Cluster Design Research Articles

Enhanced visualization of microbiome data in repeated measures designs

IntroductionRepeated measures microbiome studies, including longitudinal and clustered designs, offer valuable insights into the dynamics of microbial communities and their associations with various health outcomes. However, visualizing such multivariate data poses significant challenges, particularly in distinguishing meaningful biological patterns from noise introduced by covariates and the complexities of repeated measures.MethodsIn this study, we propose a framework to enhance the visualization of repeated measures microbiome data using Principal Coordinates Analysis (PCoA) adjusted for covariates through linear mixed models (LMM). Our method adjusts for confounding variables and accounts for the repeated measures structure of the data, enabling clearer identification of microbial community variations across time points or clusters.ResultsWe demonstrate the utility of our approach through simulated scenarios and real datasets, showing that it effectively mitigates the influence of nuisance covariates and highlights key axes of microbiome variation.DiscussionThis refined visualization technique provides a robust tool for researchers to explore and understand microbial community dynamics in repeated measures microbiome studies.

A General Framework for the Multiple Nonparametric Behrens-Fisher Problem With Dependent Replicates.

In many trials and experiments, subjects are not only observed once but multiple times, resulting in a cluster of possibly correlated observations (e.g., brain regions per patient). Observations often do not fulfill model assumptions of mixed models and require the use of nonparametric methods. In this article, we develop and present a purely nonparametric rank-based procedure that flexibly allows the unbiased and consistent estimation of the Wilcoxon-Mann-Whitney effect in clustered data designs. Compared with existing methods, we allow flexible weights to be used in effect estimation. Additionally, we develop global and multiple contrast test procedures to test null hypotheses formulated regarding the generalized Mann-Whitney effects and for the computation of range-preserving simultaneous confidence intervals in a unified way. Extensive simulation studies show that these methods control the type-I error rate well and have reasonable power to detect alternatives in various situations.

Tuning the Electronic Properties of CumAgn Bimetallic Clusters for Enhanced CO2 Activation

The urgent demand for efficient CO2 reduction technologies has driven enormous studies into the enhancement of advanced catalysts. Here, we investigate the electronic properties and CO2 adsorption properties of CumAgn bimetallic clusters, particularly Cu4Ag1, Cu1Ag4, Cu3Ag2, and Cu2Ag3, using generalized gradient approximation (GGA)/density functional theory (DFT). Our results show that the atomic arrangement within these clusters drastically affects their stability, charge transfer, and catalytic performance. The Cu4Ag1 bimetallic cluster emerges as the most stable structure, revealing superior charge transfer and effective chemisorption of CO2, which promotes effective activation of the CO2 molecule. In contrast, the Cu1Ag4 bimetallic cluster, in spite of comparable adsorption energy, indicates insignificant charge transfer, resulting in less pronounced CO2 activation. The Cu3Ag2 and Cu2Ag3 bimetallic clusters also display high adsorption energies with remarkable charge transfer mechanisms, emphasizing the crucial role of metal composition in tuning catalytic characteristics. This thorough examination provides constructive insights into the design of bimetallic clusters for boosted CO2 reduction. These findings could pave the way for the development of cost-effective and efficient catalysts for industrial CO2 reduction, contributing to global efforts in carbon management and climate change mitigation.

Trends in dietary choline and betaine intake among Chinese adults: the China Health and Nutrition Survey 1991-2011.

Choline and its derivative betaine are important methyl donors, components of cell membrane phospholipids, or precursors of the neurotransmitter acetylcholine and the gut microbial metabolite trimethylamine-N-oxide. We aimed to investigate trends in dietary intake and food sources of total choline, individual choline forms, and betaine in Chinese adults using data from the China Health and Nutrition Survey (CHNS) 1991-2011. The sample was drawn from urban and rural communities in 12 provinces or autonomous regions with a multistage, random cluster design. Dietary intake was estimated using three consecutive 24-hour dietary recalls in combination with a household food inventory. Linear mixed-effect models were constructed to calculate adjusted mean intake values and 95% confidence intervals (CIs) using R version 4.2.2. A total of 11,188 men and 12,279 women aged 18 years or older were included. Between 1991 and 2011, total choline intake increased from 219.3 (95% CI: 215.1-223.4) mg/d to 269.0 (95% CI: 265.6-272.5) mg/d in men and from 195.6 (95% CI: 191.8-199.4) mg/d to 240.4 (95% CI: 237.4-243.5) mg/d in women (both P-trends <0.001). Phosphatidylcholine was the major form of dietary choline and its contribution to total choline increased from 46.9% in 1991 to 58.8% in 2011. Cereals were the primary food source of total choline before 2000 while eggs had ranked at the top since 2004. Dietary betaine intake was relatively steady over time with a range of 134.0-151.5 mg/d in men (P-trend <0.001) and 111.7-125.3 mg/d in women (P-trend >0.05). In conclusion, Chinese adults experienced a significant increase in dietary intake of choline, particularly phosphatidylcholine during 1991-2011 and animal-derived foods have replaced plant-based foods as the main food sources of choline. Betaine intake remained relatively stable over time. Future efforts should address the health effects of these changes.

Efficacy of a Wearable Activity Tracker With Step-by-Step Goal-Setting on Older Adults' Physical Activity and Sarcopenia Indicators: Clustered Trial.

Smart wearable technology has potential benefits for promoting physical activity and preventing sarcopenia. The purpose of this study was to explore the efficacy of a wearable activity tracker with 2-stage goal-setting for daily steps on older adults' physical activity and sarcopenia indicators. The study used a clustered trial design and was conducted in March to June 2022. Participants were community-dwelling adults older than 60 years who were recruited from 4 community centers in Taipei City. The intervention was designed with 2-stage goals set to 5000 steps/day in the first 4 weeks and 7500 steps/day in the final 4 weeks while wearing a commercial wearable activity tracker. Data were collected by self-reported questionnaires, a body composition analyzer, a handle grip tester, and 5 sit-to-stand tests. All 27 participants in the experimental group and 31 participants in the control group completed the 8-week intervention. Total and light-intensity physical activities, skeletal muscle index, and muscle strength increased, while sedentary time, BMI, and the waist circumference of participants decreased in the experimental group, with significant group-by-time interactions compared to the control group. A wearable activity tracker with gradual goal-setting is an efficient approach to improve older adults' physical activity and sarcopenia indicators. Smart wearable products with behavioral change techniques are recommended to prevent sarcopenia in older adult populations.

Estimates of intra-cluster correlation coefficients from 2018 USA Medicare data to inform the design of cluster randomized trials in Alzheimer’s and related dementias

BackgroundCluster randomized trials (CRTs) are increasingly important for evaluating interventions embedded in health care systems. An essential parameter in sample size calculation to detect both overall and heterogeneous treatment effects for CRTs is the intra-cluster correlation coefficient (ICC) of both outcome and covariates of interest. However, obtaining advance estimates for the ICC can be challenging. When trial outcomes will be obtained from routinely collected data sources, there is an opportunity to obtain reliable ICC estimates in advance of the trial. Using USA national Medicare data, we estimated ICCs for a range of outcomes to inform the design of CRTs for people living with Alzheimer’s and related dementias (ADRD).MethodData from 2018 Medicare Fee-for-Service beneficiaries, specifically, 1,898,812 individuals (≥ 65 years) with diagnosis of ADRD within 3436 hospital service areas (treated as clusters) and 306 hospital referral regions (treated as fixed strata), were used to calculate unadjusted and adjusted ICC estimates for three outcomes: death, any hospitalizations, and any emergency department (ED) visits and three covariates: age, race and sex. We present both overall and stratum-specific ICC estimates. We illustrate their use in sample size calculations for overall treatment effects as well as detecting treatment effect heterogeneity.ResultsThe unadjusted overall ICCs for death, hospitalizations, and ED visits were 0.001, 0.010, and 0.017 respectively. Stratum-specific ICCs varied widely across the 306 HRRs: median 0.001, 0.010 and 0.025 for death, hospitalizations, and ED visits respectively and 0.007, 0.001, and 0.080 for age, sex and race. An interactive R Shiny app is provided that allows users to retrieve estimates overlayed on a map of the USA.ConclusionsWe presented both adjusted and unadjusted ICCs for outcomes as well as unadjusted ICCs for covariates of potential interest from population-level data in the USA and demonstrated how the estimates may be used in sample size calculations for CRTs in ADRD.

Addressing the complexities of evaluating comprehensive sexuality education: an Italian case study

Abstract Background Existing evaluation methods for assessing sexuality education programs may not be suitable for Comprehensive Sexuality Education (CSE) interventions, which are globally indicated as the most effective approach for promoting sexual health and preventing STIs. This holds particular significance in countries, such as Italy, where CSE is not integrated into school curricula. In a pilot project funded by the Italian Ministry of Health since 2019 (EduForIST), we aimed to develop a composite evaluation framework to test the CSE intervention we implemented in lower and upper secondary schools over the past 4 years. Methods Based on the latest available evidence, evaluating a CSE intervention requires addressing various dimensions. These include assessing the quality of programs and their implementation, as well as measuring outcomes and impact. Through iterative consultations with experts and providers, methods, criteria, and metrics were chosen and tested for each dimension. Results Program quality was evaluated by an expert advisory board. Instruments for implementation quality assessment included: satisfaction survey among students; reflective journals and SWOT analysis drawn up by educators delivering intervention, exploring their perceptions, practices, and attitudes; surveys among teachers and parents, measuring knowledge, attitude, and practice on CSE, and observed changes among adolescents. A four-arm, Solomon, cluster design study was designed to evaluate outcomes in terms of changes in students’ knowledge. Clinical and behavioral indicators in the short, medium and long term were selected as measures of the impact of the intervention. Conclusions In the field of CSE implementation research, a multi-component approach to evaluation is needed to provide a solid basis for informing policymakers. The involvement of providers, recipients and contextual adults is essential in evaluating CSE interventions. However, assessing long-term impact requires long time frame. Key messages • Assessing CSE interventions requires a multi-component evaluation approach, considering the impact on the personal and emotional development of individuals in order to sustain behavior changes. • The proposed evaluation framework may inform scientific community, professionals, and decision-makers on effectiveness of interventions in settings where CSE is not yet included in school curricula.

Cast-OFF 2: one week versus three to five weeks of plaster cast immobilization for non-or minimally displaced distal radius fractures, a stepped wedge cluster randomized controlled trial.

Implementation of one week of cast immobilization followed by gradually increasing wrist mobilization for non- or minimally displaced DRF and comparison of the functional outcomes and pain scores with the usual care (three to five weeks of cast immobilization). Design: A randomized stepped wedge cluster design, prospective cohort. Academic and peripheral hospitals in level 1, 2, and 3 trauma centers. All patients between 18 and 85 years old with an isolated non- or minimally and nonreduced DRF were eligible for inclusion. Participating hospitals were randomized to transition from usual care (three to five weeks of cast immobilization) to one week of cast immobilization, following the stepped wedge design. Patient characteristics, secondary dislocation, surgical treatment, visual analog scale (VAS), Patient Rated Wrist Evaluation (PRWE), Patient Reported Outcomes Measurement Information System Pain Interference (PROMIS-PI), Pain Catastrophizing Scale 4 (PCS-4), and patient satisfaction were compared between control, and intervention group at week 1, 3-5, 6, month 3, 6, and 12. A difference around 11 points on the PRWE scale was considered clinically significant. 402 patients were included (control n=197 vs intervention n=205, 267/135, female/male). There were no differences in age (53.7 ± 18.6 vs 53.3 ± 19.5, P = 0.27), sex (66% vs 67% female, P 0.44), dominant hand fractured (44% vs 53%, P 0.39), and type of fracture (39% vs 41% extra-articular, P = 0.44) After six weeks, the PRWE score showed no clinically significant differences (-4.5 [CI -12.9, 4.02], P = 0.30). No significant differences were observed for function, pain scores, and patient satisfaction between groups (all P > 0.05). Furthermore, there was no significant difference in secondary dislocation rate (control 1.5% vs intervention 1.0%, p=0.32, P = 0.32) and operation rate (control 1.5% versus 1.5% intervention P = 0.92). This study compared one week of cast immobilization followed by gradually increasing wrist mobilizationto the usual care of three to five weeks for nonreduced DRF. No clinically significant differences in function, pain scores, patient satisfaction, secondary dislocation, and operations were observed. Therefore, one week of plaster immobilization can be safely recommended for the non- or minimally displaced and nonreduced DRF treatment. Level II. See Instructions for Authors for a complete description of levels of evidence.

Solar Species: Energy Optimization of Urban Form Through an Evolutionary Design Process

This paper proposes design guidelines to enhance energy efficiency and energy generation potential in active solar buildings. Additionally, it presents a variety of optimized urban forms characterized by attributes such as shape, layout, and number of buildings on the plot. These urban configurations are classified into solar species, each associated with a distinct range of high passive and active solar potential. These results were achieved by developing and applying a simulation-driven, multi-objective optimization technique for the early-stage design of a residential building cluster in a temperate climate. This method leverages both passive and active energy indicators, employing a genetic algorithm to identify optimal forms that maximize active solar potential while also minimizing operational energy demand. The approach utilizes a parametric modelling routine that relies on vertical cores and horizontal connections to produce design iterations featuring irregular geometry, while ensuring structural continuity and means of egress. The findings reveal a significant variability in onsite energy generation, with optimized solutions differing by a factor of 2.5 solely based on shape, underscoring the critical role of active solar potential. Taken together, these results hint at the descriptive and predictive capabilities of these solar species, making them a promising heuristic model for characterizing urban form in relation to energy performance.

Sample Size Planning in the Design of Two-Level Randomized Cost-Effectiveness Trials

This study introduces recent advances in statistical power analysis methods and tools for designing and analyzing randomized cost-effectiveness trials (RCETs) to evaluate the causal effects and costs of social work interventions. The article focuses on two-level designs, where, for example, students are nested within schools, with interventions applied either at the school level (cluster design) or student level (multisite design). We explore three statistical modeling strategies—random-effects, constant-effects, and fixed-effects models—to assess the cost-effectiveness of interventions, and we develop corresponding power analysis methods and tools. Power is influenced by effect size, sample sizes, and design parameters. We developed a user-friendly tool, PowerUp!-CEA, to aid researchers in planning RCETs. When designing RCETs, it is crucial to consider cost variance, its nested effects, and the covariance between effectiveness and cost data, as neglecting these factors may lead to underestimated power.

Antenatal Care Services Uptake and Associated Factors in Somaliland: Further Analysis of the 2020 Somaliland Demographic Health Survey

Purpose Antenatal care (ANC) is vital to maintain the health of pregnant women and their unborn babies. It was linked to a positive pregnancy experience. However, ANC uptake remained low in Somaliland, and no study has been conducted to assess possible predictors. Hence, the current study aimed to determine the level of ANC utilization and to identify its associated factors in Somaliland. Materials and Methods Somaliland Demographic Health Survey (SLDHS) Data 2020 were used. A weighted sample of 3,192 women aged 15-49 years participated in the study. The survey used a two-stage cluster design for sample selection. Descriptive statistics in the form of frequency and percentage and multilevel multinomial logistic regression analysis were carried out to determine associated factors and statistical significance was declared by p&lt; 0.05. Results The findings showed that 62.4% (95% CI: 60.7-64.1) of the women did not utilize ANC services, while 23.4% (95% CI: 21.9, 24.9) partially utilized and 14.2% (95% CI: 13.0, 15.4) adequately utilized ANC services. The odds of adequately utilizing ANC service decreased by a factor of 0.88 for women aged 35 and above compared to 15 - 24 year old women (AOR= 0.88, 95% CI: 0.61, 1.26). Women with higher educational levels had significantly higher odds (AOR=4.31, 95% CI: 1.54, 12.02) of adequately utilizing ANC compared to those with no formal education. The employment status, marital status, household wealth index, residence and region were also found to be associated with the utilization of Antenatal care. Conclusion The study showed low adequate utilization of ANC services in Somaliland. The variables, including age, education, wealth index, marital status, employment status, residence and region, were significantly associated with ANC utilization. Governmental and non-governmental organizations should enact a strategy with targeted intervention focusing on women from nomadic and rural communities, those from poor households, and low educational levels.

Atomic-level design of biomimetic iron-sulfur clusters for biocatalysis.

Designing biomimetic materials with high activity and customized biological functions by mimicking the central structure of biomolecules has become an important avenue for the development of medical materials. As an essential electron carrier, the iron-sulfur (Fe-S) clusters have the advantages of simple structure and high electron transport capacity. To rationally design and accurately construct functional materials, it is crucial to clarify the electronic structure and conformational relationships of Fe-S clusters. However, due to the complex catalytic mechanism and synthetic process in vitro, it is hard to reveal the structure-activity relationship of Fe-S clusters accurately. This review introduces the main structural types of Fe-S clusters and their catalytic mechanisms first. Then, several typical structural design strategies of biomimetic Fe-S clusters are systematically introduced. Furthermore, the development of Fe-S clusters in the biocatalytic field is enumerated, including tumor treatment, antibacterial, virus inhibition and plant photoprotection. Finally, the problems and development directions of Fe-S clusters are summarized. This review aims to guide people to accurately understand and regulate the electronic structure of Fe-S at the atomic level, which is of great significance for designing biomimetic materials with specific functions and expanding their applications in biocatalysis.

Analysis of the Impact of Clusters on Productivity of Multi-Fracturing Horizontal Well in Shale Gas

Shale gas reservoirs with nanoporous media have become one of the primary resources for natural gas development. The nanopore diameters of shale reservoirs range from 5 to 200 nm, with permeability ranging from 1 × 10−9 to 1 × 10−6 μm2. The natural gas production from shale gas reservoirs is low, necessitating the use of multi-stage hydraulic fracturing in horizontal wells. Segmented multi-cluster perforation fracturing is an effective method for shale gas extraction in these wells. The number of clusters significantly impacts the productivity of horizontal wells. Therefore, it is essential to analyze the impact of cluster numbers on fracture productivity in shale gas reservoir development. In this study, the equivalent flow resistance method was applied to establish a productivity model for multi-stage hydraulic fracturing horizontal wells in shale gas reservoirs considering diffusion and slip. An approximate analytical solution was obtained, and the effects of cluster length, diffusion coefficient, and fracture network permeability on productivity were analyzed. The results show that gas production gradually increases with the increase in the number of clusters and cluster length. However, as the number of clusters increases, the interference between clusters leads to a decrease in the productivity of individual clusters. As the fracture permeability, fracture network permeability, and diffusion coefficient increase, shale gas production also gradually increases. The permeability of the fracture network has the greatest impact on productivity. These research results are beneficial for the design of clusters in horizontal well fracturing and are of great importance for the development and production of shale gas reservoirs.

Optimizing Ni-Zr-Ti metallic glasses: Cluster design and molecular dynamics evaluation of glass forming ability

Ni-based metallic glasses have garnered significant attention due to their excellent mechanical properties and wide application prospects. However, their low glass forming ability (GFA) limits broader applications. This study explores the Ni-Zr-Ti ternary system, which excludes precious metals, using clusters and mixing entropy composition design method to develop a series of Ni-based metallic glasses with improved GFA. The study found that the composition Ni60.91Zr24.97Ti14.12 (NZT3) had the best GFA, with its cluster expression Ni-Ni8Ti4 + 1.17838 Ni-Ni6Zr6. To gain a deeper understanding of the GFA of metallic glasses, molecular dynamics (MD) simulations were conducted on the designed compositions. The simulation results indicated a significant correlation between the average local five-fold symmetry (ALFFS) and the GFA of the compositions. Ni60.91Zr24.97Ti14.12 exhibited the highest ALFFS value and the best GFA. The research demonstrates that by combining clusters and mixing entropy composition design method with the simulated ALFFS, metallic glasses with excellent GFA in the Ni-Zr-Ti system can be rapidly screened. The successful application of this innovative approach in this system not only deepens the understanding of metallic glass formation mechanisms but also provides significant guidance for future materials design.

Geological and Engineering Integration Fracturing Design and Optimization Study of Liushagang Formation in Weixinan Sag

The Weixinan Sag in the Beibuwan Basin is rich in shale oil resources. However, the reservoirs exhibit rapid phase changes, strong compartmentalization, thin individual layers, and high-frequency vertical variations in the thin interbedded sandstone and mudstone. These factors can restrict the height of hydraulic fracture propagation. Additionally, the low-porosity and low-permeability shale oil reservoirs face challenges such as low production rates and rapid decline. To address these issues, the Plannar3D full 3D fracturing model was used to simulate hydraulic fracture propagation and to study the main controlling factors for fracture propagation in the second member of the Liushagang Formation. Based on the concept of geological–engineering integration, a sweet spot evaluation was conducted to identify reservoirs with relatively better brittleness, reservoir properties, and oil content as the fracturing targets for horizontal wells. The UFM model was then applied to optimize fracturing parameters. This study indicates that the matrix-type oil shale has a high clay mineral content, resulting in a low Young’s modulus and poor brittleness. This makes hydraulic fracture propagation difficult and leads to less effective reservoir stimulation. In contrast, hydraulic fractures propagate more easily in high-brittleness interlayer-type oil shale. Therefore, it is recommended to prioritize the extraction of shale oil from interlayer-type oil shale reservoirs. The difference in interlayer stress is identified as the primary controlling factor for cross-layer fracture propagation in the study area. Based on the concept of geological–engineering integration, a sweet spot evaluation standard was established for the second member of the Liushagang Formation, considering both reservoir quality and engineering quality. Four sweet spot zones of interlayer-type oil shale reservoirs were identified according to this evaluation standard. To achieve uniform fracture initiation, a differentiated segment and cluster design was implemented for certain high-angle sections of well WZ11-6-5d. Interlayer-type oil shale was selected as the fracturing target, and the UFM was used for hydraulic fracture propagation simulation. Fracturing parameters were optimized with a focus on hydraulic fracture characteristics and the estimated ultimate recovery (EUR). The optimization results were as follows: a single-stage length of 50 m, cluster spacing of 15 m, pump injection rate of 10 m3/min, fluid intensity of 25 m3/m, and proppant intensity of 3.5 t/m. The application of these optimized fracturing parameters in field operations resulted in successful fracturing and the achievement of industrial oil flow.

A review of current practice in the design and analysis of extremely small stepped-wedge cluster randomized trials.

Stepped-wedge cluster randomized trials tend to require fewer clusters than standard parallel-arm designs due to the switches between control and intervention conditions, but there are no recommendations for the minimum number of clusters. Trials randomizing an extremely small number of clusters are not uncommon, but the justification for small numbers of clusters is often unclear and appropriate analysis is often lacking. In addition, stepped-wedge cluster randomized trials are methodologically more complex due to their longitudinal correlation structure, and ignoring the distinct within- and between-period intracluster correlations can underestimate the sample size in small stepped-wedge cluster randomized trials. We conducted a review of published small stepped-wedge cluster randomized trials to understand how and why they are used, and to characterize approaches used in their design and analysis. Electronic searches were used to identify primary reports of full-scale stepped-wedge cluster randomized trials published during the period 2016-2022; the subset that randomized two to six clusters was identified. Two reviewers independently extracted information from each report and any available protocol. Disagreements were resolved through discussion. We identified 61 stepped-wedge cluster randomized trials that randomized two to six clusters: median sample size (Q1-Q3) 1426 (420-7553) participants. Twelve (19.7%) gave some indication that the evaluation was considered a "preliminary" evaluation and 16 (26.2%) recognized the small number of clusters as a limitation. Sixteen (26.2%) provided an explanation for the limited number of clusters: the need to minimize contamination (e.g. by merging adjacent units), limited availability of clusters, and logistical considerations were common explanations. Majority (51, 83.6%) presented sample size or power calculations, but only one assumed distinct within- and between-period intracluster correlations. Few (10, 16.4%) utilized restricted randomization methods; more than half (34, 55.7%) identified baseline imbalances. The most common statistical method for analysis was the generalized linear mixed model (44, 72.1%). Only four trials (6.6%) reported statistical analyses considering small numbers of clusters: one used generalized estimating equations with small-sample correction, two used generalized linear mixed model with small-sample correction, and one used Bayesian analysis. Another eight (13.1%) used fixed-effects regression, the performance of which requires further evaluation under stepped-wedge cluster randomized trials with small numbers of clusters. None used permutation tests or cluster-period level analysis. Methods appropriate for the design and analysis of small stepped-wedge cluster randomized trials have not been widely adopted in practice. Greater awareness is required that the use of standard sample size calculation methods can provide spuriously low numbers of required clusters. Methods such as generalized estimating equations or generalized linear mixed models with small-sample corrections, Bayesian approaches, and permutation tests may be more appropriate for the analysis of small stepped-wedge cluster randomized trials. Future research is needed to establish best practices for stepped-wedge cluster randomized trials with a small number of clusters.

Letting losses be lessons: Human-machine cooperation in maritime transport

Navigation safety has been a critical guarantee of global shipping, and it becomes more challenging given the increasing employment of advanced technologies and novel ship design in the era of Maritime Autonomous Surface Ships (MASS). The human-centred risk analysis of human-machine cooperation is scarce in general and emerging in maritime transport in specific. This paper aims to develop a new approach enabling the analysis of significant risk influencing factors (RIFs) in human-machine cooperation through an in-depth investigation of the occurred mistakes and violations in the cooperative operations of seafarers and machines in maritime transport. Its novelties consist of (1) a novel approach to analysing and quantifying the connectivity between humans and machines in safety-critical operations, (2) new integration of the frequency and impact of RIFs in the human-machine cooperation model, and (3) ultilisation of graph theory to generate a network to analyse critical human-machine RIFs and their interactions with the system. The connectivity analysis of RIFs is conducted through a weighted undirected network, showing the features of RIF connectivity accommodating the closed-loop system. The proposed novel approach, which combines the frequency and impact features to identify critical RIFs and analyses graphical features, will aid to realise the human-centred risk analysis for MASS. The findings make contributions for ship designers to rationalise the clustering design of function-based automation and training organisations to improve seafarer skills by rationally considering the identified risk-based human-machine cooperation features, and providing new competence schemes that can fit the demands of MASS in future.

Prevalence and correlates of mental disorders among women: results from the Saudi National Mental Health Survey

BackgroundMental disorders account for a significant proportion of the world’s disease burden and are more significant among females than males. However, most global mental health research is sex neutral, including in the Kingdom of Saudi Arabia. This study, therefore, estimated the prevalence of mental disorders and investigated the sociodemographic correlates, sex disadvantage factors, and treatment-seeking in Saudi women concerning lifetime and 12-month mental disorders.MethodThe Saudi National Mental Health Survey is a stratified multistage clustered area probability design. Lifetime and 12-month mental disorders were assessed through the Composite International Diagnostic Interview (CIDI 3.0). The correlates considered for this study included age-at-interview, education, marital status, employment status, socioeconomic status (SES), any chronic condition and household characteristics (region, urbanicity, and income), as well as domestic violence, age at marriage and in a polygamous marriage. Data was analysed using PROC SURVEYFREQ procedure as well as logistic regression in SAS 9.2.ResultsOverall, 24.7% and 35.9% of Saudi women experienced at least one of the disorders in the prior 12 months and at least once in their lifetime, respectively. Anxiety disorders were the most frequently reported 12-month and lifetime disorders, followed by mood disorders. The region, urbanicity, chronic conditions, employment status, as well as certain sex disadvantage factors were significantly associated with both 12-month and lifetime disorders. Most women did not seek treatment for 12-month mental disorders (86.2%) and lifetime disorders (73.8%).ConclusionOur study confirms that mental health issues, particularly anxiety and mood disorders, are highly prevalent among Saudi women, influenced by chronic conditions and sex-related factors like domestic violence and polygamy. Future research should focus on improving mental health literacy, using rigorous study designs to explore female-specific variables, and investigating genetic and environmental factors.

Flexibility potential quantification of electric vehicle charging clusters

A significant obstacle to providing flexibility services with electric vehicles (EVs) is the uncertainty surrounding the profitability and flexibility potential of charging clusters when utilised as a flexible load. Currently, there is a lack of comprehensive and easily applicable methods for quantifying flexibility in the literature. This paper introduces an evaluation tool and a set of flexibility indexes to assess the capability of charging clusters to deliver flexibility services. The method is designed to evaluate and quantify the flexibility potential of charging clusters in terms of short-term and long-term power adjustments and charge scheduling. Through sensitivity analysis, we examine how connection capacity, EV battery capacities, power capabilities, and the number of daily charging sessions influence the flexibility potential of charging clusters. Our findings highlight a direct relationship between the grid connection capacity of clusters and their ability to perform short-term power adjustments. Moreover, while larger batteries tend to reduce energy and time flexibility, their increased storage capability facilitates managing and scheduling a larger energy volume. Furthermore, for the days analysed, the flexibility potential showed minimal sensitivity to the number of daily charging sessions. Instead, the amount of energy requested and connection patterns emerge as key determinants of overall flexibility. In summary, this research provides valuable insights that can inform the design, monitoring, and assessment of EV charging clusters when evaluating their suitability for various flexibility services.

Unlocking high photosensitivity direct laser writing and observing atomic clustering in glass

The direct laser writing (DLW) of photoluminescent metal clusters is inspiring intensive research in functional glasses. However, understanding the influence of the host structure on cluster formation and visualizing DLW-induced clusters at the atomic scale remains challenging. In this work, we develop a highly photosensitive fluorophosphate glass through fluorine incorporation. The addition of fluorine establishes a conducive environment for Ag+ ions before DLW and enhances the availability of reducing agents and diffusion pathways during DLW. These advantages facilitate the formation of Ag clusters under low-energy single-pulsed DLW. Increasing laser energy results in a combination of Ag clusters and glasses defect, forming a dot + ring photoluminescent pattern. Atom probe tomography (APT), a technique capable of mapping the elemental spatial distribution and identifying clustering, is employed to gain more information on laser-induced clusters. Comparison of APT results between samples without and with DLW reveals the formation of Ag clusters after laser writing. The design concept and characterization enrich the understanding of Ag cluster behavior in glasses. This knowledge opens the possibility of rational design of clusters confined in glasses and inspires their synthesis for various applications.