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Accurate crop identification and spatial distribution mapping are important for crop production estimation and famine early warning, especially for food-deficit African agricultural countries. By evaluating existing preprocessing methods for classification using satellite image time series (SITS) in Kenya, this study aimed to provide a low-cost method for cultivated land monitoring in sub-Saharan Africa that lacks financial support. SITS were composed of a set of MODIS Vegetation Indices (MOD13Q1) in 2018, and the classification method included the Support Vector Machine (SVM) and Random Forest (RF) classifier. Eight datasets obtained at three levels of preprocessing from MOD13Q1 were used in the classification: (1) raw SITS of vegetation indices (R-NDVI, R-EVI, and R-NDVI + R-EVI); (2) smoothed SITS of vegetation indices (S-NDVI); and (3) vegetation phenological data (P-NDVI, P-EVI, R-NDVI + P-NDVI, and P-NDVI-1). Both SVM and RF classification results showed that the “R-NDVI + R-EVI” dataset achieved the highest performance, while the three pure phenological datasets produced the lowest accuracy. Correlation analysis between variable importance and rainfall time series demonstrated that the vegetation index SITS during rainfall periods showed higher importance in RF classifiers, thus revealing the potential of saving computational costs. Considering the preprocessing cost of SITS and its negative impact on the classification accuracy, we recommend overlaying the original NDVI with the original EVI time series to map the crop distribution in Kenya.

Recently, a large number of Transformer-based solutions have emerged for the trajectory prediction task, but there are shortcomings in the effectiveness of Transformers in trajectory prediction. Specifically, while position encoding preserves some of the ordering information, the self-attention mechanism at the core of the Transformer has its alignment invariance that leads to the loss of temporal information, which is crucial for trajectory prediction. For this reason, we design a simple and efficient strategy for temporal information extraction and prediction of trajectory sequences using the self-attention mechanism and linear layers. The experimental results show that the strategy can improve the average accuracy by 15.31%, effectively combining the advantages of the linear layer and the self-attention mechanism, while compensating for the shortcomings of the Transformer. Additionally, we conducted an empirical study to explore the effectiveness of the linear layer and sparse self-attention mechanisms in trajectory prediction.

Contact with nature is believed to enhance mental health through the process of human psychological restoration. However, prolonged indoor living limits individuals’ exposure to nature, potentially hindering the timely alleviation of stress and fatigue induced by work. While biophilic design is recognized as a potential solution, its impact on the restoration process has not been extensively studied, particularly in relation to its various design patterns. Therefore, it is important to determine the restorative effects of different patterns of biophilic design and their combination in order to guide the practical application of biophilic design. In this study, the effects of two typical biophilic design patterns and their combination on attention restoration were measured using subjective scales, cognitive tasks, and functional near-infrared spectroscopy (fNIRS) in a simulated real workspace. The results suggest a significant enhancement in the restorative impact on cognitive performance and physiological benefits when combining two biophilic design patterns, while a single design pattern does not yield the same effect. These findings contribute to a better understanding and improvement of workspaces, enhancing users’ experience and well-being.

Micro/nanoplastics (MNPs), as emerging pollutants, have been detected in both the maternal and fetal sides of the placenta in pregnant women, and their reproductive toxicity has been demonstrated in in vivo and in vitro experimental models. The Targeted Risk Assessment of Environmental Chemicals (TRAEC) strategy has been innovatively devised to facilitate valid risk assessment, encompassing a comprehensive evaluation of reliability, correlation, outcome fitness, and integrity across four dimensions based on the included published evidence and our own findings. This study serves as an application case of TRAEC, with 40 items of research evidence on the toxicity of MNPs to the placenta, which were rigorously screened and incorporated into the final scoring system. The final score for this TRAEC case study is 5.63, suggesting a moderate-to-low risk of reproductive toxicity associated with MNPs in the placenta, which may potentially increase with decreasing particle size. It is essential to emphasize that the findings also report original data from assays indicating that exposure to high-dose groups (100 μg/mL, 200 μg/mL) of 50 nm and 200 nm polystyrene nanoplastics (PS-NPs) induces HTR8/SVneo cell cycle arrest and cell apoptosis, which lead to reproductive toxicity in the placenta by disrupting mitochondrial function. Overall, this study employed the TRAEC strategy to provide comprehensive insight into the potential reproductive health effects of ubiquitous MNPs.

To address the problem of tribological failure in an aircraft piston pump valve plate pair, the friction and wear properties of the valve plate pair materials (W9Mo3Cr4V-HAl61-4-3-1) of an axial piston pump at a certain speed and load were studied using a disc-ring tester under lubrication with No. 15 aviation hydraulic oil. The results show that the friction coefficient (COF) fluctuated in the range of 0.019~0.120 when the load (L) increased from 30 N to 120 N, and the speed increased from 100 r/min to 500 r/min. With the increase in the rotational speed, the COF of the valve plate pair decreased first and then increased. When the rotation speed (V) was 300 r/min, the relative COF was the smallest. Under L lower than 60 N, abrasive wear was the main wear mechanism. Under L higher than 90 N, the main wear mechanism was adhesive wear but mild oxidation wear also occurred. In addition, based on the V, L, radius (R), and test duration (T), which affected COF, the random forest regression (RFR) algorithm, the bagging regression (BR) algorithm, and the extra trees regression (ETR) algorithm were used as machine learning methods to predict the COF of the valve plate pair. Mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2) were used to evaluate its performance, with the results showing that the ETR prediction model was the best method for predicting COF. The results of the machine learning also showed that the contributions of V, L, R, and T were 43.56%, 36.76%, 13.13%, and 6.55%, respectively, indicating that V had the greatest influence on the COF of the W9Mo3Cr4V/HAl61-4-3-1 friction pair. This study is expected to provide support for the rapid development of new valve plate pair materials.

To reduce the influence of the permanent magnet (PM) installation error on the electromagnetic characteristics of the outer rotor permanent magnet synchronous motor (OPMSM), the rotor structure of the OPMSM is optimized in this paper. The optimization method of adding auxiliary teeth on the surface of the rotor core is studied, and the influence of different auxiliary teeth heights on the electromagnetic performance of OPMSM is analyzed. It is found that adding auxiliary teeth with suitable height can greatly reduce the installation error of the PM, increase the mechanical stability of the motor, and ensure that the electromagnetic characteristics of the motor remain at a good level. Firstly, the topology and parameters of the motor proposed in this paper are introduced and analyzed. Secondly, the influence of PM installation error on the electromagnetic performance of the motor is analyzed based on the finite element method (FEM), and the necessity of eliminating PM installation error is demonstrated. Then, the parametric scanning method is used to analyze the influence of auxiliary teeth height change on the electromagnetic performance of the motor, and the selection standard of the optimal auxiliary teeth height is determined. By comparing and analyzing OPMSM with different sizes and different pole–slot ratios, the universality of the conclusions is demonstrated.

Remote sensing datasets usually have a wide range of spatial and spectral resolutions. They provide unique advantages in surveillance systems, and many government organizations use remote sensing multispectral imagery to monitor security-critical infrastructures or targets. Artificial Intelligence (AI) has advanced rapidly in recent years and has been widely applied to remote image analysis, achieving state-of-the-art (SOTA) performance. However, AI models are vulnerable and can be easily deceived or poisoned. A malicious user may poison an AI model by creating a stealthy backdoor. A backdoored AI model performs well on clean data but behaves abnormally when a planted trigger appears in the data. Backdoor attacks have been extensively studied in machine learning-based computer vision applications with natural images. However, much less research has been conducted on remote sensing imagery, which typically consists of many more bands in addition to the red, green, and blue bands found in natural images. In this paper, we first extensively studied a popular backdoor attack, BadNets, applied to a remote sensing dataset, where the trigger was planted in all of the bands in the data. Our results showed that SOTA defense mechanisms, including Neural Cleanse, TABOR, Activation Clustering, Fine-Pruning, GangSweep, Strip, DeepInspect, and Pixel Backdoor, had difficulties detecting and mitigating the backdoor attack. We then proposed an explainable AI-guided backdoor attack specifically for remote sensing imagery by placing triggers in the image sub-bands. Our proposed attack model even poses stronger challenges to these SOTA defense mechanisms, and no method was able to defend it. These results send an alarming message about the catastrophic effects the backdoor attacks may have on satellite imagery.

In this article, we are interested in the study of the following Kirchhoff–Choquard equations: −a+b∫R2|∇u|2dxΔu+V(x)u=λ(ln|x|∗u2)u+f(u),x∈R2, where λ>0,a>0,b>0, V and f are continuous functions with some appropriate assumptions. We prove that when the parameter λ is sufficiently small, the above problem has a mountain pass solution, a least energy solution and a ground state solution by applying the variational methods and building some subtle inequalities.

The ocean front has a non-negligible role in global ocean–atmosphere interactions, marine fishery production, and the marine military. Hence, obtaining the positions of the ocean front is crucial in oceanic research. At present, the positioning method of recognizing an ocean front has achieved a breakthrough in the mean dice similarity coefficient (mDSC) of above 90%, but it is difficult to use to achieve rapid extraction in emergency scenarios, including marine fisheries and search and rescue. To reduce the its dependence on machines and apply it to more requirements, according to the characteristics of an ocean front, a multi-scale model SQNet (Simple and Quick Net) dedicated to ocean front position recognition is designed, and its perception domain is expanded while obtaining current scale data. In experiments along the coast of China and the waters of the Gulf of Mexico, it was not difficult to find that SQNet exceedingly reduced running time while ensuring high-precision results (mDSC of higher than 90%). Then, after conducting intra-model self-comparison, it was determined that expanding the perceptual domain and changing the weight ratio of the loss function could improve the accuracy and operational efficiency of the model, which could be better applied in ocean front recognition.

The goal of the present paper is to investigate the critical Schrödinger-type fractional p&q-Laplacian problems. By employing the mountain pass theorem, we prove the existence and asymptotic property of nontrivial solutions for the problem.

Double-sided permanent magnet linear synchronous machine with 120° phase belt toroidal windings (120°-TWDSPMLSM) suffers from large detent force due to the end effect and slot effect. Many existing methods can reduce the detent force of the 120°-TWDSPMLSM effectively. But they also minimize the back electromotive force (back-EMF) and thrust simultaneously. To alleviate this problem, this paper adopts a method of combining slot-shift structure with rearranging the 120° phase belt toroidal windings (120°-TW) to suppress the detent force and improve the back-EMF and thrust of the 120°-TWDSPMLSM simultaneously. First, the slots on the two sides of the primary part are stagger by an optimal distance to reduce the detent force. The models of the detent force, including the end force and cogging force, is established theoretically, and then verified by finite-element analysis (FA). Then, the 120°-TW are rearranged to improve the back-EMF and average thrust. And the electromagnetic characteristics of the 120°-TWDSPMLSM under different shifted distances are analyzed and compared in detail. Finally, the result shows that the detent force of optimized machine is 70.19% lower than that of the original machine, and the thrust fluctuation reduces 19.21%. Besides, the back-EMF of optimized machine is 6.84% lower than that of the original machine, but 31.86 higher than the machine with shifting slots and without rearranging the 120°-TW. Therefore, the optimal method in this paper is available in the detent force reduction and thrust improvement of the 120°-DSPMLSM.

Telenomus remus (Nixon) is a dominant egg parasitoid of the destructive agricultural pest Spodoptera frugiperda (J. E. Smith), and so is used in augmentative biocontrol programs in several countries. An optimized mass-rearing system is essential to produce biological control products in a timely and cost-effective manner. In this study, the photoperiod, host egg:parasitoid ratio, and exposure time were evaluated to identify the optimal rearing conditions for T. remus on the alternative host Spodoptera litura (Fabricius) eggs. Results showed that increasing photoperiod above 12L:12D remarkably improved parasitoid progeny yield and life table parameters. Overlong photoperiods shortened female longevity, but within acceptable limits. There was a significant negative correlation between parasitism rate and host egg:parasitoid ratio under exposure times of 12 and 36 h, but not 24 h. Percentage of female progeny increased significantly along with increasing the host egg:parasitoid ratio. A significant negative relationship between the number of emerged adults per egg and the host egg:parasitoid ratio was observed at an exposure time of 36 h. It was concluded that T. remus may be mass-reared most efficiently on S. litura eggs using a photoperiod of more than 12L:12D, a 14–20:1 host egg:parasitoid ratio, and an exposure time of 24 h. These findings can be used to produce T. remus more efficiently and at lower costs.

Based on the principle of energy variation, a calculation model for the free vibration characteristics of a cylindrical shell with a finite submerged depth considering the influence of the free liquid surface is established in this paper. First, the Euler beam function is used instead of the shell axial displacement function to obtain the shell kinetic energy and potential energy. Then, by using the mirror image method, the analytical expression of the fluid velocity potential considering the free surface is obtained, and the flow field is added to the system energy functional in the form of fluid work. Then the energy functional is changed to obtain the shell–liquid coupled vibration equation. Solving the equation can obtain the natural frequencies and modes of the structure. The comparison with the finite element calculation results verifies the accuracy of the calculation model in this paper. The research on the influence of the free liquid surface shows that compared to the infinite domain, the free liquid surface destroys the symmetry of the entire system, resulting in a difference in the natural frequency of the positive and negative modes of the shell, and the circumferential mode shapes are no longer mutually uncoupled trigonometric functions. The existence of free liquid surface will also increase the natural frequency of the same order mode, and the closer to the free surface, the natural frequency is greater. As the immersion depth increases, the free vibration characteristics will quickly tend to the result of infinite domain. Additionally, when the immersion depth is equal to or greater than four times the radius of the shell structure, it can be considered that the free liquid surface has no effect. These law and phenomena have also been explained from the mechanism. The method in this paper provides a new analytical solution pattern for solving this type of problem.

Sand waves constitute ubiquitous geomorphology distribution in the ocean. In this paper, we quantitatively investigate the sand wave variation of topology, morphology, and evolution from the high-resolution mapping of a side scan sonar (SSS) in an Autonomous Underwater Vehicle (AUV), in favor of online sequential Extreme Learning Machine (OS-ELM). We utilize echo intensity directly derived from SSS to help accelerate detection and localization, denote a collection of Gaussian-type morphological templates, with one integrated matching criterion for similarity assessment, discuss the envelope demodulation, zero-crossing rate (ZCR), cross-correlation statistically, and estimate the specific morphological parameters. It is demonstrated that the sand wave detection rate could reach up to 95.61% averagely, comparable to deep learning such as MobileNet, but at a much higher speed, with the average test time of 0.0018 s, which is particularly superior for sand waves at smaller scales. The calculation of morphological parameters primarily infer a wave length range and composition ratio in all types of sand waves, implying the possible dominant direction of hydrodynamics. The proposed scheme permits to delicately and adaptively explore the submarine geomorphology of sand waves with online computation strategies and symmetrically integrate evidence of its spatio-temporal responses during formation and migration.

To explore factors influencing adolescents and young adults’ (AYAs) risk perception of COVID-19 and adherence to public health measures, we conducted a cross-sectional online survey of AYAs (14–22 years old) from Quebec (Canada) recruited through school and community partners in April 2020 during the first wave of the COVID-19 pandemic. The study included 3037 participants (mean age = 17.7 years, 74.6% female). AYAs had higher mean (standard deviation (SD)) risk perception of COVID-19 for their relatives (8.2 (1.9)) than for themselves (5.6 (2.6)) (p < 0.001). Factors associated with higher risk perception included higher disease knowledge (adjusted odds ratio (aOR) 1.06, 95% CI 1.01–1.11), presence of chronic disease (aOR 2.31, 95%CI 1.82–2.93) and use of immunosuppressants (aOR 2.53, 95%CI 1.67–3.87). AYAs with a higher risk perception (aOR 1.06, 95%CI 1.02–1.10) those wishing to help flatten the disease curve (aOR 1.18, 95%CI 1.12–1.25) or to protect their family/friends (aOR 1.14, 95%CI 1.05–1.24) were more likely to engage in preventive behaviors. Self-perceived risk and desire to protect others were significantly associated with adherence to preventive measures among youth. These findings may help inform public health messaging to AYAs in the current and future pandemics.

Building integrated photovoltaic (BIPV) is one of the most efficient ways to utilize renewable energy in buildings. However, the stochastic characteristic of PV power generation and load challenges the optimal dispatch of the BIPV. This paper proposes an optimal scheduling strategy of BIPV microgrid considering virtual energy storage (VES), which intends to further improve the operating economy of a BIPV microgrid. Firstly, existing research findings and gaps in this research area were analyzed. Then, the mathematical model of each unit in the system was created, meanwhile, the air conditioner-based VES (ACVES) model and electric water heater-based VES (EWHVES) model are built by exploiting the heat/cold storage capability of building and the electric water heater, respectively. Next, the optimal scheduling model considering the VES is designed. Finally, the optimal scheduling model is solved by CPLEX to obtain the optimal scheduling strategy that reduces the operating cost of the system. Simulation results demonstrate that the proposed strategy can reduce the operating cost of the system. Furthermore, the VES shows the potential to replace the battery energy storage system.

In this paper, a novel iterative sparse extended information filter (ISEIF) was proposed to solve the simultaneous localization and mapping problem (SLAM), which is very crucial for autonomous vehicles. The proposed algorithm solves the measurement update equations with iterative methods adaptively to reduce linearization errors. With the scalability advantage being kept, the consistency and accuracy of SEIF is improved. Simulations and practical experiments were carried out with both a land car benchmark and an autonomous underwater vehicle. Comparisons between iterative SEIF (ISEIF), standard EKF and SEIF are presented. All of the results convincingly show that ISEIF yields more consistent and accurate estimates compared to SEIF and preserves the scalability advantage over EKF, as well.

Underwater vision is one of the dominant senses and has shown great prospects in ocean investigations. In this paper, a hierarchical Independent Component Analysis (ICA) framework has been established to explore and understand the functional roles of the higher order statistical structures towards the visual stimulus in the underwater artificial vision system. The model is inspired by characteristics such as the modality, the redundancy reduction, the sparseness and the independence in the early human vision system, which seems to respectively capture the Gabor-like basis functions, the shape contours or the complicated textures in the multiple layer implementations. The simulation results have shown good performance in the effectiveness and the consistence of the approach proposed for the underwater images collected by autonomous underwater vehicles (AUVs).

Navigation technology is one of the most important challenges in the applications of autonomous underwater vehicles (AUVs) which navigate in the complex undersea environment. The ability of localizing a robot and accurately mapping its surroundings simultaneously, namely the simultaneous localization and mapping (SLAM) problem, is a key prerequisite of truly autonomous robots. In this paper, a modified-FastSLAM algorithm is proposed and used in the navigation for our C-Ranger research platform, an open-frame AUV. A mechanical scanning imaging sonar is chosen as the active sensor for the AUV. The modified-FastSLAM implements the update relying on the on-board sensors of C-Ranger. On the other hand, the algorithm employs the data association which combines the single particle maximum likelihood method with modified negative evidence method, and uses the rank-based resampling to overcome the particle depletion problem. In order to verify the feasibility of the proposed methods, both simulation experiments and sea trials for C-Ranger are conducted. The experimental results show the modified-FastSLAM employed for the navigation of the C-Ranger AUV is much more effective and accurate compared with the traditional methods.