In a world in which global trade is at risk, where warehouses and airports, shipping lanes and seaports try to guard against the likes of Al Qaeda and Somali pirates, and natural disaster can disrupt ...the flow of goods, even our "stuff" has a political life. The high stakes of logistics are not surprising, Deborah Cowen reveals, if we understand its genesis in war.
InThe Deadly Life of Logistics, Cowen traces the art and science of logistics over the last sixty years, from the battlefield to the boardroom and back again. Focusing on choke points such as national borders, zones of piracy, blockades, and cities, she tracks contemporary efforts to keep goods circulating and brings to light the collective violence these efforts produce. She investigates how the old military art of logistics played a critical role in the making of the global economic order-not simply the globalization of production, but the invention of the supply chain and the reorganization of national economies into transnational systems. While reshaping the world of production and distribution, logistics is also actively reconfiguring global maps of security and citizenship, a phenomenon Cowen charts through the rise of supply chain security, with its challenge to long-standing notions of state sovereignty and border management.
Though the object of corporate and governmental logistical efforts is commodity supply,The Deadly Life of Logisticsdemonstrates that they are deeply political-and, considered in the context of the long history of logistics, deeply indebted to the practice of war.
The cargo handling capacity of a port is the most basic and important indicator of port size. Based on the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) nighttime ...light data and panel model, this study attempts to estimate the cargo handling capacity of 28 coastal ports in China using satellite remote sensing. The study confirmed that there is a very close correlation between DMSP-OLS nighttime light data and the cargo handling capacity of the ports. Based on this correlation, the panel data model was established for remote sensing-based estimation of cargo handling capacity at the port and port group scales. The test results confirm that the nighttime light data can be used to accurately estimate the cargo handling capacity of Chinese ports, especially for the Yangtze River Delta Port Group, Pearl River Delta Port Group, Southeast Coastal Port Group, and Southwest Coastal Port Group that possess huge cargo handling capacities. The high accuracy of the model reveals that the remote sensing analysis method can make up for the lack of statistical data to a certain extent, which helps to scientifically analyze the spatiotemporal dynamic changes of coastal ports, provides a strong basis for decision-making regarding port development, and more importantly provides a convenient estimation method for areas that have long lacked statistical data on cargo handling capacity.
The use of di(2‐pyridyl)ketone in subcomponent self‐assembly is introduced. When combined with a flexible triamine and zinc bis(trifluoromethanesulfonyl)imide, this ketone formed a new Zn4L4 ...tetrahedron 1 bearing twelve uncoordinated pyridyl units around its metal‐ion vertices. The acid stability of 1 was found to be greater than that of the analogous tetrahedron 2 built from 2‐formylpyridine. Intriguingly, the peripheral presence of additional pyridine rings in 1 resulted in distinct guest binding behavior from that of 2, affecting guest scope as well as binding affinities. The different stabilities and guest affinities of capsules 1 and 2 enabled the design of systems whereby different cargoes could be moved between cages using acid and base as chemical stimuli.
A strategy for improving the acid resistance of a tetrahedral cage has been developed by incorporating additional free pyridyl units on its vertices. The guest binding properties of the cage are also altered compared to the analogous tetrahedron without these peripheral groups, allowing the functions of complete cargo delivery and exchange between the two capsules by using acid and base as chemical stimuli.
The International Maritime Organization introduced two energy efficiency indexes to reduce greenhouse gas emissions from ships. One of the short-run solutions is to reduce ship speed. The present ...research calculates the cost-effectiveness of reducing CO
2
emissions and the improved Energy Efficiency Design Index (EEDI) corresponded to the reduced Energy Efficiency Operational Indicator (EEOI) due to slow steaming. As a case study, RO-RO cargo vessel has been investigated. Reducing ship speed by 10% and 40% will reduce CO
2
emission by 27.05% and 78.39% with cost-effectiveness of 121.2 $/ton CO
2
and 287.6 $/ton CO
2
, respectively. The attained EEDI values will be improved by 2.04% and 35.81% with the reduced EEOI values by 8.76% and 70.65%, respectively. Although ship slow steaming by 40% would comply with the required EEDI for the first and the second phases, the complied EEDI value needs to be further reduced by 7% in the year 2025.
FPSO (floating, production, storage and offloading) units are widely used in the offshore oil and gas industry. Generally, FPSOs have excellent oil storage capacity owing to their huge oil cargo ...holds. The volume and distribution of stored oil in the cargo holds influence the strain level of hull girder, especially at critical positions of FPSO. However, strain prediction using structural analysis tools is computationally expensive and time consuming. In this study, a prediction tool based on back-propagation (BP) neural network called GAIFOA-BP is proposed to predict the strain values of concerned positions of an FPSO model under different oil storage conditions. The GAIFOA-BP combines BP model and GAIFOA which is a combination of genetic algorithm (GA) and an improved fruit fly optimization algorithm (IFOA). Results from three benchmark tests show that the GAIFOA-BP model has a remarkable performance. Subsequently, a total of 81 sets of training data and 25 sets of testing data are obtained from experiment using fiber Bragg grating (FBG) sensors installed on the surface of an FPSO model. The numerical results show that the GAIFOA-BP is capable of predicting the strain values with higher accuracy as compared with other BP models. Finally, the reserved GAIFOA-BP model is utilized to predict the strain values under the inputs of a 10-day time series of volume and distribution of stored oil. The predicted strain results are further used to calculate the fatigue consumption of measurement points.
•The stored oil in the cargo holds of FPSO influences the strain level of hull girder.•A prediction tool based on back-propagation (BP) neural network called GAIFOA-BP is proposed.•The proposed GAIFOA-BP is used to predict the strain values of concerned positions of FPSO model.•The numerical results show that the GAIFOA-BP can predict the strain values with high accuracy.•The proposed approach can be further used to calculate the fatigue consumption of measurement points.
Cargo-bearing unmanned aerial vehicles (UAVs) have tremendous potential to assist humans by delivering food, medicine, and other supplies. For time-critical cargo delivery tasks, UAVs need to be able ...to quickly navigate their environments and deliver suspended payloads with bounded load displacement. As a constraint balancing task for joint UAV-suspended load system dynamics, this task poses a challenge. This article presents a reinforcement learning approach for aerial cargo delivery tasks in environments with static obstacles. We first learn a minimal residual oscillations task policy in obstacle-free environments using a specifically designed feature vector for value function approximation that allows generalization beyond the training domain. The method works in continuous state and discrete action spaces. Since planning for aerial cargo requires very large action space (over 106 actions) that is impractical for learning, we define formal conditions for a class of robotics problems where learning can occur in a simplified problem space and successfully transfer to a broader problem space. Exploiting these guarantees and relying on the discrete action space, we learn the swing-free policy in a subspace several orders of magnitude smaller, and later develop a method for swing-free trajectory planning along a path. As an extension to tasks in environments with static obstacles where the load displacement needs to be bounded throughout the trajectory, sampling-based motion planning generates collision-free paths. Next, a reinforcement learning agent transforms these paths into trajectories that maintain the bound on the load displacement while following the collision-free path in a timely manner. We verify the approach both in simulation and in experiments on a quadrotor with suspended load and verify the method's safety and feasibility through a demonstration where a quadrotor delivers an open container of liquid to a human subject. The contributions of this work are two-fold. First, this article presents a solution to a challenging, and vital problem of planning a constraint-balancing task for an inherently unstable non-linear system in the presence of obstacles. Second, AI and robotics researchers can both benefit from the provided theoretical guarantees of system stability on a class of constraint-balancing tasks that occur in very large action spaces.
Abstract Naturally generated lipid nanoparticles termed extracellular vesicles (EVs) hold significant promise as engineerable therapeutic delivery vehicles. However, active loading of protein cargo ...into EVs in a manner that is useful for delivery remains a challenge. Here, we demonstrate that by rationally designing proteins to traffic to the plasma membrane and associate with lipid rafts, we can enhance loading of protein cargo into EVs for a set of structurally diverse transmembrane and peripheral membrane proteins. We then demonstrate the capacity of select lipid tags to mediate increased EV loading and functional delivery of an engineered transcription factor to modulate gene expression in target cells. We envision that this technology could be leveraged to develop new EV-based therapeutics that deliver a wide array of macromolecular cargo.
The flow of cargo vesicles along the secretory pathway requires concerted action among various regulators. The COPII complex, assembled by the activated SAR1 GTPases on the surface of the endoplasmic ...reticulum, orchestrates protein interactions to package cargos and generate transport vesicles en route to the Golgi. The dynamic nature of COPII, however, hinders analysis with conventional biochemical assays. Here we apply proximity-dependent biotinylation labeling to capture the dynamics of COPII transport in cells. When SAR1B was fused with a promiscuous biotin ligase, BirA*, the fusion protein SAR1B-BirA* biotinylates and thus enables the capture of COPII machinery and cargos in a GTP-dependent manner. Biochemical and pulse–chase imaging experiments demonstrate that the COPII coat undergoes a dynamic cycle of engagement–disengagement with the transmembrane cargo receptor LMAN1/ERGIC53. LMAN1 undergoes a process of concentrative sorting by the COPII coat, via a dimeric sorting code generated by oligomerization of the cargo receptor. Similar oligomerization events have been observed with other COPII sorting signals, suggesting that dimeric/multimeric sorting codes may serve as a general mechanism to generate selectivity of cargo sorting.
Wang, J., 2019. Ship dispatching system of bulk cargo port based on multi-objective genetic algorithm. In: Gong, D.; Zhu, H., and Liu, R. (eds.), Selected Topics in Coastal Research: Engineering, ...Industry, Economy, and Sustainable Development. Journal of Coastal Research, Special Issue No. 94, pp. 406–410. Coconut Creek (Florida), ISSN 0749-0208. Existing researchers in the field of ship scheduling have focused their research on the optimization of a single target for port ship scheduling. This method may be applicable to container ports with a uniform loading and unloading process, but for a wide variety of cargo types and complex loading and unloading processes. For bulk cargo ports, the single-target model is not fully applicable. This paper will start with analyzing the business characteristics of ship scheduling in bulk cargo ports, and optimize the target, the analysis and quantification of the influencing factors and the model creation according to the actual business characteristics. Combine the advantages of multi-objective optimization and genetic algorithm to solve the model and make up for the present. The ship dispatching optimization method cannot meet the shortage of the complex status of the bulk cargo port, promote and deepen the intersection and penetration of the ship scheduling method and the optimization theory, and play a guiding role in the actual ship dispatching work of the port.
PDF
