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Background: Trial registration aims to address potential bias from selective or non-reporting of findings, and therefore has a vital role in promoting transparency and accountability of clinical research. In this study, we aim to investigate the influence of trial registration on estimated harm effects in randomized controlled trials of medication interventions. Methods: We searched PubMed for systematic reviews and meta-analyses of randomized trials on medication harms indexed between January 1, 2015, and January 1, 2020. To be included in the analyses, eligible meta-analyses should have at least five randomized trials with distinct registration statuses (i.e., prospectively registered, retrospectively registered, and non-registered) and 2 by 2 table data for adverse events for each trial. To control for potential confounding, trials in each meta-analysis were analyzed within confounder-harmonized groups (e.g., dosage) identified using the Directed Acyclic Graph method. The harm estimates arising from the trials with different registration statuses were compared within the confounder-harmonized groups using hierarchical linear regression. Results are shown as ratio of odds ratio (OR) and 95% confidence interval (CI). Results: The dataset consists of 629 meta-analyses of harms with 10,069 trials. Of these trials, 74.3% were registered, and 23.9% were not registered, and for those registered, 70.6% were prospectively registered, while 26.3% were retrospectively registered. In comparison to prospectively registered trials, both non-registered trials (ratio of OR = 0.82, 95%CI 0.68 to 0.98, P = 0.03) and retrospectively registered trials (ratio of OR = 0.75, 95%CI 0.66 to 0.86, P < 0.01) had lower OR for harms based on 69 and 126 confounders-harmonized groups. The OR of harms did not differ between retrospectively registered and non-registered trials (ratio of OR = 1.02, 95%CI 0.85 to 1.23, P = 0.83) based on 76 confounders-harmonized groups. Conclusions: Medication-related harms may be understated in non-registered trials, and there was no obvious evidence that retrospective registration had a demonstrable benefit in reducing such selective or absent reporting. Prospective registration is highly recommended for future trials. [ABSTRACT FROM AUTHOR]

Bone defect is a common clinical disease. Due to the uncertainty of trauma or infection areas, customized size features are often required for bone substitutes. By inspiration of the natural bone structure, this study designs porous scaffolds with a biomimetic design perspective by using different inner and outer pore units. The outer pore units adopt body‐centered cubic (BCC) structure to simulate the weight‐bearing function of human cortical bone, while inner pore units using I‐Wrapped Package structure, a kind of three periods minimum surface, to obtain a good permeability and simulates the inner layer of cancellous bone. To further regulate the overall modulus of the scaffold within the range of natural bone modulus in the human body, the scaffold was designed to axial gradient structure. Compression experiments were conducted, and the results indicated that when the volume fraction linearly increased from 20% to 50%, the Young's modulus was close to the cortical bone modulus in the human body. In vitro cell experiments further proved that osteoblasts have good cellular activity and spreading morphology on the surface of this scaffold. The customized 3D‐printed heterogeneous porous titanium scaffold has great application potential in bone tissue engineering. [ABSTRACT FROM AUTHOR]

Background: Congenital disorders of glycosylation (CDG) are a type of inborn error of metabolism (IEM) resulting from defects in glycan synthesis or failed attachment of glycans to proteins or lipids. One rare type of CDG is caused by homozygous or compound heterozygous loss‐of‐function variants in mannosidase alpha class 2B member 2 (MAN2B2). To date, only two cases of MAN2B2‐CDG have been reported worldwide. Methods: Trio whole‐exome sequencing (Trio‐WES) was conducted to screen for candidate variants. N‐glycan profiles were measured by liquid chromatography–tandem mass spectrometry (LC–MS/MS). MAN2B2 expression was evaluated by western blotting. MX dynamin like GTPase 1 (MX1) function was estimated via Thogoto virus (THOV) minireplicon assay. Results: Trio‐WES identified compound heterozygous MAN2B2 (hg19, NM_015274.1) variants (c.384G>T; c.926T>A) in a CDG patient. This patient exhibited metabolic abnormalities, symptoms of digestive tract dysfunction, infection, dehydration, and seizures. Novel immune dysregulation characterized by abnormal lymphocytes and immunoglobulin was observed. The MAN2B2 protein level was not affected, while LC–MS/MS showed obvious disruption of N‐glycans and N‐linked glycoproteins. Conclusion: We described a CDG patient with novel phenotypes and disruptive N‐glycan profiling caused by compound heterozygous MAN2B2 variants (c.384G>T; c.926T>A). Our findings broadened both the genetic and clinical spectra of CDG. [ABSTRACT FROM AUTHOR]

With the proliferation of unmanned aerial vehicles (UAVs) in both commercial and military use, the public is paying increasing attention to UAV identification and regulation. The micro-Doppler characteristics of a UAV can reflect its structure and motion information, which provides an important reference for UAV recognition. The low flight altitude and small radar cross-section (RCS) of UAVs make the cancellation of strong ground clutter become a key problem in extracting the weak micro-Doppler signals. In this paper, a clutter suppression method based on an orthogonal matching pursuit (OMP) algorithm is proposed, which is used to process echo signals obtained by a linear frequency modulated continuous wave (LFMCW) radar. The focus of this method is on the idea of sparse representation, which establishes a complete set of environmental clutter dictionaries to effectively suppress clutter in the received echo signals of a hovering UAV. The processed signals are analyzed in the time–frequency domain. According to the flicker phenomenon of UAV rotor blades and related micro-Doppler characteristics, the feature parameters of unknown UAVs can be estimated. Compared with traditional signal processing methods, the method based on OMP algorithm shows advantages in having a low signal-to-noise ratio (−10 dB). Field experiments indicate that this approach can effectively reduce clutter power (−15 dB) and successfully extract micro-Doppler signals for identifying different UAVs. [ABSTRACT FROM AUTHOR]

• The framework is proposed using security-driven and safety-oriented methods. • SSCA is constructed through both top-down and bottom-up perspectives. • The regulatory development of SSCA is analysed among different transport modes. • It strikes new research directions for digitalised and autonomous transport vehicles. Transportation is sensitive to risk. Given the fast development of digitalisation and automation of transport systems in the past decade, new types of security risks (e.g. cyberattacks) emerge within the context of transport safety research. To enable the integrated analysis of emerging security and classical safety-related risks in a holistic manner, safety and security co-analysis (SSCA) is highly demanded for accident prevention. SSCA in transport systems will benefit the risk analysis of complex cyber physical transport systems facing challenges from both hazards and threats. However, the nature of hazard and threat-based risks is fundamentally different, which leads to the various difficulties of analysing them on the same plane. They include the use of different risk parameters, the uncertainty levels of the risk input and the methodologies of risk inference. To address such concerns, this study firstly reviews the literature on SSCA and compares the employed methodologies and their applications within the context of transport systems. Taking into account the advantages of both security-driven and safety-oriented methods, a conceptual framework is proposed to imply the insights on SSCA for transportation through both top-down and bottom-up perspectives, followed by a quantitative illustrative case study. Then, the regulatory development and evolution of SSCA in transport in practice is analysed across different transport modes, which configures initiatives' interrelations for a cross-fertilisation purpose. As a result, the findings reveal new research directions for the safety of digitalised and/or autonomous transport vehicles and aid in the formation of future transport safety study agendas. [ABSTRACT FROM AUTHOR]

Maritime accident consequences often entail substantial property losses, environmental contamination, and even loss of life. To investigate the consequences of maritime accidents in Australian waters, this paper develops a data-driven Bayesian network (BN) model with new features derived from a new accident database. The localised vital risk factors influencing bulk carrier safety in Australian waters are generated, which can help develop new measures for consequence mitigation. Compared to the previous relevant research, this article makes new contributions in that 1) manual analysis of each ATSB maritime accident report to formulate a new comprehensive database containing the key influential factors (IFs) influencing bulk carrier accident consequences. Maritime risk analysis and safety management suffer from insufficient accident databases and hence, this development will address the research gap and stimulate data-driven maritime risk analysis in future; 2) the development of a new data-driven BN model to investigate the accident consequences in Australian waters which attracts little attention compared to the other regions of high maritime traffic. The results aid in formulating a new baseline to benchmark Australian maritime accident consequences research; 3) the raw data is trained to configure and quantify the interdependence and dynamics of all the IFs. Given the country's crucial role in international dry bulk trade, this paper contributes to ensuring maritime safety from Australian national and global bulk carrier perspectives. The results reveal that the critical IFs are accident type, emergency handling, navigational condition, ship speed, visibility, safe act, time of the day, loaded or ballast condition, and lookout. Furthermore, the new BN can realise the real-time analysis of a ship's consequence severity in Australian waters and provide valuable insights for transport authorities to mitigate the consequences of accidents. • A new data-driven BN model to investigate accident consequences in Australian waters. • Analysis of dependencies among influential factors for bulk carrier accident consequences. • A new accident database from 2000 to 2022 containing all factors influencing bulk carrier safety. • Provision of rational strategies to reduce bulk carrier accident consequences in Australian waters. [ABSTRACT FROM AUTHOR]

• Transition from insomnia to stupor are rare phenotype in DRPLA. • The largest CAG expansion in ATN1 and the earliest onset age of DRPLA in China. • The onset age of DRPLA patients is negatively correlated with the repeat times of CAG. • Pneumonia and dysphagia were common causes of death in polyQ-related diseases. • Ataxia is the most common and presenting symptom in Chinese DRPLA patients. Dentatorubral-pallidoluysian atrophy (DRPLA) is an inherited neurodegenerative disease caused by CAG overexpansion (≥48 tandem copies) in ATN1. The aim of this research was to explore the genetic cause of a large Chinese DRPLA pedigree and to review the characteristics of Chinese DRPLA patients. Suspected variants were screened by high-throughput sequencing. The number of CAG repeats was assessed by polymerase chain reaction using FAM-labeled primers followed by capillary electrophoresis. Literature on previously reported DRPLA cases with overexpanded ATN1 CAG repeats in China was reviewed. After contracting a lung infection, the proband suffered early-onset DRPLA symptoms and novel phenotypes, transitioning from insomnia to stupor. The numbers of CAG repeats in the proband, her grandfather, father, mother, brother, and aunt were 8/81, 17/54, 10/57, 8/10, 10/10, and 10/17, respectively. Possible incomplete penetrance was observed in this pedigree. We described a large Chinese DRPLA pedigree in which the proband carried the largest CAG expansion reported in China. We also reviewed the characteristics of Chinese DRPLA patients and broadened the phenotypic spectrum. [ABSTRACT FROM AUTHOR]

Despite the efforts of maritime authorities to enhance seafarer competencies through the International Convention on Standards of Training, Certification and Watchkeeping for Seafarers (STCW), human error remains a leading cause of maritime accidents. To thoroughly investigate the impact of various human errors among seafarers on accidents, this paper aims to examine the relationships between seafarer competencies and maritime accidents using a data-driven approach from the perspective of bridge resource management (BRM). Through analysis of historical maritime accident reports, the dataset of seafarer competencies associated with maritime accidents is established. The least absolute shrinkage and selection operator (LASSO) method is employed to identify the critical seafarer competencies for accident prevention. Then, a data-driven Bayesian Network (BN) model, based on a Tree Augmented Naive Bayes (TAN) method, is constructed to reveal the relationship between seafarer competencies and accident types, which are validated by sensitivity analysis and case study. The results indicate that the key seafarer competencies for all maritime accidents are 'Maneuvers', 'Amend/maintain ship course', 'Decision making', 'Cognitive capacity', 'Information', 'Procedure operations', 'Situational awareness' and 'Communication'. Moreover, the study underscores the importance of leveraging lessons learned from past accidents to mitigate risks and ensure safe maritime operations. The findings contribute to a deeper understanding of the dynamics between seafarer competencies and accident types, unveiling the joint impact of different seafarer competencies on maritime accidents. This perspective offers valuable insights for maritime authorities in strengthening maritime safety regulations. • Develop a new data-driven BN model to investigate the impact of seafarer competencies on maritime accidents. • Identification of critical seafarer competencies in restricted waters using the LASSO algorithm. • Unveiling the joint impact of different seafarer competencies on maritime accidents. • Provision of rational strategies to reduce maritime accidents in restricted waters. [ABSTRACT FROM AUTHOR]

• Sepsis is a rare phenotype in adenosine kinase deficiency. • Neutrophil dysfunction contributes to sepsis in adenosine kinase deficiency. • Adenosine kinase deficiency causes metabolites disturbances in methionine pathway. • Homozygous ADK c.518_519delCA variants lead to loss-of-function of the ADK gene. Adenosine kinase deficiency (OMIM #614300) is a type of inborn errors of metabolism with multiorgan symptoms primarily neurological disorders, hepatic impairment, global developmental delay, and mild dysmorphism. The genetic causes of adenosine kinase deficiency are homozygous or compound heterozygous loss-of-function variants of ADK. To date, fewer than 25 cases of adenosine kinase deficiency have been reported worldwide and none have been reported in China. In this research, trio whole-exome sequencing (Trio-WES) identified a novel homozygous ADK (NM_001123.4) out-of-frame deletion, c.518_519delCA (p.Thr173Serfs*15), in a Chinese patient with rare phenotypes of sepsis, metabolites disruption and neutrophil dysfunction. This variant was dysfunctional, with marked reduction of ADK level in both the patient's peripheral blood and cells transfected with the corresponding variant. Additionally, metabolomics detected by high-throughput mass spectrometry showed disturbances in the methionine (Met) and energy pathway. RNA sequencing (RNA-seq) of the patient's peripheral blood suggested a defective anti-inflammatory response characterized by impaired neutrophil activation, migration, and degranulation, which might be the primary cause for the sepsis. To our knowledge, we identified the first Chinese patient of adenosine kinase deficiency with a novel homozygous out-of-frame deletion in ADK causing multiorgan disorders, metabolites disruption, rare phenotypes of sepsis, and neutrophil dysfunction. Our findings broaden the genetic spectrum and pathogenic mechanisms of adenosine kinase deficiency. [ABSTRACT FROM AUTHOR]

Introduction: Watchkeeping is a significant activity during maritime operations, and failures of sustained attention and decision‐making can increase the likelihood of a collision. Methods: A study was conducted in a ship bridge simulator where 40 participants (20 experienced/20 inexperienced) performed: (1) a 20‐min period of sustained attention to locate a target vessel and (2) a 10‐min period of decision‐making/action selection to perform an evasive maneuver. Half of the participants also performed an additional task of verbally reporting the position of their vessel. Activation of the prefrontal cortex (PFC) was captured via a 15‐channel functional near‐infrared spectroscopy (fNIRS) montage, and measures of functional connectivity were calculated frontal using graph‐theoretic measures. Results: Neurovascular activation of right lateral area of the PFC decreased during sustained attention and increased during decision‐making. The graph‐theoretic analysis revealed that density declined during decision‐making in comparison with the previous period of sustained attention, while local clustering declined during sustained attention and increased when participants prepared their evasive maneuver. A regression analysis revealed an association between network measures and behavioral outcomes, with respect to spotting the target vessel and making an evasive maneuver. Conclusions: The right lateral area of the PFC is sensitive to watchkeeping and decision‐making during operational performance. Graph‐theoretic measures allow us to quantify patterns of functional connectivity and were predictive of safety‐critical performance. [ABSTRACT FROM AUTHOR]

• Utilisation of historical records and machine learning to generate a human fatigue model. • Identification of significant factors from an objective accident/incident occurrence perspective. • Combination of LASSO and BN to propose a data-driven model to investigate human fatigue. • Providing solution to fatigue research with limited psychological data. In maritime transport, fatigue conditions can impair seafarer performance, pose a high risk of maritime incidents, and affect safety at sea. However, investigating human fatigue and its impact on maritime safety is challenging due to limited objective measures and little interaction with other risk influential factors (RIFs). This study aims to develop a novel model enabling accident data-driven fatigue investigation and RIF analysis using machine learning. It makes new methodological contributions, such as 1) the development of a human fatigue investigation model to identify significant RIFs leading to human fatigue based on historical accident and incident data; 2) the combination of least absolute shrinkage and selection operator (LASSO) and bayesian network (BN) to formulate a new machine learning model to rationalise the investigation of human fatigue in maritime accidents and incidents; 3) provision of insightful implications to guide the survey of fatigue's contribution to maritime accidents and incidents without the support of psychological data. The results show the importance of RIFs and their interdependencies for human fatigue in maritime accidents. It takes advantage of available knowledge and machine learning to open a new direction for fatigue management, which will benefit the maritime fatigue investigation and provide insights into other high-risk sectors suffering from human fatigue (e.g. nuclear and offshore). [ABSTRACT FROM AUTHOR]

Abstract 75–96% of maritime accidents are caused by human and organizational factors. Seafarers' emotion may degrade the effectivity of human behavior when tasks in onboard environment are complex and demanding. This study was concerned with the relationship between seafarers' emotion and occurring events in navigation. The Electroencephalogram (EEG) and Self-Assessment Manikin (SAM) scale rating are used to investigate the occurrence and impact of seafarers' emotions on their performance using a bridge simulator. The study was conducted and described in two sections: emotion calibration and test recognition. In the first section, two types of emotions are induced by the sound clips of the International Affective Digitized Sounds (IADS), developed by the National Institute of Mental Health Center for the Study of Human Emotions. In the second section, emotion is recognized by the Support Vector Machine (SVM) classifier, as well as self-rated after the crew-qualified test in a bridge simulator. The results indicate that SVM can identify the emotions by EEG feature extraction, with an accuracy of 77.55%. The results concerning officers' emotion in a bridge simulator test reveal that seafarers' emotion in maritime operations, relating to events exposure, affects their behavior and decision-making. In addition, negative emotion has a higher likelihood of contributing to human errors than positive emotion. Less negative emotion is the most dangerous emotion state during navigation, followed by extreme positive emotion. Highlights • Seafarers' emotion and their performance in operations in the bridge simulator revealed significant correlations. • The brain wave data of seafarers is calibrated with different types of emotions. • The effects of seafarers' emotion, relating to events exposure, on operational errors are identified and quantified. • The results from the experiment have potential application to seafarer training and qualification. [ABSTRACT FROM AUTHOR]

The incompatibility of similarity quotients and the lack of test simulation capability can be effectively addressed by overcoming "similarity constraint" using diverse model test environments (varying parameters such as atmospheric pressure and gravitational acceleration). In order to examine the scaling effects in different model test environments, this paper presents a numerical study of the prediction accuracy of a flat-head scaled-down vehicle for full-scale prototype water entry impacts in the normal compression, reduced compression and hypergravity model test environments using the experimentally validated FLUENT overlapping mesh technique. The study compared and analyzed the pressure load and flow field evolution under different initial conditions. The findings revealed that the over-simulation of the entry cushion effect of the scaled-down model and dynamic pressure in the normal compression environment led to inaccurate prediction of the full-scale prototype. The prediction results improved in the reduced compression environment than in the normal compression environment. However, the cavitation effect was over-simulated and the dynamic pressure was weakened. In contrast, the hypergravity environment enables the simulation of solid and fluid inertia to the same extent, satisfying the scaled-down model for full prediction of the prototype water entry cavity evolution and impact loads. • A method for predicting prototype loads by scaled-down models in different test environments is presented. • The similarity of water entry load for scaled-down vehicle is analyzed. • The effect of atmospheric pressure on the evolution of the inflow flow field of the vehicle is analyzed. • Density distortion correction method in the reduced compression environment is proposed. [ABSTRACT FROM AUTHOR]

The incompatibility of similarity quotients and the lack of test simulation capability can be effectively addressed by overcoming "similarity constraint" using diverse model test environments (varying parameters such as atmospheric pressure and gravitational acceleration). In order to examine the scaling effects in different model test environments, this paper presents a numerical study of the prediction accuracy of a flat-head scaled-down vehicle for full-scale prototype water entry impacts in the normal compression, reduced compression and hypergravity model test environments using the experimentally validated FLUENT overlapping mesh technique. The study compared and analyzed the pressure load and flow field evolution under different initial conditions. The findings revealed that the over-simulation of the entry cushion effect of the scaled-down model and dynamic pressure in the normal compression environment led to inaccurate prediction of the full-scale prototype. The prediction results improved in the reduced compression environment than in the normal compression environment. However, the cavitation effect was over-simulated and the dynamic pressure was weakened. In contrast, the hypergravity environment enables the simulation of solid and fluid inertia to the same extent, satisfying the scaled-down model for full prediction of the prototype water entry cavity evolution and impact loads. • A method for predicting prototype loads by scaled-down models in different test environments is presented. • The similarity of water entry load for scaled-down vehicle is analyzed. • The effect of atmospheric pressure on the evolution of the inflow flow field of the vehicle is analyzed. • Density distortion correction method in the reduced compression environment is proposed. [ABSTRACT FROM AUTHOR]

The aim of the study is to explore the effects of Nitrogen (N) rates on the speciation of Cadmium (Cd) in soil with oilseed rape (Brassica napusL.). A pot experiment was conducted at N1 (0 g/kg), N2 (0.125 g/kg), N3 (0.25 g/kg) and N4 (0.375 g/kg) rates. Correlation between Cd speciation and bioavailability, soil pH were analyzed. Cd speciation was significantly affected by N rates. Exchangeable Cd (EXC-Cd) and carbonate Cd (CAR-Cd) in soil showed a contrary pattern with residual Cd (RES-Cd) and organic Cd (ORG-Cd), respectively. Correlation analysis demonstrated EXC-Cd and CAR-Cd positively correlated with Cd bioavailability but negatively correlated with soil pH at later phase of oilseed rape (p < 0.05). Cd bioavailability could be promoted by increasing the EXC-Cd and CAR-Cd concentration at the expense of RES-Cd. Our results suggest that N rates may affect Cd speciation in regulation of soil pH, thus further affect Cd bioavailability. [ABSTRACT FROM PUBLISHER]

• Holistic use of fNIRS and maritime simulation to conduct HPM objectively. • Development of a hybrid assessment model using haemoglobin data and ANN. • Pioneering psychophysiological data-driven machine learning for seafarers' HPM. • Real case analysis for classifying seafarers of different qualifications. Human errors significantly contribute to transport accidents. Human performance measurement (HPM) is crucial to ensure human reliability and reduce human errors. However, how to address and reduce the subjective bias introduced by assessors in HPM and seafarer certification remains a key research challenge. This paper aims to develop a new psychophysiological data-driven machine learning method to realize the effective HPM in the maritime sector. It conducts experiments using a functional Near-Infrared Spectroscopy (fNIRS) technology and compares the performance of two groups in a maritime case (i.e. experienced and inexperienced seafarers in terms of different qualifications by certificates), via an Artificial Neural Network (ANN) model. The results have generated insightful implications and new contributions, including (1) the introduction of an objective criterion for assessors to monitor, assess, and support seafarer training and certification for maritime authorities; (2) the quantification of human response under specific missions, which serves as an index for a shipping company to evaluate seafarer reliability; (3) a supportive tool to evaluate human performance in complex emerging systems (e.g. Maritime Autonomous Surface Ship (MASS)) design for ship manufactures and shipbuilders. [ABSTRACT FROM AUTHOR]

[Display omitted] • FP/Ag-GOx hybrids with cascade catalytic activity were developed. • FP/Ag-GOx possessed effective antibacterial activity with the presence of glucose. • FP/Ag-GOx hybrids possessed satisfactory biocompatibility. • The synergistic antibacterial strategy accelerated the healing of infected wounds. The abuse of antibiotics seriously threatens the public health, which leads to the urgent need to develop novel antimicrobial strategies. Reactive oxygen species (ROS) have been considered as an alternative approach for bacterial inactivation. At present, magnetic materials, especially iron oxide (Fe 3 O 4) nanoparticles have gained widely application in biomedical field. Owing to the intrinsic peroxidase-like activity, Fe 3 O 4 can catalyze the decomposition of hydrogen peroxide (H 2 O 2) to generate hydroxyl radical (⋅OH) under acidic conditions. Recently, Fe 3 O 4 -based peroxidase mimics have been developed for wound infection therapy. However, the direct introduction of H 2 O 2 with relative high concentration may induce undesired effect to normal tissues, and the infection site with near neutral pH still limits the antibacterial efficiency. Herein, carboxyl modified Fe 3 O 4 immobilized silver nanoparticles (Ag NPs) and glucose oxidase (GOx) were synthesized as a self-activated cascade regent. GOx endowed the hybrids with catalytic activity, which could convert glucose into gluconic acid, which activated the peroxidase-like activity of Fe 3 O 4 by decreasing the pH value. Moreover, the self-supplied H 2 O 2 was subsequently catalyzed by Fe 3 O 4 to generate ⋅OH. By combining the excellent antimicrobial performance of Ag NPs, the enzyme assisted magnetic hybrids exhibited robust antibacterial activity, which exhibited a great potential in bacteria-infected wound therapy. [ABSTRACT FROM AUTHOR]

Global shipping flows through strategically important restricted waters such as Panama and Suez Canals, and hence accidents occurring in the Canals will cause serious disruptions to global supply chains. In this paper, a new data-driven Bayesian network (BN) based risk model is developed to investigate how risk factors jointly generate impact on different types of maritime accidents within restricted waters. A new risk database involving 25 factors has been developed by manual analysis of all the recorded accidents from 2005 to 2021 that occurred in the world's important restricted waters including key maritime canals, channels and straits. A data-driven BN model is constructed to analyse the key risk influential factors (RIFs) contributing to such accidents. The model is verified by sensitivity analysis and real accident cases. Further, it is tested by the already known information of the 2021 Suez Canal blockage case to generate useful insights and draw the lessons to learn. In a retrospective analysis using the currently available limited information on the Suez Canal case, the implication of the case study shows a plausible explanation for the observed findings by scenario analysis. The findings can be applied to backward risk cause diagnosis for accident investigation and forward risk prediction for accident prevention in restricted waters to avoid the reoccurrence of similar accident to the Suze Canal blockage. • A new data-driven risk model enables the systematic analysis of the risk factors of maritime accidents in restricted waters. • Analysis of the dependencies between RIFs for maritime accidents in restricted waters. • Forward risk prediction for accident analysis and prevention. • Backward risk diagnosis for root cause analysis of maritime accidents. • Analysis of the Suez Canal blockage case study to provide insights for accident investigation and prevention. [ABSTRACT FROM AUTHOR]

• Analyse the key risk factors influencing cargo theft from freight supply chains. • Develop a data-driven Bayesian Network model to realise cargo theft risk prediction and diagnosis. • Evaluate the critical risk factors to predict the occurrence of cargo theft accidents. • Analyse the real accidents to verify the model and provide implications for cargo theft prevention. Cargo theft has been among the most concerning risks influencing global freight supply chains, which causes serious supply chain disruptions, injuries/deaths, economic loss, and environmental damage. However, there are very few studies on the risk analysis of cargo theft, particularly in a quantitative manner, and fewer on the relevant risk factors affecting theft-related accidents in the current literature. This paper aims to analyse the risk influential factors (RIFs) of cargo theft and predict the occurrence likelihood of different types of cargo theft accidents. The historical data of 9316 cargo theft accidents that happened in the UK from 2009 to 2021 were first collected from the TAPA IIS database, and then purified and trained to construct a Bayesian network (BN) based cargo theft risk analysis model. The data-driven BN interprets the interdependency of RIFs and their combined effects on the occurrence of different types of cargo theft accidents. Compared with the previous studies, this paper makes new contributions, including that (1) The cargo theft RIFs are identified from the literature and accident records. (2) A data-driven BN is proposed to construct the model with uncertainty to realise cargo theft risk prediction and diagnosis. (3) The critical RIFs contributing to cargo theft are evaluated and prioritised to predict the occurrence of possible cargo theft accidents. (4) The real accidents are investigated to verify the model and draw useful insights for cargo theft prevention. The findings show that the most influential RIFs for the occurrence of cargo theft accidents are product category, year, location type, modus operandi (MO), and region. The findings also reveal the combined risk contributions of the RIFs, hence providing useful insights for cost-effective theft risk control in practice. [ABSTRACT FROM AUTHOR]

• Analyse the primary data to estimate the appearance frequencies of risk factors resulting in maritime accidents. • Evaluate the joint impact of human factors and other risk factors on different types of maritime accidents. • Develop an accident data-driven Bayesian network to realise human factor oriented maritime accident analysis. • Conduct an empirical study to provide insights for the prevention of a particular type of accidents involving human errors. A data-driven Bayesian network (BN) is used to investigate the effect of human factors on maritime safety through maritime accident analysis. Its novelties consist of (1) manual collection and analysis of the primary data representing frequencies of risk factors directly derived from maritime accident reports, (2) incorporation of human factors into causational analysis with respect to different maritime accident types, and (3) modelling by a historical accident data-driven approach, to generate new insights on critical human factors contributing to different types of accidents. The modelling of the interdependency among the risk influencing factors is structured by Tree Augmented Network (TAN), and validated by both sensitivity analysis and past accident records. Our findings reveal that the critical risk factors for all accident types are ship age, ship operation, voyage segment, information, and vessel condition. More importantly, the findings also present the differentiation among the vital human factors against different types of accidents. Most probable explanation (MPE) is used to provide a specific scenario in which the beliefs are upheld, observing the most probable configuration. The work pioneers the analysis of various impacts of human factors on different maritime accident types. It helps provide specific recommendations for the prevention of a particular type of accidents involving human errors. [ABSTRACT FROM AUTHOR]

The severe bacterial infection and chronic wound healing caused by the abuse of antibiotics threaten the public health, which calls the need for the development of novel antibacterial agents and alternative therapeutic strategies. Herein, magnetic carboxymethyl cellulose-ε-polylysine hybrids (FCE) were synthesized via a facile one-pot coprecipitation method and further used as matrix to anchor silver nanoparticles (Ag NPs). The as-resulted Ag/FCE hybrids were employed to inactivate pathogenic bacteria and accelerate bacteria-infected wound healing with the assistance of H 2 O 2. In vitro investigation revealed the combination of hydroxyl radical (·OH) originated from low concentration of H 2 O 2 catalyzed by Ag/FCE and the antimicrobial activity of Ag NPs endowed effective antibacterial performance to the hybrids against both Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus). Investigation on antibacterial mechanism indicated antibacterial activity of the synergetic strategy was achieved by destroying bacterial cell integrity, arresting metabolic, producing intracellular ROS, and oxidizing GSH. Additionally, in vivo assay exhibited Ag/FCE possessed satisfactory biocompatibility and effectively accelerated S. aureus -infected wound healing with the presence of low concentration of H 2 O 2. Altogether, the presented results supported the great potential application of the synergistic antibacterial strategy for the therapy of bacterial-infected wound healing. [Display omitted] • FCE hybrids with peroxidase-like activity were synthesized via a one-pot method. • FCE hybrids anchoring Ag NPs were employed for synergistic antibacterial strategy. • Ag/FCE + H 2 O 2 possessed excellent antibacterial activity against pathogenic bacteria. • The synergistic antibacterial strategy showed effective wound healing effect. [ABSTRACT FROM AUTHOR]

Human factors contribute to majority of maritime accidents. This study proposes an advanced methodology for maritime accident prevention strategy formulation from a human factor perspective. It is conducted by incorporating Bayesian network (BN) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) in a multi-criteria decision-making system. In order to develop rational accident prevention strategies, this work integrates Multiple Correspondence Analysis (MCA), Hierarchical Clustering (HC) and Classification Tree (CT) to generate strategies and describes accident types as criteria for a new multi-criteria risk-based decision-making system. Specifically, MCA is performed to detect patterns of contributory factors explaining maritime accident types. It is complemented by HC and a CT, aiming at creating different classes of vessels. Next, a Bayesian-based TOPSIS model is built to illustrate the features of multiple criteria and the relations among alternatives (i.e. strategies), so as to select the best-fit strategies for accident prevention. The results show that the information, clear order, and safety culture are the three most effective recommendations for maritime accident prevention considering human errors, which presents new insights for accident prevention practice for maritime authorities. • Accident prevention strategies are generated from a human factor perspective. • Methodology for accident prevention strategy formulation using BN and TOPSIS. • The model illustrates features of multiple criteria and relations among strategies. • The best-fit strategies are proposed for maritime accident prevention. [ABSTRACT FROM AUTHOR]

The optimal investment problem in which the asset price process is modeled by the non-extensive statistical mechanics is studied in this paper. By the methods of deterministic control and the dynamic programming, we obtain the optimal strategy with logarithmic utility function, power utility function and quadratic utility function, respectively. Moreover, the numerical results indicate that the optimal investment strategy is affected by the non-extensive parameter, the proportion invested in the risky asset decreases as the wealth increases under quadratic utility function, but it remains unchanged under power utility and logarithmic utility function. [ABSTRACT FROM AUTHOR]

The strong, transient, and high-peak impact load caused by a vehicle entering water at high speed is the primary factor threatening its structural strength and equipment safety. One of the effective methods for water entry protection is to use head-mounted buffer components. To this end, based on the arbitrary Lagrangian-Eulerian method, a numerical model of impact load reduction of water entry was established in this study. The results showed that the impact load could be characterised by a narrow pulse width and a high peak value, which gradually stabilises in a fluctuating manner. Five typical stages in the failure process of the cushion foam and four typical stages in that of the nose cap were observed. The prediction accuracy decreased as the scaling ratio decreased. Furthermore, the buffer components increased the occurrence of an error in the prediction results of the scaling model. The primary causes are the strain-rate sensitivity of the foam and dissimilarity in the failure criteria, which lead to dissimilarities in the foam failure process after scaling. This research will be useful for the designing of water load reduction structures and scaled model experimental schemes. [ABSTRACT FROM AUTHOR]

The Belt and Road (B&R) initiative was introduced by the Chinese government to promote the worldwide economic development and multilateral cooperation between China and the associated countries. As a crucial part of global supply chains, transportation plays a key role to ensure the implementation of the B&R. Safety is one of the issues with great importance in transportation research. However, its foci have been expanded from traditional risk through security to resilience and sustainability. Resilience has attracted considerable interests from both researchers and practitioners across different research domains in recent years. Various studies have been conducted on transportation resilience from different perspectives. Consequently, different definitions have been developed to define and describe resilience. This paper presents a systematic review on transportation resilience with emphasis on its definitions, characteristics, and research methods applied in different transportation systems/contexts. It aims to figure out what transportation resilience is and what kind of essential characters it usually has. More importantly, research challenges are analysed and a future research agenda on the resilience of transportation systems is proposed. This paper will provide comprehensive insights into understanding the transportation resilience, as well as establish new horizons for relevant research topics within the context of the B&R. [ABSTRACT FROM AUTHOR]

Titanium and its alloys have been widely used as bone tissue engineering scaffold due to their excellent biocompatibility and mechanical strength. In order to promote bone ingrowth, integration and match the mechanical properties of natural bone tissue, this study proposed diamond-like microstructure porous Ti-6Al-4V scaffolds for personalized bone defect repair. In order to prepare the multiple biological functional coatings on the curve surface inside of the 3D printed porous scaffolds, pulsed electrodeposition method was proposed to obtain multiple layers of Ti 3 C 2 T x /HAp composited coatings. Delaminated Ti 3 C 2 T x has an ultra-thin microstructure according to transmission electron microscopy (TEM) observation and is well dispersed in electrolyte. X-ray diffraction (XRD) characterization shows that Ti 3 C 2 T x has been synthesized and regulates more CaHPO 4 ‧2H 2 O (DCPD) to Hydroxyapatite (HAP) conversion, which is confirmed by Fourier transform infrared spectroscopy (FTIR) at 0.2 mg/mL concentration. Scanning electron microscopy (SEM) shows that the morphologies of composite coatings are closely related to Ti 3 C 2 T x content due to the availability of effective voltages for electrodeposition. By means of finite element method, deposition thickness and distribution of coatings were simulated to explore the precipitation kinetics and selection of electrodeposition parameters and a current density of 15 mA/cm2 was ultimately used. In vitro electrochemical corrosion of Ti 3 C 2 T x /HAp coatings were tested in human simulated body fluid (SBF) to verify its corrosion resistance in physiological environment and Ti 3 C 2 T x /HAp composited coatings possess the best corrosion resistance according to linear scanning voltammetry (LSV) and electrochemical impedance spectroscopy (EIS) tests. In vitro MC3T3-E1 cell culturing studies were investigated to evaluate cell behaviors and biocompatibility of these coatings. The results indicate that cells grow well on the surface of composite coatings without showing cytotoxicity and cell morphologies show a good spreading state and a trend of inward growth. • MXenes/HAp composite coatings have been prepared on the surface of 3D printed bone tissue engineering scaffolds by pulse electrodeposition. • Ti 3 C 2 T x -MXenes promotes the homogeneous nucleation of hydroxyapatite during electrodeposition process and possesses good biocompatibility and multiple biological functions. • The electrodeposition process was simulated by finite element analysis, and the deposition kinetics was studied to optimize the process parameters. • Customized parametric design of electrolytic cell and electrodeposition scaffolds for composite coating preparation process. [ABSTRACT FROM AUTHOR]

A data-driven Bayesian network model (BN) is used to analyse the relationship between the severity of marine accidents and relevant Accident Influential Factors (AIFs). Firstly, based on the marine accident investigation reports involving 1,294 ships from 2000 to 2019, the severity grades of marine accidents are classified, and a database of factors affecting the severity of marine accidents is established. Secondly, a Tree Augmented Naive Bayesian algorithm (TAN) is used to establish a data-driven BN model, and the established database of AIFs is analysed by data training and machine learning to reveal the influence of related factors on the severity of the accident and the mechanism of action. Finally, the sensitivity analysis and verification of the model are conducted. Through the analysis of the Most Probable Explanation (MPE), it explains the possible configurations in different scenarios and identifies the related potential risks. This study finds that "accident type", "engine power", "gross tonnage", "ship type" and "location" are the five most important AIFs of three accident severity grades. "Capsizing/sinking", "hull/machinery damage" and "collision" that are most likely to lead to "very serious accidents". Further, the possibility of fishing boats or other small ships leading to "very serious accidents" is also higher than that of other types of ships. The results of this study can help to analyse and predict marine accidents and ensure the safe navigation of ships and hence benefit such maritime stakeholders as safety authorities and ship owners. • A new method to analyse the relationship between the severity of marine accidents and AIFs is developed. • A database of factors affecting the severity of marine accidents is established. • A data-driven model integrated BN and TAN algorithm is proposed. • The sensitivity analysis and verification of the model are conducted. [ABSTRACT FROM AUTHOR]

Previous relevant studies have revealed that noise and poor sleep quality are two important risk factors causing seafarer fatigue. However, the relationship between marine engine noise and objective sleep parameters has rarely been studied. Using primary data collected from a 28-day on-board experiment and 1 questionnaire survey during both voyage and berthing periods, this study takes a pioneering step to address this crucial relationship. Energy indicators related to the engine noise for 28 days were estimated and 6 objective sleep parameters were used to measure the degree of seafarer fatigue. The findings reveal that as seafarers want to sleep longer to relieve their anxiety and irritability caused by the increased engine noise, the time in bed (TB) and the total sleep time (TST) increased when the engine noise level increased. Meanwhile, with the growing engine noise levels and the higher number of engine noise events, the total wake time after sleep onset (WASO) and the time for sleep onset latency (SOL) increased, and the sleep efficiency (SE) decreased. Energy indicators were significantly associated with objective sleep parameters. Finally, strengthening the content of psychological adjustment in the seafarer training link and cultivating the seafarers' character strength to improve the ability to face harsh environments are recommended. In maritime management, managers should play the role of social work intervention to adjust seafarers' sleep quality and ease fatigue. In the construction of ships, builders should emphatically consider the use of sound insulation materials to reduce noise effect on living areas. • A 28-day on-board experiment and a questionnaire survey during both sailing and berthing periods are conducted. • Six objective sleep parameters were used to measure the degree of seafarer fatigue. • Case studies are conducted using binary and ordered logistic regression model. • Appropriate psychological advice on alleviating seafarer fatigue is provided. [ABSTRACT FROM AUTHOR]

The taper leaf spring of tandem suspension of commercial vehicle is different from the traditional taper leaf spring. Thus, the professional software MSC.ADAMS/CHASSIS leaf spring, which is specially applied in traditional leaf spring, does not reliably develop the computational model of the taper leaf spring of tandem suspension. The multi-body dynamic model of the taper leaf spring of tandem suspension, which is developed in this paper, is a secondary development model that is based on the leaf spring model built by MSC.ADAMS/CHASSIS leaf spring. End contact and friction in the modified model are redefined to exhibit the hysteretic characteristics of the taper leaf spring of tandem suspension. The test of the taper leaf spring of tandem suspension is conducted to validate the reliability of the modified model. The tests in this paper are divided into two groups. The first group started at an unloaded state at an excitation frequency of 1/30 Hz and amplitude of 70 mm to acquire quasi-static behavior. The second group is conducted at various frequencies (2, 3 and 4 Hz) and various amplitudes (±1, ±3 and ±10 mm) in a loaded state to acquire dynamic behavior. A formula to calculate dynamic spring rate for leaf spring is proposed, and details about the formula are presented. The simulations are conducted under the same conditions as the test. The hysteretic characteristics and the relative error of dynamic spring rate from the test are compared with the ones from the simulations for the validation of the reliability of the modified model. [ABSTRACT FROM AUTHOR]

[Display omitted] • A heterogeneous structure was built via a Voronoi-Thiessen based parametric modeling method. • Heterogeneity had little influence on porosity, thus, mechanical/hydrodynamic properties. • Irregular local geometry mimicked cancellous bone structure and promoted cellular behavior. • 50% heterogeneity (SR structure) kept a balance of structure integrity and osteogenesis. In nature, many natural porous tissues of animals or plants follow heterogeneous structures pattern. Including turtle back pattern, leaf veins, wood porous fiber and porous cancellous bone are classic heterogeneous structures. In order to better design bionic microenvironment porous bone implants, herein, a bionic porous structure based on Voronoi-Thiessen diagram to create heterogeneous topology microenvironment promoting bone regeneration were proposed. Porous structures with tunable heterogeneity were built and achieved by selective laser melting (SLM). The topology analysis revealed the introduce of heterogeneity brought wide pore size distribution and irregular topology. The mechanical and hydrodynamics properties were predesigned and simulated by finite element analysis (FEA). The heterogeneity hardly influenced the overall strength, stiffness and permeability but the irregular stress and fluid shear rate distribution in FEA results indicated fluctuating local stiffness and permeability. In vitro and in vivo experimental results indicated that Quasi-homogeneous structure had irregular topology mimicking that of the natural bone structure and semi-heterogenous structure shown better osteogenesis. It is found that the scaffold heterogeneous microenvironment factors play important roles for bone regeneration, and which is crucial for the development of new orthopedic implants. [ABSTRACT FROM AUTHOR]

A large body of research has established diabetes-related cognitive deterioration, sometimes known as "diabetic encephalopathy". Current evidence supports that oxidative stress, neuronal apoptosis, and cerebral microcirculation weakness are associated with cognition deficits induced by diabetes. The present study explores the effect of propionate on neurological deficits, cerebral blood flow, and oxidative stress in diabetic mice. Propionate in different doses (37.5, 75 and 150 mg/kg) was orally administrated daily. Here, we show that propionate can markedly improve neurological function, which is correlated with its capabilities of stimulating nitrogen monoxide (NO) production, increasing cerebral microcirculation, suppressing oxidative stress, and reducing neuron loss in the hippocampus. In addition, the results of Western Blotting indicated that the brain-protective function of propionate in streptozocin (STZ)-induced type 1 diabetes mellitus (T1DM) mice is related to phosphoinositide 3-kinase (PI3K)/serine-threonine protein kinase (Akt)/endothelial nitrogen monoxide synthase (eNOS) signaling pathway. In a diabetic mouse model, propionate reduces cerebral microcirculation, hippocampus apoptosis, and neurological impairment. Thus, propionate, now employed as a food preservative, may also help slow diabetes-induced cognitive loss. [Display omitted] • Propionate ameliorates diabetic-induced depression-like behavior, spatial learning, and memory deficits. • Propionate increases cerebral microcirculation by enhancing NO production in diabetic mice. • Propionate suppresses oxidative stress and reduces neuron loss in the hippocampus. • The brain-protective function of propionate is related to PI3K/Akt/eNOS signaling pathway. [ABSTRACT FROM AUTHOR]

In the era of big data, mass customization (MC) systems are faced with the complexities associated with information explosion and management control. Thus, it has become necessary to integrate the mass customization system and Social Internet of Things, in order to effectively connecting customers with enterprises. We should not only allow customers to participate in MC production throughout the whole process, but also allow enterprises to control all links throughout the whole information system. To gain a better understanding, this paper first describes the architecture of the proposed system from organizational and technological perspectives. Then, based on the nature of the Social Internet of Things, the main technological application of the mass customization–Social Internet of Things (MC–SIOT) system is introduced in detail. On this basis, the key problems faced by the mass customization–Social Internet of Things system are listed. Our findings are as follows: (1) MC–SIOT can realize convenient information queries and clearly understand the user's intentions; (2) the system can predict the changing relationships among different technical fields and help enterprise R&D personnel to find technical knowledge; and (3) it can interconnect deep learning technology and digital twin technology to better maintain the operational state of the system. However, there exist some challenges relating to data management, knowledge discovery, and human–computer interaction, such as data quality management, few data samples, a lack of dynamic learning, labor consumption, and task scheduling. Therefore, we put forward possible improvements to be assessed, as well as privacy issues and emotional interactions to be further discussed, in future research. Finally, we illustrate the behavior and evolutionary mechanism of this system, both qualitatively and quantitatively. This provides some idea of how to address the current issues pertaining to mass customization systems. [ABSTRACT FROM AUTHOR]

[Display omitted] • 18β-GA (a constituent of licorice) effectively reversed the mice behavioral dysfunction after exposure to cuprizone for five weeks. • 18β-GA sufficiently alleviates demyelination in a mouse model of cuprizone-induced demyelination. • The function of 18β-GA is related to moderating M1/M2 polarisation. This research aimed to examine the nutritious supplementary function of 18β-Glycyrrhetinic acid (18β-GA) in moderating the myelin sheath destruction and behavioral impairments observed in the cuprizone model of demyelination. Mice were fed daily on food containing cuprizone (0.3 %) and given doses of 18β-GA (5 or 1 mg/kg) for a period of five weeks. The groups treated with 18β-GA exhibited improvements in exploratory behavior, locomotive activity, and weight. As assessed using luxol-fast blue and myelin basic protein (MBP) staining, which were used to detect demyelination in the brain, 18β-GA both reduced and prevented instances of cuprizone-induced demyelinating lesions; treatment with 18β-GA also caused the MBP level in the corpus callosum to increase. Furthermore, alongside these positive results following 18β-GA treatment, microglial polarisation was also observed to shift towards the beneficial M2 phenotype. The results of this research thus indicate the potential clinical application of 18β-GA for the prevention of myelin damage and behavioral dysfunction. [ABSTRACT FROM AUTHOR]

With an associated 20% death risk, epilepsy mainly involves seizures of an unpredictable and recurrent nature. This study was designed to evaluate the neuroprotective effects and underlying mechanisms of insulin on mitochondrial disruption, oxidative stress, cell apoptosis and neurological deficits after epilepsy seizures. Mice were exposed to repetitive injections of pentylenetetrazol at a dose of 37 mg per kg. The influence of insulin was assessed by many biochemical assays, histopathological studies and neurobehavioral experiments. The administration of insulin was proven to increase the latency of seizures while also decreasing their intensity. It also caused a reversal of mitochondrial dysfunction and ameliorated oxidative stress. Additionally, insulin pretreatment upregulated Bcl-2, downregulated Bax, and then played a neuroprotective role against hippocampal neuron apoptosis. Furthermore, when insulin was administered, SIRT1/PGC-1α/SIRT3 signals were activated, possibly due to the fact that insulin's neuroprotective and anti-mitochondrial damage characteristics added to its observed antiepileptic functions. Finally, insulin treatment is thus extremely valuable for effecting improvements in neurological functions, as has been estimated in a series of functional tests. In conclude, the results of this study consequently demonstrate insulin to have significant potential for future application in epilepsy management. [Display omitted] • Insulin injections declined seizure intensity. • Insulin played a neuroprotective role against hippocampal neuron apoptosis. • Neuroprotection of insulin against mitochondrial damage on animals is related to SIRT1/PGC-1α/SIRT3 signaling pathway. [ABSTRACT FROM AUTHOR]