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The study of spatial social networks has evolved from identifying structures within single networks to analyzing spatial associations between multilayer networks. However, current research primarily focuses on many-to-many flow structures, neglecting the unique advantages of one-to-many flow in characterizing local and global interactions. To bridge this gap, this paper introduces flow sequences and flow structure curves to effectively represent and visualize structures of one-to-many flows. Building on this foundation, a similarity-based comparison strategy to analyze spatial associations of one-to-many flow structures within two different networks from a local perspective is proposed. This method can be utilized to examine associations across diverse scenarios, including between distinct networks, within a single network over different temporal intervals, and between the inflow and outflow of the same network. The effectiveness and robustness of the proposed method were validated using a synthetic dataset. Case studies on migration and attention flows demonstrated its diverse applications and potential. The proposed approach enhances convergent and divergent analysis in multi-semantic spatial networks by offering tools for investigating structural consistency across networks. It emphasizes the influence of individual nodes on the entire network and the reciprocal shaping of local interaction relationships by global patterns.

For batch time-varying processeswith non-repetitive disturbances, a cross-coupling indirect iterative learning control (CC-iILC) is proposed. The set trajectory of the system on the single axis is accurately tracked by an indirect iterative learning control strategy with a PI controller for its time direction and a closed-loop feedback control strategy for the batch direction. Under the asymptotically stable condition of the two-dimensional (2D) dynamic model, the optimal control law is obtained by optimizing the H-infinity control function. To avoid the contour error during coupling, the cross-coupling technique is used to distribute the contour error to each axis for compensation. The stability condition of the indirect iterative learning controller is analyzed by a two-dimensional Fornasini-Marchesini (FM) batch dynamics model and two-dimensional robust H-infinity control theory. The feasibility and superiority of the proposed control method are verified by numerical simulation and experimental tests.

Municipal sludge is rich in nutrients and microbial populations, making it a potential soil amendment to enhance fertility. This study aimed to investigate the impact of municipal sludge application on microbial populations and assess its suitability as a fertilizer. The results indicated a significant increase in organic matter content in sandy soil after municipal sludge application (from 9.57 to 23.62 mg·kg−1). Available potassium and phosphorus levels improved from poor to intermediate, and available nitrogen reached an excellent level. Plant parameters such as wet weight, diameter, root length, and aboveground height also showed improvement with municipal sludge addition. High-throughput sequencing revealed Shannon and Simpson indices exceeding 5.26 and 0.98, respectively, across all substrates except B1, indicating enhanced microbial community structure and diversity in sandy soil. Redundancy analysis highlighted the pivotal role of total phosphorus, available phosphorus, organic matter, available nitrogen, total nitrogen, and available potassium in enriching microbial abundance and diversity. In conclusion, using municipal sludge as fertilizer is feasible and beneficial for soil safety, fertility, and microbial populations enhancement.

Pulsed electric field (PEF) technology has attracted considerable attention because it can efficiently treat pollutants that are difficult to degrade. In this study, a PEF system using iron as the electrode was constructed to investigate the effect of PEF–Fe on the growth and metabolism of aerobic denitrifying bacteria and the effectiveness of wastewater nitrogen removal. The chemical oxygen demand, NO3-–N and nitrate removal rates were 98.93%, 97.60% and 24.40 mg·L−1·h−1, respectively, under optimal conditions. As confirmed in this study, PEF–Fe could improve the key enzyme activities of W207-14. Scanning electron microscopy revealed that the surface of PEF–Fe-treated W207-14 was intact and smooth without any irreversible deformation. Flow cytometry combined with fluorescence staining analysis also confirmed reversible electroporation on the cell membrane surface of PEF–Fe-treated W207-14. Differentially expressed gene enrichment analysis showed that PEF–Fe activated the transmembrane transport function of ATP-binding cassette transporte (ABC) transport proteins and enhanced the cell membrane permeability of aerobic denitrifying bacteria. The significant differential expression of iron–sulphur cluster proteins facilitated the regulation of electron transport and maintenance of the dynamic balance of iron ions within the PEF–Fe system. HIGHLIGHTS A pulsed electric field efficiently removes nitrogen from wastewater.; Pulsed electric fields can cause reversible electroporation on the surface of microbial cell membranes.; A pulsed electric field enhances the cell membrane permeability of bacteria.; A pulsed electric field shortens the growth cycle of aerobic denitrifying bacteria.;

In this study, we present an electronic nose (e-nose) humidity compensation system based on rapid detection to solve the issue of humidity drift’s potential negative impact on the performance of electronic noses. First, we chose the first ten seconds of non-steady state (rapid detection mode) sensor data as the dataset, rather than waiting for the electronic nose to stabilize during the detection process. This was carried out in the hope of improving the detection efficiency of the e-nose and to demonstrate that the e-nose can collect gasses efficiently in rapid detection mode. The random forest approach is then used to optimize and reduce the dataset’s dimensionality, filtering critical features and improving the electronic nose’s classification capacity. Finally, this study builds an electronic nose humidity compensation system to compensate for the datasets generated via rapid real-time detection, efficiently correcting the deviation of the sensor response caused by humidity variations. This method enhanced the average resolution of the electronic nose in this trial from 87.7% to 99.3%, a 12.4% improvement, demonstrating the efficacy of the humidity compensation system based on rapid detection for the electronic nose. This strategy not only improves the electronic nose’s anti-drift and classification capabilities but also extends its service life, presenting a new solution for the electronic nose in practical detecting applications.

Macronutrients, including nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), and sulfur (S), are the most basic nutrient elements in the solution for the hydroponic system. However, the current management of hydroponic nutrient solutions usually depends on EC and pH sensors due to the lack of accurate specific macronutrient sensing equipment, which easily leads to nutritional imbalance for the cultivated plant. In this study, the UV-NIR absorption spectroscopy (200–1100 nm) was used to predict six macronutrients in hydroponic solutions; two kinds of single-task learning algorithms, including partial least squares (PLS) and least absolute shrinkage and selection operator (LASSO), and two kinds of multi-task learning algorithms, including dirty multi-task learning (DMTL) and robust multi-task learning (RMTL), were investigated to develop prediction models and assess capabilities of UV-NIR. The results showed that N and Ca could be quantitatively predicted by UV-NIR with the ratio of performance to deviation (RPD) more than 2, K could be qualitatively predicted (1.4 < RPD < 2), and P, Mg, and S could not be successfully predicted (RPD < 1.4); the RMTL algorithm outperformed others for predicting K and Ca benefit from the underlying task relationships with N; and predicting P, Mg, and S were identified as irrelevant (outlier) tasks. Our study provides a potential approach for predicting several macronutrients in hydroponic solutions with UV-NIR, especially using RMTL to improve model prediction ability.

【Objective】Cage biometrics and statistics are one of the key reference factors for marine pasture farming management. Aiming at the interference of reverberation noise and complex background, this paper constructs fish detection data sets under different lighting conditions, and uses forward-looking sonar imaging technology to propose a fish recognition lightweight model based on YOLOV5-MobilenetV3 and sonar images (LAPR-Net) to realize fish recognition in water cages in turbid or dark scenes.【Method】Taking tilapia as the research object, based on the frame structure of the YOLOV5 model, the backbone network module ado pts the lightweight Mob ileNetV3 bneck block, using the linear bottleneck inverse residual structure and depth separable convolution extract the features of fish in sonar images, applying the attention mechanism SE-Net to obtain multi-scale semantic features of sonar images and enhance the correlation between features; the neck network adopts the path aggregation network structure to perform multi-scale fusion of target features, to enhance the feature fusion ability; the prediction part adopts the maximum local search based on the non-maximum suppression method, removes the redundant detection frame, screens the detection frame with the highest confidence, and finally outputs and displays the detection result of the fish, including the position, category and detection probability of detecting an object.【Result】Four other mainstream detection models were selected for comparative experiments, including YOLOV3-ting (Darknet53), YOLOV5 (CSPdarknet53), YOLOV5 (Repvgg), and YOLOV5s (Transformer). It proposes the model parameter quantityof 3 545 453, FLOPs of 6.3 G, and the mAP of 0.957, and the average inference speed of each picture of the model is 0.08868 s. Compared with the YOLOV5 model, the mAP of the improved model has increased by 9.7%.【Conclusion】The proposed network improves the speed of training and recognition, reduces the requirements for hardware equipment, and provides a reference for the detection model of cage cultured fish in marine pastures.

【Objective】Light is the most important factor affecting plant growth, development, metabolism and external phenotype. In the process of plant growth, artificial light sources are often used instead of natural light to fill the seeds and seedlings to improve the quality, yield and efficiency of fruits and vegetables. Artificial light plant is one of the important ways for standardized and efficient seedling cultivation, but its high energy consumption, accounting for 60% to 80% of the total energy consumption of plant factories, seriously hinder the promotion and application of factory seedling technology. Therefore, reducing the energy consumption of light source and improving the utilization rate of light is one of the core goals for plant seedling.【Method】Aiming at the problems of fixed height, high energy consumption and difficult to match the light demand of seed and seedling growth, this study developed a precise light cultivation platform for seedlings based on Programmable Logic Controller (PLC) technology by combining theoretical analysis, model construction and experiment. Taking cucumber seedlings as the object, the performance of the light intensity control system of the precision light cultivation platform for seedlings was verified, and its effect on the growth of cucumber seedlings was explored.【Result】The experimental results showed that the light intensity and light distribution uniformity were improved after the optimization of the lamp bead structure. The developed platform can fill the light of cucumber seedlings by manual, automatic, Human Machine Interface (HMI) and host computer. Through the platform optical radiation energy consumption model and the layer level adjustment control strategy, the LED operating power is effectively reduced, saving about 40.65% of electric energy. The maximum and minimum relative error of the optical platform radiation model are 34.37% and 3.57%, respectively, and the average relative error is 11.45%. Cultivation experiments showed that under the same light intensity treatment, there was no significant difference in seedling phenotype parameters and strong seedling index by comparing the precision light cultivation platform with the traditional high floor fixed platform.【Conclusion】The precision light cultivation platform of seedlings based on PLC technology realizes automatic lifting and intelligent dimming, and saves up to 40.65% power without affecting the growth of seedlings.

Environmental pollution stands as one of the significant global challenges we face today. Polycyclic aromatic hydrocarbons (PAHs), a class of stubborn organic pollutants, have long been a focal point of bioremediation research. This study aims to explore the impact and mechanisms of graphene oxide (GO) on the phytoremediation effectiveness of PAHs. The results underscore the significant efficacy of GO in accelerating the degradation of PAHs. Additionally, the introduction of GO altered the diversity and community structure of endophytic bacteria within the roots, particularly those genera with potential for PAH degradation. Through LEfSe analysis and correlation studies, we identified specific symbiotic bacteria, such as Mycobacterium, Microbacterium, Flavobacterium, Sphingomonas, Devosia, Bacillus, and Streptomyces, which coexist and interact under the influence of GO, synergistically degrading PAHs. These bacteria may serve as key biological markers in the PAH degradation process. These findings provide new theoretical and practical foundations for the application of nanomaterials in plant-based remediation of polluted soils and showcase the immense potential of plant–microbe interactions in environmental restoration.

Mechanized harvesting is the key technology to solving the high cost and low efficiency of manual harvesting, and the key to realizing mechanized harvesting lies in the accurate and fast identification and localization of targets. In this paper, a lightweight YOLOv5s model is improved for efficiently identifying grape fruits and stems. On the one hand, it improves the CSP module in YOLOv5s using the Ghost module, reducing model parameters through ghost feature maps and cost-effective linear operations. On the other hand, it replaces traditional convolutions with deep convolutions to further reduce the model’s computational load. The model is trained on datasets under different environments (normal light, low light, strong light, noise) to enhance the model’s generalization and robustness. The model is applied to the recognition of grape fruits and stems, and the experimental results show that the overall accuracy, recall rate, mAP, and F1 score of the model are 96.8%, 97.7%, 98.6%, and 97.2% respectively. The average detection time on a GPU is 4.5 ms, with a frame rate of 221 FPS, and the weight size generated during training is 5.8 MB. Compared to the original YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x models under the specific orchard environment of a grape greenhouse, the proposed model improves accuracy by 1%, decreases the recall rate by 0.2%, increases the F1 score by 0.4%, and maintains the same mAP. In terms of weight size, it is reduced by 61.1% compared to the original model, and is only 1.8% and 5.5% of the Faster-RCNN and SSD models, respectively. The FPS is increased by 43.5% compared to the original model, and is 11.05 times and 8.84 times that of the Faster-RCNN and SSD models, respectively. On a CPU, the average detection time is 23.9 ms, with a frame rate of 41.9 FPS, representing a 31% improvement over the original model. The test results demonstrate that the lightweight-improved YOLOv5s model proposed in the study, while maintaining accuracy, significantly reduces the model size, enhances recognition speed, and can provide fast and accurate identification and localization for robotic harvesting.

Plant height is regarded as a key indicator that is crucial for assessing the crop growth status and predicting yield. In this study, an advanced method based on solid-state LiDAR technology is proposed, which is specifically designed to accurately capture the phenotypic characteristics of plant height during the maize growth cycle. By segmenting the scanned point cloud of maize, detailed point cloud data of a single maize plant were successfully extracted, from which stem information was accurately measured to obtain accurate plant height information. In this study, we will concentrate on the analysis of individual maize plants. Leveraging the advantages of solid-state LiDAR technology in precisely capturing phenotypic information, the data processing approach for individual maize plants, as compared to an entire maize community, will better restore the maize’s original growth patterns. This will enable the acquisition of more accurate maize plant height information and more clearly demonstrate the potential of solid-state LiDAR in capturing detailed phenotypic information. To enhance the universality of the research findings, this study meticulously selected key growth stages of maize for data validation and comparison, encompassing the tasseling, silking, and maturity phases. At these crucial stages, 20 maize plants at the tasseling stage, 40 at the flowering stage, and 40 at the maturity stage were randomly selected, totaling 100 samples for analysis. Each sample not only included actual measurement values but also included plant height information extracted using point cloud technology. The observation period was set from 20 June to 20 September 2021. This period encompasses the three key growth stages of maize described above, and each growth stage included one round of data collection, with three rounds of data collection each, each spaced about a week apart, for a total of nine data collections. To ensure the accuracy and reliability of the data, all collections were performed at noon when the natural wind speed was controlled within the range of 0 to 1.5 m/s and the weather was clear. The findings demonstrate that the root mean square error (RMSE) of the maize plant height data, procured through LiDAR technology, stands at 1.27 cm, the mean absolute percentage error (MAPE) hovers around 0.77%, and the peak R2 value attained is 0.99. These metrics collectively attest to the method’s ongoing high efficiency and precision in capturing the plant height information. In the comparative study of different stem growth stages, especially at the maturity stage, the MAPE of the plant height was reduced to 0.57%, which is a significant improvement compared to the performance at the nodulation and sprouting stage. These results effectively demonstrate that the maize phenotypic information extraction method based on solid-state LiDAR technology is not only highly accurate and effective but is also effective on individual plants, which provides a reliable reference for applying the technique to a wider range of plant populations and extending it to the whole farmland.

One of the most crucial topics in spatial interaction studies is mining patterns from extensive origin-destination (OD) flow data to capture interregional associations. However, prevailing methodologies tend to disregard the importance of using the relative closeness of interregional connections as weights, treat spatial and temporal dimensions independently, or overlook the temporal dimension completely. Consequently, the identified patterns are susceptible to inaccuracies, and the precise identification of pattern occurrence time and duration, despite their fundamental importance, remains elusive. In light of these challenges, this study proposes a strategy to calculate and combine the strength of weighted spatiotemporal flows, and develops a clustering method and evaluation metrics based on this framework. Compared to alternative density-based methods, the strength-based calculation approach demonstrates a capacity to identify flow patterns characterized by relatively high interregional closeness. Thus, the identification of flow patterns expands beyond density-based approaches, encompassing strength-based considerations and a shift from absolute to relative closeness between regions. Experiments using synthetic datasets conducted in this research demonstrate the effectiveness, efficiency, and extraction accuracy of the proposed method. Furthermore, a case study using real Chinese population migration data demonstrates the efficacy of the method in revealing implicit spatiotemporal association patterns between regions. The present study implements an interaction strength-based flow clustering and evaluation method that considers spatiotemporal continuity, making it applicable to spatial flow data analysis involving interaction volume and time attributes. As a result, this method holds promise for facilitating the modeling of intricate spatial flows within various contexts of study.

Abstract There is a high rate of recurrent hypertriglyceridemic acute pancreatitis (HTG-RAP) and risk of developing into chronic pancreatitis among recurrent hypertriglyceridemic acute pancreatitis. The key to avoiding recurrence is home-based self-management. However, self-management has proven to be difficult. Exploring experiences and perceptions of home-based self-management among patients with HTG-RAP could inform intervention development and policy making in primary care. To explore experiences and perceptions of home-based self-management among patients with HTG-RAP. This is primarily a qualitative study involving patients from eastern China. The study was designed using semi-structured interviews combined with open interviews among individuals and focus groups. Interviews with patients (n = 25) and relatives (n = 2) were conducted from October to December, 2021. Data were analyzed using the thematic analysis approach. Five themes were identified: (1) pity, (2) sense of uncertainty, (3) contradiction, (4) the way to cope, and (5) benefits. The themes constituted a continuous process where a final coping strategy was confirmed. Patients expressed sorrow, struggle, pity, adaptation, and benefits. The disease still bothered them without attack, both mentally and physically. These key points deserve considerable attention to improve the quality of life of patients and lifestyle modification. Patients with pancreatitis were more likely to manage the disease but under a tough process, and during the struggle, they experienced a continuous and contradictory period. Ultimately, the final condition was reached.

Industrialized seeding production is one of the most important methods used to raise seedlings in a standardized and efficient manner. However, its consumption of light energy limits its development. In this paper, we studied the effects of LED light intensities (30 (L1), 60 (L2), 90 (L3), 120 (L4), 150 (L5), 180 (L6), and 210 (L7) µmol·m−2·s−1) on the phenotypic parameters and health index of seedlings during different growth stages (cotyledon flattening (CF), one bud and one leaf (OBOL), and one bud and two leaves (OBTL)). Ten out of sixteen plants were selected to measure the parameters in every treatment. The control group was treated with 150 µmol·m−2·s−1 all the time. The results show that the height of the L1-treated plants was greatest during the cotyledon flattening stage. The L7 treatment produced the largest stem diameter, the greatest biomass, the largest leaf area, and the largest leaf circumference, the values of which were 2.61 mm, 3.3 g, 60.67 cm2, and 39.71 cm, respectively. During the one bud and one leaf stage, the L1 treatment produced the tallest plants. The L7 treatment resulted in the largest stem diameter, the greatest biomass, the largest leaf area, and the largest leaf circumference. During the one bud and two leaves stage, the plant height, stem diameter, leaf circumference, and biomass produced of the L6 treatment were the greatest, with values of 68.37 mm, 3.09 mm, 51.54 cm, and 4.47 g. L5 resulted in the largest leaf area (63.91 cm2). The seedling index of the three stages showed an inflection point at the L4 level. The experimental results showed that the light intensity required by the cucumber seedlings was 120 µmol·m−2·s−1, 150 µmol·m−2·s−1, and 120 µmol·m−2·s−1 during the three stages, from the cotyledon flattening stage to the one bud and two leaves stage. The results provide references regarding the light required during the cultivation of cucumber seedlings.

【Objective】In order to improve the preservation quality and prolong the shelf life of flowering cabbage, a kind of display cabinet with cyclic air curtain semiconductor refrigeration was designed.【Method】The display cabinet was taken as the display object, a heat load model based on internal and external heat transfer of the cabinet was established, and the fresh-keeping effect of the display cabinet with cyclic air curtain semiconductor refrigeration was verified by experiments.【Result】After 27 h of display, mass loss rates of three display modes (display cabinet with cyclic air curtain semiconductor refrigeration, open display cabinet and room temperature display) were 9.3%, 20.6% and 34.8%, respectively. The good vegetable rates were 35.8%, 0 and 0, respectively. And the sensory evaluation scores were 2.17, 1.08, 0, respectively. The SPAD values of the three display modes varied between 46 and 54. The mass loss rate of flowering cabbage with cyclic air curtain display cabinet was significantly lower than that of the other two modes after 3 h of display. After 6 h of display, the good vegetable rate and sensory evaluation score were significantly higher than those of the other two modes.【Conclusion】With the flowering cabbage as display object in display cabinet, a heat load model based on internal and external heat transfer of the cabinet is established, and the display cabinet with cyclic air curtain semiconductor refrigeration has a good fresh-keeping effect on flowering cabbage.

Accurate litchi identification is of great significance for orchard yield estimations. Litchi in natural scenes have large differences in scale and are occluded by leaves, reducing the accuracy of litchi detection models. Adopting traditional horizontal bounding boxes will introduce a large amount of background and overlap with adjacent frames, resulting in a reduced litchi detection accuracy. Therefore, this study innovatively introduces the use of the rotation detection box model to explore its capabilities in scenarios with occlusion and small targets. First, a dataset on litchi rotation detection in natural scenes is constructed. Secondly, three improvement modules based on YOLOv8n are proposed: a transformer module is introduced after the C2f module of the eighth layer of the backbone network, an ECA attention module is added to the neck network to improve the feature extraction of the backbone network, and a 160 × 160 scale detection head is introduced to enhance small target detection. The test results show that, compared to the traditional YOLOv8n model, the proposed model improves the precision rate, the recall rate, and the mAP by 11.7%, 5.4%, and 7.3%, respectively. In addition, four state-of-the-art mainstream detection backbone networks, namely, MobileNetv3-small, MobileNetv3-large, ShuffleNetv2, and GhostNet, are studied for comparison with the performance of the proposed model. The model proposed in this article exhibits a better performance on the litchi dataset, with the precision, recall, and mAP reaching 84.6%, 68.6%, and 79.4%, respectively. This research can provide a reference for litchi yield estimations in complex orchard environments.

Largehead hairtail Trichiurus japonicus is a major commercial fish species in the Beibu Gulf of the northwestern South China Sea. Despite much effort to protect the fishery resource, the current stock of T. japonicus is overexploited. As the impacts of climate change unfold globally, seasonal changes in the distribution of largehead hairtail in the Beibu Gulf have not yet been clarified. Maximum entropy model based on mixed layer depth and salinity were projected onto seasonal habitat changes of T. japonicus in the Beibu Gulf under a current scenario and three different Representative Concentration Pathways (126, 370, 585) to evaluate geographic distribution changes under the different climate-change scenarios. The current geographic distribution results showed variation with seasonality, as the wintering population shifts toward the northeast. Under each of three SSP scenarios, there is higher risk to habitat suitability in the 2090s as compared with that in the 2050s. The disadvantage to T. japonicus distribution is greatest in winter under each of the three climate change scenarios, both in the short- and long-term. Potential suitable habitat distributions have a minor range extension in Representative Concentration Pathway 370–2050 winter, but in the rest of the scenes and years they contract to south of the Beibu Gulf. The overall results indicate that seasonal differences in suitable habitat should be considered to ensure effective planning of future management strategies for T. japonicus.

To better understand how fish communities respond to environmental changes under extreme climate events, we examine changes in fish communities in Beibu Gulf during strong El Niño and La Niña events. Strong La Niña and El Niño events affect the composition, abundance, and distribution of fish communities in Beibu Gulf. Fish community distribution and composition change before and after La Niña and El Niño events, and dominant species within them change with stable fishing intensity. The abundance and distribution of small pelagic fish such as Japanese jack mackerel (Trachurus japonicus) and Japanese scad (Decapterus maruadsi) are the most affected. Using a generalized additive model (GAM), we explore relationships between the abundance of T. japonicus and D. maruadsi and a suite of environmental variables. The GAM results revealed that sea surface salinity and sea surface temperature best explain changes in catch per unit effort of these two species during a La Niña event; depth, sea surface temperature, and mixed layer depth during an El Niño event. The results obtained in this study will offer support for implementing more-accurate, scientific fisheries management measures.

Mg-based porous materials, as potential bone tissue engineering scaffolds, are considered an attractive strategy for bone repair owing to favorable biodegradability, good biocompatibility and suitable mechanical properties. In this work, 3D-cubic interconnected porous Mg–xZn–0.3Ca (x = 0,3,6) scaffolds were prepared to obtain desirable pore structures with a mean porosity up to 73% and main pore size of 400–500 µm, which pore structures were close to the human cancellous bone. The structure–property relationships in the present scaffolds were analyzed by experiments and theoretical models of generalized method of cells (GMC). Mg–xZn–0.3Ca scaffolds exhibited good compression properties with a maximum above 5 MPa in yield strength and about 0.4 GPa in elastic modulus. This was attributed to not only the alloy strengthening but also the large minimum solid area. On the other hand, the scaffolds showed undesirable and relatively serious degradation behavior in Hank's solution, resulting from Zn addition in Mg-based scaffolds and the high surface area ratio in the pore structure. Therefore, surface modifications are worth studying for controlled degradation in the future. In conclusion, this research would explore a novel attempt to introduce 3D-cubic pore structure for Mg-based scaffolds, and provide new insights into the preparations of Mg-based scaffolds with good service performances for bone repair.

Freshwater lake eutrophication is a global concern causing adverse effects on aquatic ecosystems. The degradation of lake aquatic–terrestrial ecotones, which are the transitional zones between terrestrial and water ecosystems, contributes to eutrophication. These ecotones play vital roles in nutrient cycling, runoff control, biodiversity conservation, and habitat provision. In the past three decades, the research on lake aquatic–terrestrial ecotones has focused on techniques for managing contaminants and runoff purification. This paper reviews the recent studies on the restoration ability of eutrophic water bodies in lake aquatic–terrestrial ecotones in recent years regarding three aspects: the establishment, restoration mechanism, and improvement of restoration function. In addition, ecological factors such as lakeshore height, water level, surface runoff, shallow groundwater level, and rainfall intensity have impacts on the restoration capacity of lake aquatic–terrestrial ecotones.

Self-healing coatings have been developed as smart surface coatings for Mg and its alloys to retain local corrosion from the coating damages. In this study, we prepared dicalcium phosphate dihydrate (DCPD) coating on biomedical Mg, and found that the artificial scratches in DCPD coating can be efficiently sealed by anti-corrosive products in both Hank's and normal saline (NS) solutions. Besides, the in-depth study revealed that DCPD was served as not only a physical barrier but also a self-healing agent, demonstrating an autonomous self-healing coating without embedded extra corrosion inhibitors. Moreover, Hank's solution provided foreign-aid film-forming ions to promote self-healing behavior. The findings might offer new opportunities for further studies and applications of efficient self-healing coatings on biodegradable Mg implants.

Functional language has been used in many multinational corporations (MNCs) as a way to overcome the problems caused by the coexistence of multiple languages in the workplace. The existing literature has explored the importance, adoption, and effectiveness of functional language. Yet, how functional language shapes host country employees’ moral cognition and behavior is insufficiently researched. Guided by the Social Identity Theory, this manuscript shows that host country employees’ functional language proficiency (i.e., English) enhances their unethical pro-organizational behavior through their linguistic group identification and moral disengagement. We tested our predictions using the data collected from 309 full-time host country employees through an online survey, and the results generally supported our hypotheses. The findings make contributions to both international management and language literature and organizational moral behavior literature.

In order to improve the leaching efficiency of gold ore and reduce the environmental treatment cost of residual sodium cyanide, continuous stirred tank reactors are often connected in a cascade manner. A gold leaching system is a multiphase chemical reaction system, and its kinetic reaction mechanism is complex and affected by random factors. Using intelligent modeling technology to establish a hybrid prediction model of the leaching system, the dynamic performance of the process can be easily analyzed. According to the reaction principle and the theory of substance conservation, a mechanism model is established to reflect the main dynamic performance of the leaching system. In order to improve the global convergence of the optimization target, a particle swarm optimization (PSO) algorithm based on simulated annealing is used to optimize the adjustment parameters in the kinetic reaction velocity model. The multilayer long short-term memory (LSTM) neural network approach is used to compensate for the prediction errors caused by the unmodeled dynamics, and a hybrid model is established. The hybrid prediction model can accurately predict the leaching rate, which provides a reliable basis for guiding production, and also provides a model basis for process optimization, controller design, and operation monitoring. Finally, the superiority and practicability of the hybrid model are verified by a practical leaching industrial system test. The prediction model of key variables in the leaching process is established for the first time using the latest time series prediction technology and intelligent optimization technology. The research results of this paper can provide a good reference and guidance for other research on complex system hybrid modeling.

The choice of spatial and temporal scales affects the performance of fisheries models and is particularly important in exploring the relationship between resource abundance and the marine environment. Traditional fishery models are constructed at a particular scale, and the results of the study hold only at that scale. Sthenoteuthis oualaniensis is one of the main target species of large-scale light falling-net fishing in the Nansha Islands in the South China Sea. We used the S. oualaniensis fishery in the Nansha Islands as an example to compare the performance of fisheries models for 12 spatial and temporal settings and to explore the relationship between the abundance of S. oualaniensis and the marine environment in the Nansha Islands under the optimal spatial and temporal settings. The results show that the spatial and temporal scale chosen in the construction of the fishery model is not as fine as possible in generalized additive models (GAMs) for abundance index-catch per unit effort (AI-CPUE)-based scenarios, and 0.5° with the season was the best spatial and temporal setting; meanwhile, in GAMs for AI-effort-based scenarios, 0.1° with the month was the best spatial and temporal setting. The distribution of S. oualaniensis resources in the Nansha Islands was characterized by significant seasonal variation, and the monthly center of gravity had a significant negative correlation with the Niño 3.4 index and the PDO index, with correlation coefficients of 100 and 1000, respectively. It is hypothesized that Pacific Decadal Oscillation and ENSO events affect the marine environment in the South China Sea by influencing the strength of the Kuroshio force and the degree of Kuroshio curvature, which in turn affects the distribution of S. oualaniensis in the Nansha Islands. The results help us to understand the influence of spatial and temporal scales on fisheries models and the environmental factors affecting the distribution of S. oualaniensis resources in the Nansha Islands. Thus, they provide a scientific basis for the sustainable development of S. oualaniensis fisheries in this region.

(1) Background: Forklifts are used widely in factories, but it shows the problem of large uncertainties when using an RGB-D sensor to recognize and locate pallets in warehouse environments. To enhance the flexibility of current autonomous forklifts in unstructured environments, the improved labeled template matching algorithm was proposed to recognize pallets. (2) Methods: The algorithm comprises four steps: (i) classifying each pixel of a color image with the color feature and obtaining the category matrix; (ii) building a labeled template containing the goods, pallet, and ground category information; (iii) compressing and matching the category matrix and template to determine the region of the pallet; and (iv) extracting the pallet pose from information in respect of the pallet feet. (3) Results: The results show that the proposed algorithm is robust against environmental influences and obstacles and that it can precisely recognize and segment multiple pallets in a warehouse with a 92.6% detection rate. The time consumptions were 72.44, 85.45, 117.63, and 182.84 ms for detection distances of 1000, 2000, 3000, and 4000 mm, respectively. (4) Conclusions: Both static and dynamic experiments were conducted, and the results demonstrate that the detection accuracy is directly related to the detection angle and distance.

Clustering has been widely used in the fields of knowledge discovery, pattern recognition and artificial intelligence. However, discovering clusters in spatial databases is still a challenging task, especially when the shape, size, and density of clusters vary a lot. Existing algorithms have sensitive parameters, clusters must be separated far enough from each other and rich prior knowledge about datasets is required. In this paper, we propose algorithm DENSS, which performs clustering on the basis of the similarity of neighbour distribution and the number of shared neighbors for two objects. Algorithm DENSS can mine clusters that differ in densities, and within a cluster the local densities are reasonably homogeneous. Adjacent objects are separated into different clusters by significant change in densities. To verify the effectiveness of the algorithm DENSS, synthetic and real-world datasets are used for testing, and it has been compared with seven clustering algorithms. Experimental results show that the proposed algorithm has a relatively high efficiency, robustness and effectiveness, and is remarkably superior to the seven algorithms. This algorithm is universal and can rapidly and efficiently identify the clusters of different densities, shapes and sizes even in the presence of noise and outliers for any object feature types.

As identifying people's movements across zones can improve our understanding of transportation patterns and recommend strategies for urban planning such as precise locations for targeted advertisements, residential zoning, and transportation development. However, when the amount of data is large or the relationships between data are complex, traditional algorithms for movement patterns between zones become ineffective. We propose a new agglomeration algorithm, namely, the density-based movement patterns between zones (DBMPZ), to mine spatial clustering of movement patterns. To validate the proposed algorithm, we use a real-world dataset of subway commuters in Shanghai and some synthetic datasets to identify movement patterns between zones. The experiment results show that the proposed algorithm can effectively mine movement patterns between zones with high precision, effectiveness, and efficiency. In addition, the proposed algorithm can also play an important role in other regions or types of transportation dataset by modifying the clustering procedure.

In recent years, the technology about IoT (Internet of Things) has been applied into finance domain, and the generated data, such as the real-time data of chattel mortgage supervision with GPS, sensors, network cameras, mobile devices, etc., has been used to improve the capability of financial credit risk management of bank loans. Financial credit risk is by far one of the most significant risks that commercial banks have to face, however, when confronting to the massively growing financial data from multiple sources including Internet, mobile networks or IoT, traditional statistical models and neural network models might not operate fairly or accurately enough for credit risk assessment with those diverse data. Hence, there is a practical need to establish more powerful risk prediction models with artificial intelligence based on big data analytics to predict default behaviors with better accuracy and capacity. In this article, a big data mining approach of Particle Swarm Optimization (PSO) based Backpropagation (BP) neural network is proposed for financial risk management in commercial banks with IoT deployment, which constructs a nonlinear parallel optimization model with Apache Spark and Hadoop HDFS techniques on the dataset of on-balance sheet item and off-balance sheet item. The experiment results indicate that this parallel risk management model has fast convergence rate and powerful predictive capacity, and performs efficiently in screening default behaviors. In the meanwhile, the distributed implementation on big data clusters largely reduces the processing time of model training and testing.

Many areas of exploratory data analysis need to deal with high-dimensional data sets. Some real life data like human gene have an inherent structure of hierarchy, which embeds multi-layer feature groups. In this paper, we propose an algorithm to search for the number of feature groups in high-dimensional data by sequential minimax method and detect the hierarchical structure of high-dimensional data. Several proper numbers of feature grouping can be discovered. The feature grouping and group weights are investigated for each group number. After the comparison of feature groupings, the multi-layer structure of feature groups is detected. The latent feature group learning (LFGL) algorithm is proposed to evaluate the effectiveness of the number of feature groups and provide a method of subspace clustering. In the experiments on several gene data sets, the proposed algorithm outstands several representative algorithms.

Frigate tuna Auxis thazard fishery is considered a potential marine resource in the open South China Sea (SCS). However, little is known about the spatial and temporal distribution of its habitat, and how this may respond to environmental changes. Using fish survey and remote sensing data from 2015–2019, we applied generalized additive models to identify relationships between environmental factors and the distribution of A. thazard in the SCS. To examine seasonal patterns in the habitat of A. thazard in the SCS, we generated a habitat suitability index model using environmental factors screened by generalized additive models. Results showed that A. thazard migrates from south to north in the SCS, and its suitable habitat is patchily distributed. Significant environmental factors affecting the habitat of A. thazard distribution were different in different seasons; we demonstrated A. thazard to be sensitive to Chl-a in spring (optimum 0.155, optimal range ~0.1252–0.1840), and in summer to be sensitive to SST (optimum 30.405, optimal range ~29.789–31.021) and SSH (optimum 0.741, optimal range ~0.618–0.864). Suitable habitat in spring occurs mainly in northeastern areas, while in summer it occurs mainly around the southeastern Nansha Islands. Compared with that in spring, the area of suitable habitat increases in summer, as does the habitat suitability index of the entire sea area. These results improve our understanding of environmental factors that affect the distribution of A. thazard habitat in the SCS, and provide a scientific basis for the development and management of A. thazard resources.

An important component of research in cultural geography involves the exploration and analysis of the laws of regional cultural differences. This topic has considerable significance in the discovery of distinctive cultures, protection of regional cultures, and in-depth understanding of cultural differences. In recent years, with the “spatial turn” of sociology, scholars have focused increasing attention to implicit spatial information in social media data, as well as the social phenomena and laws they reflect. Grasping sociocultural phenomena and their spatial distribution characteristics through texts is an important aspect. Using machine learning methods, such as the popular natural language processing (NLP) approach, this study extracts hotspot cultural elements from text data and accurately detects the spatial interaction patterns of specific cultures, as well as the characteristics of emotions toward non-native cultures. Through NLP, this study examines cultural differences among people from South and North China by analyzing 6128 answers to the question, “What are the differences between South and North China that you ever know?” posted on the Zhihu Q&A platform. Moreover, this study probes individuals’ emotions and cognition of cultural differences between South and North China in three aspects, namely, spatial interaction patterns of hotspot cultural elements, components of hotspot cultures, and emotional characteristics under the influence of cultural differences between the two regions. Results reveal that: (1) people from North and South China exhibit considerable differences in recognizing each other’s culture; (2) among numerous cultural differences, food culture is the most popular; and (3) people tend to have a negative attitude toward food cultures that differ from their own. These factors can shed light on regional cultural differences and help address cultural conflicts. In addition, this study provides effective solutions from a macro perspective, which has been challenging for new cultural geography.

This article develops a systematic model to study electric vehicle powertrain system efficiency by combining a detailed model of two-speed dual-clutch transmission system efficiency losses with an electric vehicle powertrain system model. In this model, the design factors including selection of the electric machine, gear ratios’ change, multi-plate wet clutch design, and gear shift schedule design are considered. Meanwhile, the application of detailed model for drag torque losses in the gearbox is discussed. Furthermore, the proposed model, developed with the MATLAB/Simulink platform, is applied to optimize/maximize the efficiency of the electric vehicle powertrain system using genetic algorithms. The optimization results demonstrate that the optimal results are different between simulations via New Europe Drive Cycle and Urban Dynamometer Driving Schedule, and comprehensive design and optimization of the powertrain system are necessary.

Interaction among different regions can be illustrated in the form of a stream. For example, the interaction between the flows of people and information among different regions can reflect city network structures, as well as city functions and interconnections. The popularization of big data has facilitated the acquisition of flow data for various types of individuals. The application of the regional interaction model, which is based on the summary level of individual flow data mining, is currently a hot research topic. Thus far, however, previous research on spatial interaction methods has mainly focused on point-to-point and area-to-area interaction patterns, and investigations on the patterns of interaction hotspots between two regional groups with predefined neighborhood relationships, that being with two regions, remain scarce. In this study, a method for the identification of similar interaction hotspot patterns between two regional groups is proposed, and geo-information Tupu methods are applied to visualize interaction patterns. China’s air traffic flow data are used as an example to illustrate the performance of the proposed method to identify and analyze interaction hotspot patterns between regional groups with adjoining relationships across China. Research results indicate that the proposed method efficiently identifies the patterns of interaction flow hotspots between regional groups. Moreover, it can be applied to analyze any flow space in the excavation of the patterns of regional group interaction hotspots.

Dual clutch transmissions (DCTs) are recognized as being suitable for electric drive applications as they can drive with high efficiency and achieve good shifting comfort. A two-speed DCT electric drivetrain is described in this paper, comprised of only two gear pairs and a final drive gear in the two-speed gearbox. The fundamental shifting control algorithm is provided. On the testing rig of University of Technology, Sydney (UTS) powertrain lab, shifting controls and some driving cycle controls were realized. The results demonstrated that the control algorithm functioned well both in transient shifting control process and in the driving cycle conditions.