Search Results (1,311)

Organic Light Emitting Diode (OLED) technology is preferred in modern display applications due to its superior efficiency, color quality, and flexibility. It also carries a high potential of applicability in military displays where emission color tuning is required for MIL-STD-3009 Night Vision Imaging Systems (NVISs), as compatibility is critical. Herein, we report the effects of different OLED device layer materials and thicknesses such as the hole injection layer (HIL), hole transport layer (HTL), and electron transport layer (ETL) on the color coordinates, luminance, and efficiency of OLED devices designed for night vision (NVIS) compatibility. In this study, simulation tools like SETFOS^® (Semi-conducting Emissive Thin Film Optics Simulator), MATLAB^®, and LightTools^® (Illumination Design Software) were used to verify and validate the luminance, luminance efficiency, and chromaticity coordinates of the proposed NVIS-OLED devices. We modeled the OLED device using SETFOS^®, then the selection of materials for each layer for an optimal electron–hole balance was performed in the same tool. The effective reflectivity of multiple OLED layers was determined in MATLAB^® in addition to an optimal device efficiency calculation in SETFOS^®. The optical validation of output luminance and luminous efficiency was performed in LightTools^®. Through a series of simulations for a green-emitting OLED device, we observed significant shifts in color coordinates, particularly towards the yellow spectrum, when the ETL materials and their thicknesses varied between 1 nm and 200 nm, whereas a change in the thickness of the HIL and HTL materials had a negligible impact on the color coordinates. While the critical role of ETL in color tuning and the emission characteristics of OLEDs is highlighted, our results also suggested a degree of flexibility in material selection for the HIL and HTL, as they minimally affected the color coordinates of emission. We validated via a combination of SETFOS^®, MATLAB^®, and LightTools^® that when the ETL (3TPYMB) material thickness is optimized to 51 nm, the cathode reflectivity via the ETL-EIL stack became the minimum enabling output luminance of 3470 cd/m2 through our emissive layer within the Glass/ITO/MoO₃/TAPC/(CBP:Ir(ppy)₃)/3TPYMB/LiF/Aluminum OLED stack architecture, also yielding 34.73 cd/A of current efficiency under 10 mA/cm² of current density. We infer that when stack layer thicknesses are optimized with respect to their reflectivity properties, better performances are achieved. Full article

As the acceptance of Internet of Things (IoT) systems quickens, guaranteeing their sustainability and reliability poses an important challenge. Faults in IoT systems can result in resource inefficiency, high energy consumption, reduced security, and operational downtime, obstructing sustainability goals. Thus, blockchain (BC) technology, known for its decentralized and distributed characteristics, can offer significant solutions in IoT networks. BC technology provides several benefits, such as traceability, immutability, confidentiality, tamper proofing, data integrity, and privacy, without utilizing a third party. Recently, several consensus algorithms, including ripple, proof of stake (PoS), proof of work (PoW), and practical Byzantine fault tolerance (PBFT), have been developed to enhance BC efficiency. Combining fault detection algorithms and BC technology can result in a more reliable and secure IoT environment. Thus, this study presents a sustainable BC-Driven Edge Verification with a Consensus Approach-enabled Optimal Deep Learning (BCEVCA-ODL) approach for fault recognition in sustainable IoT environments. The proposed BCEVCA-ODL technique incorporates the merits of the BC, IoT, and DL techniques to enhance IoT networks’ security, trustworthiness, and efficacy. IoT devices have a substantial level of decentralized decision-making capacity in BC technology to achieve a consensus on the accomplishment of intrablock transactions. A stacked sparse autoencoder (SSAE) model is employed to detect faults in IoT networks. Lastly, the Piranha Foraging Optimization Algorithm (PFOA) approach is used for optimum hyperparameter tuning of the SSAE approach, which assists in enhancing the fault recognition rate. A wide range of simulations was accomplished to highlight the efficacy of the BCEVCA-ODL technique. The BCEVCA-ODL technique achieved a superior FDA value of 100% at a fault probability of 0.00, outperforming the other evaluated methods. The proposed work highlights the significance of embedding sustainability into IoT systems, underlining how advanced fault detection can provide environmental and operational benefits. The experimental outcomes pave the way for greener IoT technologies that support global sustainability initiatives. Full article

To achieve accurate predictions of tail strike events during the landing phase of flight training, we propose a stacking ensemble learning prediction model that uses Random Forest (RF), Support Vector Regression (SVR), K-Nearest Neighbors (KNN), and Adaptive Boosting (AdaBoost) as base models, with Logistic Regression (LR) serving as the meta-model. This model is built on non-exceedance flight data recorded on airborne SD cards. By evaluating the importance scores of the feature parameters influencing tail strike events, we identified the optimal set of features for model input while using the landing pitch angle as the model output. We then compared the R² and RMSE of each model. The results indicate that under a prediction horizon of 5 s prior to landing, the ensemble learning model demonstrates high predictive accuracy. This capability provides flight trainees with sufficient reaction time to adjust their flight attitudes, thereby helping to avoid the occurrence of tail strike events during landing. Full article

Light is a key factor influencing the growth and quality of crops in plant factories. To explore the optimal light quality for pakchoi production, five light formulations were applied to ‘Youguan NO.3’ pakchoi: white LEDs (W; CK); white/red = 4:1 (WR); white/blue = 4:1 (WB); white/red/blue = 3:1:1 (WRB); and white/green = 4:1 (WG), all with a light intensity of 250 ± 10 µmol·m⁻²·s⁻¹. The results showed significant variations in growth indices, nutritional quality, enzyme activity, and other parameters under different light qualities. The best growth results were observed under the WRB treatment. Chloroplasts under WRB treatment appeared well-developed, with clear grana lamellae. The thylakoids in the chloroplast grana of the WRB plants were densely stacked, and a large number of starch grains were detected. The contents of total sugar, soluble sugar, soluble protein, and protein nitrogen were significantly higher under the WB, WRB, and WR treatments compared to the CK treatment, along with a significant reduction in nitrate content. Among all the treatments, WRB treatment resulted in the highest levels of total sugar, starch, free amino acids, soluble protein, total nitrogen, protein nitrogen, and ascorbic acid (AsA). Enzyme activity assays revealed that the activities of sucrose phosphate synthetase (SPS), nitrate reductase (NR), glutamine synthetase (GS), glutamate synthetase (GOGAT), and glutamate dehydrogenase (GDH) were highest under WRB treatment. Therefore, supplemental red-blue mixed light can effectively improve the growth and nutritional properties of pakchoi grown under white light. This supplementary lighting strategy provides a new way to enhance the nutritional value of leafy vegetables in plant factories. Full article

Compact high-power sub-nanosecond laser pulse sources with a wavelength of 940 nm are developed and studied. A design for laser pulse sources based on a vertical stack is proposed, which includes a semiconductor laser chip and a current switch chip. To create a compact high-speed current switch, a three-electrode heterothyristor is developed. It is found that the use of heterothyristor-based current switches allows the creation of a low-loss pump current circuit, generating short current pulses and operating the semiconductor laser in gain-switching mode. For the semiconductor laser chip, an asymmetric semiconductor heterostructure with a quantum-well active region is designed. The design of the emitting aperture of the laser chip is optimized to improve the operating characteristics of the laser beam when generating sub-ns optical pulses. It is shown that the transition to a monolithic emitting aperture design reduces the laser pulse turn-on spatial inhomogeneity, which is 90 ps over the entire range of optical powers studied. It is also demonstrated that by increasing the emitting aperture width to 400 μm, laser pulses with a peak power of 39.5 W and a pulse width at full width at half maximum (FWHM) of 120 ps can be generated. Full article

Non-invasive haemoglobin (Hb) testing devices enable large-scale haemoglobin screening, but their accuracy is not comparable to traditional blood tests. To this end, this paper aims to design a non-invasive haemoglobin testing device and propose a classification-regression prediction framework for non-invasive testing of haemoglobin using visibility graphs (VG) with network clustering of multi-sample pulse-wave-weighted undirected graphs as the features to optimize the detection accuracy of non-invasive haemoglobin measurements. Different prediction methods were compared by analyzing 608 segments of multiwavelength fingertip PPG signal data from 152 volunteers and analyzing and comparing the data and methods. The results showed that the classification using NVG with complex network clustering as features in the interval classification model was the best, with its classification accuracy (acc) of 93.35% and model accuracy of 88.28%. Among the regression models, the classification regression stack: SVM-Light Gradient Boosting Machine (LGBM) was the most effective, with a Mean Absolute Error (MAE) of 6.67 g/L, a Root Mean Square Error (RMSE) of 8.21 g/L, and an R-Square (R2) of 0.64. The results of this study indicate that the use of complex network technology in non-invasive haemoglobin detection can effectively improve its accuracy, and the detector designed in this study is promising to carry out a more accurate large-scale haemoglobin screening. Full article

The ability to measure vital signs using electromagnetic waves has been extensively investigated as a less intrusive method capable of assessing different biosignal sources while using a single device. On-body antennas, when directly coupled to the human body, offer a comfortable and effective alternative for daily monitoring. Nonetheless, on-body antennas are challenging to design primarily due to the high dielectric constant of body tissues. While the simulation process may often include a body model, a unique model cannot account for inter-individual variability, leading to discrepancies in measured antenna parameters. A potential solution is to increase the antenna’s bandwidth, guaranteeing the antenna’s impedance matching and robustness for all users. This work describes a new on-body microstrip antenna having a stacked structure with parasitic elements, designed and optimized using artificial intelligence (AI). By using an AI-driven design approach, a self-adaptive Bayesian neural network surrogate-model-assisted differential evolution for antenna optimization (SB-SADEA) method to be specific, and a stacked structure having parasitic elements and a defected ground structure with 27 tuneable design parameters, the simulated impedance bandwidth of the on-body antenna was successfully enhanced from 150 MHz to 1.3 GHz, while employing a single and simplified body model in the simulation process. Furthermore, the impact of inter-individual variability on the measured S-parameters was analyzed. The measured results relative to ten subjects revealed that for certain subjects, the SB-SADEA-optimized antenna’s bandwidth reached 1.6 GHz. Full article

This work presents an advanced computational fluid dynamics (CFD) model of a 5 kW molten carbonate fuel cell (MCFC) stack intended to provide a broad analysis and deliver improved design through optimizing flow distribution. The goal is to provide a variant analysis of flow distribution in the internal channels through the CFD model. SolidWorks was used to design the MCFC stack, and SOLIDWORKS^® Flow Simulation was utilized to model the flow distribution inside the stack. The simulated stack was validated through an experimental investigation of a 5 kW MCFC stack, empirically measuring pressure and flow distribution in an experimental laboratory station optimized for multi-scale fuel cell stack testing. The test was designed to examine a variety of internal flow distribution factors. The verified CFD model was employed for sensitivity analysis on various scales. To enhance the design, the influence of stack and single-cell constructional characteristics on the 5 kW MCFC was investigated. Full article

Stellera chamaejasme, a toxic invasive species widespread in degraded alpine grasslands, Qinghai Province, causes a significant threat to the local ecological balance. Accurate monitoring of the leaf chlorophyll content is essential for preventing its expansion over large areas. This study presents an optimal approach by integrating hierarchical dimensionality reduction, stacking ensemble learning, and 1D-CNN models to estimate leaf chlorophyll content in S. chamaejasme using hyperspectral reflectance data. Field spectrometry analysis demonstrates that the combination of Pearson correlation, first derivative, and SPA algorithms can efficiently select the most chlorophyll-sensitive wavelengths, red-edge parameters, and spectral indices related to S. chamaejasme leaves. The stacking ensemble model outperforms the 1D-CNN model in predicting leaf chlorophyll content of S. chamaejasme over the whole growth stage, while the 1D-CNN excels at prediction in each individual growth stage. Comparatively, the 1D-CNN model achieved higher accuracy (R² > 0.5) in all five growth stages, with optimal performance during the flower bud stage (R² = 0.787, RMSE = 2.476). This study underscores the potential of combining feature spectra selection with machine learning and deep learning models to monitor S. chamaejasme growth, offering valuable insights for invasive species control and ecological management. Full article

The Heifangtai Loess terrace in northwest China is frequently affected by landslides due to hydrological factors, establishing it as a significant research area for loess landslides. Advanced time-series InSAR technology facilitates the retrieval of surface deformation information, thereby aiding in the monitoring of landslide deformation status. However, existing methods that analyze deformation patterns do not fully exploit the displacement time series derived from InSAR, which hampers the exploration of potentially coexisting deformation patterns within the area. This study integrates InSAR with time-series clustering methods to reveal the surface deformation patterns and their spatial distribution characteristics in Heifangtai. Initially, utilizing the Sentinel-1 ascending and descending SAR data stack from January 2020 to June 2023, we optimize the interferometric phase based on distributed scatterer characteristics to reduce noise levels and obtain higher spatial density of measurement points. Subsequently, by combining the differential interferometric datasets from both ascending and descending orbits, the multidimensional small baseline subsets technique is employed to calculate the two-dimensional deformation time series. Finally, time-series clustering methods are utilized to extract the deformation patterns present and their spatial distribution from all measurement point time series. The results indicate that the deformation of the Heifangtai is primarily distributed around the surrounding area of the platform, with subsidence deformation being more intense than horizontal deformation. The entire terrace exhibits five deformation patterns: eastward subsidence, westward subsidence, vertical subsidence, westward movement, and eastward movement. The spatial distribution of these patterns suggests that the areas beneath the platform, namely Yanguoxia Town and Dangchuan Village, may be more susceptible to landslide threats in the future. Furthermore, wavelet analysis reveals the response relationship between rainfall and various deformation patterns, further enhancing the interpretability of these patterns. These findings hold significant implications for subsequent landslide monitoring, early warning, and risk prevention. Full article

In the past few years, deep learning has achieved remarkable advancements in the area of image compression. Remote sensing image compression networks focus on enhancing the similarity between the input and reconstructed images, effectively reducing the storage and bandwidth requirements for high-resolution remote sensing images. As the network’s effective receptive field (ERF) expands, it can capture more feature information across the remote sensing images, thereby reducing spatial redundancy and improving compression efficiency. However, the majority of these learned image compression (LIC) techniques are primarily CNN-based and transformer-based, often failing to balance the global ERF and computational complexity optimally. To alleviate this issue, we propose a learned remote sensing image compression network with visual state space model named VMIC to achieve a better trade-off between computational complexity and performance. Specifically, instead of stacking small convolution kernels or heavy self-attention mechanisms, we employ a 2D-bidirectional selective scan mechanism. Every element within the feature map aggregates data from multiple spatial positions, establishing a globally effective receptive field with linear computational complexity. We extend it to an omni-selective scan for the global-spatial correlations within our Channel and Global Context Entropy Model (CGCM), enabling the integration of spatial and channel priors to minimize redundancy across slices. Experimental results demonstrate that the proposed method achieves superior trade-off between rate-distortion performance and complexity. Furthermore, in comparison to traditional codecs and learned image compression algorithms, our model achieves BD-rate reductions of −4.48%, −9.80% over the state-of-the-art VTM on the AID and NWPU VHR-10 datasets, respectively, as well as −6.73% and −7.93% on the panchromatic and multispectral images of the WorldView-3 remote sensing dataset. Full article

This study tackles the challenge of precise wood log counting, where applications of the proposed methodology can span from automated approaches for materials management, surveillance, and safety science to wood traffic monitoring, wood volume estimation, and others. We introduce an approach leveraging Conditional Generative Adversarial Networks (cGANs) for eucalyptus log segmentation in images, incorporating specialized image processing techniques to handle noise and intersections, coupled with the Connected Components Algorithm for efficient counting. To support this research, we created and made publicly available a comprehensive database of 466 images containing approximately 13,048 eucalyptus logs, which served for both training and validation purposes. Our method demonstrated robust performance, achieving an average Accuracypixel of 96.4% and Accuracylogs of 92.3%, with additional measures such as F1 scores ranging from 0.879 to 0.933 and IoU values between 0.784 and 0.875, further validating its effectiveness. The implementation proves to be efficient with an average processing time of 0.713 s per image on an NVIDIA T4 GPU, making it suitable for real-time applications. The practical implications of this method are significant for operational forestry, enabling more accurate inventory management, reducing human errors in manual counting, and optimizing resource allocation. Furthermore, the segmentation capabilities of the model provide a foundation for advanced applications such as eucalyptus stack volume estimation, contributing to a more comprehensive and refined analysis of forestry operations. The methodology’s success in handling complex scenarios, including intersecting logs and varying environmental conditions, positions it as a valuable tool for practical applications across related industrial sectors. Full article

Accurate wind power forecasting is crucial for optimizing grid scheduling and improving wind power utilization. However, real-world wind power time series exhibit dynamic statistical properties, such as changing mean and variance over time, which make it difficult for models to apply observed patterns from the past to the future. Additionally, the execution speed and high computational resource demands of complex prediction models make them difficult to deploy on edge computing nodes such as wind farms. To address these issues, this paper explores the potential of linear models for wind power forecasting and constructs NFLM, a linear, lightweight, short-term wind power forecasting model that is more adapted to the characteristics of wind power data. The model captures both short-term and long-term sequence variations through continuous and interval sampling. To mitigate the interference of dynamic features, we propose a normalization feature learning block (NFLBlock) as the core component of NFLM for processing sequences. This module normalizes input data and uses a stacked multilayer perceptron to extract cross-temporal and cross-dimensional dependencies. Experiments with data from two real wind farms in Guangxi, China, showed that compared with other advanced wind power forecasting methods, the MSE of NFLM in the 24-step ahead forecasting of the two wind farms is respectively reduced by 23.88% and 21.03%, and the floating-point operations (FLOPs) and parameter count only require 36.366 M and 0.59 M, respectively. The results show that NFLM can achieve good prediction accuracy with fewer computing resources. Full article

Photonic crystals, which are important functional materials, are formed by the periodic arrangement of materials with different dielectric constants that have photonic bandgaps and localization properties. Their preparation methods are primarily physical and chemical. Physical methods include mechanical drilling, layer-by-layer stacking, and precision processing. Chemical methods primarily involve colloidal self-assembly methods. Various colloidal crystal self-assembly methods have been reported, each with its own advantages and disadvantages. Photonic crystals have important applications in many fields, such as optical communications, information technology, energy, biomedicine, and sensors, including high-performance optical fiber fabrication, photonic chip development, and solar cell efficiency enhancement. This paper reviews the latest progress in the preparation of photonic crystals using physical and self-assembly methods. Currently, the preparation and application of photonic crystals have made significant achievements; however, there are still challenges in terms of preparation accuracy, efficiency, cost, and application integration technology. With the future development of science and technology, breakthroughs are expected in novel structural development, preparation process optimization, and cross-field integration, which will continue to promote the progress of photonic crystals and social development. Full article

As the global population grows, vertical farming offers a promising solution by using vertically stacked shelves in controlled environments to grow crops efficiently within urban areas. However, the shading effects of farm structures make artificial lighting a significant cost, accounting for approximately 67% of total operational expenses. This study presents a novel approach to optimizing the deployment of laser illumination in rotating vertical farms by incorporating structural design, light modeling, and photosynthesis. By theoretically analyzing the beam pattern of laser diodes and the dynamics in the coverage area of rotating farm layers, we accurately characterize the light conditions on each vertical layer. Based on these insights, we introduce a new criterion, cumulative coverage, which accounts for both light intensity and coverage area. Then, an optimization framework is formulated, and a swarm intelligence algorithm, Differential Evolution (DE) is used to solve the optimization while considering the structural and operational constraints. It is found that tilting lights and placing them slightly off-center are more effective than traditional vertically aligned and center-aligned deployment. Our results show that the proposed strategy improves light coverage by 4% compared to the intensity-only optimization approach, and by 10% compared to empirical methods. This study establishes the first theoretical framework for designing energy-efficient artificial lighting deployment strategies, providing insights into enhancing the efficiency of vertical farming systems. Full article