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Police officers in the UK frequently experience traumatic events and work pressures, and these can have a detrimental impact on mental health. A psychological surveillance programme has been implemented and clinically evaluated. This paper reports an economic evaluation of the programme.

A decision analytic model was developed to explore programme costs, outcomes and return on investment for a notional cohort of 1,000 police personnel (officers and staff). The model parameters were obtained from a large data set of sequential psychological surveillance results. Changes in individual work productivity were derived from a workability measure included in the survey.

The modelled expected total cost incurred to screen 1,000 participants was £84,287 ($106,971). The expected net increase in work productivity for those receiving interventions was valued at £241,672 ($306,713). This result represents a return on investment of 187%. Sensitivity analyses showed that the model was robust to changes in key parameters.

Police personnel experiencing traumatic events may need specialist support from occupational health services or psychological therapy. Screening or surveillance can detect mental health problems, but its cost-effectiveness has been previously unknown.

The value for money of providing surveillance within the police force to detect mental health problems is assessed in this research. The gains in terms of increased work are evident. In similar environmental working conditions, this could extend to emergency services globally.

The police are often called upon to deal with the lowest points in people’s lives. The findings have implications for police forces wishing to support officers to be able to provide the best of themselves in these situations.

There have been no previous studies in policing that have concluded that running a psychological surveillance programme in policing is financially viable. Although this requires initial investment, this paper models that the extra costs incurred are more than offset by improved work productivity.

Dietary components have been mentioned as modifiable risk factors in the development of gallstone disease (GSD), but it has been less addressed. The present study aimed to investigate the potential association between different types of dietary carbohydrate and the risk of gallstone.

In this case-control study 189 patients diagnosed with GSD as a case group and 342 people as a control group were enrolled. Dietary intake of the participants was collected through a 168-item semi-quantitative food frequency questionnaire. Total intakes of calories, macronutrients and different types of carbohydrate were estimated. Crude and multivariable-adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were calculated to assess the association between carbohydrate intake and GSD.

Patients with gallstone consumed significantly more fructose and sucrose and less fiber. After fully adjustment, the logistic regression indicated significant association between GSD with dietary intake of total carbohydrate (OR = 1.65, 95% CI: 1.1–2.4, p = 0.009), sugar (OR = 1.23, 95% CI: 0.8–1.7, p = 0.014), fructose (OR = 2.5, 95% CI: 1.7–3.9, p < 0.001), glucose (OR = 1.9, 95% CI: 1.3–2.9, p = 0.002) and sucrose (OR = 1.37, 95% CI: 0.9–1.6, p = 0.042). Also, increasing intakes of lactose, galactose and maltose were associated with a decrease in the risk of GSD, but not statistically significant, although lactose was close to significance (OR = 0.71, 95% CI: 0.48–1, p = 0.051).

There is a positive and significant relationship between total carbohydrate, sugar, fructose, glucose and sucrose intake and the occurrence of gallstone.

The main aim of the current paper is to find a numerical plan for hydraulic fracturing problem with application in extracting natural gases and oil.

First, time discretization is accomplished via Crank-Nicolson and semi-implicit techniques. At the second step, a high-order finite element method using quadratic triangular elements is proposed to derive the spatial discretization. The efficiency and time consuming of both obtained schemes will be investigated. In addition to the popular uniform mesh refinement strategy, an adaptive mesh refinement strategy will be employed to reduce computational costs.

Numerical results show a good agreement between the two schemes as well as the efficiency of the employed techniques to capture acceptable patterns of the model. In central single-crack mode, the experimental results demonstrate that maximal values of displacements in x- and y- directions are 0.1 and 0.08, respectively. They occur around both ends of the line and sides directly next to the line where pressure takes impact. Moreover, the pressure of injected fluid almost gained its initial value, i.e. 3,000 inside and close to the notch. Further, the results for non-central single-crack mode and bifurcated crack mode are depicted. In central single-crack mode and square computational area with a uniform mesh, computational times corresponding to the numerical schemes based on the high order finite element method for spatial discretization and Crank-Nicolson as well as semi-implicit techniques for temporal discretizations are 207.19s and 97.47s, respectively, with 2,048 elements, final time T = 0.2 and time step size τ = 0.01. Also, the simulations effectively illustrate a further decrease in computational time when the method is equipped with an adaptive mesh refinement strategy. The computational cost is reduced to 4.23s when the governed model is solved with the numerical scheme based on the adaptive high order finite element method and semi-implicit technique for spatial and temporal discretizations, respectively. Similarly, in other samples, the reduction of computational cost has been shown.

This is the first time that the high-order finite element method is employed to solve the model investigated in the current paper.

The purpose of this paper is to investigate the nonsimultaneous impact of three impactors with spherical tip on the response of a low-velocity impact on a beam.

In this research, the third-order shear deformation theory of the beam with hyperbolic shear-strain function is used. Hamilton’s principle is applied to derive the motion equations. To simulate nonsimultaneous impacts, by using the Hertz nonlinear contact law, the contact of the impactors with different times is simulated. Comparisons with other articles are carried out in the one impactor form.

In the parametric study, the histories of the contact force and displacement of the beam are investigated in the presence of only one impactor in the center of the beam and also in the presence of three impactors, one in the center of the beam and the other two around the first impactor with a delay. One of the important and noteworthy points is that the presence of two impactors with a delay causes the maximum contact force and contact time to decrease and the maximum displacement of the beam center to increase.

The original point of this paper is what is the difference between the impact response of one projectile and three nonsimultaneous projectiles on the beam.

Violence and aggression against mental health professionals is a global concern with well-documented consequences. In the UK, mental health care is increasingly delivered in the community, yet little research has explored practitioner experiences of workplace violence (WPV) outside of inpatient settings. This study aimed to explore how mental health professionals in a UK community mental health team (CMHT) perceive, experience and cope with WPV.

Face-to-face semi-structured interviews were conducted with ten multidisciplinary professionals based in a CMHT in a UK city. Data was analysed using interpretative phenomenological analysis.

Three interconnected themes emerged. WPV was accepted as inevitable: participants carried on working despite its impact, and feeling unheard by management they gave up on change, perpetuating the perceived inevitability of WPV. Peer support and organisational resources like debriefing, counselling and occupational health improved coping. Stigma and ideas of professional responsibility were barriers to access.

To mitigate against the negative consequences of WPV, CMHTs could offer peer support initiatives, improve communication and availability of organisational resources and involve staff in post-incident decision-making. Recommendations are made to shift the attitude of acceptance of WPV and encourage help-seeking.

Flow induced by rotating disks is of great practical importance in several engineering applications such as rotating heat exchangers, turbine disks, pumps and many more. The present research has been freshly displayed regarding the implementation of an engine oil-based Casson tri-hybrid nanofluid across a rotating disk in mass and heat transferal developments. The purpose of this study is to contemplate the attributes of the flowing tri-hybrid nanofluid by incorporating porosity effects and magnetization and velocity slip effects, viscous dissipation, radiating flux, temperature slip, chemical reaction and activation energy.

The articulated fluid flow is described by a set of partial differential equations which are converted into one set of higher-order ordinary differential equations (ODEs) by using convenient conversions. The numerical solution of this transformed set of ODEs has been spearheaded by using the effectual bvp4c scheme.

The acquired results show that the heat transmission rate for the Casson tri-hybrid nanofluid is intensified by, respectively, 9.54% and 11.93% when compared to the Casson hybrid nanofluid and Casson nanofluid. Also, the mass transmission rate for the Casson tri-hybrid nanofluid is augmented by 1.09% and 2.14%, respectively, when compared to the Casson hybrid nanofluid and Casson nanofluid.

The current investigation presents an educative response on how the flow profiles vary with changes in the inevitable flow parameters. As per authors’ knowledge, no such scrutinization has been carried out previously; therefore, our results are novel and unique.

The purpose of this paper is to compare the influence of relative density (RD) and strain rates on failure mechanism and specific energy absorption (SEA) of polyamide lattices ranging from bending to stretch-dominated structures using selective laser sintering (SLS).

Three bending and two stretch-dominated unit cells were selected based on the Maxwell stability criterion. Lattices were designed with three RD and fabricated by SLS technique using PA12 material. Quasi-static compression tests with three strain rates were carried out using Taguchi's L9 experiments. The lattice compressive behaviour was verified with the Gibson–Ashby analytical model.

It has been observed that RD and strain rates played a vital role in lattice compressive properties by controlling failure mechanisms, resulting in distinct post-yielding responses as fluctuating and stable hardening in the plateau region. Analysis of variance (ANOVA) displayed the significant impact of RD and emphasised dissimilar influences of strain rate that vary to cell topology. Bending-dominated lattices showed better compressive properties than stretch-dominated lattices. The interesting observation is that stretch-dominated lattices with over-stiff topology exhibited less compressive properties contrary to the Maxwell stability criterion, whereas strain rate has less influence on the SEA of face-centered and body-centered cubic unit cells with vertical and horizontal struts (FBCCXYZ).

This comparative study is expected to provide new prospects for designing end-user parts that undergo various impact conditions like automotive bumpers and evolving techniques like hybrid and functionally graded lattices.

To the best of the authors' knowledge, this is the first work that relates the strain rate with compressive properties and also highlights the lattice behaviour transformation from ductile to brittle while the increase of RD and strain rate analytically using the Gibson–Ashby analytical model.

While performance demands in the natural world are varied, graded lattice structures reveal distinctive mechanical properties with tremendous engineering application potential. For biomechanical functions where mechanical qualities are required from supporting under external loading and permeability is crucial which affects bone tissue engineering, the geometric design in lattice structure for bone scaffolds in loading-bearing applications is necessary. However, when tweaking structural traits, these two factors frequently clash. For graded lattice structures, this study aims to develop a design-optimization strategy to attain improved attributes across different domains.

To handle diverse stress states, parametric modeling is used to produce strut-based lattice structures with spatially varied densities. The tailored initial gradients in lattice structure are subject to automatic property evaluation procedure that hinges on finite element method and computational fluid dynamics simulations. The geometric parameters of lattice structures with numerous objectives are then optimized using an iterative optimization process based on a non-dominated genetic algorithm.

The initial stress-based design of graded lattice structure with spatially variable densities is generated based on the stress conditions. The results from subsequent dual-objective optimization show a series of topologies with gradually improved trade-offs between mechanical properties and permeability.

In this study, a novel structural design-optimization methodology is proposed for mathematically optimizing strut-based graded lattice structures to achieve enhanced performance in multiple domains.

This study aims to evaluate the relationship between the estimated levels of diet advanced glycation end products (dAGEs) intake and obesity in university students.

This cross-sectional study included 301 university students aged 18–30 years. dAGEs was estimated using a food frequency questionnaire, for 549 routinely consumed food items and were reported by dividing total energy intake. Dietary intake and sociodemographic data were collected using validated questionnaires, and the anthropometric characteristics were measured. The relationship between anthropometric measurements and dAGEs intake was examined by binary logistic regression.

A total of 43.2% of the participants had high levels of dAGEs. A significant decreasing trend was found in the percentage of carbohydrate intake compared to the increasing trend in dAGEs consumption (p = 0.005). The percentage of fat intake and meat consumption were significantly higher in participants with the highest consumption, compared with the lowest consumption of dAGEs (p = 0.006). According to the dAGEs classification of participants, body mass index, waist circumference and energy intake were found to be significantly related in all model groups. Accordingly, the increase in body mass index, waist circumference and energy intake were determined as a risk factor in those with high dAGEs intakes.

The findings of this study emphasized that higher intake of dAGEs was associated with an increased risk of obesity parameters in college students.

Prediction models are essential tools for transportation agencies to forecast the condition of bridge decks based on available data, and artificial intelligence is paramount for this purpose. This study aims at proposing a bridge deck condition prediction model by assessing various classification and regression algorithms.

The 2019 National Bridge Inventory database is considered for model development. Eight different feature selection techniques, along with their mean and frequency, are used to identify the critical features influencing deck condition ratings. Thereafter, four regression and four classification algorithms are applied to predict condition ratings based on the selected features, and their performances are evaluated and compared with respect to the mean absolute error (MAE).

Classification algorithms outperform regression algorithms in predicting deck condition ratings. Due to its minimal MAE (0.369), the random forest classifier with eleven features is recommended as the preferred condition prediction model. The identified dominant features are superstructure condition, age, structural evaluation, substructure condition, inventory rating, maximum span length, deck area, average daily traffic, operating rating, deck width, and the number of spans.

The proposed bridge deck condition prediction model offers a valuable tool for transportation agencies to plan maintenance and resource allocation efficiently, ultimately improving bridge safety and serviceability.

This study provides a detailed framework for applying machine learning in bridge condition prediction that applies to any bridge inventory database. Moreover, it uses a comprehensive dataset encompassing an entire region, broadening the model’s applicability and representation.