Search Results (4,195)

In the recent era, bio-nanocomposites represent an emerging group of nanostructured hybrid materials and have been included in a new field at the frontier of materials science, life sciences, and nanotechnology. These biohybrid materials reveal developed structural and functional features of great attention for diverse uses. These materials take advantage of the synergistic assembling of biopolymers with nanometer-sized reinforcements. Conversely, polysaccharides have received great attention due to their several biological properties like antimicrobial and antioxidant performance. They mainly originated in different parts of plants, animals, seaweed, and microorganisms (bacteria, fungi, and yeasts). Polysaccharide-based nanocomposites have great features, like developed physical, structural, and functional features; affordability; biodegradability; and biocompatibility. These bio-based nanocomposites have been applied in biomedical, water treatment, food industries, etc. This paper will focus on the very recent trends in bio-nanocomposite based on polysaccharides for diverse applications. Sources and extraction methods of polysaccharides and preparation methods of their nanocomposites will be discussed. Full article

In low-altitude, GNSS-denied scenarios, Unmanned aerial vehicles (UAVs) rely on sensor fusion for self-localization. This article presents a resilient multi-sensor fusion localization system that integrates light detection and ranging (LiDAR), cameras, and inertial measurement units (IMUs) to achieve state estimation for UAVs. To address challenging environments, especially unstructured ones, IMU predictions are used to compensate for pose estimation in the visual and LiDAR components. Specifically, the accuracy of IMU predictions is enhanced by increasing the correction frequency of IMU bias through data integration from the LiDAR and visual modules. To reduce the impact of random errors and measurement noise in LiDAR points on visual depth measurement, cross-validation of visual feature depth is performed using reprojection error to eliminate outliers. Additionally, a structure monitor is introduced to switch operation modes in hybrid point cloud registration, ensuring accurate state estimation in both structured and unstructured environments. In unstructured scenes, a geometric primitive capable of representing irregular planes is employed for point-to-surface registration, along with a novel pose-solving method to estimate the UAV’s pose. Both private and public datasets collected by UAVs validate the proposed system, proving that it outperforms state-of-the-art algorithms by at least 12.6%. Full article

In the face of a significant power crisis, Bangladesh is turning towards renewable energy solutions, a move supported by the government’s initiatives. This article presents the findings of a study conducted in a residential area of Pabna, Bangladesh, using HOMER (Hybrid Optimization of Multiple Energy Resources) Pro software version 3.14.2. The study investigates the feasibility and efficiency of a grid-connected hybrid power system, combining photovoltaics (PV), a biomass generator, and wind energy. The simulation produced six competing solutions, each featuring a distinct combination of energy sources. Among the configurations analyzed, the grid-connected PV–biomass generator system emerged as the most cost-effective, exhibiting the lowest COE at USD 0.0232, a total net present cost (NPC) of USD 321,798.00, and an annual operating cost of USD 6060.59. The system presents a simple payback period of 9.25 years, highlighting its economic viability. Moreover, this hybrid model significantly reduces CO₂ emissions to 78,721 kg/year, compared to the 257,093 kg/year emissions from a solely grid-connected system, highlighting its environmental benefits. Sensitivity analyses further reveal that the system’s performance is highly dependent on solar irradiance, indicating that slight variations in solar input can significantly impact the system’s output. This study underscores the potential of integrating multiple renewable energy sources to address the power crisis in Bangladesh, offering a sustainable and economically viable solution while also mitigating environmental impacts. Full article

This paper presents a novel hybrid approach to feature detection designed specifically for enhancing Feature-Based Image Registration (FBIR). Through an extensive evaluation involving state-of-the-art feature detectors such as BRISK, FAST, ORB, Harris, MinEigen, and MSER, the proposed hybrid detector demonstrates superior performance in terms of keypoint detection accuracy and computational efficiency. Three image acquisition methods (i.e., rotation, scene-to-model, and scaling transformations) are considered in the comparison. Applied across a diverse set of remote-sensing images, the proposed hybrid approach has shown marked improvements in match points and match rates, proving its effectiveness in handling varied and complex imaging conditions typical in satellite and aerial imagery. The experimental results have consistently indicated that the hybrid detector outperforms conventional methods, establishing it as a valuable tool for advanced image registration tasks. Full article

Deep learning methods like convolution neural networks (CNNs) and transformers are successfully applied in hyperspectral image (HSI) classification due to their ability to extract local contextual features and explore global dependencies, respectively. However, CNNs struggle in modeling long-term dependencies, and transformers may miss subtle spatial-spectral features. To address these challenges, this paper proposes an innovative hybrid HSI classification method aggregating hierarchical spatial-spectral features from a CNN and long pixel dependencies from a transformer. The proposed aggregation multi-hierarchical feature network (AMHFN) is designed to capture various hierarchical features and long dependencies from HSI, improving classification accuracy and efficiency. The proposed AMHFN consists of three key modules: (a) a Local-Pixel Embedding module (LPEM) for capturing prominent spatial-spectral features; (b) a Multi-Scale Convolutional Extraction (MSCE) module to capture multi-scale local spatial-spectral features and aggregate hierarchical local features; (c) a Multi-Scale Global Extraction (MSGE) module to explore multi-scale global dependencies and integrate multi-scale hierarchical global dependencies. Rigorous experiments on three public hyperspectral image (HSI) datasets demonstrated the superior performance of the proposed AMHFN method. Full article

To reduce the impact of the agricultural sector on the environment, human health and resource depletion, several steps should be taken to develop innovative powertrain systems. The agricultural sector must be involved in this innovation, since diesel-powered tractors are an important source in terms of pollution. In this context, fuel-cell systems have gained importance, making them one of the possible substitutes due to their characteristics featuring almost zero local emissions, low refueling time and high efficiency. However, to effectively assess the sustainability of a fuel-cell tractor, a cradle-to-grave life cycle assessment, comprising production, use phase and end of life, must be performed. This article presents a comparative analysis, according to different impact categories, of the life cycle impacts of a traditional diesel-powered tractor and a fuel-cell hybrid tractor, designed considering operative requirements and functional constraints. The study was conducted according to the LCA technique (defined by ISO 14040 and ISO 14044 standards), combining secondary data, mainly derived from studies and reports available in the literature, with the use of the Ecoinvent 3.0 database. The results are presented according to ten different impact categories defined by ReCiPe 2016 v 1.03 at the midpoint level. The findings obtained showed that the fuel-cell tractor allows for a relevant reduction in all the considered categories. The highest-impact reduction, more than 92%, was obtained in the human toxicity non-carcinogenic category, while the lowest reduction, around 4.55%, was observed for the fossil fuel scarcity category, mainly due to the adoption of gray hydrogen which is produced from fossil fuels. As for the climate change category, the fuel-cell tractor showed a reduction of more than 34% in the life cycle impact. Finally, the authors also considered the case of green hydrogen produced using solar energy. In this case, further reductions in the impact on climate change and fossil fuel resource depletion were obtained. However, for the other impact categories, the results were worse compared to using gray hydrogen. Full article

Despite the appealing properties of random copolymers, the use of these biomaterials in association with phospholipids is still limited, as several aspects of their performance have not been investigated. The aim of this work is the formulation of lipid/random copolymer platforms and the comprehensive study of their features by multiple advanced characterization techniques. Both biomaterials are amphiphilic, including two phospholipids (1,2-dioctadecanoyl-sn-glycero-3-phosphocholine (DSPC), 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC)) and a statistical copolymer of oligo (ethylene glycol) methyl ether methacrylate (OEGMA) and 2-(diisopropylamino) ethyl methacrylate (DIPAEMA). We examined the design parameters, including the lipid composition, the % comonomer ratio, and the lipid-to-polymer ratio that could be critical for their behavior. The structures were also probed in different conditions. To the best of the authors’ knowledge, this is the first time that P(OEGMA-co-DIPAEMA)/lipid hybrid colloidal dispersions have been investigated from a membrane mechanics, biophysical, and morphological perspective. Among other parameters, the copolymer architecture and the hydrophilic to hydrophobic balance are deemed fundamental parameters for the biomaterial co-assembly, having an impact on the membrane’s fluidity, morphology, and thermodynamics. Exploiting their unique characteristics, the most promising candidates were utilized for methotrexate (MTX) loading to explore their encapsulation capability and potential antitumor efficacy in vitro in various cell lines. Full article

Background: Age-related macular degeneration (AMD) is a complex eye disorder with an environmental and genetic origin, affecting millions worldwide. The study aims to explore the association between retinal morphology and the polygenic risk score (PRS) for AMD using fundus images and deep learning techniques. Methods: The study used and pre-processed 23,654 fundus images from 332 subjects (235 patients with AMD and 97 controls), ultimately selecting 558 high-quality images for analysis. The fine-tuned DenseNet121 deep learning model was employed to estimate PRS from single fundus images. After training, deep features were extracted, fused, and used in machine learning regression models to estimate PRS for each subject. The Grad-CAM technique was applied to examine the relationship between areas of increased model activity and the retina’s morphological features specific to AMD. Results: Using the hybrid approach improved the results obtained by DenseNet121 in 5-fold cross-validation. The final evaluation metrics for all predictions from the best model from each fold are MAE = 0.74, MSE = 0.85, RMSE = 0.92, R² = 0.18, MAPE = 2.41. Grad-CAM heatmap evaluation showed that the model decisions rely on lesion area, focusing mostly on the presence of drusen. The proposed approach was also shown to be sensitive to artifacts present in the image. Conclusions: The findings indicate an association between fundus images and AMD PRS, suggesting that deep learning models may effectively estimate genetic risk for AMD from retinal images, potentially aiding in early detection and personalized treatment strategies. Full article

HER2-low tumors have shown promise in response to antibody–drug conjugates (ADCs) in recent clinical trials, underscoring the need to characterize this group’s clinical phenotype. In this study, we aimed to explore the clinicopathological features, survival rates, and HER2 amplicon mRNA expression of women affected with HER2-low breast cancer, compared with HER2-negative and HER2-positive groups. We included 516 breast cancer patients from Colombia, for whom we compared clinicopathological features, mRNA expression of three HER2 amplicon genes (ERBB2, GRB7 and MIEN1), survival and risk of mortality between HER2-low cases (1+ or 2+ with negative in situ hybridization (ISH) result) with HER2-positive (3+ or 2+ with positive ISH test) and HER2-negative (0+) cases. A higher proportion of patients with better-differentiated tumors and a lower proliferation index were observed for HER2-low tumors compared to the HER2-positive group. Additionally, HER2-low tumors showed higher mRNA expression of the ERBB2 gene and longer overall survival rates compared to HER2-negative cases. Nonetheless, a Cox-adjusted model by ER status and clinical stage showed no statistically significant differences between these groups. Our results show differences in important clinicopathological features between HER2-low and both HER2-positive and negative tumors. Given this unique phenotype, it is crucial to evaluate the potential advantages of ADC therapies for this emerging subtype of breast cancer. Full article

Renewable energy is the best choice for clean and sustainable energy development. A single renewable energy system reveals an intermittent disadvantage during the energy production process due to the effects of weather, season, day/night, and working environment. A generally hybrid renewable energy system (HRES) is an energy production scheme that is built based on a combination of two or more single renewable energy sources (such as solar energy, wind power, hydropower, thermal energy, and ocean energy) to produce electrical energy for energy consumption, energy storage, or a power transmission line. HRESs feature the outstanding characteristics of enhancing energy conversion efficiency and reducing fluctuations during the energy production process. Triboelectric nanogenerator (TENG) technology transduces wasted mechanical energies into electrical energy. The TENG can harvest renewable energy sources (such as wind, water flow, and ocean energy) into electricity with a sustainable working ability that can be integrated into an HRES for high power efficiency in sustainable renewable energy production. This article reviews the recent techniques and methods using HRESs and triboelectric nanogenerators (TENGs) in advanced hybrid renewable energy systems for improvements in the efficiency of harvesting energy, sustainable energy production, and practical applications. The paper mentions the benefits, challenges, and specific solutions related to the development and utilization of HRESs. The results show that the TENG is a highly potential power source for harvesting energy, renewable energy integration, application, and sustainable energy development. The results are a useful reference source for developing HRES models for practical applications and robust development in the near future. Full article

In aquaculture, the presence of dead fish on the water surface can serve as a bioindicator of health issues or environmental stressors. To enhance the precision of detecting dead fish floating on the water’s surface, this paper proposes a detection approach that integrates data-driven insights with advanced modeling techniques. Firstly, to reduce the influence of aquatic disturbances and branches during the identification process, prior information, such as branches and ripples, is annotated in the dataset to guide the model to better learn the scale and shape characteristics of dead fish, reduce the interference of branch ripples on detection, and thus improve the accuracy of target identification. Secondly, leveraging the foundational YOLOv8 architecture, a DD-IYOLOv8 (Data-Driven Improved YOLOv8) dead fish detection model is designed. Considering the significant changes in the scale of dead fish at different distances, DySnakeConv (Dynamic Snake Convolution) is introduced into the neck network detection head to adaptively adjust the receptive field, thereby improving the network’s capability to capture features. Additionally, a layer for detecting minor objects has been added, and the detection head of YOLOv8 has been modified to 4, allowing the network to better focus on small targets and occluded dead fish, which improves detection performance. Furthermore, the model incorporates a HAM (Hybrid Attention Mechanism) in the later stages of the backbone network to refine global feature extraction, sharpening the model’s focus on dead fish targets and further enhancing detection accuracy. The experimental results showed that the accuracy of DD-IYOLOv8 in detecting dead fish reached 92.8%, the recall rate reached 89.4%, the AP reached 91.7%, and the F1 value reached 91.0%. This study can achieve precise identification of dead fish, which will help promote the research of automatic pond patrol machine ships. Full article

A single-phase grounding fault often occurs in 10 kV distribution networks, seriously affecting the safety of equipment and personnel. With the popularization of urban cables, the low-resistance grounding system gradually replaced arc suppression coils in some large cities. Compared to arc suppression coils, the low-resistance grounding system features simplicity and reliability. However, when a high-resistance grounding fault occurs, a lower amount of fault characteristics cannot trigger the zero-sequence protection action, so this type of fault will exist for a long time, which poses a threat to the power grid. To address this kind of problem, in this paper, a hybrid grounding system combining the low-resistance protection device and fully controlled power module is proposed. During a low-resistance grounding fault, the fault isolation is achieved through the zero-sequence current protection with the low-resistance grounding system itself, while, during a high-resistance grounding fault, the reliable arc extinction is achieved by regulating the neutral-point voltage with a fully controlled power module. Firstly, this paper introduces the principles, topology, and coordination control of the hybrid grounding system for active voltage arc extinction. Subsequently, a dual-loop-based control method is proposed to suppress the fault phase voltage. Furthermore, a faulty feeder selection method based on the Kepler optimization algorithm and convolutional neural network is proposed for the timely removal of permanent faults. Lastly, the simulation and HIL-based emulated results verify the rationality and effectiveness of the proposed method. Full article

The atmospheric gravity waves (AGWs) generated by severe typhoons can facilitate the transfer of energy from the troposphere to the ionosphere, resulting in medium-scale traveling ionospheric disturbances (MSTIDs). However, the complex three-dimensional nature of MSTIDs over oceanic regions presents challenges for detection using ground-based Global Navigation Satellite System (GNSS) networks. This study employs a hybrid approach combining space-based and ground-based techniques to investigate the spatiotemporal characteristics of ionospheric perturbations during Typhoon Doksuri. Plane maps depict significant plasma fluctuations extending outward from the typhoon’s gale wind zone on 24 July, reaching distances of up to 1800 km from the typhoon’s center, while space weather conditions remained relatively calm. These ionospheric perturbations propagated at velocities between 173 m/s and 337 m/s, consistent with AGW features and associated propagation speeds. Vertical mapping reveals that energy originating from Typhoon Doksuri propagated upward through a 500 km layer, resulting in substantial enhancements of plasma density and temperature in the topside ionosphere. Notably, the topside horizontal density gradient was 1.5 to 2 times greater than that observed in the bottom-side ionosphere. Both modeling and observational data convincingly demonstrate that the weak background winds favored the generation of AGWs associated with Typhoon Doksuri, influencing the development of distinct MSTIDs. Full article

Blockchain technology (BCT) is regarded as one of the most important and disruptive technologies in Industry 4.0. However, no comprehensive study addresses the contributions of BCT adoption (BCA) on some special business functionalities projected as financial variables like BCA integrity, transparency, etc. Therefore, the primary objective of this study was to close this theoretical gap and determine how BCA has contributed to the four business sectors that were selected since FinTech had the greatest potential in these domains. The PRISMA approach, a systematic literature review model, was used in this work to make sure that the greatest number of studies on the topic were accessed. The PRISMA model’s output helped identify relevant publications, and an analysis of these studies served as the foundation for this paper’s findings. The findings reveal that BCA for companies with a disrupting financial technology (FinTech) attitude can help in securing corporate transaction transparency; offer knowledge, same-data, and information sharing; enhance fidelity, integrity, and trust; improve organizational procedures; and prevent fraud with cyber-hacking protection and fraudulence suspension. Moreover, blockchain’s smart contract utilization feature offers ESG and sustainability functionality. This paper’s novelty is the projection to four business sectors of the three-layer research sequence: (i) financial variables operated as BCA functionalities, (ii) issues, risks, limitations, and opportunities associated with the financial variables, and (iii) implications, theoretical contributions, questions, potentiality, and outlook of BCA/FinTech issues. And the ability of managers or practitioners to reference this sequence and make decisions on BCA matters is considered a key contribution. The proposed methodology provides business practitioners with valuable insights to reevaluate their economic challenges and explore the potential of blockchain technology to address them. This study combined a systematic literature review (SLR) with qualitative analysis as part of a hybrid research approach. Quantitative analysis was carried out on all 835 selected papers in the first step, and qualitative analysis was carried out on the top-cited papers that were screened. The current work highlights the key challenges and opportunities in established blockchain implementations and discusses the outlook potentiality of blockchain technology adoption. This study will be useful to managers, practitioners, researchers, and scholars. Full article

In complex urban road environments, vehicles inevitably experience frequent or sustained interruptions of the Global Navigation Satellite System (GNSS) signal when passing through overpasses, near tall buildings, and through tunnels. This results in the reduced accuracy and robustness of the GNSS/Inertial Navigation System (INS) integrated navigation systems. To improve the performance of GNSS and INS integrated navigation systems in complex environments, particularly during GNSS outages, we propose a convolutional neural network–gated recurrent unit (CNN-GRU)-assisted factor graph hybrid navigation method. This method effectively combines the spatial feature extraction capability of CNN, the temporal dynamic processing capability of GRU, and the data fusion strength of a factor graph, thereby better addressing the impact of GNSS outages on GNSS/INS integrated navigation. When GNSS signals are strong, the factor graph algorithm integrates GNSS/INS navigation information and trains the CNN-GRU assisted prediction model using INS velocity, acceleration, angular velocity, and GNSS position increment data. During GNSS outages, the trained CNN-GRU assisted prediction model forecasts pseudo GNSS observations, which are then integrated with INS calculations to achieve integrated navigation. To validate the performance and effectiveness of the proposed method, we conducted real road tests in environments with frequent and sustained GNSS interruptions. Experimental results demonstrate that the proposed method provides higher accuracy and continuous navigation outcomes in environments with frequent and sustained GNSS interruptions, compared to traditional GNSS/INS factor graph integrated navigation methods and long short-term memory (LSTM)-assisted GNSS/INS factor graph navigation methods. Full article