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In the health monitoring of electromechanical transmission systems, the collected state data typically consist of only a minimal amount of labeled data, with a vast majority remaining unlabeled. Consequently, deep learning-based diagnostic models encounter the challenge of scarcity in labeled data and abundance in unlabeled data. Traditional semi-supervised deep learning methods based on pseudo-label self-training, while alleviating the issue of labeled data scarcity to some extent, neglect the reliability of pseudo-label information, the accuracy of feature extraction from unlabeled data, and the imbalance in sample selection. To address these issues, this paper proposes a novel semi-supervised fault diagnosis method under imbalanced unlabeled sample class information screening. Firstly, an information screening mechanism for unlabeled data based on active learning is established. This mechanism discriminates based on the variability of intrinsic feature information in fault samples, accurately screening out unlabeled samples located near decision boundaries that are difficult to separate clearly. Then, combining the maximum membership degree of these unlabeled data in the classification space of the supervised model and interacting with the active learning expert system, label information is assigned to the screened unlabeled data. Secondly, a cost-sensitive function driven by data imbalance is constructed to address the class imbalance problem in unlabeled sample screening, adaptively adjusting the weights of different class samples during model training to guide the training of the supervised model. Ultimately, through dynamic optimization of the supervised model and the feature extraction capability of unlabeled samples, the recognition ability of the diagnostic model for unlabeled samples is significantly enhanced. Validation through two datasets, encompassing a total of 12 experimental scenarios, demonstrates that in scenarios with only a small amount of labeled data, the proposed method achieves a diagnostic accuracy increment exceeding 10% compared to existing typical methods, fully validating the effectiveness and superiority of the proposed method in practical applications.

To fabricate a highly biocompatible nanoplatform enabling synergistic therapy and real-time imaging, novel Au@Bi2S3 core shell nanobones (NBs) (Au@Bi2S3 NBs) with Au nanorods as cores were synthesized. The combination of Au nanorods with Bi2S3 film made the Au@Bi2S3 NBs exhibit ultrahigh photothermal (PT) conversion efficiency, remarkable photoacoustic (PA) imaging and high computed tomography (CT) performance; these Au@Bi2S3 NBs thus are a promising nanotheranostic agent for PT/PA/CT imaging. Subsequently, poly(N-vinylpyrrolidone)-modified Au@Bi2S3 NBs (Au@Bi2S3-PVP NBs) were successfully loaded with the anticancer drug doxorubicin (DOX), and a satisfactory pH sensitive release profile was achieved, thus revealing the great potential of Au@Bi2S3-PVP NBs in chemotherapy as a drug carrier to deliver DOX into cancer cells. Both in vitro and in vivo investigations demonstrated that the Au@Bi2S3-PVP NBs possessed multiple desired features for cancer therapy, including extremely low toxicity, good biocompatibility, high drug loading ability, precise tumor targeting and effective accumulation. Highly efficient ablation of the human liver cancer cell HepG2 was achieved through Au@Bi2S3-PVP NB-mediated photothermal therapy (PTT). As both a contrast enhancement probe and therapeutic agent, Au@Bi2S3-PVP NBs provided outstanding NIR-triggered multi-modal PT/PA/CT imaging-guided PTT and effectively inhibited the growth of HepG2 liver cancer cells via synergistic chemo/PT therapy.

Due to their tense mining succession relationship, gob-side roadways may undergo significant deformation under multi-mining pressure. In this article, many methods, such as on-site research, a theoretical analysis, a numerical simulation and an industrial experiment, are used to research the mechanism of asymmetric floor heave in a gob-side coal roadway affected by mining pressure during the mining of extra-thick coal seams. Our main research is as follows: (1) By monitoring the floor deformation in the roadway on site, it is concluded that the roadway floor shows asymmetry, indicating that the floor displacement near the coal pillar side is relatively large. (2) Based on a lateral overburden structure model of the roadway, the calculation formulas of the horizontal vertical stress caused by the roadway excavation and the excavation of the upper working face are derived separately, and the vertical stress coupling curves on both sides of the roadway during the mining of the upper working face are obtained through a numerical simulation. It is concluded that the cause of the asymmetric floor heave in the roadway is an uneven distribution of vertical stress. (3) The numerical simulation shows a symmetrical distribution of the floor displacement curve during the roadway excavation with a max. displacement of 49.5 mm. The floor displacement curve during the mining of the upper working face is asymmetric with a max. displacement of 873 mm at a distance of 1 m from the central axis near the coal pillar side. The range of the plastic zone in the roadway gradually expands with the mining of the upper working face, and the maximum depth of floor failure is 5.5 m. (4) According to the cooperative control principle of “roof + two sides + floor”, an asymmetric floor heave joint control scheme of “floor leveling + anchor cable support + concrete hardening” is proposed. The floor deformation monitoring results indicate that the max. floor heave at the measurement point near the coal pillar in the roadway is 167 mm, and the floor heave is effectively controlled.

Fragrant rapeseed oil (FRO) is a frying oil widely loved by consumers, but its quality deteriorates with increasing frying time. In this study, the effect of high-canolol phenolic extracts (HCP) on the physicochemical properties and flavor of FRO during frying was investigated. During frying, HCP significantly inhibited the increase in peroxide, acid, p-anisidine, and carbonyl values, as well as total polar compounds and degradation of unsaturated fatty acids. A total of 16 volatile flavor compounds that significantly contributed to the overall flavor of FRO were identified. HCP was effective in reducing the generation of off-flavors (hexanoic acid, nonanoic acid, etc.) and increased the level of pleasant deep-fried flavors (such as (E,E)-2,4-decadienal). Therefore, the application of HCP has a positive effect on protecting the quality and prolonging the usability of FRO.

Deep learning can be applied in the field of fault diagnosis without an accurate mechanism model. However, the accurate diagnosis of minor faults using deep learning is limited by the training sample size. In the case that only a small number of noise-polluted samples is available, it is crucial to design a new learning mechanism for the training of deep neural networks to make it more powerful in feature representation. The new learning mechanism for deep neural networks model is accomplished by designing a new loss function such that accurate feature representation driven by consistency of trend features and accurate fault classification driven by consistency of fault direction both can be secured. In such a way, a more robust and more reliable fault diagnosis model using deep neural networks can be established to effectively discriminate those faults with equal or similar membership values of fault classifiers, which is unavailable for traditional methods. Validation for gearbox fault diagnosis shows that 100 training samples polluted with strong noise are adequate for the proposed method to successfully train deep neural networks to achieve satisfactory fault diagnosis accuracy, while more than 1500 training samples are required for traditional methods to achieve comparative fault diagnosis accuracy.

This study was designed to co-load sialic acid (SA) and chitosan in a water-in-oil (W/O) emulsion and investigated its characterization and stability. Emulsions were prepared using two different oils (olive oil and maize oil) and polyglycerol polyricinoleate (PGPR) alone or in combination with lecithin (LE) as emulsifiers. The results revealed that the aqueous phase of 5% (w/v) SA and 2% (w/v) chitosan could form a stable complex and make the aqueous phase into a transparent colloidal state. Increasing the concentration of PGPR and LE presented different effects on emulsion formation between olive oil-base and maize oil-base. Two stable W/O emulsions that were olive oil-based with 1.5% (w/v) PGPR+ 0.5% (w/v) LE and maize oil-based with 2% (w/v) PGPR+ 0% (w/v) LE were obtained. Initial droplet size distribution curves of the two stable emulsions displayed unimodal distribution, and the rheological curves displayed the characteristics of shear thinning and low static shear viscosity. Moreover, the storage stability showed that there was no significant change in droplet size distribution and Sauter mean diameter of the emulsions at room temperature (25 °C) for 30 days. These results indicated that the W/O emulsions could effectively co-load and protect sialic acid and chitosan and thus could be a novel method for increasing the stability of these water-soluble bioactive compounds.

Abstract The long-standing problem of geometric problem solving in artificial intelligence education has attracted widespread attention. It is necessary to combine geometry diagrams and text descriptions to form a logical representation. This involves combining the knowledge of mathematical theorems, generating a solution sequence, and executing to obtain the answer. However, deficiencies in the feature extraction of geometry diagrams and the fusion of diagram text information can lead to poor performance in solving geometry problems. To effectively extract geometry diagram features, this study proposes an improved diagram parser DenseNet, and enhances the semantic representation of cross-modal information by adding auxiliary tasks. A structural and semantic pre-training strategy was used to parse the text description to avoid different problem solving schemes due to subtle differences in the interpretation of text content. Information fusion was realized by connecting the two modal labels, and then the information was sent to the encoder for fusion. The geometric knowledge was generated under the guidance of multi-modal information, and these programs were executed to obtain the results. Additionally, the performance of the proposed geometric neural solution method on the PGPS9K dataset is improved by 1.3% on average. Compared with the Geometry3K dataset, the effectiveness was proven.

Multi-stage attacks are one of the most critical security threats in the current cyberspace. To accurately identify multi-stage attacks, this paper proposes an anomaly-based multi-stage attack detection method. It constructs a Multi-Stage Profile (MSP) by modeling the stable system's normal state to detect attack behaviors. Initially, the method employs Doc2Vec to vectorize alert messages generated by the intrusion detection systems (IDS), extracting profound inter-message correlations. Subsequently, Hidden Markov Models (HMM) are employed to model the normal system state, constructing an MSP, with relevant HMM parameters dynamically acquired via clustering algorithms. Finally, the detection of attacks is achieved by determining the anomaly threshold through the generation probability (GP). To evaluate the performance of the proposed method, experiments were conducted using three public datasets and compared with three advanced multi-stage attack detection methods. The experimental results demonstrate that our method achieves an accuracy of over 99% and precision of 100% in multi-stage attack detection. This confirms the effectiveness of our method in adapting to different attack scenarios and ultimately completing attack detection.

Traffic signal control is essential to the efficiency of the road network's operation. In recent years, more and more detailed detection data provide potential data support for traffic signal control, such as license plate recognition (LPR) data. This study aims to develop a traffic signal control optimization method based on model predictive control (MPC) and LPR data. The proposed framework of a closed-loop control system is described in detail. First, the control objectives and queue prediction model for signalized intersection are determined. Then, online optimization and feedback compensation are discussed and implemented. Calculations of the arrival rate at the downstream are based on the LPR data detected at the upstream intersection, and dynamic optimization method of the offset is proposed for a coordinated control. The model is validated using the LPR data of two consecutive intersections with a traffic simulation platform. Results demonstrate that the model can restrain extreme long queuing, improve intersection capacity, and reduce intersection average delay. The developed model promotes the system operating efficiency and shows the general advantage of real-time optimization, feedback, and control. The proposed framework can be potentially applied by local traffic management centers to improve the quality of traffic signal control.

This study explores risk factors influencing the at-fault party in traffic accidents and analyzes their impact on traffic accident severity. Based on the traffic accident data of Shenyang City, Liaoning Province, China, from 2018 to 2020, 19 attribute variables including road attributes, time attributes, environmental attributes, and characteristics of the at-fault parties with either full responsibility, primary responsibility, or equal responsibility of the traffic accidents were extracted and analyzed in conjunction with the built environment attributes, such as road network density and POI (points of interest) density at the sites of traffic accidents. Using the RF-SHAP method to determine the relative importance of risk factors influencing the severity of traffic accidents with either motor vehicles or vulnerable groups at-fault, the top ten risk factors influencing the severity of traffic accidents with vulnerable road users as the at-fault parties are: functional zone, density of shopping POI, density of services POI, cause of accident, travel mode, collision type, season, road type, age of driver, and physical isolation. Travel mode, season, and road speed limit are more important risk factors for traffic accidents, with motor vehicle drivers as the at-fault parties. The density of service POI and cause of the accident are less critical for traffic accidents with motor vehicle drivers than traffic accidents with vulnerable road users who are at-fault. Subsequently, the Apriori algorithm based on association rules is used to analyze the important causal factors of traffic accidents, so as to explore the influence mechanism of multiple causal factors and their implied strong association rules. Our results show that most combined factors are associated with the matched Service and Shopping POI features. This study provides valuable information on the perceived risk of fatal accidents and highlights the built environment’s significant influence on fatal traffic accidents. Management strategies targeting the most typical combinations of accident risk factors are proposed for preventing fatalities and injuries in serious traffic accidents.

Abnormal gene expression is an established cause of gastric cancer (GC) initiation and progression. In this study, we aimed to identify several key genes that could be used to effectively predict progression and prognosis in patients with GC. The Cancer Genome Atlas and the Gene Expression Omnibus database were used to identify candidate genes. Fourteen genes were found to associate highly with progress, metastasis, and survival of GC. Five of these genes were overexpressed in tumor tissue compared to adjacent normal tissue. This was confirmed by reverse transcription‐polymerase chain reaction and western blotting for myosin‐Va (MYO5A), phospholipid transfer protein (PLTP), and tripeptidyl peptidase 1 (TPP1), while the CCK8 assay was used to show that these three genes promote GC cell proliferation. In summary, we demonstrate that MYO5A, PLTP, and TPP1 expression may be suitable markers for the progression and prognosis of GC.

It is very challenging to accurately understand and characterize the internal structure of three-dimensional (3D) rock masses using geological monitoring and conventional laboratory measures. One important method for obtaining 3D core images involves reconstructing their 3D structure from two-dimensional (2D) core images. However, traditional 2D–3D reconstruction methods are mostly designed for binary core images, rather than grayscale images. Furthermore, the reconstruction structure cannot reflect the gray level distribution of the core. Here, by combining the dimension promotion theory in super-dimension (SD) reconstruction and framework of deep learning, we propose a novel convolutional neural network framework, the cascaded progressive generative adversarial network (CPGAN), to reconstruct 3D grayscale core images. Within this network, we propose a loss function based on the gray level distribution and pattern distribution to maintain the texture information of the reconstructed structure. Simultaneously, by adopting SD dimension promotion theory, we set the input and output of every single node of the CPGAN network to be deep gray-padding structures of equivalent size. Through the cascade of every single node network, we thus ensured continuity and variability between the reconstruction layers. In addition, we used 3D convolution to determine the spatial characteristics of the core. The reconstructed 3D results showed that the gray level information in the 2D image were accurately reflected in the 3D space. This proposed method can help us to understand and analyze various parameter characteristics in cores.

As an alternative trip mode to the private car, carsharing mode first emerged in Europe in the 1940s. Although it possesses many merits such as convenience, affordability, and comfort, its development is far slower than the private car in recent years. Identifying the factors affecting the users’ choice between carsharing and private car is becoming very important. This paper proposes an evolution game model to explore the competitive choice process between carsharing and private car under different government policies. First, an evolution game model with incomplete information is developed to analyze the travel choice of carsharing over private car. The influences of government policy are taken into consideration. Then, the evolutionary stable strategy solution of the model is derived from replicator dynamics, and a discussion about the stable condition is presented. Finally, a case study is conducted to validate the proposed model. This study provides a rationale for agencies to improve the current carsharing choice rate between carsharing and private car.

The bottleneck normally refers to a narrowed region that decreases the flow, which is the key limiting factor in the pedestrian flow in the subway station. Due to the confined space, pedestrians are frequently forced to gather together at bottlenecks, which could not only limit the pedestrians’ efficiency and comfort but also cause serious crowd catastrophes such as stampedes. Optimization techniques for crowd congestion in subway stations should be investigated. This study proposed and demonstrated a set of optimization methods using conduction field experiments. The effects of passing time, traffic efficiency, speed, and density were explored using different design models. Results showed that optimization methods such as the design with a 45° funnel, broken guardrail, concaves, and column on the left had significant optimal effects. The optimization methods used in this study would help to implement bottlenecks in subway stations and provide design suggestions to subway designers.

Urban subway is taken by people in different frequencies, thus leading them to present different dependency varieties on this mode. Yet, how those passengers who possess low dependency on urban subway travel is less investigated. Under this background, we propose a framework to uncover passengers’ dependency variety on stations’ functions in urban subway. To begin with, nine features regarding travel frequency and time are extracted from 100 million transaction records generated by 11.45 million passengers in a month. Thus, their travel dependency on urban subway is quantified. These features are clustered into 5 distinct levels via the k-means algorithm, before an inference of subway stations’ functions from 236,040 POI data sources via the LDA approach. In this way, passengers’ travel purposes can be identified. How passengers with different dependency levels behave in subway stations in space and time is further explored in a visualization way. The intuitive experimental results, validated by priori user experiences and land-use plan of Beijing, show that among the 5 levels of dependency varieties, passengers in the first two groups present a relatively strong dependency on urban subway. Meanwhile, passengers in the rest three groups possess a low dependency on urban subway and display extreme travel patterns in time and frequency, greatly increasing management difficulties for transit operators. Findings in this research help distinguish passengers with low levels of subway dependency and grasp how those passengers without striking dependency travel by subway and what for so that practitioners can conduct an accurate risk assessment on them.

Traffic prediction is highly significant for intelligent traffic systems and traffic management. eXtreme Gradient Boosting (XGBoost), a scalable tree lifting algorithm, is proposed and improved to predict more high-resolution traffic state by utilizing origin-destination (OD) relationship of segment flow data between upstream and downstream on the highway. In order to achieve fine prediction, a generalized extended-segment data acquirement mode is added by incorporating information of Automatic Number Plate Recognition System (ANPRS) from exits and entrances of toll stations and acquired by mathematical OD calculation indirectly without cameras. Abnormal data preprocessing and spatio-temporal relationship matching are conducted to ensure the effectiveness of prediction. Pearson analysis of spatial correlation is performed to find the relevance between adjacent roads, and the relative importance of input modes can be verified by spatial lag input and ordinary input. Two improved models, independent XGBoost (XGBoost-I) with individual adjustment parameters of different sections and static XGBoost (XGBoost-S) with overall adjustment of parameters, are conducted and combined with temporal relevant intervals and spatial staggered sectional lag. The early_stopping_rounds adjustment mechanism (EAM) is introduced to improve the effect of the XGBoost model. The prediction accuracy of XGBoost-I-lag is generally higher than XGBoost-I, XGBoost-S-lag, XGBoost-S, and other baseline methods for short-term and long-term multistep ahead. Additionally, the accuracy of the XGBoost-I-lag is evaluated well in nonrecurrent conditions and missing cases with considerable running time. The experiment results indicate that the proposed framework is convincing, satisfactory, and computationally reasonable.

In order to improve the accuracy, reliability, and economy of urban traffic information collection, an optimization model of traffic sensor layout is proposed in this paper. Considering the impact of traffic big data, a set of impact factors for traffic sensor layout is established, including system cost, multisource data sharing, data demand, sensor failures, road infrastructure, and sensor type. The impacts of these influential factors are taken into account in the traffic sensor layout optimization problem, which is formulated in the form of multiobjective programming model that includes minimum system cost, maximum truncation flow, minimum path coverage, and an origin-destination (OD) coverage constraint. The model is solved by the tolerant lexicographic method based on a genetic algorithm. A case study shows that the model reflects the influence of multisource data sharing and fault conditions and satisfies the origin-destination coverage constraint to achieve the multiobjective optimization of traffic sensor layout.

Under the massive pedestrian flow, pedestrians arching phenomenon forms easily at bottleneck in subway hubs, which might stampede and crush. To explore pedestrian arching mechanism at bottleneck in subway transit hub, this paper conducts a series of simulation experiment. Firstly, movement preference characteristic in subway transit hubs was introduced into the social force model which considers multiple force. Then, after setting basic experiment scenario, unidirectional flow at different bottlenecks were simulated. Finally, the mechanism of pedestrian arching phenomenon at bottleneck was quantitative analyzed with the help of experimental data. Some main conclusions are summarized. Pedestrian arching phenomenon could be divided into four stages: Free, arching formation, arching stabilization and arching dissipation. In addition, the relationship between bottleneck scenario and passing time could be built to a function model. With the different of bottleneck width ratio, passing time presents positive correlation. The research results could give some helps for understanding the dynamic evolution process of unidirectional flow at bottleneck, improving the pedestrian efficiency at bottleneck and optimizing pedestrian facilities in subway transit hub.

Novel and stable carbon based embedded silver nano composite materials (Ag/CSs) were successfully prepared by a facile one-pot hydrothermal method with trioctylamine (TOA) as soft template and stabilizer. These as-prepared Ag/CSs exhibit well-defined shape and relatively uniform size with an average diameter of 1.5 μm and uniformly embedded Ag nanoparticles about 5 nm. The proper proportion of glucose, AgNO3 and TOA is the key to the common growth of hydrothermal carbon materials and silver nanoparticles in an embedded way. Besides, the thickness of carbon sphere matrix and the size of Ag particles can be tailored precisely by adjusting the experimental parameters. In order to facilitate comparative analysis, carbon spheres (CSs) without Ag particles embedded were also prepared with glucose under the same hydrothermal reaction conditions. The composition, structure and morphology of the as-prepared Ag/CSs and CSs were confirmed by X-ray powder diffraction (XRD), FT-IR spectroscopy, Raman spectrum, Transmission electron microscopy (TEM) and scanning electron microscopy (SEM). In addition, the possible formation mechanism of the Ag/CSs has been proposed based on experimental evidences. Finally, the as-prepared Ag/CSs and CSs were used as catalysts in the experiments of photocatalytic degradation of methylene MB in water under visible light irradiation and the high efficiency of photocatalytic performance of Ag/CSs has been verified.

This paper focuses on the optimization problem of a signal timing design based on the concept of bus priority. This optimization problem is formulated in the form of a bi-level programming model that minimizes average passenger delay at intersections and vehicle delay in lanes simultaneously. A solution framework that implements the differential evolution (DE) algorithm is developed to efficiently solve the model. A case study based on a real-world intersection in Beijing, China, is implemented to test the efficiency and applicability of the proposed modeling and computing methods. The experiment’s result shows that the optimization model can not only significantly improve the priority capacity of the buses at the intersection but also reduce the adverse impact of bus-priority approaches on the private vehicles for the intersections.

In this paper, the influence of wettability properties on the start-up characteristics of two-phase closed thermosyphons (TPCTs) is investigated. Chemical coating and etching techniques are performed to prepare the surfaces with different wettabilities that is quantified in the form of the contact angle (CA). The 12 TPCTs are processed including the same CA and a different CA combination on the inner surfaces inside both the evaporator and the condenser sections. For TPCTs with the same wettability properties, the introduction of hydrophilic properties inside the evaporator section not only significantly reduces the start-up time but also decreases the start-up temperature. For example, the start-up time of a TPCT with CA = 28° at 40 W, 60 W and 80 W is 46%, 50% and 55% shorter than that of a TPCT with a smooth surface and the wall superheat degrees is 55%, 39% and 28% lower, respectively. For TPCTs with combined hydrophilic and hydrophobic properties, the start-up time spent on the evaporator section with hydrophilic properties is shorter than that of the hydrophobic evaporator section and the smaller CA on the condenser section shows better results. The start-up time of a TPCT with CA = 28° on the evaporator section and CA = 105° on the condenser section has the best start-up process at 40 W, 60 W and 80 W which is 14%, 22% and 26% shorter than that of a TPCT with smooth surface. Thus, the hydrophilic and hydrophobic modifications play a significant role in promoting the start-up process of a TPCT.

Real-time traffic control is very important for urban transportation systems. Due to conflicts among different optimization objectives, the existing multi-objective models often convert into single-objective problems through weighted sum method. To obtain real-time signal parameters and evaluation indices, this article puts forward a Pareto front–based multi-objective traffic signal control model using particle swarm optimization algorithm. The article first formulates a control model for intersections based on detected real-time link volumes, with minimum delay time, minimum number of stops, and maximum effective capacity as three objectives. Moreover, this article designs a step-by-step particle swarm optimization algorithm based on Pareto front for solution. Pareto dominance relation and density distance are employed for ranking, tournament selection is used to select and weed out particles, and Pareto front for the signal timing plan is then obtained, including time-varying cycle length and split. Finally, based on actual survey data, scenario analyses determine the optimal parameters of the particle swarm algorithm, comparisons with the current situation and existing models demonstrate the excellent performances, and the experiments incorporating outliers in the input data or total failure of detectors further prove the robustness. Generally, the proposed methodology is effective and robust enough for real-time traffic signal control.

Real-time traffic signal control is very important for relieving urban traffic congestion. Many existing traffic control models were formulated using optimization approach, with the objective functions of minimizing vehicle delay time. To improve people’s trip efficiency, this article aims to minimize delay time per person. Based on the time-varying traffic flow data at intersections, the article first fits curves of accumulative arrival and departure vehicles, as well as the corresponding functions. Moreover, this article transfers vehicle delay time to personal delay time using average passenger load of cars and buses, employs such time as the objective function, and proposes a signal timing optimization model for intersections to achieve real-time signal parameters, including cycle length and green time. This research further implements a case study based on practical data collected at an intersection in Beijing, China. The average delay time per person and queue length are employed as evaluation indices to show the performances of the model. The results show that the proposed methodology is capable of improving traffic efficiency and is very effective for real-world applications.

With the rapid development of urbanization and motorization, urban commute trips are becoming increasingly serious due to the unbalanced distribution of residence and workplace land-use types in most Chinese cities. To explore the inherent interrelations among residence location, workplace, and commute trip, an integrated model framework of joint residence-workplace location choice and commute behavior is put forward based on the personal trip survey data of Beijing in 2005. First, to extract households’ different choice characteristics, this paper presents a latent class model, clusters all households into several groups, and analyzes the conditional probability of each group. Second, the paper integrates the residence location and workplace together as the joint choice alternative, employs the socioeconomic factors, individual attributes, household attributes, and trip characteristics as explanatory variables, and formulates the joint residence-workplace location choice model using mixed logit method. Estimations of the latent class model show that four latent groups fit the data best. Further results of the joint residence-workplace location choice model indicate that there exist significantly different choice characteristics in each latent group. Generally, the integrated model framework outperforms traditional location choice methods.

Residence location and workplace are the two most important urban land-use types, and there exist strong interdependences between them. Existing researches often assume that one choice dimension is correlated to the other. Using the mixed logit framework, three groups of choice models are developed to illustrate such choice dependencies. First, for all households, this paper presents a basic methodology of the residence location and workplace choice without decision sequence based on the assumption that the two choice behaviors are independent of each other. Second, the paper clusters all households into two groups, choosing residence or workplace first, and formulates the residence location and workplace choice models under the constraint of decision sequence. Third, this paper combines the residence location and workplace together as the choice alternative and puts forward the joint choice model. A questionnaire survey is implemented in Beijing city to collect the data of 1994 households. Estimation results indicate that the joint choice model fits the data significantly better, and the elasticity effects analyses show that the joint choice model reflects the influences of relevant factors to the choice probability well and leads to the job-housing balance.

As a new kind of highly compact and efficient micro-channel heat exchanger, the printed circuit heat exchanger (PCHE) is a promising candidate satisfying the heat exchange requirements of liquefied natural gas (LNG) vaporization at low and high pressure. The effects of airfoil fin arrangement on heat transfer and flow resistance were numerically investigated using supercritical liquefied natural gas (LNG) as working fluid. The thermal properties of supercritical LNG were tested by utilizing the REFPROF software database. Numerical simulations were performed using FLUENT. The inlet temperature of supercritical LNG was 121 K, and its pressure was 10.5 MPa. The reference mass flow rate of LNG was set as 1.22 g/s for the vertical pitch Lv = 1.67 mm and the staggered pitch Ls = 0 mm, with the Reynolds number of about 3750. The SST k-ω model was selected and verified by comparing with the experimental data using supercritical liquid nitrogen as cold fluid. The airfoil fin PCHE had better thermal-hydraulic performance than that of the straight channel PCHE. Moreover, the airfoil fins with staggered arrangement displayed better thermal performance than that of the fins with parallel arrangement. The thermal-hydraulic performance of airfoil fin PCHE was improved with increasing Ls and Lv. Moreover, Lv affected the Nusselt number and pressure drop of airfoil fin PCHE more obviously. In conclusion, a sparser staggered arrangement of fins showed a better thermal-hydraulic performance in airfoil fin PCHE.