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With the widespread attention and application of artificial intelligence (AI) and blockchain technologies, privacy protection techniques arising from their integration are of notable significance. In addition to protecting privacy of individuals, these techniques also guarantee security and dependability of data. This paper initially presents an overview of AI and blockchain, summarizing their combination along with derived privacy protection technologies. It then explores specific application scenarios in data encryption, de-identification, multi-tier distributed ledgers, and k-anonymity methods. Moreover, the paper evaluates five critical aspects of AI-blockchain-integration privacy protection systems, including authorization management, access control, data protection, network security, and scalability. Furthermore, it analyzes the deficiencies and their actual cause, offering corresponding suggestions. This research also classifies and summarizes privacy protection techniques based on AI-blockchain application scenarios and technical schemes. In conclusion, this paper outlines the future directions of privacy protection technologies emerging from AI and blockchain integration, including enhancing efficiency and security to achieve a more comprehensive privacy protection of privacy., 23 pages, 4 figures

This is an authorised Chinese translation of the article How (Not) to Write a Privacy Law written by Julie Cohen, professor of Law and Technology at Georgetown University. The original English version can be accessed at https://knightcolumbia.org/content/how-not-to-write-a-privacy-law.

Osteoarthritis (OA) is one of the most prevalent chronic degenerative joint diseases affecting adults in their middle or later years. It is characterized by symptoms such as joint pain, difficulty in movement, disability, and even loss of motion. Moreover, the onset and progression of inflammation are directly associated with OA. In this research, we evaluated the impact of Flavokawain A (FKA) on osteoarthritis. In-vitro effects of FKA on murine chondrocytes have been examined using cell counting kit-8 (CCK-8), safranin o staining, western blot, immunofluorescence staining, senescence β-galactosidase staining, flow cytometry analysis, and mRFP-GFP-LC3 adenovirus infection. An in-vivo model of destabilization of the medial meniscus (DMM) was employed to investigate FKA’s effect on OA mouse. An analysis of bioinformatics was performed on FKA and its potential role in OA. It was observed that FKA blocked interleukin (IL)-1β-induced expression of inflammatory factors, i.e., cyclooxygenase-2 (COX2) and inducible nitric oxide synthase (iNOS) in chondrocytes. In addition, FKA also downregulated the catabolic enzyme expression, i.e., aggrecanase-2 (ADAMTS5) and matrix metalloproteinases (MMPs), and helped in the upregulation of the anabolic protein expression, i.e., type II collagen (Col2), Aggrecan, and sry-box transcription factor 9 (SOX9). Moreover, FKA ameliorated IL-1β-triggered autophagy in chondrocytes, and it was observed that the FKA causes anti-inflammatory effects by the mitogen-activated protein kinase (MAPK) and phosphoinositide-3-kinase/Akt/mammalian target of rapamycin (PI3K/AKT/mTOR) signaling pathways inhibition. The results of immunohistochemical analysis and microcomputed tomography from the in vivo OA mouse model confirmed the therapeutic effect of FKA. Finally, we assessed the anti-arthritic impacts of FKA by conducting in vivo and in vitro analyses. We concluded that FKA can be employed as a useful therapeutic agent for OA therapy, but the findings require needs further clinical investigation.

Time series anomaly detection (TSAD) is an important data mining task with numerous applications in the IoT era. In recent years, a large number of deep neural network-based methods have been proposed, demonstrating significantly better performance than conventional methods on addressing challenging TSAD problems in a variety of areas. Nevertheless, these deep TSAD methods typically rely on a clean training dataset that is not polluted by anomalies to learn the "normal profile" of the underlying dynamics. This requirement is nontrivial since a clean dataset can hardly be provided in practice. Moreover, without the awareness of their robustness, blindly applying deep TSAD methods with potentially contaminated training data can possibly incur significant performance degradation in the detection phase. In this work, to tackle this important challenge, we firstly investigate the robustness of commonly used deep TSAD methods with contaminated training data which provides a guideline for applying these methods when the provided training data are not guaranteed to be anomaly-free. Furthermore, we propose a model-agnostic method which can effectively improve the robustness of learning mainstream deep TSAD models with potentially contaminated data. Experiment results show that our method can consistently prevent or mitigate performance degradation of mainstream deep TSAD models on widely used benchmark datasets.

Decentralized finance (DeFi) in Ethereum is a financial ecosystem built on the blockchain that has locked over 200 billion USD until April 2022. All transaction information is transparent and open when transacting through the DeFi protocol, which has led to a series of attacks. Several studies have attempted to optimize it from both economic and technical perspectives. However, few works analyze the vulnerabilities and optimizations of the entire DeFi system. In this paper, we first systematically analyze vulnerabilities related to DeFi in Ethereum at several levels, then we investigate real-world attacks. Finally, we summarize the achievements of DeFi optimization and provide some future directions.

Collision risk early warning is critical to sailing safety in vessel encounter situations because it provides ship officers with sufficient time to react to emergencies and take evasive actions in advance. In this study, we take spatiotemporal motion behaviors of encountering vessels into account since vessel motion behaviors have great influences on the occurrence of a dangerous situation. For this purpose, a data-driven approach is proposed to associate the motion behaviors with the future risk and early prediction of risk is achieved through classifying the behaviors into corresponding risk level. Specifically, we first derive a sequence of relative motion features between encountering vessels to characterize the spatial interactions that vary over time. Then a novel deep learning architecture, which combines bidirectional long short-term memory (BiLSTM) and attention mechanism, is developed to capture the spatial-temporal dependences of behaviors as well as their impacts on future risk. In particular, the BiLSTM is able to discover correlations among behaviors and the attention mechanism can emphasize the key information relevant to the risk prediction task. Exploiting the advantages of these two mechanisms makes the risk prediction more reasonable and reliable. Extensive experiments using ship trace data from the Yangtze River Estuary demonstrate that the proposed Attention-BiLSTM approach outperforms conventional LSTM in terms of accuracy and stability. Moreover, the real-time capability of the approach gives it a significant potential for use in predicting collisions at the early stages.

Airlines are constantly facing uncertainties such as fluctuating passenger demand, volatile prices and operational disruptions such as reduced airport capacity, unexpected aircraft unavailability, and delayed or cancelled flights. In view of this, increasing numbers of publications have appeared over the last few years dealing with uncertainty in airline scheduling. This paper provides a review of the current state-of-the-art and possible future directions for airline planning and operation under uncertainty. Major approaches, namely robust planning/scheduling and disruption management and their challenges are presented. We explore the application of a novel approach, chance constrained programming (CCP), to deal with uncertainties in airline planning. This approach is extended to compute the expected airline revenue when the ticket price and the passenger demand are uncertain and correlated. We also propose the application of risk management (Conditional Value-at-Risk) to support more robust airline planning. Finally, we discuss possible ways of integrating robust planning with disruption management and suggest an integrated approach which leverages the capabilities of major approaches.

Исследованы особенности течения воздуха через прямоточные лабиринтные уплотнения и построен алгоритм их функционирования в условиях влияния геометрических характеристик зуба. Разработано и экспериментально испытано 22 вида профиля зуба прямой зубчатой решетки. Установлено, что коэффициент текучести повышается с увеличением зазора уплотнения и толщины наконечника зуба и пропорционален высоте зуба, шагу и количеству зубьев. Показано, что при прохождении сжатого воздуха через лабиринтные уплотнения основное внимание следует уделять конструкции первых и последних зубьев в группе для повышения эффекта уплотнения.

Electric vehicles, noted EV, with smaller environmental footprint than traditional gasoline vehicles or hybrids, are growing rapidly worldwide. Several countries such as Norway and Canada have successfully established their EV networks and achieved a significant progress towards their EV deployment. While the new EV technology is becoming popular in developed countries, emerging countries are lacking behind mainly because of the huge investment hurdle to establish EV networks. This paper provides an efficient mathematical model aiming to minimize the total costs involved in establishing an EV network, using real work data from Morocco. A given set of public institutions having a fleet of EVs are first grouped into zones based on clustering algorithms. Mixed integer linear programming model are developed to optimally select charging station locations within these organizations, with an objective to minimize total cost. This paper support the minimization of the investment needed to establish EV networks. The transition towards EV networks can first take place in cities, especially for public institutions fleet that have a fixed and known operating itinerary and schedule, followed by locations among cities. The mathematical models provided through this paper aim to enhance and foster policy makers' ability in making decisions related to the migration towards EV.

This paper presents an experimental study that aimed to recycle steel slag waste as a supplementary material in metakaolin based geopolymer, resulting in a new type of geopolymer composites. The mechanical properties and microstructure analysis of geopolymer composites were carried out. The metakaolin based geopolymer composite (MGC) was prepared by a three-key parameter (the mole ratios of SiO2/Al2O3, Na2O/SiO2, and H2O/Al2O3) composition design method. The flexural strength and compressive strength test results show that the strength increment of MGC after 3 days is very limited. MGC exhibits the typical early strength property on compressive strength. Then, the steel slag powder (SS) was added to replace metakaolin to synthesize the steel slag-metakaolin based geopolymer composite (SSMGC) with the determined mix proportion. The strength test results indicate that the incorporation of SS contributes to the strength increment at a later age than MGC. The XRD patterns, SEM images, and EDS line scanning analysis indicate that SSMGC undergoes hydration in alkaline solution and accumulates hydrated calcium silicate gel with curing age besides the silica-alumina gel generated during geopolymerization. Due to the low content and low activity of steel slag active components, the degree of hydration and the amount of calcium silicate gel are inferior to the geopolymerization and silica-alumina gel caused by metakaolin, and ultimately lead to the relatively lower strength of SSMGC than that of MGC. However, the negative effect of adding SS to MGC on the mechanical strength becomes very small with curing age.

Micro-electromechanical systems (MEMS) Coriolis vibratory ring gyroscope, which utilizes Byran effect to detect rotation, is regarded as a two-dimensional derivative of vibrating shell gyroscope. The MEMS ring gyroscope has a symmetrical structure, higher mechanical sensitivity, better shock resistance and lower temperature sensitivity. Therefore, the ring gyroscope considered as candidates for high-performance MEMS gyroscopes. Moreover, ring resonator preparation is compatible with the recent popular MEMS process technology, which has a higher CSWaP (cost, size, weight, and power). This paper analyzes the progress of research into the MEMS ring gyroscopes. Firstly, working mechanism and different detection methods are introduced; secondly, process techniques and materials are reviewed; thirdly, structural styles of the ring resonators are reported; fourthly, characteristics of the ring resonators are analyzed; fifthly, error suppression and performance improvement strategies of the ring gyroscopes are investigated; and finally, some viewpoints and prospects of the MEMS ring gyroscopes are presented.

It is of critical importance for vessels to detect potentially hazardous situations as early as possible. Therefore, recognition and prediction of vessel motions require effective representations for analysis and clustering of motion trajectories. While short-term motion prediction of moving object is largely achievable, long-term prediction is more useful given the restricted maneuverability of vessels since a vessel cannot abruptly stop, turn or reverse as a land vehicle does. To this end, we propose in this study a long-term vessel motion prediction approach based on a combined trajectory classification and long short-term memory (LSTM) networks framework. As a measure for the similarity between trajectories, we introduce the longest common subsequence (LCS) algorithm to define trajectory similarity when making DBSCAN clustering. The grouped trajectories representing various motion patterns are further modeled via LSTM networks, in which vessel trajectory data are formed by regressing relative motion against current position and then the iterative prediction is applied for long-term prediction. We use the proposed approach for classifying and predicting motions in vessel traffic monitoring domains and test on real AIS data. Experiments show the benefit of this approach for long-term motion predication where parametric models such as Kalman Filters would perform poorly.

In recent years, the emergence and rapid development of tunable mid-infrared new-band ultra-intense ultrashort lasers have opened up a parametric space in the field of strong-field physics that has rarely been explored so far, providing new opportunities to explore new physics, new effects and new applications of ultra-intense ultrashort laser-matter interactions. The development trends and research directions of mid-infrared ultra-intense ultrashort lasers in recent years were summarized in this paper. The four research directions of optical parametric amplification, optical parametric chirp pulse amplification, mid-infrared pulse post-compression and new optical field modulation technology in mid-infrared were analyzed in a comprehensive manner, and the future development trend of mid-infrared ultra-intense ultrashort lasers was also prospected.

This paper aims to investigate the effects of waste glass particle size and curing temperature on the mechanical properties, microstructure and minerology of waste glass-slag-based and waste glass-fly ash-based geopolymers. Specimens synthesized from ground waste bottle glass of three different particle size distributions (D50 = 49.2 μm, 159.1 μm, and 302.1 μm), ground granulated blast furnace slag (GGBS) and Class F fly ash (FFA) were produced by reacting with alkaline activators potassium hydroxide (KOH) and sodium metasilicate nonahydrate (Na2SiO3·9H2O). The geopolymer specimens were cured at four temperatures (20 °C, 50 °C, 80 °C, 100 °C) for the initial 24 h and then at 20 °C. The compressive strength of the cylinders was tested. Scanning Electron Microscope (SEM) and X-Ray Diffraction (XRD) were utilized to evaluate the microstructural and mineral characterization. Curing at 80 °C, the geopolymer made by GGBS and waste glass of D50 = 49.2 μm achieved a compressive strength of 46.5 MPa on the 28th day. The glass powder with D50 larger than 300 μm showed relatively low chemical reactivity and hardly had any reaction with the alkaline solution. An increase of curing temperature in the initial 24 h generally accelerated the kinetics and reached a higher extent of reaction, especially specimens added waste glass. Increasing curing temperature from 50 °C to 80 °C improved the compressive strength of most of specimens containing waste glass effectively. The geopolymers synthesized from the mixture of glass powder and slag achieved much higher compressive strength compared to the ones made with pure glass or slag. This was, however, not observed in Class F fly ash due to low calcium content of Class F fly ash.

Wastewater generated during pesticide synthesis is a potential source of high concentrations of volatile organic compounds (VOCs) emissions, which would cause adverse effects on human health and the environment. Here, we provided a comprehensive study on concentrations, health risks, and olfactory effects of VOCs emitted from a pesticide wastewater storage tank. A total of 21 VOCs were identified, their concentrations ranged from 0.63 to 5023.83 μg/m3. Chlorinated compounds such as trichloroethylene (mean = 2581.29 μg/m3) and dichloromethane (mean = 2309.55 μg/m3) presented the highest concentrations. Both the cumulative chronic toxicities (514) and cancer risks (1.67 × 10−3) of VOCs were up to three orders of magnitude higher than the occupational safety limits. Trichloroethylene contributed the greatest to the cumulative chronic toxicities (88.41%) and cancer risks (74.91%). Benzene was another compound with a high cancer risk of 3.32 × 10−4. Regarding olfactory effects, triethylamine and diethylamine were the dominant contributors with a relative olfactory perception importance of 39.93% and 34.26%, respectively. The results of fuzzy synthetic evaluation revealed that benzene, diethylamine, trichloroethylene, dichloromethane, and triethylamine were the priority compounds caused the overall pollution levels, health risks, and olfactory effects.

A carrier envelope phase stable near-single cycle mid-infrared laser based on optical parametric chirped pulse amplification and hollow-core-fiber compression is demonstrated. 4 ��m laser pulses with 11.8 mJ energy are delivered from a KTA based OPCPA with 100 Hz repetition rate, and compressed to be ~105 fs by a two-grating compressor with efficiency over 50%. Subsequently, the pulse spectrum is broadened by employing a krypton gas-filled hollow-core-fiber (HCF). Then, the pulse duration is further compressed to 21.5 fs through a CaF2 bulk material with energy of 2.6 mJ and stability of 0.9% RMS, which is about 1.6 cycle for 4 ��m laser pulse. The near-single cycle 4 ��m laser pulse CEP is passively stabilized with ~370 mrad based on a CEP stable 4 ��m OPA injection.

© 2015, Springer-Verlag Berlin Heidelberg. Land surface processes play an important role in the East Asian Summer Monsoon (EASM) system. Parameterization schemes of land surface processes may cause uncertainties in regional climate model (RCM) studies for the EASM. In this paper, we investigate the sensitivity of a RCM to land surface parameterization (LSP) schemes for long-term simulation of the EASM. The Weather Research and Forecasting (WRF) Model coupled with four different LSP schemes (Noah-MP, CLM4, Pleim–Xiu and SSiB), hereafter referred to as Sim-Noah, Sim-CLM, Sim-PX and Sim-SSiB respectively, have been applied for 22-summer EASM simulations. The 22-summer averaged spatial distributions and strengths of downscaled large-scale circulation, 2-m temperature and precipitation are comprehensively compared with ERA-Interim reanalysis and dense station observations in China. Results show that the downscaling ability of RCM for the EASM is sensitive to LSP schemes. Furthermore, this study confirms that RCM does add more information to the EASM compared to reanalysis that imposes the lateral boundary conditions (LBC) because it provides 2-m temperature and precipitation that are with higher resolution and more realistic compared to LBC. For 2-m temperature and monsoon precipitation, Sim-PX and Sim-SSiB simulations are more consistent with observation than simulations of Sim-Noah and Sim-CLM. To further explore the physical and dynamic mechanisms behind the RCM sensitivity to LSP schemes, differences in the surface energy budget between simulations of Ens-Noah-CLM (ensemble mean averaging Sim-Noah and Sim-CLM) and Ens-PX-SSiB (ensemble mean averaging Sim-PX and Sim-SSiB) are investigated and their subsequent impacts on the atmospheric circulation are analyzed. It is found that the intensity of simulated sensible heat flux over Asian continent in Ens-Noah-CLM is stronger than that in Ens-PX-SSiB, which induces a higher tropospheric temperature in Ens-Noah-CLM than in Ens-PX-SSiB over land. The adaptive modulation of geopotential height gradients affects wind field (through geostrophic balance) simulation especially at lower levels, which subsequently affects the simulation of large-scale circulation, 2-m temperature and monsoon precipitation as well as RCM’s downscaling ability.

Urban sprawl significantly impacts ecosystem services and functions. The exact impacts, however are difficult to quantify and are often neglected in policy making. The evaluation of ecosystem services is conducive to clarifying the ecological and environmental changes caused by urbanization. The objective of this study is to investigate variations in ecosystem services in response to land use changes during urbanization. The aim is to provide useful information and advice for policy makers concerned with sustainable development. Shenzhen, one of the fastest growing metropolitan areas in China, is selected as the study area. A fast evaluation method for ecological service values based on land use change was proposed and applied to the city for 1996, 2000 and 2004. The total value of ecosystem services in Shenzhen was 2776.0 million Yuan in 1996, 2911.4 million Yuan in 2000 and 2544.7 million Yuan in 2004 respectively, with a decrease of 231.3 million Yuan from 1996 to 2004 mainly due to the decreasing areas of woodland, wetland and water body. The combined ecosystem service value of woodland, wetland, water body and orchard was over 90% of the total value. Water supply and waste treatment were the top two service functions with high service value, contributing about 40% of the total service value. The results suggest that a reasonable land use plan should be made with emphasis on protecting wetland, water body and woodland, which have the highest ecosystem service value.

This paper presents a solution algorithm and effective mathematical formulations for short-term scheduling of crude oil unloading, storage, and processing with multiple oil types, multiple berths, and multiple processing units. The problem can be formulated as a mixed-integer nonlinear programming (MINLP) model. However, solving a MINLP model directly results in inconsistency in solution quality and time. This paper proposes a solution algorithm that iteratively solves two mixed-integer linear programming (MIP) models and a nonlinear programming (NLP) model, resulting in better quality, stability, and efficiency than solving the MINLP model directly. The proposed MIP formulations disaggregate the three-dimensional binary decision variables in traditional formulations using two-dimensional binary variables, significantly reduce the total number of binary variables required, and hence improve the overall solution time. For handling large-scale problems, heuristics are included in the formulations to further...

Geographic modeling refers to spatially explicit modeling of the probability of presence for a specific geographic event. The problem of geographic one-class data (GOCD) is that absence data are often not available or are difficult to obtain, which challenges traditional statistical modeling methods because these methods require both presence and absence data. Therefore, the objective of this research is to address the challenges raised by GOCD, and develop effective techniques for modeling the spatial distributions of geographic events using GOCD. A novel presence and background learning (PBL) algorithm was developed to model the probability of presence conditional on predictor variables, and a novel accuracy assessment method was developed for model selection, threshold selection, and model evaluation. These methods require presence-background data, without requiring absence data, and hence they are appropriate for GOCD. To investigate their effectiveness, the novel PBL and accuracy assessment methods were applied in ecological niche modeling using simulated datasets, and one-class remote sensing classification using real datasets. Experimental results show that the PBL method is successful in modeling the conditional probability of presence, and it can produce high classification accuracy, outperforming existing methods such as the one-class support vector machines and maximum entropy; the new accuracy assessment method performs similarly to the traditional F-measure that requires both presence and absence data, and it shows promise in model selection, threshold selection, and accuracy assessment. Finally, I conclude that the proposed methods are promising for addressing the major challenges raised by GOCD. Because they do not require absence data, the proposed methods will have important applications in geographic modeling with GOCD.

Airlines are constantly facing operational disruptions such as reduced airport capacity because of bad weather or strikes, unexpected aircraft unavailability due to mechanical failures, and delayed or cancelled flights. In view of this, ROADEF organized a worldwide challenge to explore the problems encountered in real world airlines when disruptions happen and find approaches to tackle them. In this paper, a new continuous time aircraft routing model is developed which can minimize aircraft delay cost accurately and efficiently handle all types of disruptions encountered in ROADEF. Applying a new decomposition algorithm, near optimal solutions for aircraft routing can be obtained. A passenger re-accommodation model is solved subsequently using the results from the aircraft routing model as input. Competitive results are obtained applying the proposed approach to instances provided by ROADEF.

Tau is a microtubule-associated protein that promotes microtubule assembly and stability. In Alzheimer's disease and related tauopathies, tau fibrillizes and aggregates into neurofibrillary tangles. Recently, oleocanthal isolated from extra virgin olive oil was found to display non-steroidal anti-inflammatory activity similar to ibuprofen. As our unpublished data indicates an inhibitory effect of oleocanthal on amyloid beta peptide fibrillization, we reasoned that it might inhibit tau fibrillization as well. Herein, we demonstrate that oleocanthal abrogates fibrillization of tau by locking tau into the naturally unfolded state. Using PHF6 consisting of the amino acid residues VQIVYK, a hexapeptide within the third repeat of tau that is essential for fibrillization, we show that oleocanthal forms an adduct with the lysine via initial Schiff base formation. Structure and function studies demonstrate that the two aldehyde groups of oleocanthal are required for the inhibitory activity. These two aldehyde groups show certain specificity when titrated with free lysine and oleocanthal does not significantly affect the normal function of tau. These findings provide a potential scheme for the development of novel therapies for neurodegenerative tauopathies.

Boilers running at supercritical pressure with large capacity have been widely used in power generation technology. How to keep the safety of heat transfer in the riser system is an important issue. However, it is difficult to study heat transfer for the vertical smooth tubes in a boiler at sub-critical or supercritical pressure since the thermodynamic properties of water is very complex and sensitive to temperature and pressure near the critical point. Hot-state experiment of heat transfer for the tubes of diameter used in a boiler is of great value for the design of membrane wall. In order to study the heat transfer of vertical smooth tubes running at low heat flux and low mass flux for sub-critical CFB boilers, a near-supercritical pressure water test bed was built in Tsinghua University. The designed pressure is 21MPa, the mass flux of water is 550kg/m2s, and the heat flux is 136kW/m. In this paper, the design and structure of the test bed is introduced. The experimental result is analyzed, more experimental work is needed in future research.

By using electroabsorption method, we find that the built-in field (similar 105 V?cm-1) in the a-Si sublayers of a-Si:H/a-SiNx:H multilayers points towards the sample surfaces. We ascribe the internal field to the asymmetric distribution of defect states which exist primarily in the silicon side of the silicon-on-nitride interfaces, owing to the large repellent lattice stress there. We also find that the built-in potential depends strongly on x, with a maximum value near x = 1.0. We intend to explain it as a result of structural softening as more N atoms are introduced into Si networks in the range of x > 1.0.

By using electroabsorption method, we find that the built-in fields in the a-Si sublayers of a-Si/a-SiNx:H multilayers (x = 0.68 − 1.33) points towards the sample surfaces and depends obviously on composition x with a maximum value near x = 1.0. We ascribe the built-in fields to the asymmetric distribution of defect states which exist primarily on the silicon side of the silicon-on-nitride interfaces, owing to the large repellent stress there. The interfacial structure might be softened with increasing x in the range of x > 1.0 as the a-SiNx sublayers starts transforming to a-Si3N4.

Abtsract A new analytical method called “marginal value analysis” is used in this paper to provide economic information for all stream flows inside a refinery. Three types of marginal values are defined that represent respectively the de-bottlenecking effect, production cost and product value of an intermediate material flow. Important insights are generated using the analysis to find production bottlenecks, assist in decision making and price intermediate materials, etc. Sensitivity analysis and parametric programming are studied in this paper to provide more comprehensive information for pricing, retrofitting and investment evaluation. Several case studies are used to illustrate the research on marginal value analysis, sensitivity analysis and parametric programming.

This paper presents two artificial neural networks (ANN) to predict pour point (PP) and cold filter plugging point (CFPP) of diesel oil and octane number of gasoline respectively, using back-propagation (BP) algorithm with actual plant data. Appropriate topologies of ANNs were obtained and the training rates and momentum in ANNs were discussed. Detailed models of the most important process units such as Crude Distillation Unit (CDU) and Fluidized-bed Catalytic Cracking (FCC) in a refinery were developed. The developed ANN models and CDU, FCC models were integrated into the refinery planning model to replace the traditional linear processing unit models and the linear blending rule used in gasoline blending and diesel oil blending. With these accurate unit models and blending models, the planning model gives us much more reliable results which can be used for refinery business decision and configuration retrofitting, etc. Several case studies were used to illustrate the capabilities and effectiveness of the integrated model proposed.

Carbon fiber (CF) was modified by air plasma at 30 Pa and 200 W with different treatment times. Interlaminar shear strength (ILSS) of CF/BMI composites suggested that air plasma-treated CFs showed better adhesion with BMI resins than the untreated resins; the maximum increment was 20.4% after plasma treatment for 900 s. Meanwhile, we investigated the surface changes of CFs using X-ray photoelectron spectroscopy (XPS) and atomic force microscopy (AFM). The results indicated that air plasma treatment roughened the CF surfaces and improved the polarity of CFs by enhancing the polar group content. These changes were believed to be the major reasons for the improvement of interfacial adhesion. The fracture morphology observed by scanning electron microscopy (SEM) indicated that the fracture failure mode changed from adhesive failure to cohesive failure. After boiling in water for 48 h, the water absorption of the composites was low while the ILSS of the composites retained about 90%, which implied that the CF/BMI composites had good humidity resistance properties.

This paper studies a variant of the three-dimensional bin packing problem (3D-BPP), where the bin height can be adjusted to the cartons it packs. The bins and cartons to be packed are assumed rectangular in shape. The cartons are allowed to be rotated into any one of the six positions that keep the carton edges parallel to the bin edges. This greatly increases the difficulty of finding a good solution since the search space expands significantly comparing to the 3D-BPP where the cartons have fixed orientations. A mathematical (mixed integer programming) approach is modified based on [Chen, C. S., Lee, S. M., Shen, Q. S., 1995. An analytical model for the container loading problem. European Journal of Operational Research 80 (1), 68-76] and numerical experiments indicate that the mathematical approach is not suitable for the variable bin height 3D-BPP. A special bin packing algorithm based on packing index is designed to utilize the special problem feature and is used as a building block for a genetic algorithm designed for the 3D-BPP. The paper also investigates the situation where more than one type of bin are used and provides a heuristic for packing a batch of cartons using the genetic algorithm. Numerical experiments show that our proposed method yields quick and satisfactory results when benchmarked against the actual packing practice and the MIP model with the latest version of CPLEX.

Container terminal (CT) operations are often bottlenecked by slow YC (yard crane) movements. Efficient YC scheduling to reduce the PM waiting time is therefore critical in increasing a CT's throughput. This paper develops an efficient continuous time MILP model for YC scheduling. The model treats realistic operational constraints such as multiple inter-crane interference, fixed YC separation distances, simultaneous container storage/retrievals, realistic YC acceleration/deceleration stages and gantry time, and require far fewer integer variables than previous work. The model significantly improves the solution quality compared to the existing discrete time models and other heuristics found in the literature. Using heuristics and a rolling-horizon algorithm, our model can solve actual container yard (CY) problems quickly and robustly in polynomial time. Also, to cope with the last minute container arrivals which can disrupt routine CT operations, two methods for handling these last minute job insertions are discussed and compared. Refereed/Peer-reviewed