Search Results (225)

In this paper, a wideband metasurface absorber is proposed by utilizing transfer learning and a conditional deep convolutional generative adversarial network (CDCGAN). This approach involves introducing a forward prediction neural network to predict the spectral curve of a metasurface absorber, as well as a generative adversarial network for the inverse design of a metasurface absorber. After comparing different pre-trained models, a transfer learning network (TLN) based on GoogleNet-InceptionV3 is incorporated into the design process to reduce the amount of training data required. Based on the pixelated metasurface with a common effect of metallic pixels and resistive film pixels, a broadband electromagnetic absorber was designed through the CDCGAN model. For the application target of the C-band, a pixelated broadband metasurface Absorber I has been designed, which can achieve an absorption effect of less than −8 dB in the range of 6.5–8 GHz, and the absorption performance reaches less than −15 dB near the resonant frequency point of 7 GHz. Further lightweight optimization design was carried out, and the metasurface Absorber II was designed for application in the X-band, which has an absorption bandwidth below −8 dB at 9–12 GHz. The reflectivity curve measured by the experiment is in good agreement with that of the simulation result. Of note, our methodology aims to reversely engineer suitable absorbing structures based on customer-defined spectrums, which may bear some significance to the rapid design of broadband metasurface absorbers. Full article

Due to the increasing complexity of industrial systems, fault detection hinders the continuity of productivity. Also, many methods in industrial systems whose complexity increases over time have a mechanism based on human intervention. Therefore, the development of intelligent systems in fault detection is critical.. Avoiding false alarms in detecting real faults is one of the goals of these systems. Modern technology has the potential to improve strategies for detecting faults related to machine components. In this study, a hybrid approach was applied on two different datasets for fault detection. First, in this hybrid approach, data is given as input to the artificial neural network. Then, predictions are obtained as a result of training using the ANN mechanism with the feed forward process. In the next step, the error value calculated between the actual values and the estimated values is transmitted to the feedback layers. IWO (Invasive Weed Optimization) optimization algorithm is used to calculate the weight values in this hybrid structure. However the IWO optimization algorithm is designed to be initialized with fractal-based weighting. By this process sequence, it is planned to increase the global search power without getting stuck in local minima. Additionally, fractal-based initialization is an important part of the optimization process as it keeps the overall success and stability within a certain framework. Finally, a testing process is carried out on two separate datasets supplied by the Kaggle platform to prove the model’s success in failure detection. Test results exceed 98%. This success indicates that it is a successful model with high generalization ability. Full article

Lightweight composite structures like fiber metal laminates (FMLs) are widely used to manufacture aircraft structures and substitute metallic parts. While the superior mechanical performance of FMLs, including their high specific strength and excellent impact and fatigue resistance, has gained the interest of many researchers in the aerospace manufacturing industry, there are still some challenges that need to be considered. Conventional approaches like lay-up techniques and autoclave molding can achieve the relatively simple FML parts with large radii and profiles required for aircraft fuselages and flat skins. However, these methods are not suitable for forming complex-shaped structural parts due to the limited failure strain of fiber-reinforced materials and complex failure modes of the laminates. This research puts forward a new methodology that combines the hydroforming and subsequent curing process to investigate the feasibility of manufacturing complex aircraft parts like fairings made by FMLs. In this research, wrinkle formations are analyzed under various parameters during the hydroforming process. The geometrical shape of the initial blanks and the parameters, including blank holder force and cavity pressure, have been optimized to avoid flange edge wrinkles, and the addition of local support materials contributes to improving local wrinkling in the sharp corners. A finite element model (FEM) taking material laws, interlayer contacts, and boundary conditions into account is used to predict the dynamic hydroforming process of the fiber metal laminate, and experimental works are carried out for its verification. It is expected that the proposed method will reduce both costs and time, as well as reducing laminate defects. Thus, this method offers great potential for future applications related to manufacturing complex-shaped aerospace parts. Full article

Understanding the growth dynamics and ecological constraints of Porphyra spp. is essential for optimizing sustainable seaweed aquaculture. However, most existing models lack physiological detail and exhibit limited performance under variable environmental conditions. This study developed a mechanistic Dynamic Energy Budget (DEB) model to simulate structural biomass accumulation, carbon and nitrogen reserve dynamics, and blade area expansion of Porphyra under natural environmental conditions in Korean coastal waters. The model incorporates temperature, irradiance, and nutrient availability (NO₃⁻ and CO₂) as environmental drivers and was implemented using a forward difference numerical scheme. Field data from Beein Bay were used for model calibration and validation. Simulations showed good agreement with the observed biomass, reserve content, and blade area, with root-mean-square error (RMSE) typically within ±10%. Sensitivity analysis identified temperature-adjusted carbon assimilation and nitrogen uptake as the primary drivers of growth. The model was further used to estimate dynamic carrying capacity, revealing seasonal thresholds for sustainable biomass under current farming practices. Although limitations remain—such as the exclusion of reproductive allocation and tissue loss—the results demonstrate that DEB theory provides a robust framework for modeling Porphyra aquaculture. This approach supports scenario testing, spatial planning, and production forecasting, and it is adaptable for ecosystem-based management including integrated multi-trophic aquaculture (IMTA) and climate adaptation strategies. Full article

Background/Objectives: Extracting meaningful medical information from chest X-ray images and transcribing it into text is a complex task that requires a high level of expertise and directly affects clinical decision-making processes. Automatic reporting systems for this field in Turkish represent an important gap in scientific research, as they have not been sufficiently addressed in the existing literature. Methods: A deep learning-based approach called Model-SEY was developed with the aim of automatically generating Turkish medical reports from chest X-ray images. The Swin Transformer structure was used in the encoder part of the model to extract image features, while the text generation process was carried out using the cosmosGPT architecture, which was adapted specifically for the Turkish language. Results: With the permission of the ethics committee, a new dataset was created using image–report pairs obtained from Elazıg Fethi Sekin City Hospital and Indiana University Chest X-Ray dataset and experiments were conducted on this new dataset. In the tests conducted within the scope of the study, scores of 0.6412, 0.5335, 0.4395, 0.4395, 0.3716, and 0.2240 were obtained in BLEU-1, BLEU-2, BLEU-3, BLEU-4, and ROUGE word overlap evaluation metrics, respectively. Conclusions: Quantitative and qualitative analyses of medical reports autonomously generated by the proposed model have shown that they are meaningful and consistent. The proposed model is one of the first studies in the field of autonomous reporting using deep learning architectures specific to the Turkish language, representing an important step forward in this field. It will also reduce potential human errors during diagnosis by supporting doctors in their decision-making. Full article

Past archaeological practices have resulted in a distorted history of Native American cultures based upon western-biased research. This has been especially apparent in the rock art of the Klamath Basin in southern Oregon and northern California. In response to this, Native and non-Native scholars are striving to develop a counter-discourse that both challenges and replaces western constructs in research on Native American communities. The result of this approach is a growing trend within the discipline that has come to be called “Indigenous Archaeology.” Critical to this approach is that Native voices are transported from the margins of the research to its center, where they are intended to replace the Western colonialist narrative. Unfortunately, Native American tribal communities have been the targets of federal assimilation policies for the past few centuries, and as a result, much of their cultural knowledge unwittingly carries forward this distorted past. In this paper I explore a framework built upon ethnographic accounts of shamanism and rock art, along with a robust familiarity with local myth, and how this provides a foundation of traditional cultural knowledge against which to compare and evaluate the interpretive statements made in contemporary tribal members about rock art and other sacred material culture. Full article

Due to its rising carbon footprint, new paradigms for carbon-efficient computing are needed. For distributed computing systems, one option is to shift computing loads in space or time to take advantage of low-carbon electricity, a paradigm known as carbon-aware computing. We present a literature review of carbon-aware scheduling techniques, which shows that most of the literature carried out either spatial or temporal shifting but not both. Of the 28 analyzed studies, 11 considered both spatial and temporal shifting, and only 2 developed a combined optimization algorithm. Additionally, existing approaches typically focus on operational electricity alone. With the growing decarbonization of electricity, however, device production (which involves various industrial processes and cannot be easily decarbonized) is bound to become more relevant and needs to be considered. We thus suggest a novel spatio-temporal scheduling algorithm for cloud and edge computing. Our algorithm performs simultaneous spatio-temporal shifting while taking into consideration both device production and operation. As temporal shifting requires forecasts of future workloads, we also put forward a workload predictor. Although not fully implemented yet, we bring various theoretical arguments in support of our proposed algorithm. Full article

The rapid growth of Internet of Things (IoT) enabled devices in industrial environments and the associated increase in data generation are paving the way for the development of localized, distributed datacenters. In this paper, we have proposed a novel mini-datacenter in the form of wireless sensor networks to efficiently handle query-based data collection from Industrial IoT (IIoT) devices. The mini-datacenter comprises a command center, gateways, and IoT sensors, designed to manage stochastic query-response traffic flow. We have developed a duplication/aggregation query flow model, tailored to emphasize reliable transmission. We have developed a dataflow management framework that employs a multi-modal query forwarding approach to forward queries from the command center to gateways under varying environments. The query forwarding includes coarse-grain and fine-grain strategies, where the coarse-grain strategy uses a direct data flow using a single gateway at the expense of reliability, while the fine-grain approach uses redundant gateways to enhance reliability. A fuzzy-logic-based intelligence system is integrated into the framework to dynamically select the appropriate granularity of the forwarding strategy based on the resource availability and network conditions, aided by a buffer watching algorithm that tracks real-time buffer status. We carried out several experiments with gateway nodes varying from 10 to 100 to evaluate the framework’s scalability and robustness in handling the query flow under complex environments. The experimental results demonstrate that the framework provides a flexible and adaptive solution that balances buffer usage while maintaining over 95% reliability in most queries. Full article

Vancomycin, a cornerstone antibiotic against severe Gram-positive infections, is increasingly challenged by resistance in Methicillin-resistant Staphylococcus aureus (MRSA) and Vancomycin Enterococcus spp. (VRE), necessitating the development of novel therapeutic strategies. This review examines how structural modifications to vancomycin can enhance its antibacterial activity and explores the critical role of computational approaches in designing the next generation of analogs. By analyzing the existing literature, we highlight how strategic alterations, such as the introduction of lipophilic side chains, substitutions on the sugar moieties, and modifications to the aglycone core, have yielded derivatives with improved antibacterial potency. Notably, certain analogs (e.g., Vanc-83, Dipi-Van-Zn) have demonstrated expanded activity against Gram-negative bacteria and exhibited enhanced pharmacokinetic profiles, including prolonged half-lives and improved tissue penetration, crucial for effective treatment. Semisynthetic glycopeptides like telavancin, dalbavancin, and oritavancin exemplify successful translation of structural modifications, offering sustained plasma concentrations and simplified dosing regimens that improve patient compliance. Complementing these experimental efforts, computational methods, including molecular docking and molecular dynamics simulations, provide valuable insights into drug–target interactions, guiding the rational design of more effective analogs. Furthermore, physiologically based pharmacokinetic modeling aids in predicting the in vivo behavior and optimizing the pharmacokinetic properties of these novel compounds. This review highlights a critical path forward in the fight against multidrug-resistant infections. By meticulously examining the previously carried out structural refinement of vancomycin, guided by computational predictions and validated through rigorous experimental testing, we underscore its immense potential. Full article

In video transmission, maintaining high visual quality under variable network conditions, including bandwidth and efficiency, is essential for optimal viewer experience. Channel errors or malicious attacks during transmission can cause degradation in video quality, affecting its secure transmission and putting its confidentiality and integrity at risk. This paper presents a novel approach to enhancing objective video quality by integrating an energy-efficient forward error correction (FEC) technique into video encoding and transmission processes. Moreover, it ensures that the video contents remain secure and unintelligible to unauthorized parties. This is achieved by combining H.264/AVC syntax-based encryption and decryption algorithms with error correction during the video coding process to provide end-to-end confidentiality. Unlike traditional error correction strategies, our approach dynamically adjusts redundancy levels based on real-time network conditions, optimizing bandwidth utilization without compromising quality. The proposed framework is evaluated across full reference objective video quality metrics, demonstrating significant improvements in the peak signal-to-noise ratio (PSNR) and PSNR₆₁₁ of the recovered videos. Experiments are carried out on multiple test video sequences with different video resolutions having various characteristics, i.e., colors, motions, and structures, and confirm that the FEC-based solution effectively detects and corrects packet loss and transmission errors without the need for retransmission, reducing the impact of channel noise and accidental disruptions on visual quality in challenging network environments. This study contributes to the development of resilient video transmission systems with reduced computational complexity of the codec and provides insights into the role of FEC in addressing quality degradation in modern multimedia applications where low latency is crucial. Full article

Cultural tourism can act as a driver for inner area development, bringing about a range of socio-economic benefits through economic stimulation, quality of life improvement, and cultural heritage preservation. Inner territories, set apart by geographic marginality and low population density, hold a rich cultural and environmental heritage that, however, remains off the radar and left behind. Guided by the principles of endogenous local development, this article seeks to contribute to the existing body of research by proposing potential strategies for local growth rooted in cultural tourism. From this perspective, we identified the Basilicata region (Southern Italy) as a proper test area. The region is rich in archaeological, monumental and museum evidence, but is characterized, except in a few areas, by a low rate of tourist turnout. Through a replicable, comprehensive, and flexible methodology—drawing on bibliographic research, analysis of archaeological, archival, erudite and antiquarian sources, and carrying out field surveys—the different points of interest in the region have been brought together under specific cultural themes. Results include the design of three detailed routes (Via Herculia, Frederick II’s, and St Michael’s cultural routes) useful for three different types of tourism (sustainable, emotional, and accessible). Possible scenarios for valorization and fruition are also proposed, paying particular attention to digital technologies. Thus, this research aligns with Sustainable Development Goals (SDGs) 8 and 11 promoting cultural heritage valorization and preservation, shoring up economic revitalization, stepping up community engagement, and pushing forward environmentally friendly tourism practices. Research findings can attract the interest of a wide range of stakeholders such as tourism professionals, local authorities, cultural and creative industries, local communities and entrepreneurs, as well as academics and researchers. The methodological approach can be considered for the valorization and tourist enjoyment of inner areas in other countries, with particular focus on those falling within the Mediterranean region which is rich in cultural heritage, environmental value, and socio-economic potential. Full article

Driven by the remarkable capabilities of machine learning, brain–computer interfaces (BCIs) are carving out an ever-expanding range of applications across a multitude of diverse fields. Notably, electroencephalogram (EEG) signals have risen to prominence as the most prevalently utilized signals within BCIs, owing to their non-invasive essence, exceptional portability, cost-effectiveness, and high temporal resolution. However, despite the significant strides made, the paucity of EEG data has emerged as the main bottleneck, preventing generalization of decoding algorithms. Taking inspiration from the resounding success of generative models in computer vision and natural language processing arenas, the generation of synthetic EEG data from limited recorded samples has recently garnered burgeoning attention. This paper undertakes a comprehensive and thorough review of the techniques and methodologies underpinning the generative models of the general EEG, namely the variational autoencoder (VAE), the generative adversarial network (GAN), and the diffusion model. Special emphasis is placed on their practical utility in augmenting EEG data. The structural designs and performance metrics of the different generative approaches in various application domains have been meticulously dissected and discussed. A comparative analysis of the strengths and weaknesses of each existing model has been carried out, and prospective avenues for future enhancement and refinement have been put forward. Full article

The study block is located on the eastern edge of the Ordos Basin and is one of the typical medium coalbed methane blocks in China that have previously been subjected to exploration and development work. The rich CBM resource base and good exploration and development situation in this block mean there is an urgent need to accelerate development efforts, but compared with the current situation for tight sandstone gas where development is in full swing in the area, the production capacity construction of CBM wells in the area shows a phenomenon of lagging to a certain degree. In this study, taking the 4 + 5 coal seam of the YJP block in the Ordos Basin as the research object, we carried out technical research on an integrated program concerning CBM geology and engineering and put forward a comprehensive seismic geology analysis method for the prediction of the CBM content. The study quantitatively assessed the tectonic conditions, depositional environment, and coal seam thickness as potential controlling factors using gray relationship analysis, trend surface analysis, and seismic geological data integration. The results show that tectonic conditions, especially the burial depth, residual deformation, and fault development, are the main controlling factors affecting the coalbed methane content, showing a strong correlation (gray relational value greater than 0.75). The effects of the depositional environment (sand–shale ratio) and coal bed thickness were negligible. A weighted fusion model incorporating seismic attributes and geological parameters was developed to predict the gas content distribution, achieving relative prediction errors of below 15% in validation wells, significantly outperforming traditional interpolation methods. The integrated approach demonstrated enhanced spatial resolution and accuracy in delineating the lateral CBM distribution, particularly in structurally complex zones. However, limitations persist due to the seismic data resolution and logging data reliability. This method provides a robust framework for CBM exploration in heterogeneous coal reservoirs, emphasizing the critical role of tectonic characterization in gas content prediction. Full article

Structural damage detection is crucial for ensuring the safety and durability of engineering systems. Conventional detection methods based on the frequency response function (FRF) in linear systems tend to fail when small early damage occurs in engineered structures. Nonlinear output frequency response functions (NOFRFs), which are extensions of the FRF in linear systems to weak nonlinear systems, have been applied in nonlinear system analysis. In this study, we extended the structural damage detection method based on NOFRFs to multi-degree-of-freedom systems and beam structures. Due to the presence of multiple modal frequencies in these structures, the nonlinear characteristic frequencies exhibited by the system are often more complex than those of typical rotor systems, significantly increasing the difficulty of system identification and the feasibility of frequency-domain analysis. To improve the accuracy of the Nonlinear Auto-Regressive with eXogenous inputs (NARX) model and reduce the impact of noise interference, we proposed a Multi-input Multi-output Forward Regression Orthogonal Least Squares (MFROLS) algorithm for processing multi-input multi-output data to identify the NARX model of the same structural system. Next, a numerical simulation study was conducted using the combined NARX model and Generalized Associated Linear Equations (GALEs) method, taking a one-dimensional multi-degree-of-freedom (MDOF) system as an example. Nonlinear stiffness terms were introduced into the MDOF system to simulate structural damage, and a comparative study was performed with a least squares method (LSM). The results show that the proposed method can capture the trends of dynamic characteristic changes in the one-dimensional MDOF system under the influence of different nonlinear stiffnesses, whereas the LSM fails to do so. Finally, experimental research was carried out on simply supported beams with varying degrees of damage. The results demonstrate that the frequency-domain analysis method based on nonlinear systems can detect differences in damage levels in beam structures, providing a new approach for structural damage detection. Full article

Background: The increasing demand for elective surgery makes optimizing preoperative assessment a priority. Value-based healthcare aims to provide the highest value for patients at the lowest possible cost through various mechanisms, including reorganizing care into integrated practice units (IPUs). However, few studies have analyzed the effectiveness of implementing virtually led IPUs for preoperative assessment. Methods: We performed a retrospective observational cohort study including patients undergoing elective surgery at a teaching hospital in Madrid, Spain from 1 January 2018 to 31 December 2023, analyzing changes in surgical complications, efficiency, and patient satisfaction between the pre-implementation (2018–2019) and post-implementation (2020–2023) periods. Anesthesiologists’ satisfaction with the virtual assessments was described. During the post-implementation period, preoperative assessment was reorganized as a virtually led IPU. At the IPU appointment, preoperative testing and physical (including airway) examinations were performed by a nurse anesthesiologist. The results were uploaded to the electronic health records, and asynchronous virtual anesthesiologist assessment using a store-and-forward approach was performed. Digital patient education was carried out over the Patient Portal mobile application. Results: A total of 40,233 surgical procedures were included, of which 31,259 were from the post-intervention period. During the post-intervention period, no increase in surgical complications was observed, while same-day cancellations decreased from 4.3% to 2.8% of the total procedures (p < 0.001). The overall process time did not increase, despite the rising number of surgical procedures per year. Patient satisfaction improved. The median time to complete anesthesiologist assessment was significantly lower for virtual assessment (4.5 versus 10 min (p < 0.001), signifying estimated time savings of 716 person-hours per year. Anesthesiologists agreed that virtual assessment was more efficient than in-person evaluation, and half of the participants agreed that virtual preoperative care improved their work–life balance and reduced burnout. Conclusions: A digitally enhanced value-based model of preoperative care can improve efficiency and satisfaction metrics, reducing unnecessary costs and potentially improving the quality of care. Full article