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Refueling decisions of liner ships are facing challenges from both fuel price fluctuations and carbon emission constraints. This paper proposes a multistage stochastic programming model to tackle the refueling problem for dual-fuel ships under carbon intensity indicator (CII) rating limit and carbon tax costs. The model also takes into account various factors, including fuel consumption of main and auxiliary engines, fuel availability at ports of call, and fuel price fluctuations. The proposed model is solved using scenario size selection and moment matching methods, and a greedy heuristic algorithm is adopted to speed up the process. Managerial insights are obtained from multinomial logistic regression and sensitivity analyses. Our numerical results reveal that low sulfur fuel oil (LSFO) refueling decisions are closely linked to the difference of LSFO and liquefied natural gas (LNG) fuel prices and that LSFO becomes more attractive when the variance of LSFO fuel price or the LNG availability decreases. Besides, carbon emission costs are found to become a true consideration when carbon taxes exceed a certain threshold. These insights can help practitioners better understand the coupling influence of carbon emissions and fuel price fluctuations on the ship refueling problem.

Technological advancements in unmanned aerial vehicles (UAVs) have revolutionized various industries, enabling the widespread adoption of UAV-based solutions. In engineering management, UAV-based inspection has emerged as a highly efficient method for identifying hidden risks in high-risk construction environments, surpassing traditional inspection techniques. Building on this foundation, this paper delves into the optimization of UAV inspection routing and scheduling, addressing the complexity introduced by factors such as no-fly zones, monitoring-interval time windows, and multiple monitoring rounds. To tackle this challenging problem, we propose a mixed-integer linear programming (MILP) model that optimizes inspection task assignments, monitoring sequence schedules, and charging decisions. The comprehensive consideration of these factors differentiates our problem from conventional vehicle routing problem (VRP), leading to a mathematically intractable model for commercial solvers in the case of large-scale instances. To overcome this limitation, we design a tailored variable neighborhood search (VNS) metaheuristic, customizing the algorithm to efficiently solve our model. Extensive numerical experiments are conducted to validate the efficacy of our proposed algorithm, demonstrating its scalability for both large-scale and real-scale instances. Sensitivity experiments and a case study based on an actual engineering project are also conducted, providing valuable insights for engineering managers to enhance inspection work efficiency.

Ships are traditionally powered by fossil fuels such as heavy fuel oil (HFO) and marine diesel oil (MDO), where the emissions, such as particulates, hydrocarbons (HC), carbon monoxide (CO), nitrogen oxides (NOX) and carbon dioxide (CO2), negatively affect the environment and human health. The International Maritime Organization (IMO) encourages shipping companies to use liquefied natural gas (LNG), which is a green fuel source to power shipping activities and is easy to store, to replace traditional marine fuels. There are three common methods of LNG bunkering: ship-to-ship, truck-to-ship, and port-to-ship. The objective of this study is to determine the optimal bunkering method at a port using an integer linear programming (ILP) model considering three kinds of costs: fixed, variable, and extra. To find the optimal bunkering method, the three methods and their related constraints are modeled into the ILP model. The results indicate that ship-to-ship is the optimal bunkering method for LNG under the scenario of the port considered. Numerical experiments are conducted to validate model performance and generate managerial insights.

Annually, over 10 billion tons of construction and demolition waste is transported globally from sites to reception facilities. Optimal and effective planning of waste transportation holds the potential to mitigate cost and carbon emissions, and alleviate road congestion. A major challenge for developing an effective transportation plan is the uncertainty of the precise volume of waste at each site during the planning stage. However, the existing studies have assumed known demand in planning models but the assumption does not reflect real-world volatility. Taking advantage of the problem structure, this study adopts the stochastic programming methodology to approach the construction waste planning problem. An integer programming model is developed that adeptly addresses the uncertainty of the amount of waste in an elegant manner. The proposed stochastic programming model can efficiently handle practical scale problems. Our numerical experiments amass a comprehensive dataset comprising nearly 4300 records of the actual amount of construction waste generated in Hong Kong. The results demonstrate that incorporating demand uncertainty can reduce the transportation cost by 1% correlating with an increase in profit of 14% compared to those that do not consider the demand uncertainty.

Machine learning (ML) techniques are extensively applied to practical maritime transportation issues. Due to the difficulty and high cost of collecting large volumes of data in the maritime industry, in many maritime studies, ML models are trained with small training datasets. The relative predictive performances of these trained ML models are then compared with each other and with the conventional model using the same test set. The ML model that performs the best out of the ML models and better than the conventional model on the test set is regarded as the most effective in terms of this prediction task. However, in scenarios with small datasets, this common process may lead to an unfair comparison between the ML and the conventional model. Therefore, we propose a novel process to fairly compare multiple ML models and the conventional model. We first select the best ML model in terms of predictive performance for the validation set. Then, we combine the training and the validation sets to retrain the best ML model and compare it with the conventional model on the same test set. Based on historical port state control (PSC) inspection data, we examine both the common process and the novel process in terms of their ability to fairly compare ML models and the conventional model. The results show that the novel process is more effective at fairly comparing the ML models with the conventional model on different test sets. Therefore, the novel process enables a fair assessment of ML models' ability to predict key performance indicators in the context of limited data availability in the maritime industry, such as predicting the ship fuel consumption and port traffic volume, thereby enhancing their reliability for real-world applications.

Due to European Union (EU) oil sanctions, tanker shipping companies need to redeploy their tankers by moving tankers between ship routes with the consideration of flag states of tankers, but the literature lacks quantitative methods for this problem. To fill this research gap, this paper studies an integrated problem of fleet deployment, fleet repositioning, round trip completion, and speed optimization with the consideration of flag states of tankers. The problem is formulated as a nonlinear integer programming model to minimize the total cost, including the fleet repositioning cost, the mismatch cost, and the fuel cost, during the planning period while satisfying the total crude oil transportation demand of each voyage and the minimum shipping frequency. Some linearization methods are used to transform the nonlinear model to a linear one which can be directly solved by Gurobi. The average solving time required for 17 computational instances is 4.5 minutes, which validates the effectiveness of the proposed model. Sensitivity analyses, including the influences of the unit fuel price, the total crude oil transportation demand, the mismatch cost of completing a round trip by a deployed tanker, and the repositioning cost for each deployed tanker, on operations decisions, are conducted to obtain managerial insights.

The development of the Internet of things (IoT) and online platforms enables companies and governments to collect data from a much broader spatial and temporal area in the logistics industry. The huge amount of data provides new opportunities to handle uncertainty in optimization problems within the logistics system. Accordingly, various prescriptive analytics frameworks have been developed to predict different parts of uncertain optimization problems, including the uncertain parameter, the combined coefficient consisting of the uncertain parameter, the objective function, and the optimal solution. This tutorial serves as the pioneer to introduce existing literature on state-of-the-art prescriptive analytics methods, such as the predict-then-optimize framework, the smart predict-then-optimize framework, the weighted sample average approximation framework, the empirical risk minimization framework, and the kernel optimization framework. Based on these frameworks, this tutorial further proposes possible improvements and practical tips to be considered when we use these methods. We hope that this tutorial will serve as a reference for future prescriptive analytics research on the logistics system in the era of big data.

Limiting carbon dioxide emissions is one of the main concerns of green shipping. As an important carbon intensity indicator, the Energy Efficiency Operational Index (EEOI) represents the energy efficiency level of each ship and can be used to guide the operations of ship fleets for liner companies. Few studies have investigated an integrated optimization problem of fleet deployment, voyage planning and speed optimization with consideration of the influences of sailing speed, displacement and voyage option on fuel consumption. To fill this research gap, this study formulates a nonlinear mixed-integer programming model capturing all these elements and subsequently proposes a tailored exact algorithm for this problem. Extensive numerical experiments are conducted to show the efficiency of the proposed algorithm. The largest numerical experiment, with 7 ship routes and 32 legs, can be solved to optimality in four minutes. Moreover, managerial insights are obtained according to sensitivity analyses with crucial parameters, including the weighting factor, unit price of fuel, Suez Canal toll fee per ship, weekly fixed operating cost and cargo load in each leg.

Ship fouling has significant adverse effects on vessel performance and environmental sustainability. Therefore, this research study develops a bi-level programming model to simultaneously optimize cleaning equipment deployment by cleaning service providers in the upper level and cleaning decisions by shipping companies in the lower level. To address the interaction within the bi-level problem, the model is transformed into a single-level formulation using the big-M method. This transformation greatly simplifies the complexity of the computation and reduces computation time. Numerical experiments are conducted using real-world data to evaluate the performance of the proposed models. In addition, sensitivity analyses are performed to investigate the influence of key parameters. The results indicate that cleaning service providers primarily purchase equipment in the first year based on the demand distribution. To maximize profit, they may choose to forgo a portion of the demand. The sensitivity analysis reveals that sacrificing part of the demand can lead to an additional USD 27 million in profits compared with satisfying all demand. Moreover, increasing the cleaning price reduces both demand and equipment purchases but increases total profits. Conversely, increasing purchase costs reduces profits and the total amount of equipment purchased. When service providers can no longer generate profits, they are likely to exit the market. These findings offer valuable insights for service providers and shipping companies in the practical deployment of cleaning equipment and foul cleaning decisions, respectively.

BACKGROUND The development of urban industrialization causes soil heavy metal pollution and other environmental problems. Studies have shown that the damage caused by soil heavy metals to the ecological environment is not only related to the total content, but also depends on the speciation of heavy metals. OBJECTIVES To investigate the content characteristics, speciation and risk status of heavy metals in soil of a city in the Hetao Plain. METHODS 52 soil samples were collected from different functional areas of the Hetao Plain, heavy metals were analyzed by inductively coupled plasma-mass spectrometry (ICP-MS), atomic fluorescence spectrometry (AFS) and other analytical methods to study the total content and speciation. The ecological risk of soil heavy metals was evaluated using the RAC risk assessment method. RESULTS The results showed that the contents of heavy metal elements (except As) in surface soil were higher than the background values in the Hetao Plain, the variation coefficients of Pb, Cr, Mn, Cd and Zn were large and the spatial distribution was not uniform, which were significantly affected by human activities. The heavy metal content in the industrial area exceeded the background value, especially Cd (5.83 times of background value) and Pb (3.58 times of background value). Road heavy metal contents were also significantly higher than the background value, Cd was nearly 4 times the background value. Except for Cd in industrial areas, other heavy metals were mainly in residual fraction. The residual fractions of Cr, Ni, Cu and As were more than 70%. The residual fraction of Cd in each functional region accounted for about 30%. RAC risk assessment results showed that Cd and Pb in industrial areas reached the high-risk level. CONCLUSIONS The research results provide scientific basis for identifying the potential environmental risks in urban area soil and proposing effective prevention.

BACKGROUND The exploitation of minerals releases heavy metals into the surrounding soil, which can cause health hazards when biological entities are exposed to contaminated soil. Iron ore is one of the most widely distributed minerals in China, but there are few studies on the health risk of soil heavy metals in iron mines, especially in arid areas. The ecosystem of the arid area is fragile and difficult to repair once it has been polluted. OBJECTIVES To perform environmental health risk assessment for topsoil surrounding iron deposits from Guyang County, Baotou City. METHODS 101 topsoil samples were collected from an iron mine in Guyang County, Baotou City. Heavy metals were measured by inductively coupled plasma-mass spectrometry/optical emission spectrometry and atomic fluorescence spectrometry, to study element characteristics. The health risk assessment method was used to evaluate and calculate the screening value. RESULTS The results showed that the variation coefficients of copper, chromium, and nickel in the study area were higher than 0.5, and the average contents of these elements were significantly higher than that in the Hetao Plain. The high value areas of copper were mainly distributed around tailings ponds and concentrators, while the high value areas of chromium or nickel were mainly distributed in bedrock mountains. The assessment of health hazards showed that the hazard quotient of all elements was less than 1, and the order was As>Cr>Ni>Cu>Cd>Zn>Hg. The average of total hazard quotient was 0.542. There was a sample in the bedrock mountain south of the iron mine, which had a total hazard quotient exceeding 1, and the contribution rates of chromium, nickel and arsenicwere 59%, 25%, and 15%, respectively. The carcinogenic hazard index of all elements was below 10-4. Soil screening levels of chromium, nickel and arsenic in the study area were 579mg/kg, 622mg/kg and 32.8mg/kg base on human health. CONCLUSIONS Chromium and nickel in the study soil are mainly affected by parent materials of soil formation, while copper is mainly affected by mineral processing activities. In general, the health hazard of heavy metals in soil is relatively low, and is greatly influenced by the parent materials of soil formation. However, it is still necessary to pay attention to the accumulation of polluted elements in the areas around tailings ponds, and to avoid oral intake of pollutants. And it is also necessary to further study the feature of soil heavy metals from different kinds of iron mines and different regions that have not been studied before, because the associated elements of iron ore in different regions or types are not the same.

Objective To investigate the role of Ofactory Test of Parkinson's Disease (KinPamor) in the evaluation of olfactory function in patients with Parkinson's disease (PD). Methods From October 2018 to March 2021, 66 patients with PD diagnosed in General Hospital of Ningxia Medical University and The University of Hong Kong ⁃ Shenzhen Hospital were selected. Olfactory function was evaluated by Argentine Hyposmia Rating Scale and KinPamor, positive rate was compared between these two tests. The error rate of each odor in KinPamor was analyzed. Multifactor linear stepwise regression was used to analyze the linear quantitative relationship between KinPamor score and clinical data. Results The incidence of olfactory dysfunction in 66 PD patients was 92.42% (61/66) using KinPamor, higher than 25.76% (17/66) using Argentine Hyposmia Rating Scale (χ2= 40.333, P = 0.000). The difference in error rate of 12 odors of KinPamor was statistically significant (χ2= 52.049, P = 0.000). Error rate of garlic odor was lower than the other 9 odors, banana (P = 0.000), apple (P = 0.049), aniseed (P = 0.049), rose (P = 0.000), lemon (P = 0.000), mint (P = 0.049), resin (P = 0.000), camphor (P = 0.000) and wood (P = 0.000). Multifactor linear stepwise regression showed KinPamor score decreased by 0.077 points for every increasing year of age (P = 0.031). There was no linear regression relationship between male, disease course, smoking, Mini-Mental State Examination (MMSE) score, Unified Parkinson's Disease Rating Scale Ⅲ (UPDRS Ⅲ) score and KinPamor score. Conclusions KinPamor is a simple and convenient tool to screening olfactory dysfunction in PD patients, which can be used as an auxiliary test for early diagnosis of PD. Except for easily recognition of garlic odor, selective hyposmia was not found by KinPamor in PD patients.

The European Union (EU) has implemented a sub-quota of 2% for renewable marine fuels to be utilized by vessels operating within its jurisdiction, effective starting from 2034. This progressive policy signifies a significant leap towards reducing carbon emissions and promoting sustainable development. However, it also presents notable challenges for shipping companies, particularly in terms of fuel costs. In order to support shipping companies in devising optimal strategies within the framework of this new policy, this study proposes a mixed-integer linear programming model. This model aims to determine the optimal decisions for fuel choice, sailing speed and the number of vessels on various routes. Furthermore, we showcase the adaptability of our model in response to fluctuations in fuel prices, relevant vessel costs, and the total fleet size of vessels. Through its innovative insights, this research provides invaluable guidance for optimal decision-making processes within shipping companies operating under the new EU policy, enabling them to minimize their total costs effectively.

Objective: The uncertainty of determination of Isocarbophos residues in tea by using QuEChERS-gas chromatography-tandem mass spectrometry techniqne was evaluated. Methods: Isocarbophos residues in tea was determined by using "GB 23200.113—2018 National food safety Standard-Determination of 208 pesticides and metabolites residues in plant origin-Gas chromatography-tandem mass spectrometry method", and factors in the determination of isocarbophos were analyzed by establishing a mathematical model, including sample weighing, volume, calibration of standard solutions, repeatability and recovery rate, etc.. Results: When the residual amount of isocarbophos in tea was 0.050 mg/kg, the expanded uncertainty was 0.007 mg/kg, and the determination result was expressed as: ω=（0.050±0.007） mg/kg, k=2. Conclusion: The main sources of uncertainty in this method werecalibration of standard solutions and the recovery rate.

Maritime transport serves as the backbone of international trade, accounting for more than 90% of global trade. Although maritime transport is cheaper and safer than other modes of transport, it often means long sailing distances, which often results in substantial fuel consumption and emissions. Liner shipping, a vital component of maritime transport, plays an important role in achieving sustainable maritime operations, necessitating the implementation of green liner shipping practices. Therefore, this study formulates a nonlinear integer programming model for a multi-fuel engine selection optimization problem to optimally determine ship order choice in terms of the fuel engine type, fleet deployment, fuel selection, and speed optimization, with the aim of minimizing the total weekly cost containing the weekly investment cost for ship orders and the weekly fuel cost. Given the complexity of solving nonlinear models, several linearization techniques are applied to transform the nonlinear model into a linear model that can be directly solved by Gurobi. To evaluate the performance of the linear model, 20 sets of numerical instances with, at most, seven routes are conducted. The results show that among 20 numerical instances, 16 sets of numerical instances are solved to optimality within two hours. The average gap value of the remaining four sets of numerical instances that cannot be solved to optimality within two hours is 0.51%. Additionally, sensitivity analyses are performed to examine crucial parameters, such as the weekly investment cost for ordering ships, the ship ordering budget, and the potential application of new fuel engine types, thereby exploring managerial insights. In conclusion, our findings indicate that equipping ships with low-sulfur fuel oil engines proves to be the most economical advantageous option in the selected scenarios. Furthermore, ordering ships with low-sulfur fuel, oil + methanol + liquefied natural gas engines, is beneficial when the weekly investment cost for such engines does not exceed $13,000, under the current parameter value setting.

The greenhouse effect caused by carbon dioxide (CO2) emissions has forced the shipping industry to actively reduce the amount of CO2 emissions emitted directly into the atmosphere over the past few years. Carbon capture, utilization, and storage (CCUS) is one of the main technological methods for reducing the amount of CO2 emissions emitted directly into the atmosphere. CO2 transport, i.e., shipping CO2 to permanent or temporary storage sites, is a critical intermediate step in the CCUS chain. This study formulates a mixed-integer programming model for a carbon storage and transport problem in the CCUS chain to optimally determine ship allocation, ship departure scheduling, and CO2 storage and transport. Taking advantage of the structure of the problem, we transform the mixed-integer programming model into a simpler model that can be computed efficiently. To evaluate the performance of the simpler model, numerous computational experiments are conducted. The results show that all small-scale instances (each with 10 power plants) and medium-scale instances (each with 30 power plants) can be solved optimality by Gurobi within 14.33 s. For large-scale instances with 60 and 65 power plants, feasible solutions with average gap values of 0.06% and 6.93% can be obtained by Gurobi within one hour, which indicates that the proposed methodology can be efficiently applied to practical problems. In addition, important parameters, including the unit fuel price, the time-charter cost, and the ship sailing speed, are examined in sensitivity analyses to investigate the impacts of these factors on operations decisions. In summary, a lower fuel price, a lower charter cost, or a higher ship sailing speed can increase the profit of the CCUS chain.

Abstract Hepatocellular carcinoma (HCC), commonly caused by liver fibrosis, is a global challenge with high morbidity. Activation of hepatic stellate cells (HSCs) contributes to hepatic fibrosis. Exosomes are small vesicles that play a significant role in cell-to-cell communication. Smoothened (SMO) is the key signal transducer for Hedgehog pathway. This study was designed to study the function and underlying mechanism of SMO in HSC activation. Functional assays including 5-Ethynyl-2´-deoxyuridine, colony formation, wound healing, transwell, and sphere formation assays disclosed the function of SMO. Western blot analysis of exosome biomarkers, immunofluorescence staining assay, electron microscope, and flow cytometry revealed the existence of exosomes. Bioinformatics analyses and mechanistic assays uncovered the interplays between RNAs. Nude mice xenograft model was established to evaluate HCC tumor growth. We uncovered that SMO was an oncogene in HCC cells and was low-expressed in quiescent HSCs. Then, SMO was upregulated in HSCs cultured with HCC cells-conditioned medium. Next, it was revealed that HCC cells-derived exosomes activated HSCs by transmitting SMO to HSCs. Subsequently, we recognized that microRNA let-7b host gene (MIRLET7BHG) served as the competing endogenous RNA against miR-330-5p to upregulate SMO. In turn, SMO induced hedgehog pathway to promote GLI family zinc finger 1 (Gli1), leading to transcriptional activation of MIRLET7BHG in activated HSCs. In summary, this study demonstrated that Gli1-induced MIRLET7BHG facilitated HCC by activating HSCs through exosomal SMO to stimulate hedgehog pathway, providing a new road for HCC treatment.

Maritime transport is the backbone of international trade and globalization. Maritime transport research can be roughly divided into two categories, namely the shipping side and the port side. Most of the classic approaches adopted to address practical problems in these research topics are based on long-term observations and expert knowledge, while few of them are based on historical data accumulated from practice. In recent years, emerging approaches, which we refer to as machine learning and deep learning techniques in this essay, have been receiving a wider attention to solve practical problems. As a relatively conservative industry, there are some initial trials of applying the emerging approaches to solve practical problems in the maritime sector. The objective of this essay is to review the application of emerging approaches to maritime transport research. The main research topics in maritime transport and classic methods developed to solve them are first presented. The introduction of emerging approaches and their suitability to be applied in maritime transport research is then discussed. Related existing studies are then reviewed according to problem settings, main data sources, and emerging approaches adopted. Challenges and solutions in the process are also discussed from the perspectives of data, model, users, and targets. Finally, promising future research directions are identified. This essay is the first to give a comprehensive review of existing studies on developing machine learning and deep learning models together with popular data sources used to address practical problems in maritime transport.

Maritime decarbonization and strict international regulations have forced liner companies to find new solutions for reducing fuel consumption and greenhouse gas emissions in recent years. Green technology is regarded as one of the most promising alternatives to achieve environmental benefits despite its high initial investment costs. Therefore, a scientific method is required to assess the possibility of green technology adoption for liner companies. This study formulates a mixed-integer nonlinear programming model to determine whether to retrofit their ship fleets with green technology and how to deploy ships while taking maritime decarbonization into account. To convert the nonlinear model into a linear model that can be solved directly by off-the-shelf solvers, several linearization techniques are applied in this study. Sensitivity analyses involving the influences of the initial investment cost, fuel consumption reduction rate of green technology, unit fuel cost, and fixed operating cost of a ship on operation decisions are conducted. Green technology may become more competitive when modern technology development makes it efficient and economical. As fuel and fixed operating costs increase, more ships retrofitted with green technology will be deployed on all shipping routes.

Maritime transport plays an important role in global economic development but also inevitably faces increasing pressures from all sides, such as ship operating cost reduction and environmental protection. An ideal innovation to address these pressures is electric ships, which are more environmentally friendly than conventional manned fuel oil ships. The electric ship is in its early stages. To provide high-quality transportation services, the service network needs to be designed carefully. Therefore, this research simultaneously studies the location of charging stations, charging plans, route planning, ship scheduling, and ship deployment under service time requirements. The problem is formulated as a mixed-integer linear programming model with the objective of minimizing total cost comprised of charging cost, construction cost of charging stations, and fixed cost of ships. A case study using the data of the shipping network along the Yangtze River is conducted in order to evaluate the performance of the model. Valuable managerial insights are also derived from sensitivity analyses.

Reducing air pollution and greenhouse gas emissions has become one of the primary tasks for the shipping industry over the past few years. Among alternative marine fuels, liquefied natural gas (LNG) is regarded as one of the most popular alternative marine fuels because it is one of the cleanest fossil marine fuels. Therefore, a practical way to implement green shipping is to deploy dual-fuel ships that can burn conventional fuel oil and LNG on various ship routes. However, a severe problem faced by dual-fuel ships is methane slip from the engines of ships. Therefore, this study formulates a nonlinear mixed-integer programming model for an integrated optimization problem of fleet deployment, ship refueling, and speed optimization for dual-fuel ships, with the consideration of fuel consumption of both main and auxiliary engines, ship carbon emissions, availability of LNG at different ports of call, and methane slip from the main engines of ships. Several linearization techniques are applied to transform the nonlinear model into a linear model that can be directly solved by off-the-shelf solvers. A large number of computational experiments are carried out to assess the model performance. The proposed linearized model can be solved quickly by Gurobi, namely shorter than 0.12 s, which implies the possibility of applying the proposed model to practical problems to help decision-makers of shipping liners make operational decisions. In addition, sensitivity analyses with essential parameters, such as the price difference between the conventional fuel oil and LNG, carbon tax, and methane slip amount, are conducted to investigate the influences of these factors on operational decisions to seek managerial insights. For example, even under the existing strictest carbon tax policy, shipping liners do not need to deploy more ships and slow steaming to reduce the total weekly cost.

The plastic deformation with respect to dislocation distribution of titanium foil under ultrasonic vibration was studied. The influencing rule of different ultrasonic vibration modes on the stress-strain,elongation and dislocation distribution of titanium foil during plastic deformation was studied by uniaxial tensile test and microstructure analysis. The results show that the flow stress of titanium foil can be reduced by about 80% and the elongation can increase from 40.33% to 54.46% during ultrasonic vibration. TEM shows that the dislocations tend to be parallel to each other without large amount of entanglement, and the distribution of dislocations in the samples without ultrasonic vibration is chaotic and seriously entangled.

As a malignant tumor, liver cancer is mainly treated with chemotherapy, while chemotherapeutic drugs, such as doxorubicin (DOX), may lead to toxicity, drug resistance and poor prognosis. The targeted delivery systems of combining natural products and chemotherapeutic drugs are useful to eliminate cancers with reduced toxicity and increased efficiency. In this study, a diosgenin-based liposome loaded with DOX (Dios-DOX-LP) was developed for synergistic treatment of liver cancer, in which Dios not only replaced cholesterol as the membrane regulator to keep stability of liposomes, but also became the chemotherapy adjuvant of DOX for synergistic treatment. Dios-DOX-LP was characterized by particle size (99.4 ± 6.2 nm), zeta potential (−33.3 ± 2.5 mV), and entrapment efficiency (DOX: 98.77 ± 2.04%, Dios: 87.75 ± 2.93%), which had a good stability and slow-release effect. Compared with commercial DOX liposome (CHOL-DOX-LP), Dios-DOX-LP had an improved anti-tumor effect in vitro and in vivo by inducing the apoptosis and inhibiting the proliferation of the tumor cell, which was 1.6 times better than CHOL-DOX-LP in cytotoxicity, and had 78% of the tumor inhibition rate on tumor-bearing nude mice. Dios-DOX-LP provided a novel idea to achieve synergistic tumor treatment using diosgenin as a liposome material.

China has emerged as the second largest economy in the world during the globalization in the last forty years. However, in the last decade, Chinese manufacturing has also demonstrated its dark side causing wide range of concerns globally and directly jeopardize people’s health because of serious pollutions. How could the world keep its industrialization yet without damages to the natural environment? The paper proposes a new framework entitled ‘IE3’ by integrating three domains of knowledge—Industrial Entrepreneurship, Industrial Engineering and Industrial Ecology. The IE3 model provides a potential answer to the future development pathway for industrialization, changing from pursuit of quantity to quality via considering resources efficiency and ecology efficiency. The novelty of the research lies in incorporating three originally separated theories into a comprehensive system.