Being able to represent and remember verbally-encoded information about spatial environments from different perspectives is important for numerous daily activities. The present study examined how frequently individuals spontaneously engage in visual mental imagery and verbal rehearsal strategies in memory recall of spatial descriptions, and whether using certain strategies is associated with better recall performance. Memory recall for route (person-centred) and survey (object-centred) spatial descriptions was examined in a sample of 105 neurotypical individuals, who also reported to what extent they used route- and survey-based mental imagery and verbal rehearsal strategies in each description. Results showed that participants favoured a path visualisation strategy to recall the route description and a map visualisation strategy to recall the survey description. Regression models further showed that employing both imagery and verbal strategies was associated with better recall for both route and survey descriptions, although imagery strategies held a higher predictive power. The present findings highlight the fact that the spontaneous use of internal strategies in the form of visual imagery and verbal rehearsal is ubiquitous when recalling spatial descriptions and a core part of efficient spatial memory functioning. Full article

Hematopoietic acute radiation syndrome (H-ARS) involves injury to multiple organ systems following total body irradiation (TBI). Our laboratory demonstrated that captopril, an angiotensin-converting enzyme inhibitor, mitigates H-ARS in Göttingen minipigs, with improved survival and hematopoietic recovery, as well as the suppression of acute inflammation. However, the effects of captopril on the gastrointestinal (GI) system after TBI are not well known. We used a Göttingen minipig H-ARS model to investigate captopril’s effects on the GI following TBI (⁶⁰Co 1.79 or 1.80 Gy, 0.42–0.48 Gy/min), with endpoints at 6 or 35 days. The vehicle or captopril (0.96 mg/kg) was administered orally twice daily for 12 days, starting 4 h post-irradiation. Ilea were harvested for histological, protein, and RNA analyses. TBI increased congestion and mucosa erosion and hemorrhage, which were modulated by captopril. GPX-4 and SLC7A11 were downregulated post-irradiation, consistent with ferroptosis at 6 and 35 days post-irradiation in all groups. Interestingly, p21/waf1 increased at 6 days in vehicle-treated but not captopril-treated animals. An RT-qPCR analysis showed that radiation increased the gene expression of inflammatory cytokines IL1B, TNFA, CCL2, IL18, and CXCL8, and the inflammasome component NLRP3. Captopril suppressed radiation-induced IL1B and TNFA. Rectal microbiome analysis showed that 1 day of captopril treatment with radiation decreased overall diversity, with increased Proteobacteria phyla and Escherichia genera. By 6 days, captopril increased the relative abundance of Enterococcus, previously associated with improved H-ARS survival in mice. Our data suggest that captopril mitigates senescence, some inflammation, and microbiome alterations, but not ferroptosis markers in the intestine following TBI. Full article

A clear trend of replacing synthetic cosmetic ingredients with natural ones can be observed in modern cosmetology. This entails the need to search for bioactive ingredients in the natural environment, especially in plants. This paper presents a comprehensive overview of dermatological, cosmetic, and pharmacological properties of highly potent plants, namely Acmella oleracea (A. oleracea), Centella asiatica (C. asiatica), Psoralea corylifolia (P. corylifolia), Plantago lanceolata L. (P. lanceolata L.), and Solidago virgaurea L. (S. virgaurea L.). Biological activity and phytochemical constituents are presented for all plants, but special attention is paid to ingredients of particular value to the cosmetics industry. The advantages of spilanthol and bakuchiol as a replacement for the popular botulinum toxin and retinol are discussed. Natural habitats, ethnomedical importance, cultivation area, as well as extraction methods of active plant ingredients are presented in detail. A wide spectrum of biological activity indicates the enormous potential of the presented plants in formulating new cosmetic and dermatological preparations. Full article

Ephemeral gully (EG) erosion is an important type of water erosion. Understanding the spatial distribution of EGs and other influencing factors at a regional scale is crucial for developing effective soil and water management strategies. Unfortunately, this area has not been sufficiently studied. The present study visually interpreted the EGs based on Google Earth images in 137 small watersheds uniformly distributed in the Loess Plateau, compared them with measured results, and analyzed the factors influencing EG formation and density using GeoDetector. The results showed that visually interpreting EGs from Google Earth images was suitable for EG regional studies. Out of the 137 small watersheds, 33.6% had EG occurrence with an average density of 3.41 km/km². Rainfall (R) and slope gradient (S) were the primary factors influencing the formation of EGs, while the area proportion of sloping farmland (APSF) and soil erodibility (K) were the main factors affecting EG density. The interaction of dual factors had a greater influence compared to single factors, with the interaction between S and Normalized Difference Vegetation Index (NDVI) having the greatest impact on EG formation and the interaction between K and NDVI on EG density. Although natural forces significantly influence whether EGs can form in a specific area, human activities greatly affect the density of the gullies that develop. This underscores the importance of proper land management in controlling gully erosion. These findings could provide theoretical support for EG prediction models and a scientific basis for soil and water loss control strategies at the regional scale. Full article

Underground coal mining of gas-bearing coal seams is accompanied by the emission of large amounts of methane, which increases with depth. Coal seam methane is not only a major cause of major accidents in coal mines, but is also a greenhouse gas that has a significant negative impact on the Earth’s atmosphere. Analysis of the efficiency of underground coal mining suggests that as the depth of mining increases, the productivity of a longwall decreases by a factor of 3–5 or more, while the specific volume of methane emitted increases manifold and the efficiency of methane management decreases. Effective management of coal seam methane can only be achieved by monitoring its content at key points in a system of workings. Monitoring of methane not only eliminates the risk of explosions, but also lets us assess the effectiveness of using methane management techniques and their parameters to improve efficiency and reduce the cost of methane management (including a methane drainage) for ensuring sustainable underground coal mining. The aim of this article is to develop a software and hardware complex for monitoring methane in a coal mine by creating a simulation model for monitoring methane. The Arduino Uno board and the methane sensor MQ-4 were used for this purpose. In this article, the causes of methane emissions in coal mines, gas control systems, the structure of the mine monitoring system, and the causes of risks and occurrence of accidents in coal mines are considered. As a result of the work, the mathematical model of the methane measurement sensor was developed; the Arduino Uno board developed a simulation system for methane monitoring; and the numerical results of the research are presented in the graphs. Full article

Background/Objective: Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by lifelong impacts on functional social and daily living skills, and restricted, repetitive behaviors (RRBs). Applied behavior analysis (ABA), the gold-standard treatment for ASD, has been extensively validated. ABA access is hindered by limited availability of qualified professionals and logistical and financial barriers. Scientifically validated, parent-led ABA can fill the accessibility gap by overcoming treatment barriers. This retrospective cohort study examines how our ABA treatment model, utilizing parent behavior technicians (pBTs) to deliver ABA, impacts adaptive behaviors and interfering behaviors (IBs) in a cohort of children on the autism spectrum with varying ASD severity levels, and with or without clinically significant IBs. Methods: Clinical outcomes of 36 patients ages 3–15 years were assessed using longitudinal changes in Vineland-3 after 3+ months of pBT-delivered ABA treatment. Results: Within the pBT model, our patients demonstrated clinically significant improvements in Vineland-3 Composite, domain, and subdomain scores, and utilization was higher in severe ASD. pBTs utilized more prescribed ABA when children initiated treatment with clinically significant IBs, and these children also showed greater gains in their Composite scores. Study limitations include sample size, inter-rater reliability, potential assessment metric bias and schedule variability, and confounding intrinsic or extrinsic factors. Conclusion: Overall, our pBT model facilitated high treatment utilization and showed robust effectiveness, achieving improved adaptive behaviors and reduced IBs when compared to conventional ABA delivery. The pBT model is a strong contender to fill the widening treatment accessibility gap and represents a powerful tool for addressing systemic problems in ABA treatment delivery. Full article

To combat climate change, one approach is to manufacture products from biomass-fungi composite materials instead of petroleum-based plastics. These products can be used in packaging, furniture, and construction industries. A 3D printing-based manufacturing method was developed for these biomass-fungi composite materials, eliminating the need for molds, and enabling customized product design. However, previous studies on the 3D printing-based method showed significant shrinkage of printed samples. In this paper, an approach is proposed to reduce the shrinkage by incorporating ionic crosslinking into biomass-fungi composite materials. This paper reports two sets of experiments regarding the effects of sodium alginate (SA) and calcium chloride (CaCl₂) on fungal growth and fungal viability. The first set of experiments was conducted using Petri dishes with fungi isolated from colonized biomass-fungi material and different concentrations of SA and CaCl₂. Fungal growth was measured by the circumference of fungal colonies. The results showed that concentrations of SA and CaCl₂ had significant effects on fungal growth and no fungal growth was observed on Petri dishes with 15% CaCl₂. Some of these Petri dishes were also observed under confocal microscopy. The results confirmed the differences obtained by measuring the circumference of fungal colonies. The second set of experiments was conducted using Petri dishes with biomass-fungi mixtures that were treated with different concentrations of SA and exposure times in a CaCl₂ (crosslinking) solution. Fungal viability was measured by counting colony-forming units. The results showed that the addition of the SA solution and exposure times in the crosslinking solution had statistically significant effects on fungal viability. The 2SA solution was prepared by dissolving 2 g of SA in 100 mL of water, the 5SA solution was prepared by dissolving 5 g of SA in 100 mL of water, and the crosslinking solution was prepared by dissolving 5 g of CaCl₂ in 100 mL of water. The results also showed that fungal viability was not too low in biomass-fungi mixtures that included 2SA solution and were exposed to the crosslinking solution for 1 min. Full article

Spinal Muscular Atrophy is a neurodegenerative disease which can lead to muscle weakness, paralysis, and in some cases death. There are many factors that contribute to the severity of symptoms and those factors can be used to determine the best course of treatment for the patients. We looked through published literature to create a set of considerations for treatment in patients with Spinal Muscular Atrophy including age, type, SMN2 copies, and any familial considerations. This can serve as a guide for what to consider in the treatment of SMA patients clinically. Full article

β-tricalcium phosphate (β-TCP) is a promising material in regenerative traumatology for the creation of bone implants. Previously, it was established that doping the structure with certain cations can reduce the growth of bacterial activity. Recently, much attention has been paid to co-doped β-TCP, that is explained by their ability, on the one hand, to reduce cytotoxicity for cells of the human organism, on the other hand, to achieve a successful antibacterial effect. Sr, Cu-co-doped solid solutions of the composition Ca_9.5–xSr_xCu(PO₄)₇ was obtained by the method of solid-phase reactions. The Rietveld method of structural refinement revealed the presence of Sr²⁺ ions in four crystal sites: M1, M2, M3, and M4. The M5 site is completely occupied by Cu²⁺. Isomorphic substitution of Ca²⁺ → (Sr²⁺and Cu²⁺) expands the concentration limits of the existence of the solid solution with the β-TCP structure. No additional phases were formed up to x = 4.5 in Ca_9.5–xSr_xCu(PO₄)₇. Biocompatibility tests were performed on cell lines of human bone marrow mesenchymal stromal cells (hMSC), human fibroblasts (MRC-5) and osteoblasts (U-2OS). It was demonstrated that cytotoxicity exhibited a concentration dependence, along with an increase in osteogenesis and cell proliferation. Ca_9.5–xSr_xCu(PO₄)₇ powders showed significant inhibitory activity against pathogenic strains Escherichia coli and Staphylococcus aureus. Piezoelectric properties of Ca_9.5–xSr_xCu(PO₄)₇ were investigated. Possible ways to achieve high piezoelectric response are discussed. The combination of bioactive properties of Ca_9.5–xSr_xCu(PO₄)₇ renders them multifunctional materials suitable for bone substitutes. Full article

Wastewater treatment plants are highly energy-intensive systems. This research uses Life Cycle Assessment (LCA) to determine the impacts generated during the operation of a wastewater treatment plant. Three different scenarios are analyzed: a baseline scenario that considers a conventional activated sludge treatment technology exploiting data from an existing plant located in central Italy, a second scenario that involves the implementation of MBR technology applied to the baseline scenario, and finally a third scenario that consists of the addition of an anaerobic digester that allows energy recovery from biogas production, followed by a photovoltaic plant capable of supplying the plant energy demand. Global warming potential, eutrophication, and acidification are the environmental categories considered most relevant to emissions. The results showed that the effluent had the highest impact in terms of CO₂ equivalent in all three situations due to the presence of N₂O. Since emissions from biological processes, transportation, and wastewater are almost similar in all three scenarios, it is preferable to focus on the environmental impacts associated with energy consumption. The third scenario involves careful resource management and the use of treatment technologies that allow for a reduction in the use of nonrenewable energy sources in favor of renewable ones. Full article

Defect inspection is a critical task in ensuring the surface quality of steel plates. Deep neural networks have the potential to achieve excellent inspection accuracy if defect samples are sufficient. Nevertheless, it is very different to collect enough samples using cameras alone. To a certain extent, generative models can alleviate this problem but poor sample quality can greatly affect the final inspection performance. A sample generation method, which employs a generative adversarial network (GAN), is proposed to generate high-quality defect samples for training accurate inspection models. To improve generation quality, we propose a production-and-elimination, two-stage sample generation process by simulating the formation of defects on the surface of steel plates. The production stage learns to generate defects on defect-free background samples, and the elimination stage learns to erase defects on defective samples. By minimizing the differences between the samples at both stages, the proposed model can make generated background samples close to real ones while guiding the generated defect samples to be more realistic. Experimental results show that the proposed method has the ability to generate high-quality samples that can help train powerful inspection models and thereby improve inspection performance. Full article

In addressing the challenges associated with low convergence accuracy and unstable optimization results in the original gazelle optimization algorithm (GOA), this paper proposes a novel approach incorporating chaos mapping termed multi-strategy particle swarm optimization with gazelle optimization algorithm (MPSOGOA). In the population initialization stage, segmented mapping is integrated to generate a uniformly distributed high-quality population which enhances diversity, and global perturbation of the population is added to improve the convergence speed in the early iteration and the convergence accuracy in the late iteration. By combining particle swarm optimization (PSO) and GOA, the algorithm leverages individual experiences of gazelles, which improves convergence accuracy and stability. Tested on 35 benchmark functions, MPSOGOA demonstrates superior performance in convergence accuracy and stability through Friedman tests and Wilcoxon signed-rank tests, surpassing other metaheuristic algorithms. Applied to engineering optimization problems, including constrained implementations, MPSOGOA exhibits excellent optimization performance. Full article

The activities of health care workers and early childhood educators have received increased attention both in lay public discourse and in scientific discourse. These professional groups play a significant role in shaping the health behaviours of those they interact with; thus, understanding the patterns they convey is of paramount importance. The aim of our study is a comparative analysis of health conditions and health behaviours of professionals working in Hungarian early childhood education and nurses working in the healthcare system (n = 1591). We carried out our quantitative, cross-sectional research using convenience sampling among healthcare professionals working in nursing job positions (n = 581) and as early childhood educators (n = 1010), in south-west Hungary. Diagnosed chronic illnesses affect early childhood educators at a significantly higher rate (p < 0.05): the prevalence of musculoskeletal disorders is particularly high among them, as a result of which they reported a significant degree of physical limitation in relation to work. In the context of mental health, comparing the professional groups, nurses’ indicators were significantly (p < 0.001) more unfavourable in all examined dimensions. Moreover, the comparison in terms of educational attainment directed attention to the worse indicators of non-graduates. In this context, early childhood educators are less affected by all three dimensions of burnout (p < 0.001). As for health behaviour, the smoking habits of nurses are more unfavourable (p < 0.05). Regarding screening tests, participation in cytological testing was significantly higher among nurses, whereas early childhood educators showed increased participation in mammography (p < 0.001). Our findings draw attention to the fact that early childhood educators are primarily affected by chronic musculoskeletal disorders, while healthcare workers are more affected by problems related to mental health. Mental well-being can be further endangered by the fact that both professional groups perceive low social appreciation for the work they carry out. Full article

The present article is intended to serve an educational purpose for data scientists and students who already have experience with the R language and which to start using it for geospatial analysis and map creation. The basic concepts of raster data, vector data, CRS and datum are first presented along with a basic workflow to conduct reproducible geospatial research in R. Examples of important types of maps (scatter, bubble, choropleth, hexbin and faceted) created from open-source environmental data are illustrated and their practical implementation in R is discussed. Through these examples, essential manipulations on geospatial vector data are demonstrated (reading , transforming CRS, creating geometries from scratch, buffer zones around existing geometries and intersections between geometries). Full article

Dictyophora rubrovolvata volva, an agricultural by-product, is often directly discarded resulting in environmental pollution and waste of the proteins’ resources. In this study, D. rubrovolvata volva proteins (DRVPs) were recovered using the ultrasound-assisted extraction (UAE) method. Based on one-way tests, orthogonal tests were conducted to identify the effects of the material–liquid ratio, pH, extraction time, and ultrasonic power on the extraction rate of DRVPs. Moreover, the impact of UAE on the physicochemical properties, structure characteristics, intermolecular forces, and functional attributes of DRVPs were also examined. The maximum protein extraction rate was achieved at 43.34% under the best extraction conditions of UAE (1:20 g/mL, pH 11, 25 min, and 550 W). UAE significantly altered proteins’ morphology and molecular size compared to the conventional alkaline method. Furthermore, while UAE did not affect the primary structure, it dramatically changed the secondary and tertiary structure of DRVPs. Approximately 13.42% of the compact secondary structures (α-helices and β-sheets) underwent a transition to looser structures (β-turns and random coils), resulting in the exposure of hydrophobic groups previously concealed within the molecule’s core. In addition, the driving forces maintaining and stabilizing the sonicated protein aggregates mainly involved hydrophobic forces, disulfide bonding, and hydrogen bonding interactions. Under specific pH and temperature conditions, the water holding capacity, oil holding capacity, foaming capacity and stability, emulsion activity, and stability of UAE increased significantly from 2.01 g/g to 2.52 g/g, 3.90 g/g to 5.53 g/g, 92.56% to 111.90%, 58.97% to 89.36%, 13.85% to 15.37%, and 100.22% to 136.53%, respectively, compared to conventional alkali extraction. The findings contributed to a new approach for the high-value utilization of agricultural waste from D. rubrovolvata. Full article

Repairing damaged tissue caused by bacterial infection poses a significant challenge. Traditional antibacterial hydrogels typically incorporate various components such as metal antimicrobials, inorganic antimicrobials, organic antimicrobials, and more. However, drawbacks such as the emergence of multi-drug resistance to antibiotics, the low antibacterial efficacy of natural agents, and the potential cytotoxicity associated with metal antibacterial nanoparticles in hydrogels hindered their broader clinical application. In this study, we successfully developed imidazolium poly(ionic liquids) (PILs) polymer microspheres (APMs) through emulsion polymerization. These APMs exhibited notable antibacterial effectiveness and demonstrated minimal cell toxicity. Subsequently, we integrated the APMs into a gelatin methacryloyl (GelMA)—polyethylene glycol (PEG) hydrogel. This composite hydrogel not only showcased strong antibacterial and anti-inflammatory properties but also facilitated the migration of human skin fibroblasts (HSF) and human umbilical vein endothelial cells (HUVECs) and promoted osteogenic differentiation in vitro. Full article

Autonomous underwater vehicles (AUVs) are now widely used in both civilian and military applications; however, wireless charging underwater often faces difficulties such as disturbances from ocean currents and errors in device positioning, making proper alignment of the charging devices challenging. Misalignment between the primary and secondary coils can significantly impact the efficiency and power of the wireless charging system energy transfer. To address the issue of misalignment in wireless charging systems, this paper proposes a multiple transfer coil wireless power transfer (MTCWPT) system based on backpropagation (BP) neural network control combined with nonsingular terminal sliding mode control (NTSMC) to enhance further the system robustness and efficiency. To achieve WPT in the ocean, a coil shielding case structure was equipped. In displacement experiments, the proposed multi-transmitting coil system could achieve stable power transfer of 40 W and efficiency of over 78.5% within a displacement range of 8 cm. The system robustness was also validated. This paper presents a new AUV energy supply solution based on MTCWPT. The proposed MTCWPT system can significantly improve the navigation performance of AUVs. Full article

The focus of this paper was Jouguet detonation in an ideal gas flow in a magnetic field. A modified Hugoniot detonation equation has been obtained, taking into account the influence of the magnetic field on the detonation process and the parameters of the detonation wave. It was shown that, under the influence of a magnetic field, combustion products move away from the detonation front at supersonic speed. As the magnetic field strength increases, the speed of the detonation products also increases. A dependence has been obtained that allows us to evaluate the influence of heat release on detonation parameters. Full article

The primary function of a mine hoist is the transportation of personnel and equipment, serving as a crucial link between underground and surface systems. The proper functioning of key components such as work braking and safety braking is essential for ensuring the safety of both personnel and equipment, thereby playing a critical role in the safe operation of coal mines. As coal mining operations extend to greater depths, they introduce heightened challenges for safe transportation, compounded by increased equipment loss. Consequently, there is a pressing need to enhance safety protocols to safeguard personnel and materials. Traditional maintenance and repair methods, characterized by routine equipment inspections and scheduled downtime, often fall short in addressing emerging issues promptly, leading to production delays and heightened risks for maintenance personnel. This underscores the necessity of adopting predictive maintenance strategies, leveraging digital twin models to anticipate and prevent potential faults in mine hoists. In summary, the implementation of predictive maintenance techniques grounded in digital twin technology represents a proactive and scientifically rigorous approach to ensuring the continued safe operation of mine hoists amidst the evolving challenges of deepening coal mining operations. In this study, we propose the integration of a CNN-LSTM algorithm within a digital twin framework for predicting faults in mine hoist braking systems. Utilizing software such as AMESim 2019 and MATLAB 2016b, we conduct joint simulations of the hoist braking digital twin system. Subsequently, leveraging the simulation model, we establish a fault diagnosis platform for the hoist braking system. Finally, employing the CNN-LSTM network model, we forecast failures in the mine hoist braking system. Experimental findings demonstrate the effectiveness of our proposed algorithm, achieving a prediction accuracy of 95.35%. Comparative analysis against alternative algorithms confirms the superior performance of our approach. Full article

Hyperspectral imaging has emerged as a novel imaging modality in the medical field, offering the ability to acquire images of biological tissues while simultaneously providing biochemical insights for in-depth tissue analysis. This approach facilitates early disease diagnosis, presenting advantages over traditional medical imaging techniques. Addressing challenges such as the computational burden of existing convolutional neural networks (CNNs) and imbalances in sample data, this paper introduces a lightweight GhostMRNet for the classification of microscopic hyperspectral images of human blood cells. The proposed model employs Ghost Modules to replace conventional convolutional layers and a cascading approach with small convolutional kernels for multiscale feature extraction, aiming to enhance feature extraction capabilities while reducing computational complexity. Additionally, an SE (Squeeze-and-Excitation) module is introduced to selectively allocate weights to features in each channel, emphasizing informative features and efficiently achieving spatial–spectral feature extraction in microscopic hyperspectral imaging. We evaluated the performance of the proposed GhostMRNet and compared it with other state-of-the-art models using two real medical hyperspectral image datasets. The experimental results demonstrate that GhostMRNet exhibits a superior performance, with an overall accuracy (OA), average accuracy (AA), and Kappa coefficient reaching 99.965%, 99.565%, and 0.9925, respectively. In conclusion, the proposed GhostMRNet achieves a superior classification performance at a smaller computational cost, thereby providing a novel approach for blood cell detection. Full article

Accurate crop mapping is crucial for ensuring food security. Recently, many studies have developed diverse crop mapping models based on deep learning. However, these models generally rely on a large amount of labeled crop samples to investigate the intricate relationship between the crop types of the samples and the corresponding remote sensing features. Moreover, their efficacy is often compromised when applied to other areas owing to the disparities between source and target data. To address this issue, a new multi-modal deep adaptation crop classification network (MDACCN) was proposed in this study. Specifically, MDACCN synergistically exploits time series optical and SAR images using a middle fusion strategy to achieve good classification capacity. Additionally, local maximum mean discrepancy (LMMD) is embedded into the model to measure and decrease domain discrepancies between source and target domains. As a result, a well-trained model in a source domain can still maintain satisfactory accuracy when applied to a target domain. In the training process, MDACCN incorporates the labeled samples from a source domain and unlabeled samples from a target domain. When it comes to the inference process, only unlabeled samples of the target domain are required. To assess the validity of the proposed model, Arkansas State in the United States was chosen as the source domain, and Heilongjiang Province in China was selected as the target domain. Supervised deep learning and traditional machine learning models were chosen as comparison models. The results indicated that the MDACCN achieved inspiring performance in the target domain, surpassing other models with overall accuracy, Kappa, and a macro-averaged F1 score of 0.878, 0.810, and 0.746, respectively. In addition, the crop-type maps produced by the MDACCN exhibited greater consistency with the reference maps. Moreover, the integration of optical and SAR features exhibited a substantial improvement of the model in the target domain compared with using single-modal features. This study indicated the considerable potential of combining multi-modal remote sensing data and an unsupervised domain adaptive approach to provide reliable crop distribution information in areas where labeled samples are missing. Full article

This study presents a comprehensive approach for simulating the additive manufacturing process of semi-crystalline composite polymers using Fused Deposition Modeling (FDM). By combining thermomechanical Finite Element Analysis (FEA) with experimental validation, our main objective is to comprehend and model the complex behaviors of 50 wt.% carbon fiber-reinforced Polyphenylene Sulfide (CF PPS) during FDM printing. The simulations of the FDM process encompass various theoretical aspects, including heat transfer, orthotropic thermal properties, thermal dissipation mechanisms, polymer crystallization, anisotropic viscoelasticity, and material shrinkage. We utilize Abaqus user subroutines such as UMATHT for thermal orthotropic constitutive behavior, UEPACTIVATIONVOL for progressive activation of elements, and ORIENT for material orientation. Mechanical behavior is characterized using a Maxwell model for viscoelastic materials, incorporating a dual non-isothermal crystallization kinetics model within the UMAT subroutine. Our approach is validated by comparing nodal temperature distributions obtained from both the Abaqus built-in AM Modeler and our user subroutines, showing close agreement and demonstrating the effectiveness of our simulation methods. Experimental verification further confirms the accuracy of our simulation techniques. The mechanical analysis investigates residual stresses and distortions, with particular emphasis on the critical transverse in-plane stress component. This study offers valuable insights into accurately simulating thermomechanical behaviors in additive manufacturing of composite polymers. Full article