Search Results (41)

Ferritic stainless steels (FSSs) have attracted considerable attention due to their excellent corrosion resistance and significantly lower cost compared with nickel-bearing austenitic stainless steels. However, the high-temperature wear behavior of additively manufactured FSS 430 has not yet been thoroughly investigated. This study aims to examine the microstructural characteristics and wear properties of laser powder directed energy deposition (LP-DED) FSS 430 fabricated under varying laser powers and hatch distances. Wear testing was conducted at 25 °C and 300 °C after subjecting the samples to solution heat treating at 815 °C and 980 °C for 1 h, followed by forced fan cooling. For comparison, an AISI 430 commercial plate was also tested under the same test conditions. The microstructural evolution and worn surfaces were analyzed using SEM-EDS and EBSD techniques. The wear performance was evaluated based on the friction coefficients and cross-sectional profiles of wear tracks, including wear volume, maximum depth, and scar width. The average friction coefficients (AFCs) of the samples solution heat treated at 980 °C were higher than those treated at 815 °C. Additionally, the AFCs increased with hatch distance at both testing temperatures. A strong correlation was observed between Rockwell hardness and wear resistance, indicating that higher hardness generally results in improved wear performance. Full article

This paper presents a simulation-based investigation of passive frequency tunability in frequency-selective surfaces (FSSs) enabled by Moiré pattern interference. By overlapping two identical hexagonal FSS layers and introducing rotational misalignment between them, we demonstrate that the resulting Moiré patterns induce significant shifts in the resonance frequency without any external bias or active components. Using full-wave simulations in HFSS, we show that rotating the second layer from 0° to 30° can shift the resonant frequency from 4.4 GHz down to 1.2 GHz. This tunable behavior emerges solely from geometrical manipulation, offering a low-complexity alternative to active tuning methods that rely on varactors or micro-electromechanical systems (MEMSs). We discuss the theoretical basis for this tuning mechanism based on effective periodicity modulation via rotational interference and highlight potential applications in passive reconfigurable filters and refractive index sensors. The proposed approach provides a promising route for implementing tunable electromagnetic structures without compromising simplicity, power efficiency, or integration compatibility. Full article

This paper proposes a structural silicon heterojunction photosensitive element with a simple form, low manufacturing cost, and efficient performance, which has a high-intensity photoelectric effect and a high frequency range of use. It can be applied as microwave switches to active frequency selective surfaces (AFSSs) to replace PIN diodes. Meanwhile, we explore the crucial role of pentacene/silicon heterojunction in the photoelectric conversion process. It is found that due to the inherent photovoltaic effect and the built-in electric field interaction between the two materials, the insertion loss of the heterojunction formed is reduced to 4.5 dB, which is 2.5 dB lower than that of the high-resistivity silicon wafer. In order to further reduce the insertion loss, the surface of the silicon wafer is etched and then heterojunction is prepared, which can further reduce insertion loss to within 2.5 dB, and the bandwidth difference between the presence and absence of pump excitation exceeds 10 dB extends to 12 GHz, indicating that the light collecting ability of structural silicon significantly enhances its photoelectric effect. The research results demonstrate the potential of using structural silicon heterojunctions in photoelectric devices, providing new technology for high-performance microwave switches and implementing optically controlled FSSs. Full article

Wastes resulting from the depulping of tropical fruits such as siriguela (Spondias purpurea), umbu (Spondias tuberosa), and juçara (Euterpe edulis) can be used as a source of bioactive compounds and nutrients. Therefore, the aim of this work was to chemically characterize the flours of siriguela seeds and peels (FSSs and FSPs), umbu seeds and peels (FUSs and FUPs), umbu pulp refine cake (FUC), and defatted juçara pulp refine cake (FJC) based on their proximate composition and mineral content, fatty acids, total phenolic content (TPC) and antioxidant capacity (ABTS^•+, DPPH^•, and FRAP). The results were expressed on a dry basis. The FJC had the highest lipid and protein percentage (10% and 31%, respectively), while for carbohydrates; FUS samples had the highest value (80%). FSSs presented the highest levels of Ca (239.7 mg 100 g⁻¹), Mg (183.3 mg 100 g⁻¹), and FSP of K (1403.9 mg 100 g⁻¹). Regarding the fatty acid profiles, palmitic acid (C16:0) was found as the main fatty acid in FSSs (28.87%), FSPs (69.31%), and FUC (45.68%), while oleic acid (C18:1) was found as the main fatty acid in FUSs (32.63%), FUPs (48.24%), and FJC (61.58%). The FUP sample exhibited the highest antioxidant potential (1852.81 mg GAE 100 g⁻¹, 130 µmol Trolox g⁻¹, 131 µmol Trolox g⁻¹, and 590 µmol Fe²⁺ g⁻¹ by TPC, ABTS^•+, DPPH^•, and FRAP, respectively). As the first comparative study of these specific fruits wastes, the results showed that their flours are promising sources of nutrients and bioactive compounds. In addition, their use can contribute to the circular economy and Sustainable Development Goals (SDGs) 2 and 12 of the 2030 Agenda. Full article

Conventional design methodologies for Frequency Selective Surfaces (FSSs) are often plagued by challenges such as difficulties in determining unit cell structures, a plethora of optimization parameters, and substantial computational demands. In response, researchers have developed deep learning-based approaches for FSS design, highlighting their advantages in terms of high efficiency and low resource consumption. However, these methods are typically confined to designing FSSs within the spectral ranges defined by their datasets, significantly limiting their applicability. This paper systematically analyzes the impact of material and geometric parameters of FSSs on their spectral characteristics, thereby establishing a theoretical foundation for the cross-band transfer learning capability of neural networks. Building on this foundation, we utilized COMSOL (Version 6.0) and MATLAB (Version R2021b) co-simulations to recollect 6000 sets of FSS data in the millimeter-wave band. Using only 23.1% of the data volume, we achieved training results comparable to those obtained with the full dataset in a significantly shorter time frame, with a mean absolute error of 0.07 on the test set. This demonstrates the feasibility of transfer learning and successfully implements cross-band transfer learning of convolutional neural networks from the terahertz band to the millimeter-wave band. The findings of this study provide valuable insights for the integration of deep learning with FSSs, enhancing data utilization efficiency, and further advancing the development of efficient, concise, and universal FSS design methodologies. This advancement extends the scope from solving specific problems to addressing a broader class of issues. Full article

Functional somatic symptoms (FSSs) in children—such as headaches, stomachaches, and muscle pain without clear medical explanations—pose a significant clinical challenge, often leading to repeated healthcare visits and impairments in daily functioning. While the role of parental psychological factors in shaping children’s FSSs has been suggested, empirical evidence remains limited and fragmented. This study addresses this gap by systematically examining the associations between parents’ reflective functioning, emotion regulation, alexithymia, and physical and mental health, and the frequency and severity of children’s FSSs. A total of 339 parents of children aged 6–12 completed surveys assessing their capacity to understand mental states, regulate emotions, and identify or describe feelings, as well as their self-reported physical and mental health. They also indicated whether their child experienced FSSs (e.g., headaches, stomachaches) more than once per week. Results revealed that parents of children with FSSs reported significantly lower levels of reflective functioning (lower certainty, higher uncertainty), higher alexithymic traits, and greater emotion regulation difficulties, alongside poorer physical and mental health indices. Logistic regression analyses demonstrated that emotion regulation difficulties and poorer mental health significantly increased the likelihood of a child exhibiting FSSs, while lower reflective functioning also emerged as a significant predictor. Furthermore, multiple linear regression indicated that emotion regulation challenges and poor mental health predicted greater severity of FSSs. These findings offer novel insights into how parents’ psychological and health characteristics can shape children’s somatic symptom expression, highlighting the need for family-focused interventions. By identifying and addressing parental emotional and cognitive difficulties, clinicians may be able to mitigate the intergenerational transmission of maladaptive stress responses, ultimately reducing the burden of FSSs in children. Full article

This dataset comprises specklegram images acquired from a multimode optical fiber subjected to varying thermal conditions. Designed for training neural networks focused on developing Fiber Optic Specklegram Sensors (FSSs), these experimental data enable the detection of changes in speckle patterns corresponding to applied temperature variations. The dataset includes 24,528 images captured over a temperature range from 25 °C to 200 °C, with incremental steps of approximately 0.175 °C. Key acquisition parameters include a wavelength of 633 nm, a sensing zone length of 20 mm, and a multimode fiber with a core diameter of 62.5 μm. This dataset supports developing and validating temperature-sensing models using fiber optic technology and can facilitate benchmarking against other experimental or synthetic datasets. Finally, an implementation is presented for utilizing the dataset in a deep learning interrogation scheme. Full article

Heavy rainfall events are the main cause of flooding, especially in regions like Kerala, India. Kerala is vulnerable to extreme weather due to its geographical location in the Western Ghats. Accurate forecasting of rainfall events is essential for minimizing the impact of floods on life, infrastructure, and agriculture. For accurate forecasting of heavy rainfall events in this region, region-specific evaluations of NWP model performance are very important. This study evaluated the performance of six Numerical Weather Prediction (NWP) models—NCEP, NCMRWF, ECMWF, CMA, UKMO, and JMA—in forecasting heavy rainfall events in Kerala. A comprehensive assessment of these models was performed using traditional performance metrics, categorical precipitation metrics, and Fractional Skill Scores (FSSs) across different forecast lead times. FSSs were calculated for different rainfall thresholds (100 mm, 50 mm, 5 mm). The results reveal that all models captured rainfall patterns well for the lower threshold of 5 mm, but most of the models struggled to accurately forecast heavy rainfall, especially for longer lead times. JMA performed well overall in most of the metrics except False Alarm Ratio (FAR). It showed high FAR, which revealed that it may predict false rainfall events. ECMWF demonstrated consistent performance. NCEP and UKMO performed moderately well. CMA, and NCMRWF had the lowest accuracy either due to more errors or biases. The findings underscore the trade-offs in model performance, suggesting that model selection should depend on the accuracy required or rainfall event prediction capability. This study recommends the use of Multi-Model Ensembles (MME) to improve forecasting accuracy, integrate the strengths of the best-performing models, and reduce biases. Future research can also focus on expanding observational networks and employing advanced data assimilation techniques for more reliable predictions, particularly in regions with complex terrain such as Kerala. Full article

The viability of the family-scale solar still (F-SSS) desalination plant in nine low- and middle-income Central American and Caribbean sites, with improper water treatment facilities and supply networks, has been analyzed and reported in detail. The sizing of the desalination plant was done based on the still’s performance, clean water requirement and solar radiation potential. The still’s performance was estimated using an experimentally validated thermodynamic model. Annual desalinated water productivity per still was about 979.0 L (highest) and 836.0 L (lowest) in Port-au-Prince and Belize City, respectively. The lowest and highest potable water production price was observed in Havana (19.75 to 20.22 USD/m³) and Port-au-Prince (59.23 to 60.62 USD/m³) due to their low and high local interest rates, respectively. The decarbonization potential of the F-SSS desalination plant with a 25-year lifetime ranged between 37 and 641 tons of CO₂ emission. The specific CO₂ generated was found to be the least and highest in San Salvador (4.24 to 4.34 g/L of desalinated water) and Port-au-Price (13.70 to 14.04 g/L of desalinated water), respectively. The energy, finance payback time and sustainability index of the F-SSS desalination plant ranged between 0.59 and 0.67 years, 1.2 and 18.0 months, and 1.03 and 1.04, respectively. The performance, economic and environmental aspects revealed positive signs on the applicability of the F-SSS desalination plant in Central American and Caribbean sites for reliable and sustainable clean water supply. However, this process can be ratified if the concerned governments implement a reasonable subsidy, as is the case with other renewable energy systems. Full article

This paper evaluates the role of federated satellite systems (FSSs) in enhancing unmanned vehicle-supported military medical evacuation (MEDEVAC) missions. An FSS integrates multiple satellite systems, thus improving imaging and communication capabilities compared with standalone satellite systems. A simulation model is developed for a MEDEVAC mission where the FSS control of an unmanned aerial vehicle is distributed across different countries. The model is utilized in a simulation experiment in which the capabilities of the federated and standalone systems in MEDEVAC are compared. The performance of these systems is evaluated by using the most meaningful metrics, i.e., mission duration and data latency, for evacuation to enable life-saving procedures. The simulation results indicate that the FSS, using low-Earth-orbit constellations, outperforms standalone satellite systems. The use of the FSS leads to faster response times for urgent evacuations and low latency for the real-time control of unmanned vehicles, enabling advanced remote medical procedures. These findings suggest that investing in hybrid satellite architectures and fostering international collaboration promote scalability, interoperability, and frequent-imaging opportunities. Such features of satellite systems are vital to enhancing unmanned vehicle-supported MEDEVAC missions in combat zones. Full article

In recent years, weakly supervised semantic segmentation (WSSS) has garnered significant attention in remote sensing image analysis due to its low annotation cost. To address the issues of inaccurate and incomplete seed areas and unreliable pseudo masks in WSSS, we propose a novel WSSS method for remote sensing images based on the Siamese Affinity Network (SAN) and the Segment Anything Model (SAM). First, we design a seed enhancement module for semantic affinity, which strengthens contextual relevance in the feature map by enforcing a unified constraint principle of cross-pixel similarity, thereby capturing semantically similar regions within the image. Second, leveraging the prior notion of cross-view consistency, we employ a Siamese network to regularize the consistency of CAMs from different affine-transformed images, providing additional supervision for weakly supervised learning. Finally, we utilize the SAM segmentation model to generate semantic superpixels, expanding the original CAM seeds to more completely and accurately extract target edges, thereby improving the quality of segmentation pseudo masks. Experimental results on the large-scale remote sensing datasets DRLSD and ISPRS Vaihingen demonstrate that our method achieves segmentation performance close to that of fully supervised semantic segmentation (FSSS) methods on both datasets. Ablation studies further verify the positive optimization effect of each module on segmentation pseudo labels. Our approach exhibits superior localization accuracy and precise visualization effects across different backbone networks, achieving state-of-the-art localization performance. Full article

This review provides a comprehensive analysis of the design, materials, fabrication techniques, and applications of flexible wearable antennas, with a primary focus on their roles in Wireless Body Area Networks (WBANs) and healthcare technologies. Wearable antennas are increasingly vital for applications that require seamless integration with the human body while maintaining optimal performance under deformation and environmental stress. Return loss, gain, bandwidth, efficiency, and the SAR are some of the most important parameters that define the performance of an antenna. Their interactions with human tissues are also studied in greater detail. Such studies are essential to ensure that wearable and body-centric communication systems perform optimally, remain safe, and are in compliance with regulatory standards. Advanced materials, including textiles, polymers, and conductive composites, are analyzed for their electromagnetic properties and mechanical resilience. This study also explores innovative fabrication techniques, such as inkjet printing, screen printing, and embroidery, which enable scalable and cost-effective production. Additionally, solutions for SAR optimization, including the use of metamaterials, electromagnetic band gap (EBG) structures, and frequency-selective surfaces (FSSs), are discussed. This review highlights the transformative potential of wearable antennas in healthcare, the IoT, and next-generation communication systems, emphasizing their adaptability for real-time monitoring and advanced wireless technologies, such as 5G and 6G. The integration of energy harvesting, biocompatible materials, and sustainable manufacturing processes is identified as a future direction, paving the way for wearable antennas to become integral to the evolution of smart healthcare and connected systems. Full article

Frequency-selective surfaces (FSSs) have attracted great attention owing to their unique feature to manipulate transmission performance over the frequency domain. In this work, a filtering antenna-filtering antenna (FA-FA) FSS with a wide passband and double-side sharp roll-off characteristics is presented by inter-using the filtering antenna and receiving–transmitting metasurface methods. First, a dual-polarized filtering antenna element was designed by employing a parasitic band-stop structure with an L-probe feed. Then, the FA-FA-based FSS unit was constructed by placing two such filtering antennas back to back, with their feedings connected through metallic vias. Finally, the FSS with a wide passband and high selectivity was realized by arraying the FA-FA units periodically. The full-wave simulation results demonstrated that the designed FA-FA-based FSS had a wide passband from 13.06 GHz to 14.46 GHz with a flat in-band frequency response. The lower and upper roll-off bandwidths were sharp, reaching 1% and 1.2% of the center frequency. The proposed FA-FA-based FSS was fabricated and measured, achieving the coincident performance according to the theoretical prediction. The wideband band-pass FSS obtained a sharp double-side roll-off feature, which can be applied in various studies such as an antenna array, metasurface, communication, etc. Full article

Frequency-Selective Surfaces (FSSs) are structures that act as frequency-dependent electromagnetic filters, enabling innovative designs for energy-efficient building envelopes. This paper explores their potential for energy harvesting and integration into construction materials, offering insights into design strategies, performance analysis, and potential applications of FSS sin future architectural projects. A range of FSS designs are presented and systematically classified based on their performance and adaptability for building integration. This includes their use as part of traditional construction elements or as independent components of building walls. Critical issues such as the limitations, challenges, and durability of FSSs in real-world applications are also examined to provide a comprehensive view of their practical feasibility. Additionally, incorporating the electromagnetic properties of these materials into Building Information Modelling (BIM) systems is recommended. Doing so will enable architects and engineers to better utilize the novel opportunities that FSSs offer, fostering more innovative, energy-efficient building envelopes. Overall, this paper provides valuable insights into how FSSs can transform the future of sustainable architecture and energy management in buildings. Full article

Industry 4.0 supply chains, characterized by dynamic environments, uncertainty, and intricate interdependencies, necessitate robust decision-making tools. While existing models have made strides in addressing these complexities, they often struggle to effectively handle the high degree of uncertainty inherent in such systems. To bridge this gap, this research introduces a novel framework grounded in the axioms of Cubic Type-2 Fuzzy Soft Sets (CT2FSSs). By leveraging the enhanced flexibility and uncertainty-handling capabilities of CT2FSSs, our proposed framework empowers decision-makers to navigate complexities, optimize supply chain processes, and mitigate risks while maintaining symmetry in decision-making. Through rigorous theoretical analysis and practical applications, this study not only advances fuzzy set theory but also demonstrates its efficacy in the context of Industry 4.0. The unique contribution of this research lies in the development of a CT2FSS-based framework that offers superior adaptability to uncertain and complex environments, thereby enhancing the resilience and performance of supply chains in symmetrical scenarios. Full article