Fatigue Detection for Ship OOWs Based on Input Data Features, from The Perspective of Comparison with Vehicle Drivers: A Review

Ninety percent of the world’s cargo is transported by sea, and the fatigue of ship officers of the watch (OOWs) contributes significantly to maritime accidents. The fatigue detection of ship OOWs is more difficult than that of vehicles drivers owing to an increase in the automation degree. In this study, research progress pertaining to fatigue detection in OOWs is comprehensively analysed based on a comparison with that in vehicle drivers. Fatigue detection techniques for OOWs are organised based on input sources, which include the physiological/behavioural features of OOWs, vehicle/ship features, and their comprehensive features. Prerequisites for detecting fatigue in OOWs are summarised. Subsequently, various input features applicable and existing applications to the fatigue detection of OOWs are proposed, and their limitations are analysed. The results show that the reliability of the acquired feature data is insufficient for detecting fatigue in OOWs, as well as a non-negligible invasive effect on OOWs. Hence, low-invasive physiological information pertaining to the OOWs, behaviour videos, and multisource feature data of ship characteristics should be used as inputs in future studies to realise quantitative, accurate, and real-time fatigue detections in OOWs on actual ships

DiffBIR: Towards Blind Image Restoration with Generative Diffusion Prior

We present DiffBIR, which leverages pretrained text-to-image diffusion models for blind image restoration problem. Our framework adopts a two-stage pipeline. In the first stage, we pretrain a restoration module across diversified degradations to improve generalization capability in real-world scenarios. The second stage leverages the generative ability of latent diffusion models, to achieve realistic image restoration. Specifically, we introduce an injective modulation sub-network -- LAControlNet for finetuning, while the pre-trained Stable Diffusion is to maintain its generative ability. Finally, we introduce a controllable module that allows users to balance quality and fidelity by introducing the latent image guidance in the denoising process during inference. Extensive experiments have demonstrated its superiority over state-of-the-art approaches for both blind image super-resolution and blind face restoration tasks on synthetic and real-world datasets. The code is available at https://github.com/XPixelGroup/DiffBIR

EFFECTS OF SHADOW BANKING ON HOUSE PRICES - EMPIRICAL STUDY BASED ON CANADIAN REAL ESTATE MARKET

Canadian housing market, known for its robustness and stabilization, has continuedbooming for over 10 years even during the housing bubbles burst period. Especially in the past fewyears, real estate GDP has represented approximately half of Canada’s Economic growth, wheremost of the outsized contribution lies in the rising section of shadow banking system.In this paper, we introduce the current situation of shadow banking system, analyse thecorrelation between mortgage balances from shadow banks, chartered banks and Canadian housingprices by building up Vector Auto Regression model, Impulse Response Function, and VariationDecomposition. The empirical results show that the house prices respond to the mortgage balancefrom shadow banks to some extent, and chartered banks also play a role. However, the mortgageoutstanding in shadow banks do not respond to change in house prices in return. In the end, weoffer some corresponding policy suggestions based on the analysis

Routing Optimisation of Urban Medical Waste Recycling Network considering Differentiated Collection Strategy and Time Windows

The increasing gap between medical waste production and disposal stresses the urgency of further development of urban medical waste recycling. This paper investigates an integrated optimisation problem in urban medical waste recycling network. It combines the vehicle routing problem of medical facilities with different requirements and the collection problem of clinics’ medical waste to the affiliated hospital. To solve this problem, a compact mixed-integer linear programming model is proposed, which takes account of the differentiated collection strategy and time windows. Since the medical waste recycling operates according to a two-day pattern, the periodic collection plan is also embedded in the model. Moreover, we develop a particle swarm optimisation (PSO) solution approach for problem-solving. Numerical experiments are also conducted to access the solution efficiency of the proposed algorithm, which can obtain a good solution in solving large-scale problem instances within a reasonable computation time. Based on the results, some managerial implications can be recommended for the third-party recycling company

Prognostic Implication of Plasma Metabolites in Gastric Cancer

Gastric cancer (GC) typically carries a poor prognosis as it is often diagnosed at a late stage. Altered metabolism has been found to impact cancer outcomes and affect patients’ quality of life, and the role of metabolites in gastric cancer prognosis has not been sufficiently understood. We aimed to establish a prognostic prediction model for GC patients based on a metabolism-associated signature and identify the unique role of metabolites in the prognosis of GC. Thus, we conducted untargeted metabolomics to detect the plasma metabolites of 218 patients with gastric adenocarcinoma and explored the metabolites related to the survival of patients with gastric cancer. Firstly, we divided patients into two groups based on the cutoff value of the abundance of each of the 60 metabolites and compared the differences using Kaplan–Meier (K-M) survival analysis. As a result, 23 metabolites associated with gastric cancer survival were identified. To establish a risk score model, we performed LASSO regression and Cox regression analysis on the 60 metabolites and identified 8 metabolites as an independent prognostic factor. Furthermore, a nomogram incorporating clinical parameters and the metabolic signature was constructed to help individualize outcome predictions. The results of the ROC curve and nomogram plot showed good predictive performance of metabolic risk features. Finally, we performed pathway analysis on the 24 metabolites identified in the two parts, and the results indicated that purine metabolism and arachidonic acid metabolism play important roles in gastric cancer prognosis. Our study highlights the important role of metabolites in the progression of gastric cancer and newly identified metabolites could be potential biomarkers or therapeutic targets for gastric cancer patients

Two-dimensional titanium carbonitride mxene for high-performance sodium ion batteries

Two-dimensional layered MXenes are attractive materials for energy storage devices due to their superior electrochemical performance and encouraging capacitances as anode materials in sodium ion batteries (SIBs). Herein, we report the use of Ti3CN as a new promising anode material in SIBs and the interfacial kinetic properties of Ti3CN electrodes. The Ti3CN electrode delivered a high specific capacity of 507 and 211.5 mAh g–1 for the first discharge/charge process, respectively, at a current density of 20 mA g–1. When tested at 500 mA g–1, the discharge specific capacity of the Ti3CN electrode was 98.9 mAh g–1, which is 1.65 times as much as that of pristine Ti3C2. The density functional theory (DFT) calculations demonstrate that the introduction of more electronegative nitrogen into the lattice grid of Ti3C increases the electron count of MXenes. The significantly improved capacities and long-term cycling performance of Ti3CN compared to those of its counterpart Ti3C2 SIBs indicate that Ti3CN can be considered promising anode materials for sodium-based energy storage devices

Clinical Distribution and Drug Resistance of Pseudomonas aeruginosa in Guangzhou, China from 2017 to 2021

The aim of the current study was to analyse the distribution of antimicrobial drug resistance (AMR) among Pseudomonas aeruginosa (P. aeruginosa, PA) isolates from Guangdong Provincial People’s Hospital (GDPH) from 2017 to 2021, and the impact of the COVID-19 outbreak on changes in the clinical distribution and drug resistance rate of P. aeruginosa to establish guidelines for empiric therapy. Electronic clinical data registry records from 2017 to 2021 were retrospectively analysed to study the AMR among P. aeruginosa strains from GDPH. The strains were identified by VITEK 2 Compact and MALDI-TOF MS, MIC method or Kirby–Bauer method for antibiotic susceptibility testing. The results were interpreted according to the CLSI 2020 standard, and the data were analysed using WHONET 5.6 and SPSS 23.0 software. A total of 3036 P. aeruginosa strains were detected in the hospital from 2017 to 2021, and they were primarily distributed in the ICU (n = 1207, 39.8%). The most frequent specimens were respiratory tract samples (59.6%). The detection rate for P. aeruginosa in 5 years was highest in September, and the population distribution was primarily male(68.2%). For the trend in the drug resistance rate, the 5-year drug resistance rate of imipenem (22.4%), aztreonam (21.5%) and meropenem (19.3%) remained at high levels. The resistance rate of cefepime decreased from 9.4% to 4.8%, showing a decreasing trend year by year (p < 0.001). The antibiotics with low resistance rates were aminoglycoside antibiotics, which were gentamicin (4.4%), tobramycin (4.3%), and amikacin (1.4%), but amikacin showed an increasing trend year by year (p = 0.008). Our analysis indicated that the detection rate of clinically resistant P. aeruginosa strains showed an upwards trend, and the number of multidrug-resistant (MDR) strains increased year by year, which will lead to stronger pathogenicity and mortality. However, after the outbreak of COVID-19 in 2020, the growth trend in the number of MDR bacteria slowed, presumably due to the strict epidemic prevention and control measures in China. This observation suggests that we should reasonably use antibiotics and treatment programs in the prevention and control of P. aeruginosa infection. Additionally, health prevention and control after the outbreak of the COVID-19 epidemic (such as wearing masks, washing hands with disinfectant, etc., which reduced the prevalence of drug resistance) led to a slowdown in the growth of the drug resistance rate of P. aeruginosa in hospitals, effectively reducing the occurrence and development of drug resistance, and saving patient’s treatment costs and time

Discovering Cathodic Biocompatibility for Aqueous Zn–MnO2 Battery: An Integrating Biomass Carbon Strategy

Highlights γ-MnO2 loaded on N-doped biomass carbon from grapefruit peel is firstly developed. The splendid cathodic properties (e.g., coulombic efficiency: ~ 100%, energy density: 553.12 Wh kg−1) are gained. The biomass strategy guaranteed via cytotoxicity test shows a clinical potential. Zn-ion storage efficiency is boosted mainly by regulating Mn–O bond and Mn domains