Search Results (3,619)

A series of rivastigmine hybrids, incorporating rivastigmine fragments (RIV) and a set of different antioxidant scaffolds, were designed, synthesized, and evaluated as multifunctional agents for the potential therapy of Alzheimer’s disease (AD). In vitro bioactivity assays indicated that some compounds have very good antioxidant (radical-scavenging) activity. The compounds also displayed good inhibitory activity against cholinesterases, though the bigger-sized hybrids showed higher inhibitory ability for butyrylcholinesterase (BChE) than for acetylcholinesterase (AChE), due to the larger active site cavity of BChE. All the hybrids exhibited an inhibition capacity for self-induced amyloid-β (Aβ_1–42) aggregation. Furthermore, cell assays demonstrated that some compounds showed capacity for rescuing neuroblastoma cells from toxicity induced by reactive oxygen species (ROS). Among these RIV hybrids, the best in vitro multifunctional capacity was found for the caffeic acid derivatives enclosing catechol moieties (4AY5, 4AY6), though the Trolox derivatives (4AY2, 4BY2) presented the best cell neuroprotective activity against oxidative damage. Molecular-docking studies provided structural insights into the binding modes of RIV-based hybrids to the cholinesterases, revealing key interaction patterns despite some lack of correlation with inhibitory potency. Overall, the balanced multifunctional profiles of these hybrids render them potentially promising candidates for treating AD, thus deserving further investigation. Full article

This paper presents a hybrid indoor localization framework combining time difference of arrival (TDoA) measurements with a swarm intelligence optimization technique. To address the nonlinear optimization challenges in three-dimensional (3D) indoor localization via TDoA measurements, we systematically evaluate the artificial bee colony (ABC) algorithm and chimpanzee optimization algorithm (ChOA). Through comprehensive Monte Carlo simulations in a cubic 3D environment with eight beacons, our comparative analysis reveals that the ChOA achieves superior localization accuracy while maintaining computational efficiency. Building upon the ChOA framework, we introduce a multi-beacon fusion strategy incorporating a local outlier factor-based linear weighting mechanism to enhance robustness against measurement noise and improve localization accuracy. This approach integrates spatial density estimation with geometrically consistent weighting of distributed beacons, effectively filtering measurement outliers through adaptive sensor fusion. The experimental results show that the proposed algorithm exhibits excellent convergence performance under the condition of a low population size. Its anti-interference capability against Gaussian white noise is significantly improved compared with the baseline algorithms, and its anti-interference performance against multipath noise is consistent with that of the baseline algorithms. However, in terms of dealing with UWB device failures, the performance of the algorithm is slightly inferior. Meanwhile, the algorithm has relatively good time-lag performance and target-tracking performance. The study provides theoretical insights and practical guidelines for deploying reliable localization systems in complex indoor environments. Full article

Reservoir-induced landslides threaten infrastructures and downstream communities, making continuous deformation monitoring vital. Time-series InSAR, notably the SBAS algorithm, provides high-precision surface-displacement mapping but suffers from voids due to layover/shadow effects and temporal decorrelation. Existing deep-learning approaches often operate on fixed-size patches or ignore irregular spatio-temporal dependencies, limiting their ability to recover missing pixels. With this objective, a hybrid spatio-temporal Graph Attention (ST-GAT) framework was developed and trained on SBAS-InSAR values using 24 influential features. A unified spatio-temporal graph is constructed, where each node represents a pixel at a specific acquisition time. The nodes are connected via inverse distance spatial edges to their K-nearest neighbors, and they have bidirectional temporal edges to themselves in adjacent acquisitions. The two spatial GAT layers capture terrain-driven influences, while the two temporal GAT layers model annual deformation trends. A compact MLP with per-map bias converts the fused node embeddings into normalized LOS estimates. The SBAS-InSAR results reveal LOS deformation, with 48% of missing pixels and 20% located near the Dasu dam. ST D-GAT reconstructed fully continuous spatio-temporal displacement fields, filling voids at critical sites. The model was validated and achieved an overall R² (0.907), ρ (0.947), per-map R² ≥ 0.807 with RMSE ≤ 9.99, and a ROC-AUC of 0.91. It also outperformed the six compared baseline models (IDW, KNN, RF, XGBoost, MLP, simple-NN) in both RMSE and R². By combining observed LOS values with 24 covariates in the proposed model, it delivers physically consistent gap-filling and enables continuous, high-resolution landslide monitoring in radar-challenged mountainous terrain. Full article

The COVID-19 pandemic’s rapid shift to working from home has fundamentally challenged residential acoustic design, which traditionally prioritises rest and relaxation rather than sustained concentration. However, a clear gap exists in understanding how acoustic needs and the subjective evaluation of soundscape appropriateness (SA) differ between these conflicting activities within the same domestic space. Addressing this gap, this study reveals critical differences in how people experience and evaluate home soundscapes during work versus relaxation activities in the same residential spaces. Through an online survey of 247 Chinese participants during lockdown, we assessed soundscape perception attributes, the perceived saliencies of various sound types, and soundscape appropriateness (SA) ratings while working and relaxing at home. Our findings demonstrate that working at home creates a more demanding acoustic context: participants perceived indoor soundscapes as significantly less comfortable and less full of content when working compared to relaxing (p < 0.001), with natural sounds becoming less noticeable (−13.3%) and distracting household sounds more prominent (+7.5%). Structural equation modelling revealed distinct influence mechanisms: while comfort significantly mediates SA enhancement in both activities, the effect is stronger during relaxation (R² = 0.18). Critically, outdoor man-made noise, building-service noise, and neighbour sounds all negatively impact SA during work, with neighbour sounds showing the largest detrimental effect (total effect size = −0.17), whereas only neighbour sounds and outdoor man-made noise significantly disrupt relaxation activities. Additionally, natural sounds act as a positive factor during relaxation. These results expose a fundamental mismatch: existing residential acoustic environments, designed primarily for rest, fail to support the cognitive demands of work activities. This study provides evidence-based insights for acoustic design interventions, emphasising the need for activity-specific soundscape considerations in residential spaces. As hybrid work arrangements become the norm post-pandemic, our findings highlight the urgency of reimagining residential acoustic design to accommodate both focused work and restorative relaxation within the same home. Full article

In recent years, significant progress has been made in breast reconstructive surgery, particularly with the use of three-dimensional (3D) disassemblable scaffolds. Reconstructive plastic surgery aimed at restoring the shape and size of the mammary gland offers medical, psychological, and social benefits. Using autologous tissues allows surgeons to recreate the appearance of the mammary gland and achieve tactile sensations similar to those of a healthy organ while minimizing the risks associated with implants; 3D disassemblable scaffolds are a promising solution that overcomes the limitations of traditional methods. These constructs offer the potential for patient-specific anatomical adaptation and can provide both temporary and long-term structural support for regenerating tissues. One of the most promising approaches in post-mastectomy breast reconstruction involves the use of autologous cellular and tissue components integrated into either synthetic scaffolds—such as polylactic acid (PLA), polyglycolic acid (PGA), poly(lactic-co-glycolic acid) (PLGA), and polycaprolactone (PCL)—or naturally derived biopolymer-based matrices, including alginate, chitosan, hyaluronic acid derivatives, collagen, fibrin, gelatin, and silk fibroin. In this context, two complementary research directions are gaining increasing significance: (1) the development of novel hybrid biomaterials that combine the favorable characteristics of both synthetic and natural polymers while maintaining biocompatibility and biodegradability; and (2) the advancement of three-dimensional bioprinting technologies for the fabrication of patient-specific scaffolds capable of incorporating cellular therapies. Such therapies typically involve mesenchymal stromal cells (MSCs) and bioactive signaling molecules, such as growth factors, aimed at promoting angiogenesis, cellular proliferation, and lineage-specific differentiation. In our review, we analyze existing developments in this area and discuss the advantages and disadvantages of 3D disassemblable scaffolds for mammary gland reconstruction, as well as prospects for their further research and clinical use. Full article

Sheep face recognition technology is critical in key areas such as individual sheep identification and behavior monitoring. Existing sheep face recognition models typically require high computational resources. When these models are deployed on mobile or embedded devices, problems such as reduced model recognition accuracy and increased recognition time arise. To address these problems, an improved Parameter Fusion Lightweight You Only Look Once (PFL-YOLO) sheep face recognition model based on YOLOv8n is proposed. In this study, the Efficient Hybrid Conv (EHConv) module is first integrated to enhance the extraction capability of the model for sheep face features. At the same time, the Residual C2f (RC2f) module is introduced to facilitate the effective fusion of multi-scale feature information and improve the information processing capability of the model; furthermore, the Efficient Spatial Pyramid Pooling Fast (ESPPF) module was used to fuse features of different scales. Finally, parameter fusion optimization work was carried out for the detection head, and the construction of the Parameter Fusion Detection (PFDetect) module was achieved, which significantly reduced the number of model parameters and computational complexity. The experimental results show that the PFL-YOLO model exhibits an excellent performance–efficiency balance in sheep face recognition tasks: mAP@50 and mAP@50:95 reach 99.5% and 87.4%, respectively, and the accuracy is close to or equal to the mainstream benchmark model. At the same time, the number of parameters is only 1.01 M, which is reduced by 45.1%, 83.7%, 66.6%, 71.4%, and 61.2% compared to YOLOv5n, YOLOv7-tiny, YOLOv8n, YOLOv9-t, and YOLO11n, respectively. The size of the model was compressed to 2.1 MB, which was reduced by 44.7%, 82.5%, 65%, 72%, and 59.6%, respectively, compared to similar lightweight models. The experimental results confirm that the PFL-YOLO model maintains high accuracy recognition performance while being lightweight and can provide a new solution for sheep face recognition models on resource-constrained devices. Full article

Hybridization of solar and wind energy sources is a promising solution to enhance the dispatch capability of renewables. The complementarity of wind and solar radiation, as well as the sharing of transmission lines and other infrastructures, can notably benefit the deployment of renewable power. Mapping of hybrid solar–wind potential can help identify new emplacements or existing power facilities where an extension with a hybrid system might work. This paper presents an analysis of a hybrid solar–wind potential by considering a reference power plant of 40 MW in the Iberian Peninsula and comparing the hybrid and non-hybrid energy generated. The generation of energy is estimated using SAM for a typical meteorological year, using PVGIS and ERA5 meteorological information as input. Modeling the hybrid plant in relation to individual PV and wind power plants minimizes the dependence on technical and economic input data, allowing for the expression of potential hybridization analysis in relative numbers through maps. Correlation coefficient and capacity factor maps are presented here at different time scales, showing the complementarity in most of the spatial domain. In addition, economic analysis in comparison with non-hybrid power plants shows a reduction of around 25–30% in the LCOE in many areas of interest. Finally, a sizing sensitivity analysis is also performed to select the most beneficial sharing between PV and wind. Full article

Background: Chryseobacterium indologenes, an environmental bacterium, is increasingly recognized as an emerging nosocomial pathogen, particularly in Asia, and is often characterized by multidrug resistance. Objectives: This study aimed to investigate the genomic features of clinical C. indologenes isolates from Maharaj Nakorn Chiang Mai Hospital, Thailand, to understand their mechanisms of multidrug resistance, virulence factors, and mobile genetic elements (MGEs). Methods: Twelve C. indologenes isolates were identified, and their antibiotic susceptibility profiles were determined. Whole genome sequencing (WGS) was performed using a hybrid approach combining Illumina short-reads and Oxford Nanopore long-reads to generate complete bacterial genomes. The hybrid assembled genomes were subsequently analyzed to detect antimicrobial resistance (AMR) genes, virulence factors, and MGEs. Results: C. indologenes isolates were primarily recovered from urine samples of hospitalized elderly male patients with underlying conditions. These isolates generally exhibited extensive drug resistance, which was subsequently explored and correlated with genomic determinants. With one exception, CMCI13 showed a lower resistance profile (Multidrug resistance, MDR). Genomic analysis revealed isolates with genome sizes of 4.83–5.00 Mb and GC content of 37.15–37.35%. Genomic characterization identified conserved resistance genes (bla_IND-2, bla_CIA-4, adeF, vanT, and qacG) and various virulence factors. Phylogenetic and pangenome analysis showed 11 isolates clustering closely with Chinese strain 3125, while one isolate (CMCI13) formed a distinct branch. Importantly, each isolate, except CMCI13, harbored a large genomic island (approximately 94–100 kb) carrying significant resistance genes (bla_OXA-347, tetX, aadS, and ermF). The absence of this genomic island in CMCI13 correlated with its less resistant phenotype. No plasmids, integrons, or CRISPR-Cas systems were detected in any isolate. Conclusions: This study highlights the alarming emergence of multidrug-resistant C. indologenes in a hospital setting in Thailand. The genomic insights into specific resistance mechanisms, virulence factors, and potential horizontal gene transfer (HGT) events, particularly the association of a large genomic island with the XDR phenotype, underscore the critical need for continuous genomic surveillance to monitor transmission patterns and develop effective treatment strategies for this emerging pathogen. Full article

The use of aluminum honeycomb structures is fast expanding in advanced sectors such as the aeronautics, aerospace, marine, and automotive industries. However, processing these structures represents a major challenge for producing parts that meet the strict standards. To address this issue, an innovative manufacturing method using longitudinal ultrasonic vibration-assisted cutting, combined with a CDZ10 hybrid cutting tool, was developed to optimize the efficiency of traditional machining processes. To this end, a 3D numerical model was developed using the finite element method and Abaqus/Explicit 2017 software to simulate the complex interactions among the cutting tool and the thin walls of the structures. This model was validated by experimental tests, allowing the study of the influence of milling conditions such as feed rate, cutting angle, and vibration amplitude. The numerical results revealed that the hybrid technology significantly reduces the cutting force components, with a decrease ranging from 10% to 42%. In addition, it improves cutting quality by reducing plastic deformation and cell wall tearing, which prevents the formation of chips clumps on the tool edges, thus avoiding early wear of the tool. These outcomes offer new insights into optimizing industrial processes, particularly in fields with stringent precision and performance demands, like the aerospace sector. Full article

Low regression rate is the most critical issue for the development and application of hybrid rocket motors (HRMs). Paraffin-based fuels are potential candidates for HRMs due to their high regression rates but adding polymers to improve strength results in insufficient regression rates for HRMs applications. In this work, Computational Fluid Dynamics (CFD) modeling and analysis were used to investigate the mixing and combustion of gaseous fuels and oxidizers in HRMs for various fuel grains and injector combinations. In addition, the regression rate characteristics and combustion efficiency were evaluated using a ground test. The results showed that the swirling flow with both high mixing intensity and high velocity could be formed by using the swirl injector. The highest mixing degree attained for the star-swirl grain and swirl injector was 86%. The reported combustion efficiency calculated by the CFD model attained a maximum of 93% at the nozzle throat. In addition, a spatially averaged regression rate of 1.40 mm·s⁻¹ was achieved for the star-swirl grain and swirl injector combination when the mass flux of N₂O was 89.94 kg·m⁻²·s⁻¹. This is around 191% higher than the case of non-swirling flow. However, there were obvious local regression rate differences between the root of the star and the slot. The regression rate increase was accompanied by a decrease in the combustion efficiency for the strong swirling flow condition due to the remarkable higher mass flow rate of gasified fuels. It was shown that the nano-sized aluminum was unfavorable for the combustion efficiency, especially under extreme fuel-rich conditions. Full article

This paper presents a large-scale empirical study aimed at identifying the optimal local deep learning model and data volume for deploying intrusion detection systems (IDS) on resource-constrained IoT devices using federated learning (FL). While previous studies on FL-based IDS for IoT have primarily focused on maximizing accuracy, they often overlook the computational limitations of IoT hardware and the feasibility of local model deployment. In this work, three deep learning architectures—a deep neural network (DNN), a convolutional neural network (CNN), and a hybrid CNN+BiLSTM—are trained using the CICIoT2023 dataset within a federated learning environment simulating up to 150 IoT devices. The study evaluates how detection accuracy, convergence speed, and inference costs (latency and model size) vary across different local data scales and model complexities. Results demonstrate that CNN achieves the best trade-off between detection performance and computational efficiency, reaching ~98% accuracy with low latency and a compact model footprint. The more complex CNN+BiLSTM architecture yields slightly higher accuracy (~99%) at a significantly greater computational cost. Deployment tests on Raspberry Pi 5 devices confirm that all three models can be effectively implemented on real-world IoT edge hardware. These findings offer practical guidance for researchers and practitioners in selecting scalable and lightweight IDS models suitable for real-world federated IoT deployments, supporting secure and efficient anomaly detection in urban IoT networks. Full article

The article investigates the accuracy of nonparametric univariate density estimation methods applied to various Gaussian mixture models. A comprehensive comparative analysis is performed for four popular estimation approaches: adaptive kernel density estimation, projection pursuit, log-spline estimation, and wavelet-based estimation. The study is extended with modified versions of these methods, where the sample is first clustered using the EM algorithm based on Gaussian mixture components prior to density estimation. Estimation accuracy is quantitatively evaluated using MAE and MAPE criteria, with simulation experiments conducted over 100,000 replications for various sample sizes. The results show that estimation accuracy strongly depends on the density structure, sample size, and degree of component overlap. Clustering before density estimation significantly improves accuracy for multimodal and asymmetric densities. Although no formal statistical tests are conducted, the performance improvement is validated through non-overlapping confidence intervals obtained from 100,000 simulation replications. In addition, several decision-making systems are compared for automatically selecting the most appropriate estimation method based on the sample’s statistical features. Among the tested systems, kernel discriminant analysis yielded the lowest error rates, while neural networks and hybrid methods showed competitive but more variable performance depending on the evaluation criterion. The findings highlight the importance of using structurally adaptive estimators and automation of method selection in nonparametric statistics. The article concludes with recommendations for method selection based on sample characteristics and outlines future research directions, including extensions to multivariate settings and real-time decision-making systems. Full article

This study investigates strategies for urban-scale renewable energy integration through a photovoltaic-centric approach, with a case study of a district in Barcelona. The methodology integrates spatial and morphological data using a geographic information system (GIS)-based and clustering framework to address challenges of CO₂ emissions, air pollution, and energy inefficiency. Rooftop availability and photovoltaic (PV) design constraints are analysed under current urban regulations. The spatial analysis incorporates building geometry and solar exposure, while an evolutionary optimisation algorithm in Grasshopper refines shading analysis, energy yield, and financial performance. Clustering methods (K-means and 3D proximity) group PV panels by solar irradiance uniformity and spatial coherence to enhance system efficiency. Eight PV deployment scenarios are evaluated, incorporating submodule integrated converter technology under a solar power purchase agreement model. Results show distinct trade-offs among PV scenarios. The standard fixed tilted (31.5° tilt, south-facing) scenario offers a top environmental and performance ratio (PR) = 66.81% but limited financial returns. In contrast, large- and huge-sized modules offer peak financial returns, aligning with private-sector priorities but with moderate energy efficiency. Medium- and large-size scenarios provide balanced outcomes, while a small module and its optimised rotated version scenarios maximise energy output yet suffer from high capital costs. A hybrid strategy combining standard fixed tilted with medium and large modules balances environmental and economic goals. The district’s morphology supports “solar neighbourhoods” and demonstrates how multi-scenario evaluation can guide resilient PV planning in Mediterranean cities. Full article

We propose a tri-fold learning algorithm, called Neuroevolution of Hybrid Neural Networks in a Robotic Agent (acronym in Spanish, NRNH-AR), based on deep reinforcement learning (DRL), with self-supervised learning (SSL) and unsupervised learning (USL) steps, specifically designed to be implemented in a small autonomous navigation robot capable of operating in constrained physical environments. The NRNH-AR algorithm is designed for a small physical robotic agent with limited resources. The proposed algorithm was evaluated in four critical aspects: computational cost, learning stability, required memory size, and operation speed. The results obtained show that the performance of NRNH-AR is within the ranges of the Deep Q Network (DQN), Deep Deterministic Policy Gradient (DDPG), and Twin Delayed Deep Deterministic Policy Gradient (TD3). The proposed algorithm comprises three types of learning algorithms: SSL, USL, and DRL. Thanks to the series of learning algorithms, the proposed algorithm optimizes the use of resources and demonstrates adaptability in dynamic environments, a crucial aspect of navigation robotics. By integrating computer vision techniques based on a Convolutional Neuronal Network (CNN), the algorithm enhances its abilities to understand visual observations of the environment rapidly and detect a specific object, avoiding obstacles. Full article

Based on the experience gained worldwide from potential solutions to the fouling problem of fisheries and aquaculture infrastructure, we attempted to design, construct and test the antifouling efficiency of a new hybrid filament created from non-laminated copper wire braided with synthetic fibers made of Dyneema. The design involved the creation of a hybrid twine substituting a percentage of the synthetic fibers with 0.1–0.15 mm diameter copper wire at 5%, 10%, 20% and 40% levels. There is limited information in the international literature for comparison with our results, since there has never been any attempt to create such a hybrid net. The results showed that for the 6 mm mesh, the maximum openness obtained after the 8-month experimental period was 8.72%, with Cu wire substitution at 35%. For the 12 mm mesh, these values were 27.07% at 26%, and for the 20 mm mesh, they were 33.68% at 28%. A conservative average independent from mesh size to achieve optimum openness in the long term is 30 ± 4.73% Cu wire substitution. In addition, we found that both the mesh size (mm) and the copper substitution percentage affected the fouling process during the experimental period, which lasted 8 months. Full article