Compliant physical interactions, interactive learning, and robust position control are crucial to improving the effectiveness and safety of rehabilitation robots. This paper proposes a human–robot cooperation control strategy (HRCCS) for lower limb rehabilitation robots. The high-level trajectory planner of the HRCCS consists of a trajectory generator, a trajectory learner, a desired trajectory predictor, and a soft saturation function. The trajectory planner can predict and generate a smooth desired trajectory through physical human–robot interaction (pHRI) in a restricted joint space and can learn the desired trajectory using the locally weighted regression method. Moreover, a triple-step controller was designed to be the low-level position controller of the HRCCS to ensure that each joint tracks the desired trajectory. A nonlinear disturbance observer is used to observe and compensate for total disturbances. The radial basis function neural networks (RBFNN) approximation law and robust term are adopted to compensate for observation errors. The simulation results indicate that the HRCCS is robust and can achieve compliant pHRI and interactive trajectory learning. Therefore, the HRCCS has the potential to be used in rehabilitation robots and other fields involving pHRI. Full article

The healthcare industry is confronted with the challenge to offer an increasing variety of healthcare services while in the meantime controlling rapidly increasing healthcare costs. Mass customization has been proven to be an effective strategy to fulfill customers’ individual specific needs with high efficiency and low cost in the manufacturing industry. This paper investigates the theoretical feasibility and practical applicability of adopting mass customization as a conceptual framework for designing a healthcare service delivery system. The nature of healthcare delivery systems and their evolution are discussed relative to those of manufacturing systems. Recent research in personalized medicine, consumer-driven healthcare, consumer healthcare informatics, and integrated healthcare delivery is reviewed as enabling technologies towards mass customization of healthcare services. By synthesizing these scattered efforts in different streams of literature, this paper concludes that mass customization can contribute to the redesign of healthcare service systems, and delineates a roadmap for future research. Full article

Processing ground motion signals at early stages can be advantageous for issuing public warnings, deploying first-responder teams, and other time-sensitive measures. Multiple Deep Learning (DL) models are presented herein, which can predict triaxial ground motion accelerations upon processing the first-arriving 0.5 s of recorded acceleration measurements. Principal Component Analysis (PCA) and the K-means clustering algorithm were utilized to cluster 17,602 accelerograms into 3 clusters using their metadata. The accelerograms were divided into 1 million input–output pairs for training, 100,000 for validation, and 420,000 for testing. Several non-overlapping forecast horizons were explored (1, 10, 50, 100, and 200 points). Various architectures of Artificial Neural Networks (ANNs) were trained and tested, such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) networks, and CNN-LSTMs. The utilized training methodology applied different aspects of supervised and unsupervised learning. The LSTM model demonstrated superior performance in terms of short-term prediction. A prediction horizon of 10 timesteps in the future with a Root Mean Squared Error (RMSE) value of 8.43 × 10⁻⁶ g was achieved. In other words, the LSTM model exhibited a performance improvement of 95% compared to the baseline benchmark, i.e., ANN. It is worth noting that all the considered models exhibited acceptable real-time performance (0.01 s) when running in testing mode. The CNN model demonstrated the fastest computational performance among all models. It predicts ground accelerations under 0.5 ms on an Intel Core i9-10900X CPU (10 cores). The models allow for the implementation of real-time structural control responses via intelligent seismic protection systems (e.g., magneto-rheological (MR) dampers). Full article

A domain alignment-based hyperspectral image (HSI) classification method was designed to address the heterogeneity in resolution and band between the source domain and target domain datasets of cross-scene hyperspectral images, as well as the resulting reduction in common features. Firstly, after preliminary feature extraction, perform two domain alignment operations: image alignment and distribution alignment. Image alignment aims to align hyperspectral images of different bands or time points, ensuring that they are within the same spatial reference framework. Distribution alignment adjusts the distribution of features of samples of different categories in the feature space to reduce the distribution differences of the same type of features between two domains. Secondly, adjust the consistency of the two alignment methods to ensure that the features obtained through different alignment methods exhibit consistency in the feature space, thereby improving the comparability and reliability of the features. In addition, this method considers multiple losses in the model from different perspectives and makes comprehensive adjustments through a unified optimization process to more comprehensively capture and utilize the correlation information between data. Experimental results on Houston 2013 and Houston 2018 datasets can improve the hyperspectral prediction performance between datasets with different resolutions and bands, effectively solving the problems of high cost and limited training samples in HSI labeling and significantly improving cross-scene HSI classification performance. Full article

Activated carbon/BiOI nanocomposites were successfully synthesized through a simplistic method. The produced composites were then characterized using XRD, TEM, SEM-EDX, and XPS. The results showed that BiOI with a tetragonal crystal structure had been formed. The interaction between activated carbon and BiOI was confirmed via all the mentioned tools. The obtained nanocomposites’ electrical conductivity, dielectric properties, and Ac impedance were studied at 59 KHz−1.29 MHz. AC and dc conductivities were studied at temperatures between 303 and 573 K within the frequency range of 59 KHz–1.29 MHz. The 10% activated carbon/BiOI nanocomposite possessed dc and AC conductivity values of 5.56 × 10⁻⁴ and 2.86 × 10⁻⁴ Ω⁻¹.cm⁻¹, respectively, which were higher than BiOI and the other nanocomposites. Every sample exhibited increased electrical conductivity values as the temperature and frequency rose, suggesting that all samples had semiconducting behavior. The loss and dielectric constants (ε′ and ε″) also dropped as the frequency increased, leading to higher dielectric loss. The Nyquist plot unraveled single semicircle arcs and a decreased bulk resistance, indicating decreased grain boundary resistance. Consequently, the electrical characteristics of BiOI, 1C/BiOI, 5C/BiOI, and 10C/BiOI implied their applicability as dielectric absorbers, charge-stored capacitors, and high-frequency microwave devices. Full article

[Background] Intermediate care is a limited-time service founded on patient-centered care (PCC) that ensures continuity and quality of care during the transition between home and acute care services, promotes recovery, and restores independence and confidence. In Japan, systematic education on intermediate care for care providers is lacking. [Method] The present study explored the relationship between a Japanese scale used to evaluate individualized discharge support skills, a Japanese version of a tool for evaluating intermediate care based on PCC, and a tool that measures the shared decision making of care providers, which is the pinnacle of PCC. [Results] Clear correlations were found between the concepts evaluated using these three tools. Some concepts were not correlated between the Japanese scale that evaluated individualized discharge support skills and intermediate care based on PCC. [Conclusions] Elucidating the perspectives that help expand discharge care to intermediate care based on PCC will contribute to future education on intermediate care for Japanese care providers and to enriching patient-centered intermediate care. Full article

Prospective environmental and technological assessment of emerging chemical processes is necessary to identify, analyze and evaluate the technologies that are highly imperative in the transition towards climate neutrality. The investigation of the environmental impacts and material and energy requirements of the processes at the low technology readiness level (TRL) is important in making early decisions about the feasibility of adapting and upscaling the process to the industrial level. However, the upscaling of new chemical processes has always been a major challenge; and in this context, there is no general methodological guidance available in the literature. Hence, a new comprehensive methodological framework for upscaling of novel chemical processes is designed and presented based on thermodynamic process modeling and simulation. The practical implementation of the proposed methodology is extensively discussed by developing a scaled-up novel carbon capture and utilization (CCU) process comprised of sequestration of carbon dioxide (CO₂) from blast furnace gas with a capacity of 1000 liter per hour (L/h) using methanol and its utilization as a precursor to produce methane (CH₄). It was found that thermodynamic process modeling and simulations based on the perturbed-chain statistical associating (PC-SAFT) equation of state (EOS) can precisely estimate the CO₂ solubility in methanol and conversion to CH₄ at various temperature and pressure conditions. The achieved thermophysical property and kinetics parameters can be employed in process simulations to estimate scaled-up environmental flows and material and energy requirements of the process. Full article

The hydro-pneumatic suspension, known for enhancing vehicle ride comfort and stability, finds widespread use in engineering vehicles. Presently, the majority of mining trucks employ hydro-pneumatic suspension with a fixed damping hole, underscoring the critical importance of selecting appropriate damping hole parameters. Initially, an equilibrium mathematical model of the ¼ hydro-pneumatic suspension is established, and the influencing factors of the damping characteristics are analyzed. Subsequently, the simulation model and experimental bench for the hydro-pneumatic suspension are constructed. Sinusoidal signals with different frequencies and amplitudes serve as the excitation signals to analyze the variation trend of the force on the rod with displacement changes. The simulation and experimental results demonstrate a high degree of consistency, validating the rationality and validity of the simulation model. Building upon this foundation, various damping apertures are then selected to study the damping characteristics of the hydro-pneumatic suspension. The research indicates that as the damping aperture increases, the setting time of the hydro-pneumatic suspension system after excitation extends, accompanied by a decrease in the acceleration overshoot. As a result, a comprehensive evaluation index is developed, considering various factors, such as different weight setting times and peak longitudinal accelerations to assess the ride comfort of the suspension. This approach is then employed to determine the optimal damping aperture under both full-load and no-load conditions. The findings of this research offer valuable insights for the development of adaptive variable damping hydraulic suspensions, especially under variable load conditions. Full article

The present study aims to determine the effect of miscellaneous meals (rapeseed meal, cottonseed meal, and sunflower meal) replacing soybean meal in feed on growth performance, apparent digestibility of nutrients, serum biochemical parameters, serum free amino acid content, microbiota composition and SCFAs content in growing pigs (25–50 kg). A total of 72 (Duroc × Landrace × Yorkshire) growing pigs with initial weights of 25.79 ± 0.23 kg were randomly divided into three treatments. The pigs were fed corn–soybean meal (CON), corn–soybean–miscellaneous meals (CSM), and corn–miscellaneous meals (CMM). Each treatment included six replicates with four pigs per pen (n = 24, 12 barrows and 12 gilts). Soybean meal accounted for 22.10% of the basal diet in the CON group. In the CSM group, miscellaneous meals partially replaced soybean meal with a mixture of 4.50% rapeseed meal, 3.98% cottonseed meal, and 4.50% sunflower meal. In the CMM group, miscellaneous meals entirely replaced soybean meal with a mixture of 8.50% rapeseed meal, 8.62% cottonseed meal, and 8.5% sunflower. The results showed that compared with the CON, the CSM and CMM groups significantly improved the average daily gain (ADG) of growing pigs during the 25–50 kg stage (p < 0.05) but had no effects on average daily feed intake (ADFI) and average daily feed intake/average daily gain (F/G) (p > 0.05). Moreover, the CMM group significantly reduced nutrient apparent digestibility of gross energy compared with the CON group. The serum biochemical parameters results showed that the CSM group significantly improved the contents of total protein (TP) compared with the CON group (p < 0.05). The CMM group significantly improved the contents of total protein (TP), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) compared with the CON group in serum (p < 0.05). In comparison with the CON group, the CMM group also significantly improved lysine (Lys), threonine (Thr), valine (Val), isoleucine (Ile), leucine (Leu), phenylalanine (Phe), arginine (Arg), and citrulline (Cit) levels in serum (p < 0.05). However, the CMM group significantly decreased non-essential amino acid content glycine (Gly) in serum compared with CON (p < 0.05), while compared with the CON group, the CSM and CMM groups had no significant effects on the relative abundance, the alpha-diversity, or the beta-diversity of fecal microbiota. Moreover, compared with the CON group, the CSM group significantly increased butyric acid and valeric acid contents of short-chain fatty acids (SCFAs) in feces (p < 0.05). In contrast to the CON group, the CMM group significantly reduced the contents of SCFAs in feces, including acetic acid, propionic acid, and isobutyric acid (p < 0.05). Collectively, the results of the present study indicate that miscellaneous meals (rapeseed meal, cottonseed meal, and sunflower meal) can partially replace the soybean meal and significantly improve the growth performance of growing pigs during the 25–50 kg stage. Thus, miscellaneous meals are a suitable protein source as basal diets to replace soybean meals for 25–50 kg growing pigs. These results can be helpful to further develop miscellaneous meals as a functional alternative feed ingredient to soybean meal. Full article

The mechanical characteristics and sliding friction behaviors of AA7075 samples were studied in regard to structural states formed in them by ECAP and depending on the ECAP pass number. In addition, the effect of a counterbody’s material on the tribological characteristics of the samples was investigated by the examples of AISI 52100 steel, alumina Al₂O₃ and silicon nitride Si₃N₄. Vibration acceleration and acoustic emission signals with parameters such as acoustic emission energy and median frequency were used for characterizing the sliding regimes. The structural state and mechanical properties of the ECAPed AA7075 samples significantly affected their wear behaviors in dry sliding. The counterbody material had a significant influence on the formation of a transfer layer and the subsurface deformation of samples. The dynamic behavior of the tribosystem was studied and the relationship between the sliding parameters, vibrometry and acoustic emission signals was established. Full article

Background and Objectives: The influence of montelukast (MK), an antagonist of cysLT1 leukotriene receptors, on lung lesions caused by experimental diabetes was studied. Materials and Methods: The study was conducted on four groups of six adult male Wistar rats. Diabetes was produced by administration of streptozotocin 65 mg/kg ip. in a single dose. Before the administration of streptozotocin, after 72 h, and after 8 weeks, the serum values of glucose, SOD, MDA, and total antioxidant capacity (TAS) were determined. After 8 weeks, the animals were anesthetized and sacrificed, and the lungs were harvested and examined by optical microscopy. Pulmonary fibrosis, the extent of lung lesions, and the lung wet-weight/dry-weight ratio were evaluated. Results: The obtained results showed that MK significantly reduced pulmonary fibrosis (3.34 ± 0.41 in the STZ group vs. 1.73 ± 0.24 in the STZ+MK group p < 0.01) and lung lesion scores and also decreased the lung wet-weight/dry-weight (W/D) ratio. SOD and TAS values increased significantly when MK was administered to animals with diabetes (77.2 ± 11 U/mL in the STZ group vs. 95.7 ± 13.3 U/mL in the STZ+MK group, p < 0.05, and 25.52 ± 2.09 Trolox units in the STZ group vs. 33.29 ± 1.64 Trolox units in the STZ+MK group, respectively, p < 0.01), and MDA values decreased. MK administered alone did not significantly alter any of these parameters in normal animals. Conclusions: The obtained data showed that by blocking the action of peptide leukotrienes on cysLT1 receptors, montelukast significantly reduced the lung lesions caused by diabetes. The involvement of these leukotrienes in the pathogenesis of fibrosis and other lung diabetic lesions was also demonstrated. Full article

This study introduces a novel sensitivity analysis approach to assess the resilience and susceptibility of hydrologic systems to the stresses of climate change, moving away from conventional top-down methodologies. By exploring the hydrological sensitivity of the upper Kızılırmak River basin using the Variable Infiltration Capacity (VIC) hydrologic model, we employed a sensitivity-based approach as an alternative to the traditional Global Climate Model (GCM)-based methods, providing more insightful information for water managers. Considering the consistent projections of increasing temperature over this region in GCMs, the hydrologic system was perturbed to examine gradients of a more challenging climate characterized by warming and drying conditions. The sensitivity of streamflow, snow water equivalent, and evapotranspiration to temperature (T) and precipitation (P) variations under each perturbation or “reference” climate was quantified. Results indicate that streamflow responds to T negatively under all warming scenarios. As the reference climates become drier, streamflow sensitivity to P increases, indicating that meteorological drought impacts on water availability could be exacerbated. These results suggest that there will be heightened difficulty in managing water resources in the region if it undergoes both warming and drying due to the following setbacks: (1) water availability will shift away from the summer season of peak water demand due to the warming effects on the snowpack, (2) annual water availability will likely decrease due to a combination of warming and lower precipitation, and (3) streamflow sensitivity to hydroclimatic variability will increase, meaning that there will be more extreme impacts to water availability. Water managers will need to plan for a larger set of extreme conditions. Full article

This study examines the effects of three interactive voice assistant (VA) features (responsiveness, ubiquitous connectivity, and personalization) on consumer happiness. An online survey was administered to 316 VA consumers, and the data were analyzed using structural equation modeling with SmartPLS 4 software. The results indicate that VA responsiveness, ubiquitous connectivity, and personalization have significant effects on consumer happiness. This study also provides evidence that consumer happiness is influenced by VA features through the mediating roles of autonomy and timeliness. Notably, perceived privacy risk has a dual effect, negatively affecting happiness but positively moderating the relationship between autonomy and happiness, suggesting a complex interplay between benefits and concerns in user interactions with VAs. This study highlights the need for VA businesses to consider both the enhancing and mitigating factors of technology for user experiences. Furthermore, our findings have significant implications for VA businesses and executives, suggesting that improved interactions through these VA features can better serve consumers and enhance their experiences. Full article

Facing the increasingly complex and uncertain external environment, the reasonable control of investment risk is the key to realizing the sound operation and high-quality development of enterprises. Based on the innovation perspective, this paper takes A-share non-financial listed companies from 2007 to 2021 as the research sample to explore the impact of the corporate risk-taking level on the high-quality development of enterprises and examines the transmission mechanism of the relationship between the two from the perspectives of innovation efficiency, innovation input, and innovation output. It is found that enterprise risk-taking significantly inhibits the high-quality development of enterprises, by reducing innovation efficiency; innovation efficiency plays a mediating role in the influence of the relationship between the two, which is mainly due to the fact that enterprise risk-taking increases the innovation inputs of enterprises but reduces the innovation outputs of enterprises, and then reduces the innovation efficiency of enterprises and inhibits the high-quality development of enterprises. This heterogeneity study finds that the inhibitory effect of corporate risk-taking on the high-quality development of firms is more pronounced among manufacturing firms, small-scale firms, and firms with higher capital intensity. The findings of this study provide both guidance to help enterprises to reduce risky investment decision-making behaviors and experience for regulators to effectively promote the formulation of policies related to the high-quality development of the real economy. Full article

Recently, many floating renewable energy platforms have been deployed in coastal regions, where sloping bottoms are an important factor when evaluating their safety. In this article, a simplified method coupling an eigenfunction matching method (EMM) and a finite-depth Green’s function (FDGF) is developed to evaluate floating-body motion responses over a sloping bottom for which bathymetry is homogeneous in the longshore direction. We propose an extended EMM to create an incident wave model over the sloping bottom, thereby obtaining the Froude–Krylov (F–K) force and Neumann data on the wet surfaces of the floating body for the diffraction problem. An equivalent depth is introduced to account for the interaction between the sloping bottom and floating bodies when dealing with the diffraction and radiation problems. The accuracy of the present method is validated through a comprehensive comparison with numerical and/or experiment results for a liquefied natural gas (LNG) ship and a floating hemisphere from the literature. Generally, the present, simplified method can give predictions with sufficient accuracy. Full article

The controlled manufacturing of semiconductor photocatalysts is crucial to their development for drinking water treatment. In this study, TiO₂-coated meltblown nonwoven fabrics prepared via Atomic Layer Deposition (ALD) are applied for the inactivation of Escherichia coli (E. coli). It is observed that in the presence of an ultraviolet light-emitting diode (UV-LED) light source (255 nm), 1.35 log E. coli inactivation is achieved. However, exposure to catalyst-coated fabrics in addition to the light source resulted in >4 log E. coli inactivation, suggesting a much higher rate of hydroxyl radical formation on the surface, leading to cell death. Full article

Despite the end of the pandemic, coronavirus disease 2019 (COVID-19) remains a major public health concern. The first waves of the virus led to a better understanding of its pathogenesis, highlighting the fact that there is a specific pulmonary vascular disorder. Indeed, COVID-19 may predispose patients to thrombotic disease in both venous and arterial circulation, and many cases of severe acute pulmonary embolism have been reported. The demonstrated presence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) within the endothelial cells suggests that direct viral effects, in addition to indirect effects of perivascular inflammation and coagulopathy, may contribute to pulmonary vasculopathy in COVID-19. In this review, we discuss the pathological mechanisms leading to pulmonary vascular damage during acute infection, which appear to be mainly related to thromboembolic events, an impaired coagulation cascade, micro- and macrovascular thrombosis, endotheliitis and hypoxic pulmonary vasoconstriction. As many patients develop post-COVID symptoms, including dyspnea, we also discuss the hypothesis of pulmonary vascular damage and pulmonary hypertension as a sequela of the infection, which may be involved in the pathophysiology of long COVID. Full article

Optical Coherence Tomography (OCT) is a growing family of biophotonic imaging techniques, but in the literature there is a lack of easy-to-use tools to universally directly evaluate a device’s theoretical performance for a given metric. Modern computing tools mean that direct numerical modeling can, from first principles, simulate the performance metrics of a specific device directly without relying on analytical approximations and/or complexities. Here, we present two different direct numerical models, along with the example MATLAB code for the reader to adapt to their own systems. The first model is of photo-electron shot noise at the detector, the primary noise source for OCT. We use this firstly to evaluate the amount of additional noise present (1.5 dB) for an experimental setup. Secondly, we demonstrate how to use it to precisely quantify the expected shot noise SNR limit difference between time-domain and Fourier-domain OCT systems in a given hypothetical experiment. The second model is used to demonstrate how USAF 1951 test chart images should be interpreted for a given lateral PSF shape. Direct numerical modeling is an easy and powerful basic tool for researchers and developers, the wider use of which may improve the rigor of the OCT literature. Full article

Facing the significant challenge of 3D object detection in complex weather conditions and road environments, existing algorithms based on single-frame point cloud data struggle to achieve desirable results. These methods typically focus on spatial relationships within a single frame, overlooking the semantic correlations and spatiotemporal continuity between consecutive frames. This leads to discontinuities and abrupt changes in the detection outcomes. To address this issue, this paper proposes a multi-frame 3D object detection algorithm based on a deformable spatiotemporal Transformer. Specifically, a deformable cross-scale Transformer module is devised, incorporating a multi-scale offset mechanism that non-uniformly samples features at different scales, enhancing the spatial information aggregation capability of the output features. Simultaneously, to address the issue of feature misalignment during multi-frame feature fusion, a deformable cross-frame Transformer module is proposed. This module incorporates independently learnable offset parameters for different frame features, enabling the model to adaptively correlate dynamic features across multiple frames and improve the temporal information utilization of the model. A proposal-aware sampling algorithm is introduced to significantly increase the foreground point recall, further optimizing the efficiency of feature extraction. The obtained multi-scale and multi-frame voxel features are subjected to an adaptive fusion weight extraction module, referred to as the proposed mixed voxel set extraction module. This module allows the model to adaptively obtain mixed features containing both spatial and temporal information. The effectiveness of the proposed algorithm is validated on the KITTI, nuScenes, and self-collected urban datasets. The proposed algorithm achieves an average precision improvement of 2.1% over the latest multi-frame-based algorithms. Full article

The receptor for advanced glycation end-products (RAGE) has a central function in orchestrating inflammatory responses in multiple disease states including chronic obstructive pulmonary disease (COPD). RAGE is a transmembrane pattern recognition receptor with particular interest in lung disease due to its naturally abundant pulmonary expression. Our previous research demonstrated an inflammatory role for RAGE following acute exposure to secondhand smoke (SHS). However, chronic inflammatory mechanisms associated with RAGE remain ambiguous. In this study, we assessed transcriptional outcomes in mice exposed to chronic SHS in the context of RAGE expression. RAGE knockout (RKO) and wild-type (WT) mice were delivered nose-only SHS via an exposure system for six months and compared to control mice exposed to room air (RA). We specifically compared WT + RA, WT + SHS, RKO + RA, and RKO + SHS. Analysis of gene expression data from WT + RA vs. WT + SHS showed FEZ1, Slpi, and Msln as significant at the three-month time point; while RKO + SHS vs. WT + SHS identified cytochrome p450 1a1 and Slc26a4 as significant at multiple time points; and the RKO + SHS vs. WT + RA revealed Tmem151A as significant at the three-month time point as well as Gprc5a and Dynlt1b as significant at the three- and six-month time points. Notable gene clusters were functionally analyzed and discovered to be specific to cytoskeletal elements, inflammatory signaling, lipogenesis, and ciliogenesis. We found gene ontologies (GO) demonstrated significant biological pathways differentially impacted by the presence of RAGE. We also observed evidence that the PI3K-Akt and NF-κB signaling pathways were significantly enriched in DEGs across multiple comparisons. These data collectively identify several opportunities to further dissect RAGE signaling in the context of SHS exposure and foreshadow possible therapeutic modalities. Full article

This paper presents an overview of the status and future prospects of fuel-cell electric vehicles (FC-EVs). As global concerns about emissions escalate, FC-EVs have emerged as a promising substitute for traditional internal combustion engine vehicles. This paper discusses the fundamentals of fuel-cell technology considering the major types of fuel cells that have been researched and delves into the most suitable fuel cells for FC-EV applications, including comparisons with mainstream vehicle technologies. The present state of FC-EVs, ongoing research, and the challenges and opportunities that need to be accounted for are discussed. Furthermore, the comparison between promising proton-exchange membrane fuel cell (PEMFC) and solid oxide fuel cell (SOFC) technologies used in EVs provides valuable insights into their respective strengths and challenges. By synthesizing these aspects, the paper aims to provide a comprehensive understanding and facilitate decision-making for future advancements in sustainable FC-EV transportation, thereby contributing to the realization of a cleaner, greener, and more environmentally friendly future. Full article

This paper describes the creation of a fast, deterministic, 3D fractal cloud renderer for the AFIT Sensor and Scene Emulation Tool (ASSET). The renderer generates 3D clouds by ray marching through a volume and sampling the level-set of a fractal function. The fractal function is distorted by a displacement map, which is generated using horizontal wind data from a Global Forecast System (GFS) weather file. The vertical windspeed and relative humidity are used to mask the creation of clouds to match realistic large-scale weather patterns over the Earth. Small-scale detail is provided by the fractal functions which are tuned to match natural cloud shapes. This model is intended to run quickly, and it can run in about 700 ms per cloud type. This model generates clouds that appear to match large-scale satellite imagery, and it reproduces natural small-scale shapes. This should enable future versions of ASSET to generate scenarios where the same scene is consistently viewed from both GEO and LEO satellites from multiple perspectives. Full article