Search Results (26)

A realistic description of halo nuclei, characterized by low-lying breakup thresholds, requires a proper treatment of continuum effects. We have developed an ab initio approach, the No-Core Shell Model with Continuum (NCSMC), capable of describing both bound and unbound states in light nuclei in a unified way. With chiral two- and three-nucleon interactions as the only input, we can predict the structure and dynamics of halo and other light nuclei and, by comparing to available experimental data, test the quality of chiral nuclear forces. We review NCSMC calculations of weakly bound states and resonances of the exotic halo nuclei ⁶He, ⁸B, ¹¹Be, and ¹⁵C. For the latter, we discuss its production in the capture reaction ¹⁴C(n,$\gamma$)¹⁵C. We highlight the challenges of a description of ⁶He as a Borromean n-n-⁴He system. Finally, we present our calculations of excited states in ¹⁰Be exhibiting a one-neutron halo structure and a large scale No-Core Shell Model investigation of ¹¹Li as a precursor of a full n-n-⁹Li NCSMC study. Full article

The nuclear force is central to our understanding of complex nuclear phenomena and to the applications of nuclear techniques. The non-perturbative nature of low-energy strong interaction and color confinement have provided an ab initio understanding of nuclear force, a challenge for almost a century, since the pioneering work of Yukawa. Since 1990, chiral effective field theory (ChEFT) has become the de facto standard for describing nuclear interactions; most prior studies employed heavy-baryon chiral perturbation theory. Only recently, there have been successful attempts to construct a chiral nuclear force employing covariant baryon chiral perturbation theory. In this work, we review recent developments and applications of relativistic chiral nuclear forces. We first elaborate on the necessity of relativistic/covariant theories, then present the construction of the first high-precision relativistic chiral nuclear force up to next-to-next-to-leading order (NNLO), and discuss the ongoing progress in higher-order nucleon–nucleon (NN) and n-d scattering, as well as their applications in nuclear matter, finite nuclei, and hypernuclear systems. Finally, we summarize the achievements and outline the future outlook of this research field. Full article

Background/Objectives: Antibiotic-resistant strains have been reported in aquatic ecosystems, with varying prevalence and resistance patterns by region. In Tamaulipas, Mexico, little information has been generated on this topic, making it difficult to estimate their potential risk to environmental and human health. Therefore, the objective of this study was to evaluate the presence and virulence of antibiotic-resistant strains of Pseudomonas aeruginosa in environmental water from Tamaulipas, Mexico. Methods: One hundred water samples were collected from different water bodies in Tamaulipas to identify P. aeruginosa by PCR and MALDI-TOF, virulence gene detection, antimicrobial susceptibility testing, and detection class 1 integrons. Results: In this study, 109 P. aeruginosa strains were isolated. Eight virulence genes were identified in 47.7% to 80.7% of the strains, with the rhlAB gene being the most frequent. The strains showed resistance or intermedia resistance to 10 of the 16 antibiotics tested, in a range of resistance values 0.9–66.2%. In total, 100% (109/109) were susceptible to ceftazidime (CAZ), gentamicin (GM), amikacin (AN), netilmicin (NET), tobramycin (NN) and norfloxacin (NOR), and 65.7% were resistant to ticarcillin/clavulanic acid and 53.5% to ticarcillin; the resistance to the remaining antibiotics was between 19.4% and 0.9%. The class 1 integron was not identified in any of the strains analyzed. Conclusions:P. aeruginosa in environmental waters of Tamaulipas showed potential to cause infections and low rates of resistance to most of the antibiotics tested. However, 20% were resistant to one of the most common treatments, which could pose a risk to public health. Full article

Transgenic Bt maize commercialization has become a critical pest management strategy against lepidopteran insects in southwest China, but its ecological impact on arthropod biodiversity remains insufficiently characterized. This two-year field investigation (2023–2024) conducted in Bazhong City, Sichuan Province utilized systematic field monitoring to compare arthropod community dynamics between conventional maize and Bt-Cry1Ab maize (DBN9936) cultivation systems. This study documented 575,970 arthropod specimens representing 80 species/types across 45 families and 17 orders. Analysis of variance revealed significant differences (p < 0.05) between non-Bt and Bt maize in the abundance and species richness of target herbivorous pests, non-target herbivorous pests, and natural enemy insects. Field investigations revealed a notable absence of Macrocentrus cingulum, a key larval parasitoid of Ostrinia furnacalis, in Bt-maize plots compared to conventional counterparts. The populations of non-target herbivorous pests and natural enemies such as Aphididae, Chrysoperla sinica, Frankliniella tenuicornis, and Orius sauteri were higher in Bt maize fields than in non-Bt maize fields, while the populations of target herbivorous pests including O. furnacalis and Mythimna loreyi were lower than those in non-Bt maize fields. However, no significant differences (p > 0.05) were observed in arthropod abundance, species richness, or in a suite of ecological indices including the Simpson diversity index, Shannon–Wiener diversity index, Pielou evenness index, McIntosh diversity index, and community stability indices (N_n/N_p, N_d/N_p, and S_d/S_p). Redundancy analysis identified maize growth stages (6.75% variance explained) and interannual variations (2.44%) as principal drivers of arthropod community dynamics, with maize genotype contributing minimally (1.53%). These findings demonstrate that Bt-Cry1Ab maize (DBN9936) cultivation maintains functional arthropod community structure while effectively controlling target pests, providing substantial empirical evidence to support its sustainable deployment in southern China’s agricultural landscapes. Full article

NaNbO₃ (NN), known for the complexity of its phase transition sequence, is antiferroelectric (AFE) at room temperature, while both LiNbO₃ (LN) and KNbO₃ (KN) are ferroelectric (FE). The origin of ferroelectricity in ABO₃ perovskites is believed to lie in the B-O hybridization, but the origin of antiferroelectricity remains unclear. Recent ab initio studies have shown that the same B-O hybridization is necessary in AFE and proposed an additional, anharmonic contribution to the potential of the A-site atom as the crucial difference between FE and AFE perovskites. We used structure factors obtained from X-ray diffraction experiments in combination with the Maximum Entropy Method to obtain electron densities for LN, KN, and NN and identify differences in their bonding behavior. We present experimental evidence for anharmonic A-site contributions of varying strength in alkali niobates, pointing at a new path for the design of (anti-)ferroelectric materials. Full article

This study presents a novel artificial intelligence-driven architecture for real-time sensor fusion in autonomous vehicles (AVs), leveraging Apache Kafka and MongoDB for synchronous and asynchronous data processing to enhance resilience against sensor failures and dynamic conditions. We introduce Chaotic AdaBoost (CAB), an advanced variant of AdaBoost that integrates a logistic chaotic map into its weight update process, overcoming the limitations of deterministic ensemble methods. CAB is evaluated alongside k-Nearest Neighbors (kNNs), Artificial Neural Networks (ANNs), standard AdaBoost (AB), Gradient Boosting (GBa), and Random Forest (RF) for speed and acceleration prediction using CARLA simulator data. CAB achieves a superior 99.3% accuracy (MSE: 0.018 for acceleration, 0.010 for speed; MAE: 0.020 for acceleration, 0.012 for speed; R²: 0.993 for acceleration, 0.997 for speed), a mean Time-To-Collision (TTC) of 3.2 s, and jerk of 0.15 m/s³, outperforming AB (98.5%, MSE: 0.15, TTC: 2.8 s, jerk: 0.22 m/s³), GB (99.1%), ANN (98.2%), RF (97.5%), and kNN (87.0%). This logistic map-enhanced adaptability, reducing MSE by 88% over AB, ensures robust anomaly detection and data fusion under uncertainty, critical for AV safety and comfort. Despite a 20% increase in training time (72 s vs. 60 s for AB), CAB’s integration with Kafka’s high-throughput streaming maintains real-time efficacy, offering a scalable framework that advances operational reliability and passenger experience in autonomous driving. Full article

Low-cost air quality sensors (LCSs) are increasingly being used in environmental monitoring due to their affordability and portability. However, their sensitivity to environmental factors can lead to measurement inaccuracies, necessitating effective calibration methods to enhance their reliability. In this study, an Internet of Things (IoT)-based air quality monitoring system was developed and tested using the most commonly preferred sensor types for air quality measurement: fine particulate matter (PM_2.5), carbon dioxide (CO₂), temperature, and humidity sensors. To improve sensor accuracy, eight different machine learning (ML) algorithms were applied: Decision Tree (DT), Linear Regression (LR), Random Forest (RF), k-Nearest Neighbors (kNN), AdaBoost (AB), Gradient Boosting (GB), Support Vector Machines (SVM), and Stochastic Gradient Descent (SGD). Sensor performance was evaluated by comparing measurements with a reference device, and the best-performing ML model was determined for each sensor. The results indicate that GB and kNN achieved the highest accuracy. For CO₂ sensor calibration, GB achieved R² = 0.970, RMSE = 0.442, and MAE = 0.282, providing the lowest error rates. For the PM_2.5 sensor, kNN delivered the most successful results, with R² = 0.970, RMSE = 2.123, and MAE = 0.842. Additionally, for temperature and humidity sensors, GB demonstrated the highest accuracy with the lowest error values (R² = 0.976, RMSE = 2.284). These findings demonstrate that, by identifying suitable ML methods, ML-based calibration techniques can significantly enhance the accuracy of LCSs. Consequently, they offer a viable and cost-effective alternative to traditional high-cost air quality monitoring systems. Future studies should focus on long-term data collection, testing under diverse environmental conditions, and integrating additional sensor types to further advance this field. Full article

This study examines the potential of machine learning (ML) and deep learning (DL) techniques for classifying microplastics using Fourier-transform infrared (FTIR) spectroscopy. Six commonly used industrial plastics (PET, HDPE, PVC, LDPE, PP, and PS) were analyzed. A significant contribution of this research is the use of broader and more varied spectral ranges than those typically reported in the state of the art. Furthermore, the impact of different normalization techniques (Min-Max, Max-Abs, Sum of Squares, and Z-Score) on classification accuracy was evaluated. The study assessed the performance of ML algorithms, such as k-nearest neighbors (k-NN), support vector machines (SVM), naive Bayes (NB), random forest (RF), and artificial neural networks architectures (including convolutional neural networks (CNNs) and multilayer perceptrons (MLPs)). Models were trained and validated using the FTIR-PLASTIC-c4 dataset with a 10-fold cross-validation approach to ensure robustness. The results showed that Z-score normalization significantly improved stability and generalization across most models, with CNN, MLP, and RF achieving near-perfect values in accuracy, precision, recall, and F1-score. In contrast, the sum of squares normalization was less effective, particularly for CNNs, due to its sensitivity to scale and data distribution. Notably, naive Bayes consistently underperformed because of its limitations in analyzing complex spectral data. The findings highlight the effectiveness of FTIR spectra with broad and variable ranges for the automated classification of microplastics using ML techniques, along with appropriate normalization methods. Full article

This study presents a novel approach for filament recognition in fused filament fabrication (FFF) processes using a multi-spectral spectroscopy sensor module combined with machine learning techniques. The sensor module measures 18 wavelengths spanning the visible to near-infrared spectra, with a custom-designed shroud to ensure systematic data collection. Filament samples include polylactic acid (PLA), thermoplastic polyurethane (TPU), thermoplastic copolyester (TPC), carbon fibre, acrylonitrile butadiene styrene (ABS), and ABS blended with Carbon fibre. Data are collected using the Triad Spectroscopy module AS7265x (composed of AS72651, AS72652, AS72653 sensor units) positioned at three measurement distances (12 mm, 16 mm, 20 mm) to evaluate recognition performance under varying configurations. Machine learning models, including k-Nearest Neighbors (kNN), Logistic Regression, Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP), are employed with hyperparameter tuning applied to optimise classification accuracy. Results show that the data collected on the AS72651 sensor, paired with the SVM model, achieves the highest accuracy of 98.95% at a 20 mm measurement distance. This work introduces a compact, high-accuracy filament recognition module that can enhance the autonomy of multi-material 3D printing by dynamically identifying and switching between different filaments, optimising printing parameters for each material, and expanding the versatility of additive manufacturing applications. Full article

Unreinforced masonry buildings are highly vulnerable to earthquake damage due to their limited ability to withstand lateral loads, compared to other structures. Therefore, a detailed assessment of the seismic response and resultant damage associated with such buildings becomes necessary. The present study employs machine learning models to effectively predict the seismic response and classify the damage level for a benchmark unreinforced masonry building. In this regard, eight regression-based models, namely, Linear Regression (LR), Stepwise Linear Regression (SLR), Ridge Regression (RR), Support Vector Machine (SVM), Gaussian Process Regression (GPR), Decision Tree (DT), Random Forest (RF), and Neural Networks (NN), were used to predict the building’s responses. Additionally, eight classification-based models, namely, Naïve Bayes (NB), Discriminant Analysis (DA), K-Nearest Neighbours (KNN), Adaptive Boosting (AB), DT, RF, SVM, and NN, were explored for the purpose of categorizing the damage states of the building. The material properties of the masonry and the earthquake intensity were considered as the input parameters. The results from the regression models indicate that the GPR model efficiently predicts the seismic response with larger coefficients of determination and smaller root mean square error values than other models. Among the classification-based models, the RF, AB, and NN models effectively classify the damage states with accuracy levels of 92.9%, 91.1%, and 92.6%, respectively. In conclusion, the overall performance of the non-parametric models, such as GPR, NN, and RF, was found to be better than that of the parametric models. Full article

The safety of the airspace could be improved by the use of visual methods for the detection and tracking of aircraft. However, in the case of the small angular size of airplanes and the high noise level in the image, sufficient use of such methods might be difficult. By using the ConvNN (Convolutional Neural Network), it is possible to obtain a detector that performs the segmentation task for aircraft images that are very small and lost in the background noise. In the learning process, a database of actual aircraft images was used. Using the Monte Carlo method, four types of Max algorithms, i.e., Pixel Value, Min. Pixel Value, and Max. Abs. Pixel Value, were compared with ConvNN’s forward architecture. The obtained results showed superior detection with ConvNN. For example, if the standard deviation equals 0.1, it was twice as large. Deep dream analysis for network layers is presented, which shows a preference for images with horizontal contrast lines. The proposed solution uses the processed image values for the tracking process with the raw data using the Track-Before-Detect method. Full article

Effectively addressing the formation control of ABS-assisted hovercraft systems with heterogeneities, unavailable leaders’ convex combination states, nonlinearities, and mixed disturbances poses significant challenges. This paper proposes a distributed neuroadaptive formation tracking strategy of ABS-assisted hovercraft systems for the first time, where aerial base stations (ABSs) are composed of unmanned aerial vehicles (UAVs) for data distribution and computation offloading. Firstly, UAVs are designed to track the virtual-leader while shaping a fixed formation, and the observer is devised for each follower hovercraft to estimate the convex combination states of UAVs. Then, output regulation equations are employed to transform heterogeneous systems into a compact form via the Kronecker product, while neural networks (NNs) are introduced to compensate for model nonlinearities. Furthermore, based on random differential equations (RDEs) combined with Lyapunov theory, the noise-to-state practical stability in probability (NSPS-P) property of the error dynamics under mixed disturbances can be obtained. Finally, simulation examples demonstrate that the outputs of follower hovercrafts rapidly achieve a time-varying formation and rotate around convex combination states of leader UAVs simultaneously. Full article

Natural Killer (NK) cells have the potential to eliminate HIV-1-infected cells by antibody-dependent cellular cytotoxicity (ADCC). NK cell activation is tightly regulated by the engagement of its inhibitory and activating receptors. The activating receptor CD16 drives ADCC upon binding to the Fc portion of antibodies; NK cell activation is further sustained by the co-engagement of activating receptors NTB-A and 2B4. During HIV-1 infection, Nef and Vpu accessory proteins contribute to ADCC escape by downregulating the ligands of NTB-A and 2B4. HIV-1 also evades ADCC by keeping its envelope glycoproteins (Env) in a “closed” conformation which effectively masks epitopes recognized by non-neutralizing antibodies (nnAbs) which are abundant in the plasma of people living with HIV. To achieve this, the virus uses its accessory proteins Nef and Vpu to downregulate the CD4 receptor, which otherwise interacts with Env and exposes the epitopes recognized by nnAbs. Small CD4-mimetic compounds (CD4mc) have the capacity to expose these epitopes, thus sensitizing infected cells to ADCC. Given the central role of NK cell co-activating receptors NTB-A and 2B4 in Fc-effector functions, we studied their contribution to CD4mc-mediated ADCC. Despite the fact that their ligands are partially downregulated by HIV-1, we found that both co-activating receptors significantly contribute to CD4mc sensitization of HIV-1-infected cells to ADCC. Full article

The vast majority of antibodies generated against a virus will be non-neutralising. However, this does not denote an absence of protective capacity. Yet, within the field, there is typically a large focus on antibodies capable of directly blocking infection (neutralising antibodies, NAbs) of either specific viral strains or multiple viral strains (broadly-neutralising antibodies, bNAbs). More recently, a focus on non-neutralising antibodies (nNAbs), or neutralisation-independent effects of NAbs, has emerged. These can have additive effects on protection or, in some cases, be a major correlate of protection. As their name suggests, nNAbs do not directly neutralise infection but instead, through their Fc domains, may mediate interaction with other immune effectors to induce clearance of viral particles or virally infected cells. nNAbs may also interrupt viral replication within infected cells. Developing technologies of antibody modification and functionalisation may lead to innovative biologics that harness the activities of nNAbs for antiviral prophylaxis and therapeutics. In this review, we discuss specific examples of nNAb actions in viral infections where they have known importance. We also discuss the potential detrimental effects of such responses. Finally, we explore new technologies for nNAb functionalisation to increase efficacy or introduce favourable characteristics for their therapeutic applications. Full article

This study aims to analyze the variables that affect the accidents experienced by e-scooter users and to estimate the probability of an accident during travel with an e-scooter vehicle. The data of e-scooter drivers, offered for use via rental application in 15 different cities of Turkey, were run in this study. The methodology of this study consists of testing the effects of the input parameters with the statistical analysis of the data, estimating the probability of an e-scooter accident with machine learning, and calculating the optimum values of the input parameters to minimize e-scooter accidents. By running SVM, RF, AB, kNN, and NN algorithms, four statuses (completed, injured, material damage, and nonapplicable) likely to be encountered by shared e-scooter drivers during the journey are estimated in this study. The F1 score values of the SVM, RF, kNN, AB, and NN algorithms were calculated as 0.821, 0.907, 0.839, 0.928, and 0.821, respectively. The AB algorithm showed the best performance with high accuracy. In addition, the highest consistency ratio in the ML algorithms belongs to the AB algorithm, which has a mean value of 0.930 and a standard deviation value of 0.178. As a result, the rental experience, distance, driving time, and driving speed for a female driver were calculated as 100, 10.44 km, 48.33 min, and 13.38 km/h, respectively, so that shared e-scooter drivers can complete their journey without any problems. The optimum values of the independent variables of the rental experience, distance, driving time, and driving speed for male drivers were computed as 120, 11.49 km, 52.20 min, and 17.28 km/h, respectively. Finally, this study generally provides a guide to authorized institutions so that customers who use shared and rentable micro-mobility e-scooter vehicles do not have problems during the travel process. Full article