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

Copper Price Discovery on Comex, the LME and the SHFE, 2001-2013

Over the past two decades, China has come to dominate international commerce in copper. The importance of the Shanghai Futures Exchange(SHFE) has increased in response to this development. We look at the distribution of price discovery between the SHFE and the two historically important copper futures exchanges, Comex and the LME. The resultsindicate that it is Comex, followed by the SHFE, not the LME which plays the most important role in copper price discovery. We also highlighta number of problems associated with both the calculation and interpretation of the standard IS and PT price discovery measures when used to look at overlapping price change on non-synchronous markets.The results offer a clearer interpretation in terms of trading slots (European, North American and Asian trading days) than in terms of exchanges

The theory of magnetic field induced domain-wall propagation in magnetic nanowires

A global picture of magnetic domain wall (DW) propagation in a nanowire driven by a magnetic field is obtained: A static DW cannot exist in a homogeneous magnetic nanowire when an external magnetic field is applied. Thus, a DW must vary with time under a static magnetic field. A moving DW must dissipate energy due to the Gilbert damping. As a result, the wire has to release its Zeeman energy through the DW propagation along the field direction. The DW propagation speed is proportional to the energy dissipation rate that is determined by the DW structure. An oscillatory DW motion, either the precession around the wire axis or the breath of DW width, should lead to the speed oscillation.Comment: 4 pages, 2 figure

Perceptions and Barriers of Survivorship Care in Asia: Perceptions From Asian Breast Cancer Survivors.

PurposeWith the long-term goal to optimize post-treatment cancer care in Asia, we conducted a qualitative study to gather in-depth descriptions from multiethnic Asian breast cancer survivors on their perceptions and experiences of cancer survivorship and their perceived barriers to post-treatment follow-up.MethodsTwenty-four breast cancer survivors in Singapore participated in six structured focus group discussions. The focus group discussions were voice recorded, transcribed verbatim, and analyzed by thematic analysis.ResultsBreast cancer survivors were unfamiliar with and disliked the term "survivorship," because it implies that survivors had undergone hardship during their treatment. Cognitive impairment and peripheral neuropathy were physical symptoms that bothered survivors the most, and many indicated that they experienced emotional distress during survivorship, for which they turned to religion and peers as coping strategies. Survivors indicated lack of consultation time and fear of unplanned hospitalization as main barriers to optimal survivorship care. Furthermore, survivors indicated that they preferred receipt of survivorship care at the specialty cancer center.ConclusionBudding survivorship programs in Asia must take survivor perspectives into consideration to ensure that survivorship care is fully optimized within the community

Molecular characterizations of PCR-positive Mycoplasma pneumoniae specimen collected from Australia and China.

Mycoplasma pneumoniaeis an important cause of community-acquired pneumonia (CAP). In this study,M. pneumoniae strains in PCR-positive specimens collected from patients in Sydney, Australia (30 samples), and Beijing, China (83 samples), were characterized using multilocus variable-number tandem-repeat (VNTR) analysis (MLVA), P1-restriction fragment length polymorphism (RFLP) analysis, and sequencing of domain V of the 23S rRNA gene to compare genotype distribution and macrolide resistance rates between locations. Eighteen distinct MLVA types were identified in specimens from Sydney, of which 10 were known (types E, G, J, M, N, P, U, V, S, and X) and 8 previously unknown. Strains were equally distributed between P1-RFLP type 1 and type 2 variants. Among samples from Beijing, MLVA types E, G, J, P, U, X, and Z and four new types were identified. Most specimens belonged to P1-RFLP type 1. A nomenclature based on five VNTR loci is proposed to designate MLVA patterns. Macrolide resistance-associated mutations were identified in only 1 of 30 specimens (3.3%) from Sydney and 71 of 83 (85.5%) from Beijing (P<0.05). This study demonstrated that although multiple individualM. pneumoniaestrains were circulating in Beijing, the genotypes were less diverse than those in Sydney. However, the greatest regional difference was in the incidence of macrolide resistance, which may reflect differences in antibiotic use and/or measures in resistance controlthe Beijing Natural Science Foundation (7112019) and the Beijing City talent training project fund (20071 A0303200118

Predictors of employment in bipolar disorder : a systematic review

Background Severe work impairment can be present for a considerable proportion of the course of bipolar disorder (BD) and is costly for governments, services and individuals. Understanding predictors of employment in BD is therefore crucial as some may be susceptible to interventions. We conducted a systematic review of prospective studies in order to identify predictors of employment in people with BD. Methods We searched Medline, PsychInfo, EMBASE and Web of Science databases, hand searched 3 journals and used predetermined criteria to select papers for full text inclusion. Sixty seven papers were identified. Nine met inclusion criteria, with a total sample of 3184. Results Studies included in this review identified cognitive deficits (67%, n=4), depression (43%, n=3) and level of education (33%, n=2) as predictors of employment in BD patients. Bipolar depression not only affects whether someone is employed but also time off work. Even sub-syndromal depression appears to damage employment prospects. Verbal memory and executive functioning appear to be predictors of work functioning. Limitations Conclusions are based on a relatively small number of studies and are therefore subject to change with the addition of further studies. A formal meta-regression was not possible due to differences between measures of employment and work functioning. Conclusions Better assessment and management of depression and cognitive difficulties could improve the occupational functioning of BD patients. There is a need for high quality longitudinal studies specifically designed to investigate predictors of employment in large bipolar disorder samples

Employment outcomes in people with bipolar disorder : a systematic review

Objective: Employment outcome in bipolar disorder is an under investigated, but important area. The aim of this study was to identify the long-term employment outcomes of people with bipolar disorder. Method: A systematic review using the Medline, PsychInfo and Web of Science databases. Results: Of 1962 abstracts retrieved, 151 full text papers were read. Data were extracted from 25 papers representing a sample of 4892 people with bipolar disorder and a mean length of follow-up of 4.9 years. Seventeen studies had follow-up periods of up to 4 years and eight follow-up of 5–15 years. Most studies with samples of people with established bipolar disorder suggest approximately 40–60% of people are in employment. Studies using work functioning measures mirrored this result. Bipolar disorder appears to lead to workplace underperformance and 40–50% of people may suffer a slide in their occupational status over time. Employment levels in early bipolar disorder were higher than in more established illness. Conclusion: Bipolar disorder damages employment outcome in the longer term, but up to 60% of people may be in employment. Whilst further studies are necessary, the current evidence provides support for extending the early intervention paradigm to bipolar disorder