Prescriptive analytics for a maritime routing problem

Port state control (PSC) serves as the final defense against substandard ships in maritime transportation. The port state control officer (PSCO) routing problem involves selecting ships for inspection and determining the inspection sequence for available PSCOs, aiming to identify the highest number of deficiencies. Port authorities face this problem daily, making decisions without prior knowledge of ship conditions. Traditionally, a predict-then-optimize framework is employed, but its machine learning (ML) models’ loss function fails to account for the impact of predictions on the downstream optimization problem, potentially resulting in suboptimal decisions. We adopt a decision-focused learning framework, integrating the PSCO routing problem into the ML models’ training process. However, as the PSCO routing problem is NP-hard and plugging it into the training process of ML models requires that it be solved numerous times, computational complexity and scalability present significant challenges. To address these issues, we first convert the PSCO routing problem into a compact model using undominated inspection templates, enhancing the model’s solution efficiency. Next, we employ a family of surrogate loss functions based on noise-contrastive estimation (NCE) for the ML model, requiring a solution pool treating suboptimal solutions as noise samples. This pool represents a convex hull of feasible solutions, avoiding frequent reoptimizations during the ML model’s training process. Through computational experiments, we compare the predictive and prescriptive qualities of both the two-stage framework and the decision-focused learning framework under varying instance sizes. Our findings suggest that accurate predictions do not guarantee good decisions; the decision-focused learning framework’s performance may depend on the optimization problem size and the training dataset size; and using a solution pool containing noise samples strikes a balance between training efficiency and decision performance

Prescriptive analytics for a maritime routing problem

Port state control (PSC) serves as the final defense against substandard ships in maritime transportation. The port state control officer (PSCO) routing problem involves selecting ships for inspection and determining the inspection sequence for available PSCOs, aiming to identify the highest number of deficiencies. Port authorities face this problem daily, making decisions without prior knowledge of ship conditions. Traditionally, a predict-then-optimize framework is employed, but its machine learning (ML) models’ loss function fails to account for the impact of predictions on the downstream optimization problem, potentially resulting in suboptimal decisions. We adopt a decision-focused learning framework, integrating the PSCO routing problem into the ML models’ training process. However, as the PSCO routing problem is NP-hard and plugging it into the training process of ML models requires that it be solved numerous times, computational complexity and scalability present significant challenges. To address these issues, we first convert the PSCO routing problem into a compact model using undominated inspection templates, enhancing the model’s solution efficiency. Next, we employ a family of surrogate loss functions based on noise-contrastive estimation (NCE) for the ML model, requiring a solution pool treating suboptimal solutions as noise samples. This pool represents a convex hull of feasible solutions, avoiding frequent reoptimizations during the ML model’s training process. Through computational experiments, we compare the predictive and prescriptive qualities of both the two-stage framework and the decision-focused learning framework under varying instance sizes. Our findings suggest that accurate predictions do not guarantee good decisions; the decision-focused learning framework’s performance may depend on the optimization problem size and the training dataset size; and using a solution pool containing noise samples strikes a balance between training efficiency and decision performance

Research on Method of Health Assessment about the Destruction Equipment for High-risk Hazardous Chemical Waste

AbstractThe destroying tasks of high-risk hazardous chemical waste have a strict request to the health status of destruction equipment.The paper proposes the health status classification method based on time between failures for the destruction of equipment, set up health status assessment model based on Time-varying Bayesian Networks and the time slice, which can take advantage of history fault information and health status monitoring indicator information to health status assessment for the destruction equipment, and which provides a reliable and safe evaluation method

Boosting oxygen reduction and hydrogen evolution at the edge sites of a web-like carbon nanotube-graphene hybrid

Identifying catalytically active sites in graphene-based catalysts is critical to improved oxygen reduction reaction (ORR) electrocatalysts for fuel-cell applications. To generate abundant active edge sites on graphene-based electrocatalysts for superior electrocatalytic activity, rather than at their basal plane, has been a challenge. A new type of ORR electrocatalyst produced using fluidization process and based on a three-dimensional hybrid consisting of horizontally-aligned carbon nanotube and graphene (CNT-G), featured abundant active edge sites and a large specific surface area (863\ua0m\ua0g). The Pt-doped CNT-G exhibited an increase of about 55% in mass activity over the state-of-the-art commercial Pt/C and about 164% over Pt/N-graphene in acidic medium, and approximately 54% increase in kinetic limiting current than the Pt/C at low overpotential in alkaline medium. The higher mass activity indicates that less Pt is required for the same performance, reducing the cost of fuel cell electrocatalyst. In hydrogen evolution reaction (HER), both the metal-free CNT-G and Pt/CNT-G exhibited superior electrocatalytic activity compared to N-doped graphene and commercial Pt/C, respectively

How does the coupling coordination between high-quality development and eco-environmental carrying capacity in the Yellow river basin over time?

Introduction: The Yellow River Basin is an important national energy base and ecological protection area, and it is of great significance to promote the coordinated development of high-quality development and eco-environmental carrying capacity in the region.Methods: Taking the 73 prefecture-level cities along the Yellow River as the study unit, this paper measures the changes of high-quality development level and eco-environmental carrying capacity of municipalities from 2005 to 2020, using the coupling coordination degree model and fuzzy logic algorithm.Results and discussion: 1) The capital city and its surrounding cities have a high level of high-quality development, with the lower and middle reaches of the Yellow River having higher levels than the upper reaches. From 2005 to 2020, the level of high-quality development showed an upward trend. 2) The eco-environmental carrying capacity of cities in the lower reaches is higher than that in the upper reaches. From 2005 to 2020, the eco-environmental carrying capacity of cities in the lower reaches of the Yellow River increased first and then decreased. 3) The provincial capital cities have a high degree of coupling coordination, with cities in the lower reaches having a higher level than those in the middle and upper reaches. A high degree of coupling coordination reduces spatial differences, but dominated by primary coordination. 4) From 2005 to 2020, the eco-environmental carrying capacity tended to be coordinated with the high-quality development, close to a high level and system optimization. In the end, we conclude with policy recommendations to promote high-quality urban development and harmony between people and nature in the region

Pressure-stabilized divalent ozonide CaO3 and its impact on Earth's oxygen cycles.

High pressure can drastically alter chemical bonding and produce exotic compounds that defy conventional wisdom. Especially significant are compounds pertaining to oxygen cycles inside Earth, which hold key to understanding major geological events that impact the environment essential to life on Earth. Here we report the discovery of pressure-stabilized divalent ozonide CaO3 crystal that exhibits intriguing bonding and oxidation states with profound geological implications. Our computational study identifies a crystalline phase of CaO3 by reaction of CaO and O2 at high pressure and high temperature conditions; ensuing experiments synthesize this rare compound under compression in a diamond anvil cell with laser heating. High-pressure x-ray diffraction data show that CaO3 crystal forms at 35 GPa and persists down to 20 GPa on decompression. Analysis of charge states reveals a formal oxidation state of -2 for ozone anions in CaO3. These findings unravel the ozonide chemistry at high pressure and offer insights for elucidating prominent seismic anomalies and oxygen cycles in Earth's interior. We further predict multiple reactions producing CaO3 by geologically abundant mineral precursors at various depths in Earth's mantle

Solution Structure of Tensin2 SH2 Domain and Its Phosphotyrosine-Independent Interaction with DLC-1

Background: Src homology 2 (SH2) domain is a conserved module involved in various biological processes. Tensin family member was reported to be involved in tumor suppression by interacting with DLC-1 (deleted-in-liver-cancer-1) via its SH2 domain. We explore here the important questions that what the structure of tensin2 SH2 domain is, and how it binds to DLC-1, which might reveal a novel binding mode. Principal Findings: Tensin2 SH2 domain adopts a conserved SH2 fold that mainly consists of five b-strands flanked by two a-helices. Most SH2 domains recognize phosphorylated ligands specifically. However, tensin2 SH2 domain was identified to interact with nonphosphorylated ligand (DLC-1) as well as phosphorylated ligand. Conclusions: We determined the solution structure of tensin2 SH2 domain using NMR spectroscopy, and revealed the interactions between tensin2 SH2 domain and its ligands in a phosphotyrosine-independent manner

Ni-based bimetallic heterogeneous catalysts for energy and environmental applications

Bimetallic catalysts have attracted extensive attention for a wide range of applications in energy production and environmental remediation due to their tunable chemical/physical properties. These properties are mainly governed by a number of parameters such as compositions of the bimetallic systems, their preparation method, and their morphostructure. In this regard, numerous efforts have been made to develop “designer” bimetallic catalysts with specific nanostructures and surface properties as a result of recent advances in the area of materials chemistry. The present review highlights a detailed overview of the development of nickel-based bimetallic catalysts for energy and environmental applications. Starting from a materials science perspective in order to obtain controlled morphologies and surface properties, with a focus on the fundamental understanding of these bimetallic systems to make a correlation with their catalytic behaviors, a detailed account is provided on the utilization of these systems in the catalytic reactions related to energy production and environmental remediation. We include the entire library of nickel-based bimetallic catalysts for both chemical and electrochemical processes such as catalytic reforming, dehydrogenation, hydrogenation, electrocatalysis and many other reactions