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Existing unconventional intersection designs can enhance the capacity of intersections to deal with repaid pressure on urban commuting, whereas they force vehicles to multi-stop and reorganize, resulting in safety risks and high fuel consumption. This paper proposes the Intermittent Lane Intersection (ILI) that combines the advantages of variable lane intersection and CTE (Combining the tandem control and exit lanes for left-turn control). An integrated optimization model is established to obtain the optimal geometric and signal control parameters, and a homothermic genetic algorithm is used to solve the model. Then, the application of ILI in different traffic demands of the Tianfu Third Street intersection is analyzed. Compared with the benchmark design (TI, CTE, and CTE with signal coordinated control), ILI can effectively reduce intersection delay, fuel consumption, and parking times. Additionally, ILI has great application superiority when the traffic arrival rate is [1500,1700] and the left-turn ratio is [0.40,0.60]. [ABSTRACT FROM AUTHOR]

Background Results Conclusion The Pickering emulsion delivery technique is widely acknowledged for its efficacy in serving as a carrier that can encapsulate functional components effectively. Previous studies have shown significant differences in the stability of Pickering emulsions composed of different oil phases and in the bioaccessibility of the encapsulated functional ingredients. This study therefore investigated the effects of different carrier oils in the betulin self‐stabilized water‐in‐oil (W/O) Pickering emulsion on the stability of the emulsion and bioaccessibility of betulin.The results showed that the oil type was one of the main factors affecting the stability of the emulsion. Palm oil and coconut oil provided better storage stability and centrifugal stability due to the high saturated fatty acid content. The bioavailability of betulin correlated significantly with the composition and characteristics of fatty acids in carrier oils. Carrier oils rich in low‐saturation long‐chain fatty acids tended to release more free fatty acids (FFAs), thus forming larger and more mixed micelles with stronger swelling and dissolution ability, resulting in a relatively high bioaccessibility of betulin. In contrast, the bioaccessibility of betulin in the emulsion prepared by coconut oil (with high saturated fatty acid content) was relatively low (1.17%).The results of this study indicate that selecting an appropriate carrier oil is important for the design of self‐stabilized W/O Pickering emulsions to improve the bioaccessibility of betulin and other lipophilic bioactivities effectively. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Actuator failure on a remotely deployed robot results in decreased efficiency or even renders it inoperable. Robustness to these failures will become critical as robots are required to be more independent and operate out of the range of repair. To address these challenges, we present two approaches based on modular robotic architecture to improve robustness to actuator failure of both fixed-configuration robots and modular reconfigurable robots. Our work uses modular reconfigurable robots capable of modifying their style of locomotion and changing their designed morphology through ejecting modules. This framework improved the distance travelled and decreased the effort to move through the environment of simulated and physical robots. When the deployed robot was allowed to change its locomotion style, it showed improved robustness to actuator failure when compared to a robot with a fixed controller. Furthermore, a robot capable of changing its locomotion and design morphology statistically outlasted both tests with a fixed morphology. Testing was carried out using a gazebo simulation and validated in multiple tests in the field. We show for the first time that ejecting modular failed components can improve the overall mission length. [ABSTRACT FROM AUTHOR]

Objective To map the immunodominant linear B-cell epitopes of Campylobacter jejuni (C.jejuni) PEB1 antigen and evaluate the protective immune responses elicited by these epitopes in a mouse model of oral infection with C.jejuni. Methods The B-cell immunodominant PEB1 epitopes were identified using synthetic overlapping peptide ELISA. BALB/c mice were immunized with the immunodominant PEB1 peptides conjugated with KLH plus CFA/IFA, and the IgG titers against these peptides were detected using ELISA. Seven days after the last immunization, the mice were orally infected with C.jejuni 11168, and the bacterial burden and expression of tumor necrosis factor-α (TNF-α) mRNA in the jejunum were analyzed using qRT-PCR in 28 d after the challenge. We assessed the effect of the antibodies against the immunodominant PEB1 epitopes in mediating the opsonophagocytic killing of C.jejuni by HL-60 cells. We also assessed the bacterial burden in the jejunum following C.jejuni challenge in a B cell-knockout mice immunized with the peptides. Results The immunodominant peptides PEB155-72aa, PEB197-114aa, and PEB1211-228aa all induced strong IgG responses to PEB1 antiserum, and the antisera of these immunodominant peptides also showed strong IgG responses to recombinant PEB1. Compared with the antiserum of CFA/IFA, the antisera of these immunodominant peptides induced significantly enhanced opsonophagocytic activity of HL-60 cells (P < 0.01). Both the bacterial burdens and TNF-α mRNA expression level in the jejunum were significantly lowered in the mice immunized with the 3 immunodominant peptides in comparison with the control mice immunized with CFA/IFA (P < 0.01). In B cell-knockout mice, immunization with the 3 peptides did not provide immune protection against C.jejuni, and the bacterial burden in the jejunum after C.jejuni challenge was significantly greater than that in immunized wild-type mice (P < 0.01). Conclusion We successfully identified 3 linear B-cell epitopes of C.jejuni PEB1 (PEB155-72aa, PEB197-114aa, and PEB1211-228aa), which exhibit good immunogenicity and immunoprotective activities and may facilitate the future development of vaccines against C.jejuni infection.

Finding the shortest path of the traveling salesman problem (TSP) is a typical NP-hard problem and one of the basic optimization problems. TSP in three-dimensional space (3D-TSP) is an extension of TSP. It plays an important role in the fields of 3D path planning and UAV inspection, such as forest fire patrol path planning. Many existing studies have focused on the expected length of the shortest path of TSP in 2D space. The expected length of the shortest path in 3D space has not yet been studied. To fill this gap, this research focuses on developing models to estimate the expected length of the shortest path of 3D-TSP. First, different experimental scenarios are designed by combining different service areas and the number of demand points. Under each scenario, the specified number of demand points is randomly generated, and an improved genetic algorithm and Gurobi are used to find the shortest path. A total of 500 experiments are performed for each scenario, and the average length of the shortest path is calculated. The models to estimate the expected length of the shortest path are proposed. Model parameters are estimated and k-fold cross-validation is used to evaluate the goodness of fit. Results show that all the models fit the data well and the best model is selected. The developed models can be used to estimate the expected length of the shortest path of 3D-TSP and provide important references for many applications., Author(s): Hongtai Yang [1]; Xiaoqian Lu [1]; Xinan Zhou [1]; Rong Zheng [1]; Yugang Liu (corresponding author) [1]; Siyu Tao [1] 1. Introduction The traveling salesman problem (TSP) is a [...]

Aim. The potential of olfactory ensheathing cells (OECs) as a cell therapy for spinal cord reconstruction and regeneration after injury has drawn significant attention in recent years. This study attempted to investigate the influences of nano-fibrous scaffolds on the growth status and functional properties of OECs. Methods. The ultra-morphology of the scaffolds was visualized using scanning electron microscopy (SEM). To culture OECs, donated cells were subcultured and identified with p75. Cell proliferation, apoptosis, and survival rates were measured through MTT assay, Annexin-V/PI staining, and p75 cell counting, respectively. The adhesion of cells cultured on scaffolds was observed using SEM. Additionally, the functions of OECs cultured on scaffolds were assessed by testing gene expression levels through real time polymerase chain reaction. Results. The electrospun type I collagen-based nano-fibers exhibited a smooth surface and uniform distribution. It was indicated that the proliferation and survival rates of OECs cultured on both randomly oriented and aligned type I collagen-based nano-fibrous scaffolds were higher than those observed in the collagen-coated control. Conversely, apoptosis rates were lower in cells cultured on scaffolds. Furthermore, OEC adhesion was better on the scaffolds than on the control. The expression levels of target genes were significantly elevated in cells cultured on scaffolds versus the controls. Conclusion. As a whole, the utilization of aligned collagen nanofibers has demonstrated significant advantages in promoting cell growth and improving cell function. These findings have important implications for the field of regenerative medicine and suggest that the approach may hold promise for the future therapeutic applications. [ABSTRACT FROM AUTHOR]

Direct ridership models can predict station-level urban rail transit ridership. Previous research indicates that the direct modeling of urban rail transit ridership uses different coverage overlapping area processing methods (such as naive method or Thiessen polygons), area analysis units (such as census block group and census tract), and various regression models (such as linear regression and negative binomial regression). However, the selection of these methods and models seems arbitrary. The objective of this research is to suggest methods of station-level urban rail transit ridership model selection and evaluate the impact of this selection on ridership model results and prediction accuracy. Urban rail transit ridership data in 2010 were collected from five cities: New York, San Francisco, Chicago, Philadelphia, and Boston. Using the built environment characteristics as the independent variables and station-level ridership as the dependent variable, an analysis was conducted to examine the differences in the model performance in ridership prediction. Our results show that a large overlap of circular coverage areas will greatly affect the accuracy of models. The equal division method increases model accuracy significantly. Most models show that the generalized additive models have lower mean absolute percentage errors (MAPE) and higher adjusted R[sup.2] values. By comparison, the Akaike information criterion (AIC) values of the negative binomial models are lower. The influence of different basic spatial analysis unit on the model results is marginal. Therefore, the selection of basic area unit can use existing data. In terms of model selection, advanced models seem to perform better than the linear regression models., Author(s): Hongtai Yang [1]; Chaojing Li [1]; Xuan Li [1]; Jinghai Huo [1]; Yi Wen [2]; Emma G. P. Sexton [2]; Yugang Liu (corresponding author) [1] 1. Introduction Urban rail [...]

Direct demand modeling is a useful tool to estimate the demand of urban rail transit stations and to determine factors that significantly influence such demand. The construction of a direct demand model involves determination of the catchment area. Although there have been many methods to determine the catchment area, the choice of those methods is very arbitrary. Different methods will lead to different results and their effects on the results are still not clear. This paper intends to investigate this issue by focusing on three aspects related to the catchment area: size of the catchment area, processing methods of the overlapping areas, and whether to apply the distance decay function on the catchment area. Five catchment areas are defined by drawing buffers around each station with radius distance ranging from 300 to 1500 meters with the interval of 300 meters. Three methods to process the overlapping areas are tested, which are the naïve method, Thiessen polygon, and equal division. The effect of distance decay is considered by applying lower weight to the outer catchment area. Data from five cities in the United States are analyzed. Built environment characteristics within the catchment area are extracted as explanatory variables. Annual average weekday ridership of each station is used as the response variable. To further analyze the effect of regression models on the results, three commonly used models, including the linear regression, log-linear regression, and negative binomial regression models, are applied to examine which type of catchment area yields the highest goodness-of-fit. We find that the ideal buffer sizes vary among cities, and different buffer sizes do not have a great impact on the model's goodness-of-fit and prediction accuracy. When the catchment areas are heavily overlapping, dividing the overlapping area by the number of times of overlapping can improve model results. The application of distance decay function could barely improve the model results. The goodness-of-fit of the three models is comparable, though the log-linear regression model has the highest prediction accuracy. This study could provide useful references for researchers and planners on how to select catchment areas when constructing direct demand models for urban rail transit stations., Author(s): Hongtai Yang [1]; Xuan Li [1]; Chaojing Li [1]; Jinghai Huo [1]; Yugang Liu (corresponding author) [1] 1. Introduction Transit-oriented development (TOD) plays a pivotal role in urban planning. [...]