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With the continuous progress of contemporary science and technology and the increasing requirements for marine vehicles in various fields, the intelligence and automation of ships have become a general trend. The autonomous control of surface Unmanned Surface Vessel (USV) generally covers the USV path planning, path tracking control, and autonomous collision avoidance control. But in the whole navigation process of USV, autonomous berthing is also a crucial part. And the research on the algorithm of the automatic berthing process of the USV is less. Mature USV autonomous berthing technology can effectively reduce the cost of human and material resources and financial resources while reducing the accident rate reasonably and safely. Therefore, it is of great importance to comprehensively promote the development of USV autonomous berthing technology.

It is challenging for a patient without medical knowledge to select a ‘capable’ physician based on nontransparent medical information. The situation becomes pervasive in specialty care. Motivated by this existing patient-physician matching problem, we propose a novel physician matching index (PMI) obtained by an analytical framework integrated with an improved multi-disease pre-diagnosing Bayesian network (BN) model. The pre-diagnosis BN structure learning is critical since it provides the causal map among diseases and symptoms, but it has been proved to be NP-hard. To improve the computational tractability of the BN structure learning, we propose a dynamic programming based cache calculation algorithm integrated with expert knowledge. The optimal BN structure is obtained through an improved branch-and-bound algorithm. Given patients’ symptoms and physicians’ specialty information, we apply the trained pre-diagnosis BN model to obtain PMI, which can be extended to the weighted matching index by considering patient preferences. A case study of the patient-physician matching problem in the ear, nose, and throat (ENT) department is conducted. The branch-and-bound algorithm with the proposed cache calculation algorithm learns the optimal BN structure with high pre-diagnosing accuracy and time efficiency. We disclose that the proposed PMI can rectify the misdiagnosis since the highly related diseases usually belong to one specialty. Moreover, we demonstrate the significance of the consistency between the physicians’ specialty and the patients’ disease distribution. We also highlight that the proposed PMI guides the patients in choosing physicians more appropriately under independent patient preferences. Note to Practitioners—This research is motivated by a potential healthcare quality issue caused by the mismatch between patient and physician in specialty care, which is critical for both referral and non-referral systems. We propose a novel physician matching index (PMI) according to patients’ symptoms and physicians’ specialties based on potential disease(s) pre-diagnosed by a Bayesian network model. In the case study of the ear, nose, and throat (ENT) department, we show that PMI can rectify a large portion of misdiagnosed cases. Under the consideration of independent patient preferences, we also find that PMI improves the matching performance. To improve the systematic matching outcome, we suggest that physicians’ specialty cross-training programs be consistent with the patient’s disease distribution. However, we assume that patients’ symptoms are known and physicians’ specialties are clearly described, which might need to be further investigated. The framework proposed in this research can be extended to other specialty care departments, e.g., neurology and orthopedics.

Batch processing machines, which operate multiple jobs at a time, are commonly used in energy-intensive industries. A significant amount of energy can be saved in such industries using production scheduling as an approach to enhance efficiency. This study deals with an energy-aware scheduling problem for parallel batch processing machines with incompatible families and job release times. In such an environment, a machine may need to wait until all the jobs in the next batch become ready. During waiting time, a machine can be switched off or kept on standby for more energy-efficient scheduling. We first present a mixed-integer linear programming (MILP) model to solve the problem. However, the presented MILP model can only solve small problem instances. We therefore propose an energy-efficient tabu search (ETS) algorithm for solving larger problem instances. The proposed solution framework incorporates multiple neighborhood methods for efficient exploration of the search space. An energy-related heuristic is also integrated into the ETS for minimizing energy consumption during the waiting time. The performance of our proposed ETS algorithm is validated by comparing it with CPLEX for small problem instances and with two other heuristic algorithms for larger problem instances. The contribution of different components in ETS is also established in our experimental studies. The proposed solution framework is expected to bring many benefits in energy-intensive industries both economically and environmentally.

Coronary artery wall contrast enhancement (CE) has been applied to non-invasive visualization of changes to the coronary artery wall in systemic lupus erythematosus (SLE). This study investigated the feasibility of quantifying CE to detect coronary involvement in IgG4-related disease (IgG4-RD), as well as the influence on disease activity assessment.

As an important element to maintain pressure stability in pipelines, back pressure valves play a vital role in high-pressure fluid control. With the continuous development of high-pressure flow control technology, the performance and adaptability of back pressure valves put forward higher requirements. At present, domestic and foreign back pressure valve products have problems such as poor adaptability to fluid media, insufficient wear resistance and easy to jam. Aiming at these problems, a new type of back pressure valve with jeweled ball seat structure is developed in this study, and the effects of opening degree, initial pump flow and stroke on the internal flow field and performance of the back pressure valve are investigated using water as the flow medium. Numerical simulation and experimental verification show that the inlet pressure can be up to 30 MPa, and under the highest pressure design condition, the fluid pressure flowing through the flow path between the spools will be reduced from high pressure to atmospheric pressure, with a jet appearing in the center of the outlet and a vortex forming in the area near the outlet wall of the slit.

Acute pancreatitis (AP), resulting from inflammation of the pancreas, accounts for more than 300,000 US hospital discharges per year. Although glucose intolerance has been known as a complication of severe AP, this effect was thought to be transient. Recently, cohort studies and meta-analysis of 24 published studies of 1100 patients who survived one or more episodes of AP revealed that 30% to 40% of patients developed diabetes or impaired glucose tolerance within 3 to 4 years of even a single episode of AP. The National Institute of Diabetes and Digestive and Kidney Diseases funded the Type 1 Diabetes in Acute Pancreatitis Consortium (T1DAPC) to undertake a prospective observational study of the occurrence of diabetes during an AP episode or subsequently, with emphasis on type 1 diabetes. Key factors for funding T1DAPC are the increasing incidence and prevalence of AP, its association with the development of type 1 diabetes and other forms of diabetes after AP, its complications, and associated health care cost. The T1DAPC structure, governance, and research objectives are described in this article. The DREAM (Diabetes RElated to Acute pancreatitis and its Mechanisms) studies to be undertaken by the T1DAPC are described in other articles in this journal's issue.

The heart–liver axis is of growing importance. Previous studies have identified independent association of liver dysfunction and fibrosis with adverse cardiac outcomes, but mechanistic pathways remain uncertain. We sought to understand the relations between the degree of hepatic fibrosis identified by the Fibrosis-4 (Fib-4) risk score and comprehensive cardiac MRI (CMR) measures of subclinical cardiac disease. We conducted a retrospective single-center cohort study of patients between 2011 and 2021. We identified consecutive patients who underwent a comprehensive CMR imaging protocol including contrast enhanced with stress/rest perfusion, and lacked pre-existing cardiovascular disease or perfusion abnormalities on CMR. We examined the association of hepatic fibrosis, using the Fib-4 score, with subclinical cardiac disease on CMR while adjusting for cardiometabolic traits. Given known associations of hepatic disease and coronary microvascular dysfunction, we prioritized analyses with the myocardial perfusion reserve index (MPRI), a marker of coronary microvascular function. Of the 66 patients in our study cohort, 54 were female (81%) and the mean age was 53.7 ± 15.3 years. We found that higher Fib-4 was associated with reduction in the MPRI (β [SE] − 1.12 [0.46], P = 0.02), after adjusting for cardiometabolic risk factors. Importantly, Fib-4 was not significantly associated with any other CMR phenotypes including measures of cardiac remodeling, inflammation, fibrosis, or dysfunction. We found evidence that hepatic fibrosis associated with coronary microvascular dysfunction, in the absence of overt associations with any other subclinical cardiac disease measures. These findings highlight a potentially important precursor pathway leading to development of subsequent heart–liver disease.

The predominant substrate of high-intensity coronary plaque at MRI in stable coronary artery disease was intraplaque hemorrhage, not lipid.

Purpose The trend of atherosclerotic plaque feature evolution is unclear in stroke patients with and without recurrence. We aimed to use three-dimensional whole-brain magnetic resonance vessel wall imaging to quantify the morphological changes of causative lesions during medical therapy in patients with symptomatic intracranial atherosclerotic disease.Methods Patients with acute ischemic stroke attributed to intracranial atherosclerotic disease were retrospectively enrolled if they underwent both baseline and follow-up magnetic resonance vessel wall imaging. The morphological features of the causative plaque, including plaque volume, peak normalized wall index, maximum wall thickness, degree of stenosis, pre-contrast plaque-wall contrast ratio, and post-contrast plaque enhancement ratio, were quantified and compared between the non-recurrent and recurrent groups (defined as the recurrence of a vascular event within 18 months of stroke).Results Twenty-nine patients were included in the final analysis. No significant differences were found in plaque features in the baseline scan between the non-recurrent (n= 22) and recurrent groups (n= 7). The changes in maximum wall thickness (–13.32% vs. 8.93%, P= 0.026), plaque-wall contrast ratio (–0.82% vs. 3.42%, P= 0.005) and plaque enhancement ratio (–11.03% vs. 9.75%, P= 0.019) were significantly different between the non-recurrent and recurrent groups. Univariable logistic regression showed that the increase in plaque-wall contrast ratio (odds ratio 3.22, 95% confidence interval 1.55–9.98, P= 0.003) was related to stroke recurrence.Conclusion Morphological changes of plaque features on magnetic resonance vessel wall imaging demonstrated distinct trends in symptomatic intracranial atherosclerotic disease patients with and without stroke recurrence.

ObjectiveEstablished preclinical imaging assessments of heart failure (HF) risk are based on macrostructural cardiac remodelling. Given that microstructural alterations may also influence HF risk, particularly in women, we examined associations between microstructural alterations and incident HF.MethodsWe studied N=2511 adult participants (mean age 65.7±8.8 years, 56% women) of the Framingham Offspring Study who were free of cardiovascular disease at baseline. We employed texture analysis of echocardiography to quantify microstructural alteration, based on the high spectrum signal intensity coefficient (HS-SIC). We examined its relations to incident HF in sex-pooled and sex-specific Cox models accounting for traditional HF risk factors and macrostructural alterations.ResultsWe observed 94 new HF events over 7.4±1.7 years. Individuals with higher HS-SIC had increased risk for incident HF (HR 1.67 per 1-SD in HS-SIC, 95% CI 1.31 to 2.13; p<0.0001). Adjusting for age and antihypertensive medication use, this association was significant in women (p=0.02) but not men (p=0.78). Adjusting for traditional risk factors (including body mass index, total/high-density lipoprotein cholesterol, blood pressure traits, diabetes and smoking) attenuated the association in women (HR 1.30, p=0.07), with mediation of HF risk by the HS-SIC seen for a majority of these risk factors. However, the HS-SIC association with HF in women remained significant after adjusting for relative wall thickness (representing macrostructure alteration) in addition to these risk factors (HR 1.47, p=0.02).ConclusionsCardiac microstructural alterations are associated with elevated risk for HF, particularly in women. Microstructural alteration may identify sex-specific pathways by which individuals progress from risk factors to clinical HF.

High-intensity plaque (HIP) on magnetic resonance imaging (MRI) has been documented as a powerful predictor of periprocedural myocardial injury (PMI) following percutaneous coronary intervention (PCI). Despite the recent proposal of three-dimensional HIP quantification to enhance the predictive capability, the conventional pulse sequence, which necessitates the separate acquisition of anatomical reference images, hinders accurate three-dimensional segmentation along the coronary vasculature. Coronary atherosclerosis T1-weighted characterization (CATCH) enables the simultaneous acquisition of inherently coregistered dark-blood plaque and bright-blood coronary artery images. We aimed to develop a novel HIP quantification approach using CATCH and to ascertain its superior predictive performance compared to the conventional two-dimensional assessment based on plaque-to-myocardium signal intensity ratio (PMR).

Pancreatic ductal adenocarcinoma is most frequently detected at an advanced stage. Such late detection restricts treatment options and contributes to a dismal 5-year survival rate of 3–15%. Pancreatic ductal adenocarcinoma is relatively uncommon and screening of the asymptomatic adult population is not feasible or recommended with current modalities. However, screening of individuals in high-risk groups is recommended. Here, we review groups at high risk for pancreatic ductal adenocarcinoma, including individuals with inherited predisposition and patients with pancreatic cystic lesions. We discuss studies aimed at finding ways of identifying pancreatic ductal adenocarcinoma in high-risk groups, such as among individuals with new-onset diabetes mellitus and people attending primary and secondary care practices with symptoms that suggest this cancer. We review early detection biomarkers, explore the potential of using social media for detection, appraise prediction models developed using electronic health records and research data, and examine the application of artificial intelligence to medical imaging for the purposes of early detection.

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Despite the proliferation of health and nursing informatics applications in the past decade, factors influencing consumer acceptance of the applications are not well understood. This study was conducted to investigate factors affecting acceptance of a consumer-used nursing informatics application (ie, online health information portal) within the framework of the Technology Acceptance Model. A cross-sectional study was conducted in which 201 Chinese young adults were invited to participate in usability testing with a typical health information portal and to complete a self-report questionnaire measuring the model's constructs and five hypothesized variables drawn from consumer and portal characteristics. Hierarchical regression analyses were used to test research hypotheses. Fifteen of the 22 research hypotheses were supported. Perceived ease of use and perceived usefulness predicted satisfaction and behavioral intention, respectively, over and above the portal and consumer characteristics examined in the study. All portal and consumer characteristics had significant, although varied, impacts on the original model constructs. This study demonstrated that an adapted Technology Acceptance Model, extended with portal and consumer characteristics, provides an effective means to understand consumer acceptance of health portals. The findings hold important implications for design and implementation strategies to increase the likelihood of acceptance of consumer-used nursing informatics applications.

Lift-off velocity of saltating sand particles in wind-blown sand located at 1.0 mm above the sand bed surface was measured using a phase Doppler particle analyzer in a wind tunnel. The results show that the probability distribution of lift-off velocity can be expressed as a lognormal function, while that of lift-off angle follows an exponential function. The probability distribution of lift-off angle conditioned for each lift-off velocity also follows an exponential function, with a slope that becomes steeper with increasing lift-off velocity. This implies that the probability distribution of lift-off velocity is strongly dependent on the lift-off angle. However, these lift-off parameters are generally treated as an independent joint probability distribution in the literature. Numerical simulations were carried out to investigate the effects of conditional versus independent joint probability distributions on the vertical sand mass flux distribution. The simulation results derived from the conditional joint probability distribution agree much better with experimental data than those from the independent ones. Thus, it is better to describe the lift-off velocity of saltating sand particles using the conditional joint probability distribution. These results improve our understanding of saltation processes in wind-blown sand.

Quantification of chamber size and systolic function is a fundamental component of cardiac imaging. However, the human heart is a complex structure with significant uncharacterized phenotypic variation beyond traditional metrics of size and function. Examining variation in cardiac shape can add to our ability to understand cardiovascular risk and pathophysiology.

We demonstrated that contrast agent–enhanced, self-navigated whole-heart coronary 3-T MR angiography is a promising technique for detecting significant coronary stenosis, and future efforts are warranted to further improve the image quality and conduct studies on a patient cohort with coronary artery disease.

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This study aimed to compare the coronary plaque characterization by cardiovascular magnetic resonance (CMR) and near-infrared spectroscopy (NIRS)-intravascular ultrasound (IVUS) (NIRS-IVUS), and to determine whether pre–percutaneous coronary intervention (PCI) evaluation using CMR identifies high-intensity plaques (HIPs) at risk of peri-procedural myocardial infarction (pMI). Although there is little evidence in comparison with NIRS-IVUS findings, which have recently been shown to identify vulnerable plaques, we inferred that CMR-derived HIPs would be associated with vulnerable plaque features identified on NIRS-IVUS.