Shared decision making in rural general practices:a qualitative exploration of older rural South Australians'perceived involvement in clinical consultations with doctors

Background:Shared decision-making(SDM)implementation is a priority for Australian health systems,including general practices but it remains complex for specific groups like older rural Australians.We initiated a qualitative study with older rural Australians to explore barriers to and facilitators of SDM in local general practices.Methods:We conducted a patient-oriented research,partnering with older rural Australians,families,and health service providers in research design.Participants who visited general practices were purposively sampled from five small rural towns in South Australia.A semi-structured interview guide was used for interviews and reflexive thematic coding was conducted.Results:Telephone interviews were held with 27 participants.Four themes were identified around older rural adults’involvement in SDM:(1)Understanding of"patient involvement";(2)Positive and negative outcomes;(3)Barriers to SDM;and(4)Facilitators to SDM.Understanding of patient involvement in SDM considerably varied among participants,with some reporting their involvement was contingent on the“opportunity to ask questions”and the“treatment choices”offered to them.Alongside the opportunity for involvement,barriers such as avoidance of cultural care and a lack of continuity of care are new findings.Challenges encountered in SDM implementation also included resource constraints and time limitations in general practices.Rural knowledge of general practitioners and technology integration in consultations were viewed as potential enablers..Conclusion:Adequate resources and well-defined guidelines about the process should accompany the implementation of SDM in rural general practices of South Australia.Innovative strategies by general practitioners promoting health literacy and culturally-tailored communication approaches could increase older rural Australians'involvement in general.

The quality of older adults'involvement in clinical communication with general practitioners:evidence from rural towns in Australia

Objective:A study was conducted about the putative links of older rural Australians'health knowledge and preparation with their quality of involvement in patient-general practitioner(GP)communication during health intake visits.Methods:It was a cross-sectional study between January 2021 and April 2022.The 32-item quality of involvement in communication scale was designed and incorporated into the SurveyGizmo software.This online survey was administered by sending an email request to the Renmark Rotary Club,which actively promoted this study across five rural towns in South Australia.121 participants completed the surveys.Mean-sum scores were calculated based on the questionnaire responses to evaluate outcomes,specifically initiation of information,active participation,and emotional expression.We employed different methods including t-tests,ANOVA,and leaner regressions to analyse data.Results:The demographic profile of participants characterised by a female predominance(58.7%,71/121),a majority falling within the 65-<70 age bracket(47.1%,57/121),and a high level of educational attainment(58.7%had completed high school or higher,71/121).Additionally,35%of the participants predominantly spoke a language other than English at home.Regarding the initiation of information with GPs,the mean sum-score was(20.5+3.7),indicating a marginally above-average level of engagement.Contrarily,the active participation was suboptimal,as suggested by a mean sum score of(35.9±6.3).Furthermore,the emotional expression was relatively low,with a mean score of(13.9±1.8).Substantial variations were discerned in the quality of patient-GP communication,contingent upon factors such as educational background,language spoken at home,health literacy,and preparatory measures for clinical visits.Participants who predominantly spoke a language other than English at home demonstrated significantly lower levels of information initiation with their GPs(P<0.o01).Higher educational attainment was positively correlated with increased active participation(P<0.001).Enhanced health literacy and thorough visit preparation were significantly associated with increased levels of active participation(P<0.001).Conclusion:Meaningful engagement through recognition,empowerment,and support(health literacy programs)for older rural adults is suggested for improving their quality of involvement in communication with GPs.

Prediction of rock fragmentation in a fiery seam of an open-pit coal mine in India

Spontaneous combustion of coal increases the temperature in adjoining overburden strata of coal seams and poses a challenge when loading blastholes.This condition,known as hot-hole blasting,is dangerous due to the increased possibility of premature explosions in loaded blastholes.Thus,it is crucial to load the blastholes with an appropriate amount of explosives within a short period to avoid premature detonation caused by high temperatures of blastholes.Additionally,it will help achieve the desired fragment size.This study tried to ascertain the most influencial variables of mean fragment size and their optimum values adopted for blasting in a fiery seam.Data on blast design,rock mass,and fragmentation of 100 blasts in fiery seams of a coal mine were collected and used to develop mean fragmentation prediction models using soft computational techniques.The coefficient of determination(R^(2)),root mean square error(RMSE),mean absolute error(MAE),mean square error(MSE),variance account for(VAF)and coefficient of efficiency in percentage(CE)were calculated to validate the results.It indicates that the random forest algorithm(RFA)outperforms the artificial neural network(ANN),response surface method(RSM),and decision tree(DT).The values of R^(2),RMSE,MAE,MSE,VAF,and CE for RFA are 0.94,0.034,0.027,0.001,93.58,and 93.01,respectively.Multiple parametric sensitivity analyses(MPSAs)of the input variables showed that the Schmidt hammer rebound number and spacing-to-burden ratio are the most influencial variables for the blast fragment size.The analysis was finally used to define the best blast design variables to achieve optimum fragment size from blasting.The optimum factor values for RFA of S/B,ld/B and ls/ld are 1.03,1.85 and 0.7,respectively.

Applications of Soft Computing Methods in Backbreak Assessment in Surface Mines: A Comprehensive Review

Geo-engineering problems are known for their complexity and high uncertainty levels,requiring precise defini-tions,past experiences,logical reasoning,mathematical analysis,and practical insight to address them effectively.Soft Computing(SC)methods have gained popularity in engineering disciplines such as mining and civil engineering due to computer hardware and machine learning advancements.Unlike traditional hard computing approaches,SC models use soft values and fuzzy sets to navigate uncertain environments.This study focuses on the application of SC methods to predict backbreak,a common issue in blasting operations within mining and civil projects.Backbreak,which refers to the unintended fracturing of rock beyond the desired blast perimeter,can significantly impact project timelines and costs.This study aims to explore how SC methods can be effectively employed to anticipate and mitigate the undesirable consequences of blasting operations,specifically focusing on backbreak prediction.The research explores the complexities of backbreak prediction and highlights the potential benefits of utilizing SC methods to address this challenging issue in geo-engineering projects.

Waterbird use of farm dams in south-eastern Australia:abundance and influence of biophysical and landscape characteristics

Background: While agriculture has taken much environmental water in Australia's Murray-Darling Basin, agricultural expansion has resulted in a vast number of farm dams, almost three-quarters of a million in the Murray-Darling Basin alone.Methods: Over a summer we studied(1) waterbird abundance and species richness and(2) the influence of biophysical and landscape characteristics across 49 farm dams at a large mixed-enterprise farm in northern Victoria on the southern reach of the Murray-Darling Basin.Results: On average, dams were found to host 27.1 ± 71.1 individuals/ha and 1.8 ± 2.9 species per pond. Such densities are comparable to those on natural wetlands. Dam surface area and perimeter and amount of vegetation were positively and strongly correlated with the Rallidae density(birds/ha), but no other parameters were strongly correlated with any other functional group. The landscape in which the dams were embedded had a highly significant effect(p < 0.001) on the number of birds found on a dam.Conclusions: Our research needs to be complemented with further studies in other parts of the Basin and on other taxa, but given at our site they supported similar densities of individuals and species to natural wetlands, and given the fact that there are 710,539 farm dams in the Murray-Darling Basin, which hosts much of Australia's waterbird fauna, it is reasonable to suggest that farm dams are overlooked, and possibly very important, avian biodiversity hotspots. It also highlights the importance of a landscape setting, in which dams are situated, on the number of birds using the dams.

A true triaxial strength criterion for rocks by gene expression programming

Rock strength is a crucial factor to consider when designing and constructing underground projects.This study utilizes a gene expression programming(GEP)algorithm-based model to predict the true triaxial strength of rocks,taking into account the influence of rock genesis on their mechanical behavior during the model building process.A true triaxial strength criterion based on the GEP model for igneous,metamorphic and magmatic rocks was obtained by training the model using collected data.Compared to the modified Weibols-Cook criterion,the modified Mohr-Coulomb criterion,and the modified Lade criterion,the strength criterion based on the GEP model exhibits superior prediction accuracy performance.The strength criterion based on the GEP model has better performance in R2,RMSE and MAPE for the data set used in this study.Furthermore,the strength criterion based on the GEP model shows greater stability in predicting the true triaxial strength of rocks across different types.Compared to the existing strength criterion based on the genetic programming(GP)model,the proposed criterion based on GEP model achieves more accurate predictions of the variation of true triaxial strength(s1)with intermediate principal stress(s2).Finally,based on the Sobol sensitivity analysis technique,the effects of the parameters of the three obtained strength criteria on the true triaxial strength of the rock are analysed.In general,the proposed strength criterion exhibits superior performance in terms of both accuracy and stability of prediction results.

Decentralized content sharing in mobile ad-hoc networks:A survey

The evolution of smart mobile devices has significantly impacted the way we generate and share contents and introduced a huge volume of Internet traffic.To address this issue and take advantage of the short-range communication capabilities of smart mobile devices,the decentralized content sharing approach has emerged as a suitable and promising alternative.Decentralized content sharing uses a peer-to-peer network among colocated smart mobile device users to fulfil content requests.Several articles have been published to date to address its different aspects including group management,interest extraction,message forwarding,participation incentive,and content replication.This survey paper summarizes and critically analyzes recent advancements in decentralized content sharing and highlights potential research issues that need further consideration.

大理岩RHT模型参数确定研究

RHT模型共计包含34个参数,其中A,n,f_s*,f_t*,Q0,g_c*,ξ,D_1,ε_p^m,A_f,n_f,p_(comp)和N因试验获取途径复杂较难确定,多数学者在利用RHT模型模拟岩石爆破破碎时,不管岩石种类如何变化都只是简单引用混凝土的相关参数,为了较为准确地获得大理岩的相关参数取值,以静力学试验、声波测试试验和SHPB冲击试验为基础,LSDyna数值模拟为手段,通过正交试验的方法对以上参数进行优化确定,并将参数优化前、后的模拟曲线分别与SHPB冲击大理岩的试验曲线进行对比.结果表明:以上参数经过正交模拟试验优化确定后,缩小了模拟曲线与SHPB冲击试验曲线的对比误差,得到了适用于大理岩的RHT模型参数.

岩石RHT模型主要参数敏感性及确定方法研究

在34个RHT模型参数中,19个参数可以通过试验、理论研究等手段获得,但还剩15个参数较难确定.为了较为准确的获得相关参数,在只改变参数N、p_(comp)、f_t~*、f_s~*、A、n、Q_0、g_c~*、ξ、D_1、ε_p^m、A_f、n_f、B、g_t~*的情况下,利用LS-DYNA对石灰岩进行单轴、三轴压缩模拟计算,通过对比RHT模型的弹性、线性强化以及损伤软化段的曲线对以上15个参数的敏感性进行研究,并以红砂岩为例,利用SHPB冲击红砂岩获得应力-应变曲线,并通过静力学、声波测试等试验获得红砂岩相关物理参数,以LS-DYNA模拟SHPB冲击试验作为手段,通过正交试验的方法得到了红砂岩的RHT模型参数,为了验证所确定参数的正确性,将LS-DYNA模拟SHPB冲击红砂岩的曲线与SHPB冲击试验曲线进行了对比,取得了令人满意的效果.

失独者抑郁的心理弹性多因素作用模型

目的:中国失独家庭已多达百万个,65.6%的失独者存在严重而持久的抑郁情绪,需要及时干预。本研究旨在探索心理弹性与其影响因素的交互作用,比较心理弹性及其影响因素对于失独者抑郁的效应。方法:采用Connor-Davidson心理弹性量表、Zung抑郁自评量表、简易应对方式问卷、艾森克简式量表中国版、社会支持评定量表、一般自我效能感量表对长沙市9个行政区域212名失独者进行入户调查。基于Kumpfer心理弹性框架和心理应激多因素作用过程的理论模式构建假设模型并进行验证。结果:心理弹性的影响因素有:积极应对(总效应值为0.480)、支持利用(总效应值为0.359)、精神质(总效应值为-0.326)、消极应对(总效应值为0.279)、内外倾(总效应值为0.219)、客观支持(总效应值为0.077)。心理弹性对抑郁的影响以直接效应为主(总效应值为-0.344,占67.1%),也通过自我效能感发挥间接效应(总效应值为-0.169),心理弹性的总效应值占所有变量总效应的20.1%。结论:心理弹性受到人格、积极应对、社会支持等应激中介变量的直接和间接作用;心理弹性对抑郁主要产生直接负性效应,是定位于自我效能感之前的重要应激中介变量。个体-环境互动作用过程对心理弹性的影响较大,人格和支持利用通过影响消极应对、积极应对进而间接影响心理弹性。提高心理弹性应当关注个体-环境作用过程,即利用支持、积极应对、适当的消极应对,进而影响个体对自我的积极评价,这将是开发心理弹性干预方案以改善失独者抑郁的重要内容。

Optimization of cement-based grouts using chemical additives

Grout injection is used for sealing or strengthening the ground in order to prevent water entrance or any failure after excavation.There are many methods of grouting.Permeation grouting is one of the most common types in which the grout material is injected to the pore spaces of the ground.In grouting operations,the grout quality is important to achieve the best results.There are four main characteristics for a grout mixture including bleeding,setting time,strength,and viscosity.In this paper,we try to build some efficient grouting mixtures with different water to cement ratios considering these characteristics.The ingredients of grout mixtures built in this study are cement,water,bentonite,and some chemical additives such as sodium silicate,sodium carbonate,and triethanolamine(TEA).The grout mixtures are prepared for both of the sealing and strengthening purposes for a structural project.Effect of each abovementioned ingredient is profoundly investigated.Since each ingredient may have positive or negative aspect,an optimization of appropriate amount of each ingredient is determined.The optimization is based on 200 grout mixture samples with different percentages of ingredients.Finally,some of these grout mixtures are chosen for the introduced project.It should be mentioned that grouting operations depend on various factors such as pressure of injection,ground structure and grain size of soils.However,quality of a grout can be helpful to make an injection easier and reasonable.For example,during the injection,a wrong estimated setting time can destroy the injected grout by washing the grout or setting early which prevents grouting.This paper tries to show some tests in easy way to achieve a desirable sample of grout.

Proposing a novel comprehensive evaluation model for the coal burst liability in underground coal mines considering uncertainty factors

Coal burst is a severe hazard that can result in fatalities and damage of facilities in underground coal mines.To address this issue,a robust unascertained combination model is proposed to study the coal burst hazard based on an updated database.Four assessment indexes are used in the model,which are the dynamic failure duration(DT),elastic energy index(WET),impact energy index(KE)and uniaxial compressive strength(RC).Four membership functions,including linear(L),parabolic(P),S and Weibull(W)functions,are proposed to measure the uncertainty level of individual index.The corresponding weights are determined through information entropy(EN),analysis hierarchy process(AHP)and synthetic weights(CW).Simultaneously,the classification criteria,including unascertained cluster(UC)and credible identification principle(CIP),are analyzed.The combination algorithm,consisting of P function,CW and CIP(P-CW-CIP),is selected as the optimal classification model in function of theory analysis and to train the samples.Ultimately,the established ensemble model is further validated through test samples with 100%accuracy.The results reveal that the hybrid model has a great potential in the coal burst hazard evaluation in underground coal mines.

A DEM investigation on simple shear behavior of dense granular assemblies

A micromechanical investigation on simple shear behavior of dense granular assemblies was carried out by discrete element method.Three series of numerical tests were performed to examine the effects of initial porosity,vertical stress and particle shape on simple shear behavior of the samples,respectively.It was found that during simple shear the directions of principal stress and principal strain increment rotate differently with shear strain level.The non-coaxiality between the two directions decreases with strain level and may greatly affect the shear behavior of the assemblies,especially their peak friction angles.The numerical modelling also reveals that the rotation of the principal direction of fabric anisotropy lags behind that of the major principal stress direction during simple shear,which is described as fabric hyteresis effect.The degrees of fabric and interparticle contact force anisotropies increase as particle angularity increases,whereas the orientations of these anisotropies have not been significantly influenced by particle shape.An extended stress–dilatancy relationship based on ROWE-DAVIS framework was proposed to consider the non-coaxiality effect under principal stress rotation.The model was validated by present numerical results as well as some published physical test and numerical modelled data.

Scientific evidence is just the starting point: A generalizable process for developing sports injury prevention interventions

Background: The 2 most cited sports injury prevention research frameworks incorporate intervention development, yet little guidance is available in the sports science literature on how to undertake this complex process. This paper presents a generalizable process for developing implementable sports injury prevention interventions, including a case study applying the process to develop a lower limb injury prevention exercise training program(Footy First) for community Australian football.Methods: The intervention development process is underpinned by 2 complementary premises:(1) that evidence-based practice integrates the best available scientific evidence with practitioner expertise and end user values and(2) that research evidence alone is insufficient to develop implementable interventions.Results: The generalizable 6-step intervention development process involves(1) compiling research evidence, clinical experience, and knowledge of the implementation context;(2) consulting with experts;(3) engaging with end users;(4) testing the intervention;(5) using theory; and(6)obtaining feedback from early implementers. Following each step, intervention content and presentation should be revised to ensure that the final intervention includes evidence-informed content that is likely to be adopted, properly implemented, and sustained over time by the targeted intervention deliverers. For Footy First, this process involved establishing a multidisciplinary intervention development group, conducting 2targeted literature reviews, undertaking an online expert consensus process, conducting focus groups with program end users, testing the program multiple times in different contexts, and obtaining feedback from early implementers of the program.Conclusion: This systematic yet pragmatic and iterative intervention development process is potentially applicable to any injury prevention topic across all sports settings and levels. It will guide researchers wishing to undertake intervention development.

Experimental investigations on mechanical performance of rocks under fatigue loads and biaxial confinements

In this research,a series of biaxial compression and biaxial fatigue tests were conducted to investigate the mechanical behaviors of marble and sandstone under biaxial confinements.Experimental results demonstrate that the biaxial compressive strength of rocks under biaxial compression increases firstly,and subsequently decreases with increase of the intermediate principal stress.The fatigue failure characteristics of the rocks in biaxial fatigue tests are functions of the peak value of fatigue loads,the intermediate principal stress and the rock lithology.With the increase of the peak values of fatigue loads,the fatigue lives of rocks decrease.The intermediate principal stress strengthens the resistance ability of rocks to fatigue loads except considering the strength increasing under biaxial confinements.The fatigue lives of rocks increase with the increase of the intermediate principal stress under the same ratio of the fatigue load and their biaxial compressive strength.The acoustic emission(AE)and fragments studies showed that the sandstone has higher ability to resist the fatigue loads compared to the marble,and the marble generated a greater number of smaller fragments after fatigue failure compared to the sandstone.So,it can be inferred that the rock breaking efficiency and rock burst is higher or severer induced by fatigue loading than that induced by monotonous quasi-static loading,especially for hard rocks.

Prediction of blasting mean fragment size using support vector regression combined with five optimization algorithms

The main purpose of blasting operation is to produce desired and optimum mean size rock fragments.Smaller or fine fragments cause the loss of ore during loading and transportation,whereas large or coarser fragments need to be further processed,which enhances production cost.Therefore,accurate prediction of rock fragmentation is crucial in blasting operations.Mean fragment size(MFS) is a crucial index that measures the goodness of blasting designs.Over the past decades,various models have been proposed to evaluate and predict blasting fragmentation.Among these models,artificial intelligence(AI)-based models are becoming more popular due to their outstanding prediction results for multiinfluential factors.In this study,support vector regression(SVR) techniques are adopted as the basic prediction tools,and five types of optimization algorithms,i.e.grid search(GS),grey wolf optimization(GWO),particle swarm optimization(PSO),genetic algorithm(GA) and salp swarm algorithm(SSA),are implemented to improve the prediction performance and optimize the hyper-parameters.The prediction model involves 19 influential factors that constitute a comprehensive blasting MFS evaluation system based on AI techniques.Among all the models,the GWO-v-SVR-based model shows the best comprehensive performance in predicting MFS in blasting operation.Three types of mathematical indices,i.e.mean square error(MSE),coefficient of determination(R^(2)) and variance accounted for(VAF),are utilized for evaluating the performance of different prediction models.The R^(2),MSE and VAF values for the training set are 0.8355,0.00138 and 80.98,respectively,whereas 0.8353,0.00348 and 82.41,respectively for the testing set.Finally,sensitivity analysis is performed to understand the influence of input parameters on MFS.It shows that the most sensitive factor in blasting MFS is the uniaxial compressive strength.

Sensitivity Analysis,Determination and Optimization of Granite RHT Parameters

The RHT model has 34 parameters,among which 19 parameters can be obtained by experiments or theoretical calculations and the remaining 15 parameters are difficult to acquire.In this study,firstly,10 Hopkinson impact tests were conducted to acquire the typical stress-strain curves of granite under dynamic loads.Through the sensitivity analysis,it is found that 13 of the 15 difficult-acquired parameters are effective to affect the shape of the stress-strain curve,and the other two parameters have no effect.Following the initial determination of model parameters with reference to the concrete RHT model,a new approach is proposed to optimize the 13 influential parameters through the LS-DYNA numerical simulation and orthogonal experiments.Finally,the determined granite RHT model parameters are verified by the results of Hopkinson impact tests conducted in this study and the bullet penetration test by Wang et al.Both results of the numerical simulations are in a good agreement with the tested results,which validates the suitability of the proposed method to acquire RHT model parameters for granite and the other rocks.

An evolutionary adaptive neuro-fuzzy inference system for estimating field penetration index of tunnel boring machine in rock mass

Field penetration index(FPI) is one of the representative key parameters to examine the tunnel boring machine(TBM) performance.Lack of accurate FPI prediction can be responsible for numerous disastrous incidents associated with rock mechanics and engineering.This study aims to predict TBM performance(i.e.FPI) by an efficient and improved adaptive neuro-fuzzy inference system(ANFIS) model.This was done using an evolutionary algorithm,i.e.artificial bee colony(ABC) algorithm mixed with the ANFIS model.The role of ABC algorithm in this system is to find the optimum membership functions(MFs) of ANFIS model to achieve a higher degree of accuracy.The procedure and modeling were conducted on a tunnelling database comprising of more than 150 data samples where brittleness index(BI),fracture spacing,α angle between the plane of weakness and the TBM driven direction,and field single cutter load were assigned as model inputs to approximate FPI values.According to the results obtained by performance indices,the proposed ANFISABC model was able to receive the highest accuracy level in predicting FPI values compared with ANFIS model.In terms of coefficient of determination(R^(2)),the values of 0.951 and 0.901 were obtained for training and testing stages of the proposed ANFISABC model,respectively,which confirm its power and capability in solving TBM performance problem.The proposed model can be used in the other areas of rock mechanics and underground space technologies with similar conditions.

Optimization of the catch bench design using a genetic algorithm

Rockfalls are one of the hazards that may be associated with open pit mining. The majority of rockfalls occur due to the existing conditions of slopes, such as back break, fractures and joints. Constructing a berm on the catch bench is a popular method for the mitigation of rockfall hazards in open pit mining.The width of the catch bench and the height of the berm play a major role in the open pit bench design.However, there is no systematic method currently available to optimize the size of these parameters. This study proposes a novel methodology which calculates the optimum catch bench width by integrating the rockfall simulation model and genetic algorithm into a Simulation-Optimization Model. The proposed methodology is useful when used to determine the minimum catch bench width, or the maximum overall slope angle, insuring that a sufficient factor of safety of the slope is included while maximizing the overall profitability of the open pit mine.

The epistemic basis of distance running injury research:A historical perspective

1. Introduction For both recreational and competitive purposes, distance running is an ideal activity for increasing endurance capacity and improving cardiovascular health. Running is an accessible and relatively simple form of exercise that is performed by able bodied individuals in a variety of locations worldwide. Accord- ingly, the popularity of running in developed countries has increased dramatically in recent times, demonstrated by the growth in fun runs, marathons, and fundraising events.