diagnosis focus on contactless diagnostic technologies. In this paper, novel higher order spectral technologies, based on spectral moduli, are proposed. The proposed technologies estimate statistical dependencies between moduli of harmonics of bearing defect frequencies. Moduli of harmonics of bearing defect frequencies, which appear due to bearing faults, are statistically dependent. The Third Order Modulus (TOM) is a novel higher order spectral signal processing technology developed for rotating machinery diagnostics. The paper presents mathematical expressions for new technologies as well as a detailed description of the signal processing algorithm of motor current for bearings diagnostics. The TOM technology is comprehensively validated via experimental trials for motor bearing diagnosis via motor current signature analysis. Results of experimental trials clearly show that the TOM technology is highly effective for diagnosis of bearing defects. Estimates of the total probabilities of correct diagnosis provided by the TOM technology are 100%. The TOM technology is experimentally compared with the classic bicoherence (CB) technology using eight bearings: four pristine bearings and four damaged bearings with two damage types. Comparison has shown that the TOM technology is more effective than the CB technology. Full article

(1) Background: Fiberoptic Endoscopic Evaluation of Swallowing (FEES) is one of the two gold-standard tools for assessing oropharyngeal dysphagia (alongside Videofluoroscopic Swallowing Study). Although generally considered safe, concerns about complications persist, particularly in systems where FEES is not routine and professional roles differ. The aim of this study was to evaluate the safety of FEES performed by both speech-language pathologists (SLPs) and physicians, in order to provide evidence of its safety in a healthcare system where the procedure is not yet widely established and to identify patient subgroups potentially at higher risk of procedure-related complications. (2) Methods: This retrospective study analyzed 964 consecutive FEES procedures. Examinations were carried out by trained SLPs or physicians. Data included demographics, clinical status, operator qualifications, setting, and complications, classified as minor (vomiting, poor tolerance, early termination) or major (laryngospasm, epistaxis). (3) Results: The overall complication rate was 1.14% (11/964): 0.6% minor and 0.5% major. All events were self-limiting. Complication rates did not differ between SLPs (1.05%) and physicians (1.23%) or by experience, setting, drug use, penetration–aspiration scale score, or nasogastric tube. Four complications occurred in amyotrophic lateral sclerosis patients, suggesting higher risk. (4) Conclusions: FEES is safe and well tolerated when performed by either physicians or SLPs. These findings underscore the value of task sharing in dysphagia diagnostics, demonstrating that a shared model increases service capacity, reduces delays, and facilitates timely management of dysphagia. Full article

To meet the increasing demands of energy efficiency and real-time performance in autonomous driving systems, this paper presents a lightweight and robust LiDAR SLAM framework designed with power-aware considerations. The proposed system introduces three core innovations. First, it replaces traditional ordered point cloud indexing with a 2D range image projection, significantly reducing memory usage and enabling efficient feature extraction with curvature-based criteria. Second, a multi-stage outlier rejection mechanism is employed to enhance feature robustness by adaptively filtering occluded and noisy points. Third, we propose a dynamically filtered local mapping strategy that adjusts keyframe density in real time, ensuring geometric constraint sufficiency while minimizing redundant computation. These components collectively contribute to a SLAM system that achieves high localization accuracy with reduced computational load and energy consumption. Experimental results on representative autonomous driving datasets demonstrate that our method outperforms existing approaches in both efficiency and robustness, making it well-suited for deployment in low-power and real-time scenarios within intelligent transportation systems. Full article

This work addresses the problem of synurbization, with its causes and effects specified using the example of wild boar (Sus scrofa). It presents basic biological parameters of the species, including those that promote its synurbization—small habitat demands, omnivorism, as well as ecological, behavioral, and demographic flexibility. It also discusses intra-species transformations stemming from wild boar adaptation to the urban space and pinpoints habitat fragmentation, ecological restoration, and phenotypic flexibility as the underlying causes of people–wild boar interactions. These interactions are primarily negative because wild boars attack humans and domestic animals and cause many traffic accidents. An analysis of the literature included in this study shows that, unfortunately, there are currently no fully effective methods that could protect urban areas and their inhabitants from the threats posed by wild boars. In order for sustainable urban development policies to be effectively implemented, there is a need for intensive, holistic research and cooperation between experts in many fields: wildlife, economics, public health, sociology, ethics, psychology, and urban planning. The synurbanization of wild boars is a large and growing social problem, but from an ecological perspective, there is a need to take action and develop methods to mitigate human/wild animal conflicts, not only from a human perspective. A one-sided view and action can be a threat to many animal species. Full article

Speculative execution attacks significantly compromise the security of modern processors by enabling information leakage. These well-known attacks exploit speculative cache-based covert channels to effectively exfiltrate secret data by altering cache states. Existing hardware defenses specifically designed to prevent cache-based covert channels are effective at blocking explicit channels. However, their protection against implicit attack variants remains limited, since these hardware defenses do not fully eliminate secret-dependent microarchitectural changes in caches. In this paper, we propose TrackRISC, a framework which comprises (i) a refined implicit attack flow model specifically for the exploration and analysis of implicit cache-based speculative execution attacks which severely compromise the security of existing hardware defenses, and (ii) a security-enhanced tracking and mitigation microarchitecture, termed TrackRISC-Defense, designed to mitigate both implicit and explicit attack variants that use speculative cache-based covert channels. To obtain realistic hardware evaluation results, we implement and evaluate both TrackRISC-Defense and a representative existing defense on top of the Berkeley’s out-of-order RISC-V processor core (SonicBOOM) using the VCU118 FPGA platform running Linux. Compared to the representative existing defense which incurs a performance overhead of 13.8%, TrackRISC-Defense ensures stronger security guarantees with a performance overhead of 19.4%. In addition, TrackRISC-Defense can mitigate both explicit and implicit speculative cache-based covert channels with a register-based hardware resource overhead of 0.4%. Full article

While weighted multi-model approaches are widely used to improve predictive capability, hydrological models (HMs) and their weighted combinations that perform well under past conditions may not guarantee robustness under future climate scenarios. Furthermore, the extent to which weighting schemes influence the propagation of runoff projection uncertainty remains insufficiently explored. Therefore, this study evaluates the capacity of strategies that weight monthly scale HMs to narrow runoff projection uncertainty. Since standard approaches rely only on historical simulation skill and offer static weighting, this study introduces a refined framework, the Uncertainty Optimizing Multi-Model Ensemble (UO-MME), which dynamically considers the trade-offs between calibration performance and projection uncertainty. In performing the uncertainty decomposition, a total of 140 ensemble runoff projections, generated through a modelling chain comprising five GCMs, two emission scenarios, two downscaling methods, and seven HMs, were analyzed for Beydag and Tahtali watersheds in Türkiye. Results indicate that standard techniques, such as Bayesian model averaging, ordered weighted averaging, and Granger–Ramanathan averaging, led to either marginal reductions or noticeable increases in projection uncertainty, depending on the case and projection period. Conversely, the UO-MME achieved average reductions in projection uncertainty of around 30% across the two watersheds by balancing the influences of climate signals produced by GCMs that are reflected in the projections through HMs while maintaining high simulation accuracy, as indicated by Nash–Sutcliffe efficiency values exceeding 0.75. Although not designed to eliminate inherently irreducible uncertainty, the UO-MME framework helps temper the inflation of noisy GCM signals in runoff responses, providing more balanced hydrological projections for water resources planning. Full article