A Bi-Objective Fuzzy Credibilistic Chance-Constrained Programming Approach for the Hazardous Materials Road-Rail Multimodal Routing Problem under Uncertainty and Sustainability

Hazardous materials transportation involves extensive risk and cannot be avoided in practice. An advanced routing, however, can help to reduce the risk by planning the best transportation routes for hazardous materials that can make effective tradeoffs between the risk objective and the economic objective. In this study, we explore the hazardous materials routing problem in the road-rail multimodal transportation network with a hub-and-spoke structure, in which the risk is measured by the multiplication of population exposure and the associated volume of hazardous materials, and minimizing the total risk of all the transportation orders of hazardous materials is set as the risk objective. It is difficult to estimate the population exposure exactly during the routing decision-making process, which results in its uncertainty. In this study, we formulate the uncertain population exposure from a fuzzy programming perspective by using triangular fuzzy numbers. Moreover, the carbon dioxide emission constraint is formulated to realize the sustainable transportation of hazardous materials. To optimize the problem under the above framework, we first establish a bi-objective fuzzy mixed integer nonlinear programming model, and then develop a three-stage exact solution strategy that the combines fuzzy credibilistic chance constraint, linearization technique, and the normalized weighting method. Finally, a computational experiment is carried out to verify the feasibility of the proposed method in dealing with the problem. The experimental results indicate that tradeoffs between the two conflicting objectives can be effectively made by using the Pareto frontier to the hazardous materials routing problem. Furthermore, the credibility level and carbon dioxide emission cap significantly influence the hazardous materials routing optimization. Their effects on the optimization result are quantified by using sensitivity analysis, which can draw some useful insights to help decision makers to better organize the hazardous materials road-rail multimodal transportation under uncertainty and sustainability. Document type: Articl

Differences of serum glucose and lipid metabolism and immune parameters and blood metabolomics regarding the transition cows in the antepartum and postpartum period

This study aims to investigate differences in metabolism regarding the transition cows. Eight cows were selected for the test. Serum was collected on antepartum days 14th (ap14) and 7th (ap7) and postpartum days 1st (pp1), 7th (pp7), and 14th (pp14) to detect biochemical parameters. The experiment screened out differential metabolites in the antepartum (ap) and postpartum (pp) periods and combined with metabolic pathway analysis to study the relationship and role between metabolites and metabolic abnormalities. Results: (1) The glucose (Glu) levels in ap7 were significantly higher than the other groups (p < 0.01). The insulin (Ins) levels of ap7 were significantly higher than pp7 (p = 0.028) and pp14 (p < 0.01), and pp1 was also significantly higher than pp14 (p = 0.016). The insulin resistance (HOMA-IR) levels of ap7 were significantly higher than ap14, pp7, and pp14 (p < 0.01). The cholestenone (CHO) levels of ap14 and pp14 were significantly higher than pp1 (p < 0.01). The CHO levels of pp14 were significantly higher than pp7 (p < 0.01). The high density lipoprotein cholesterol (DHDL) levels of pp1 were significantly lower than ap14 (p = 0.04), pp7 (p < 0.01), and pp14 (p < 0.01), and pp14 was also significantly higher than ap14 and ap7 (p < 0.01). (2) The interferon-gamma (IFN-γ) and tumor necrosis factor α (TNF-α) levels of ap7 were significantly higher than pp1 and pp7 (p < 0.01); the immunoglobulin A (IgA) levels of pp1 were significantly higher than ap7 and pp7 (p < 0.01); the interleukin-4 (IL-4) levels of pp7 were significantly higher than ap7 and pp1 (p < 0.01), the interleukin-6 (IL-6) levels of ap7 and pp1 were significantly higher than pp7 (p < 0.01). (3) Metabolomics identified differential metabolites mainly involved in metabolic pathways, such as tryptophan metabolism, alpha-linolenic acid metabolism, tyrosine metabolism, and lysine degradation. The main relevant metabolism was concentrated in lipid and lipid-like molecules, organic heterocyclic compounds, organic acids, and their derivatives. The results displayed the metabolic changes in the transition period, which laid a foundation for further exploring the mechanism of metabolic abnormalities in dairy cows in the transition period

I329L protein-based indirect ELISA for detecting antibodies specific to African swine fever virus

African swine fever (ASF) is a disease that causes severe economic losses to the global porcine industry. As no vaccine or drug has been discovered for the prevention and control of ASF virus (ASFV), accurate diagnosis and timely eradication of infected animals are the primary measures, which necessitate accurate and effective detection methods. In this study, the truncated ASFV I329L (amino acids 70–237), was induced using IPTG and expressed in Escherichia coli cells. The highly antigenic viral protein I329L was used to develop an indirect enzyme-linked immunosorbent assay (iELISA), named I329L-ELISA, which cut-off value was 0.384. I329L-ELISA was used to detect 186 clinical pig serum samples, and the coincidence rate between the indirect ELISA developed here and the commercial kit was 96.77%. No cross-reactivity was observed with CSFV, PRRSV, PCV2, or PRV antibody-positive pig sera, indicating good specificity. Both intra- assay and inter-assay coefficients were below 10%, and the detection sensitivity of the iELISA reached 1:3200. In this study, an iELISA for ASFV antibody detection was developed based on the truncated ASFV I329L protein. Overall, the I329L-ELISA is a user-friendly detection tool that is suitable for ASFV antibody detection and epidemiological surveillance

A Bi-Objective Fuzzy Credibilistic Chance-Constrained Programming Approach for the Hazardous Materials Road-Rail Multimodal Routing Problem under Uncertainty and Sustainability

Hazardous materials transportation involves extensive risk and cannot be avoided in practice. An advanced routing, however, can help to reduce the risk by planning the best transportation routes for hazardous materials that can make effective tradeoffs between the risk objective and the economic objective. In this study, we explore the hazardous materials routing problem in the road-rail multimodal transportation network with a hub-and-spoke structure, in which the risk is measured by the multiplication of population exposure and the associated volume of hazardous materials, and minimizing the total risk of all the transportation orders of hazardous materials is set as the risk objective. It is difficult to estimate the population exposure exactly during the routing decision-making process, which results in its uncertainty. In this study, we formulate the uncertain population exposure from a fuzzy programming perspective by using triangular fuzzy numbers. Moreover, the carbon dioxide emission constraint is formulated to realize the sustainable transportation of hazardous materials. To optimize the problem under the above framework, we first establish a bi-objective fuzzy mixed integer nonlinear programming model, and then develop a three-stage exact solution strategy that the combines fuzzy credibilistic chance constraint, linearization technique, and the normalized weighting method. Finally, a computational experiment is carried out to verify the feasibility of the proposed method in dealing with the problem. The experimental results indicate that tradeoffs between the two conflicting objectives can be effectively made by using the Pareto frontier to the hazardous materials routing problem. Furthermore, the credibility level and carbon dioxide emission cap significantly influence the hazardous materials routing optimization. Their effects on the optimization result are quantified by using sensitivity analysis, which can draw some useful insights to help decision makers to better organize the hazardous materials road-rail multimodal transportation under uncertainty and sustainability

A Fuzzy Programming Method for Modeling Demand Uncertainty in the Capacitated Road–Rail Multimodal Routing Problem with Time Windows

Demand uncertainty is an important issue that influences the strategic, tactical, and operational-level decision making in the transportation/logistics/supply chain planning. In this study, we explore the effect of demand uncertainty on the operational-level freight routing problem in the capacitated multimodal transportation network that consists of schedule-based rail transportation and time-flexible road transportation. Considering the imprecise characteristic of the demand, we adopt fuzzy set theory to model its uncertainty and use trapezoidal fuzzy numbers to represent the fuzzy demands. We set multiple transportation orders as the optimization object and employ soft time windows to reflect the customer requirement on on-time transportation. Under the above situation, we establish a fuzzy mixed integer nonlinear programming (FMINLP) model to formulate the capacitated road–rail multimodal routing problem with demand uncertainty and time windows. We first use the fuzzy expected value model and credibility measure based fuzzy chance-constrained programming to realize the defuzziness of the model and then adopt linearization technique to reformulate the crisp model to finally generate an equivalent mixed integer linear programming (MILP) model that can be solved by standard mathematical programming software. Finally, a numerical case is presented to demonstrate the feasibility of the proposed method. Sensitivity analysis and fuzzy simulation are combined to quantify the effect of demand uncertainty on the routing problem and also reveal some helpful insights and managerial implications

Random Field Modeling of Track Irregularity of Beijing-Guangzhou High-Speed Railway with Karhunen-Loève Expansion

As one of the largest civil engineering systems in China, the network of high-speed railway expanded fast in the last decade. The safety of railway operation is of great concern for the whole country. Railway track irregularity is a potential threat to safety of operation and comfort of passengers, and remains a challenging issue for researchers and engineers. Currently, track irregularity data are recorded by various sensors in the comprehensive inspection cars in China. To reveal relations between the operation of high-speed trains and the track geometry, the data mining and random modeling of track irregularity are needed. In this paper, different methods to evaluate the track irregularities are presented at first. A case study of a section in Beijing-Guangzhou high-speed railway is studied using the Karhunen-Loève expansion. Track geometries that are a representation of this railway network are generated along with statistical and frequency validations. As an application based on generated random track geometries, accelerations of train body under different traveling velocities are calculated and analyzed using a simulation model

The role of protein contents in promoting wastewater phosphorus and bioenergy recovery during anaerobic digestion

Proteins present in domestic and industrial wastewater, yet their role in nutrient and energy recovery during anaerobic digestion (AD) has not been well understood. This study aimed to examine the impact of feedwater protein content on the calcium phosphate (CaP) precipitation under a low supersaturation condition and the methane production in an AD process. Four 1.0 L upflow anaerobic sludge blanket (UASB) reactors were fed with the synthetic feeds with different protein contents. The results showed that the amino acid degradation caused the pH elevation, triggering the CaP precipitation. A 25% bovine serum albumin (BSA) content in the feedwater helped shape a superior microbial community that contributed to a methanisation rate of 82.7%. The high prevalence of the phylum Synergistetes and the methanogen Methanosaeta suggested that methane was mainly produced through the acetate utilization.</p

The role of protein contents in promoting wastewater phosphorus and bioenergy recovery during anaerobic digestion

Proteins present in domestic and industrial wastewater, yet their role in nutrient and energy recovery during anaerobic digestion (AD) has not been well understood. This study aimed to examine the impact of feedwater protein content on the calcium phosphate (CaP) precipitation under a low supersaturation condition and the methane production in an AD process. Four 1.0 L upflow anaerobic sludge blanket (UASB) reactors were fed with the synthetic feeds with different protein contents. The results showed that the amino acid degradation caused the pH elevation, triggering the CaP precipitation. A 25% bovine serum albumin (BSA) content in the feedwater helped shape a superior microbial community that contributed to a methanisation rate of 82.7%. The high prevalence of the phylum Synergistetes and the methanogen Methanosaeta suggested that methane was mainly produced through the acetate utilization.</p

Effluent recirculation weakens the hydrolysis of high-solid content feeds in upflow anaerobic sludge blanket reactors

Effluent recirculation in an upflow anaerobic sludge blanket (UASB) reactor is a commonly used strategy to improve both mixing and upflow velocity of the reactor. This study aimed to assess the impact of effluent recirculation on methane production of UASB reactors treating substrates of different solid contents. Two 2.0 L UASB reactors were operated for 219 d under mesophilic conditions. When the UASB reactors were fed with a high-solid content substrate, effluent recirculation led to significantly reduced methanisation rate (from 47.9% without recirculation to 25.5% with recirculation) and hydrolysis efficiency of particulate organic matter (from 45.5% without recirculation to 22% with recirculation). In comparison to the high-solid content substrate, a low-solid content substrate led to an increase in methanisation rate for both UASB reactors with and without effluent recirculation, but the difference in methane production for the two reactors reduced significantly. Results demonstrated that the lower methane production in the presence of effluent recirculation arose from the inefficient hydrolysis of particulate organic matter, which was mitigated when the reactors were fed with a low-solid content substrate. Turbulence due to effluent recirculation enhanced biomass transport but limited the accessibility of adsorption sites on particulate matter. An insufficient attachment between microorganisms/enzymes and particles could have lowered the hydrolysis efficiency of particulate organic matter.</p