Facilitators and barriers to interprofessional collaboration among health professionals in primary healthcare centers in Qatar: a qualitative exploration using the "Gears" model.

BACKGROUND: The number of patients seeking medical care is increasing, necessitating more access to primary healthcare services. As several of these patients usually present with complex medical conditions, the need for interprofessional collaboration (IPC) among health professionals in primary care is necessary. IPC is essential for facing the increasing and challenging healthcare demands. Therefore, the facilitators of and the barriers to IPC should be studied in the hope that the results will be used to promote such endeavors. OBJECTIVES: This study aimed to explore the perspectives of different health professionals regarding the facilitators of and the barriers to IPC in the primary healthcare settings in Qatar. METHODS: A qualitative study using focus groups was conducted within the Primary Health Care Corporation (PHCC) in Qatar. Several health professionals were invited to participate in the focus groups. The focus groups were uniprofessional for general practitioners (GPs), nurses, and dentists, while they were interprofessional for the other health professionals. Focus groups were audio-recorded and transcribed verbatim and validated by the research team. The data were analyzed by deductive thematic analysis using the "Gears" Conceptual Model as a coding framework. RESULTS: Fourteen focus groups were conducted involving 58 participants (including 17 GPs, 12 nurses, 15 pharmacists, 3 dentists, and 11 allied health professionals) working in PHCC in Qatar. The findings revealed a spectrum of factors influencing IPC, categorized into four main domains: Macro, Meso, Micro, and individual levels, with each accompanied by relevant barriers and facilitators. Key challenges identified included a lack of communication skills, insufficient professional competencies, and power imbalances, among others. To address these challenges, recommendations were made to implement dedicated training sessions on IPC, reduce hierarchical barriers among different health professionals, and enhance the effectiveness of existing systems. Conversely, it was emphasized that projects and campaigns focused on IPC, alongside the development of enhanced communication skills and the presence of supportive leadership, as essential for facilitating effective IPC in PHCCs. CONCLUSION: The interplay between the meso, macro, micro, and individual levels highlight the significance of a multifaceted approach to interventions, aiming to enhance the successes of IPC. While initiatives like interprofessional education training are underway, numerous challenges persist before achieving improved collaboration and more efficient integration of IPC in the PHCC setting.

Directional Bias in Molecular Photogearing Evidenced by LED-Coupled Chiral Cryo-HPLC.

Molecular gearing systems are technomimetic nanoscale analogues to complex geared machinery in the macroscopic world. They are defined as systems incorporating intermeshed movable parts which perform correlated rotational motions by mechanical engagement. Only recently, new methods to actively drive molecular gearing motions instead of relying on passive thermal activation have been developed. Further progress in this endeavor will pave the way for unidirectional molecular gearing devices with a distinct type of molecular machine awaiting its realization. Within this work an essential step towards this goal is achieved by evidencing directional biases for the light-induced rotations in our molecular photogear system. Using a custom-designed LED-coupled chiral cryo-HPLC setup for the in situ irradiation of enantiomeric analytes, an intrinsic selectivity for clockwise or counterclockwise rotations was elucidated experimentally. Significant directional biases in the photogearing processes and light-induced single bond rotations (SBRs) are observed for our photogear with directional preferences of up to 4.8 : 1. Harnessing these effects will allow to rationally design and construct a fully directional molecular gearing motor in the future.

Synthesis of Ce(IV) Heteroleptic Double-Decker Complex with a New Helical Naphthalocyanine as a Potential Gearing Subunit.

This paper describes the synthesis of a cerium(IV)-based molecular gear composed of a thioether functionalized phthalocyanine anchoring ligand, and a helical naphthalocyanine rotating cogwheel functionalized with four carbazoles. The naphthalocyanine ligand 9 was obtained after eleven steps (overall yield of 0.2%) as a mixture of three geometrical isomers, two of which are chiral and exhibit high levels of steric hindrance, as shown by DFT calculations. Their attributions have been made using 1H-NMR based on their different symmetry groups. The ratio of isomers was also determined and the prochiral C4h naphthalocyanine shown to be the major compound (55%). Its heteroleptic complexation with cerium (IV) and the anchoring phthalocyanine ligand 10 gave the targeted molecular gear in a 16% yield.

Intelligent Inspection Method and System of Plastic Gear Surface Defects Based on Adaptive Sample Weighting Deep Learning Model.

After injection molding, plastic gears often exhibit surface defects, including those on end faces and tooth surfaces. These defects encompass a wide range of types and possess complex characteristics, which pose challenges for inspection. Current visual inspection systems for plastic gears suffer from limitations such as single-category defect inspection and low accuracy. There is an urgent industry need for a comprehensive and accurate method and system for inspecting defects on plastic gears, with improved inspection capability and higher accuracy. This paper presents an intelligent inspection algorithm network for plastic gear defects (PGD-net), which effectively captures subtle defect features at arbitrary locations on the surface compared to other models. An adaptive sample weighting method is proposed and integrated into an improved Focal-IoU loss function to address the issue of low inspection accuracy caused by imbalanced defect dataset distributions, thus enhancing the regression accuracy for difficult defect categories. CoordConv layers are incorporated into each inspection head to improve the model's generalization capability. Furthermore, a dataset of plastic gear surface defects comprising 16 types of defects is constructed, and our algorithm is trained and tested on this dataset. The PGD-net achieves a comprehensive mean average precision (mAP) value of 95.6% for the 16 defect types. Additionally, an online inspection system is developed based on the PGD-net algorithm, which can be integrated with plastic gear production lines to achieve online full inspection and automatic sorting of plastic gear defects. The entire system has been successfully applied in plastic gear production lines, conducting daily inspections of over 60,000 gears.

Technological optimization and fatigue evaluation of carbon reinforced polyamide 3D printed gears.

Nowadays, this is even more challenging while additive manufacturing technology is used for prototyping, as well as the production of gearing systems. Presented is an optimization and characterization of the additively manufactured (AM) gears made of carbon-reinforced polyamide material in correlation to conventional polymer gearings. Based on the obtained results, the fatigue life data for AM carbon-reinforced Polyamide gears is calculated and compared to traditionally produced Polyamide 66-based gears. Results show that the durability of AM-produced carbon-reinforced Polyamide gears can be compared to conventional-produced Polyamide 66-based gears. Furthermore, deduced that wear of the AM-reinforced Polyamide teeth, can be closely connected with the meshing temperature in correlation with fatigue.

Helical Gearbox Defect Detection with Machine Learning Using Regular Mesh Components and Sidebands.

The current paper presents helical gearbox defect detection models built from raw vibration signals measured using a triaxial accelerometer. Gear faults, such as localized pitting, localized wear on helical pinion tooth flanks, and low lubricant level, are under observation for three rotating velocities of the actuator and three load levels at the speed reducer output. The emphasis is on the strong connection between the gear faults and the fundamental meshing frequency GMF, its harmonics, and the sidebands found in the vibration spectrum as an effect of the amplitude modulation (AM) and phase modulation (PM). Several sets of features representing powers on selected frequency bands or/and associated peak amplitudes from the vibration spectrum, and also, for comparison, time-domain and frequency-domain statistical feature sets, are proposed as predictors in the defect detection task. The best performing detection model, with a testing accuracy of 99.73%, is based on SVM (Support Vector Machine) with a cubic kernel, and the features used are the band powers associated with six GMF harmonics and two sideband pairs for all three accelerometer axes, regardless of the rotation velocities and the load levels.

Optimization of machine tool settings for Spirac hypoid gears by controlling symmetry of contact paths.

A novel optimization method to control the symmetry of contact paths on the concave and convex tooth surfaces of the gear then improves the meshing quality was proposed. By modifying the angular setting of the head cutter when cutting the pinion, the direction angles of the two contact paths are equated to estimate their symmetry. The relation between the direction angles is formulated precisely, the influence of the angular setting on the contact paths is investigated, and the equations for obtaining the values of the machine tool settings are derived. The proposed method is applied to a numerical example of a Spirac hypoid gear pair, and the results reveal that the contact paths on the concave and convex tooth surfaces are approximately symmetrical and the transmission errors of both sides are comparable.

A Computational Model for Analysing the Dry Rolling/Sliding Wear Behaviour of Polymer Gears Made of POM.

This study presents a computational model to determine the wear behaviour of polymer gears. Using PrePoMax finite element numerical calculation software, a proposed computational model was built to predict dry rolling/sliding wear behaviour based on Archard's wear model. This allows the calculation of the wear depth in each loading cycle with constant mesh updating using the finite element method. The developed computational model has been evaluated on a spur gear pair, where the pinion made of POM was meshed with a support gear made of steel. The computational results obtained were compared with the analytical results according to the VDI 2736 guidelines. Based on this comparison, it was concluded that the proposed computational model could be used to simulate the wear behaviour of contacting mechanical elements like gears, bearings, etc. The main advantage of the model, if compared to the standardised procedure according to the VDI 2736 guidelines, is the geometry updating after a chosen number of loading cycles, which enables a more accurate prediction of wear behaviour under rolling/sliding loading conditions.

Computational Model for Analysing the Tooth Deflection of Polymer Gears.

A computational model for analysing the tooth deflection of polymer gears is presented in this paper. Because polymer gears have less stiffness compared to metal gears, the proposed approach considers a comprehensive analysis to determine the most suitable numerical model, i.e., the number of teeth that should be modelled for a given gear's geometry and material. The developed computational model has been evaluated using a spur gear pair, where the pinion made of POM was meshed with a support gear made of steel. Material properties were assigned with linear elastic characteristics for the gear, while the pinion was characterised by hyperelastic properties using POM material. Furthermore, a nonlubricated frictional contact between the gear and pinion was considered in the numerical computations. The computational results that were obtained were compared to the empirical results according to VDI 2736 guidelines. Here, the computational approach showed more accurate results due to the hyperelastic material characteristics of POM and the simulation of multiple teeth meshing. However, VDI 2736 calculation showed comparability with the computational results, with a slightly larger deviation at higher loads. In this respect, the proposed computational approach is more suitable for analysing the tooth deflection of polymer gears under higher loads.

Data-Driven Multi-Objective Optimization Approach to Loaded Meshing Transmission Performances for Aerospace Spiral Bevel Gears.

Loaded meshing transmission performance optimization has been an increasingly significant target for the design and manufacturing of aerospace spiral bevel gears with low noise and high strength. An innovative data-driven multi-objective optimization (MOO) method is proposed for the loaded meshing transmission performances of aerospace spiral bevel gears. Data-driven tooth surface modeling is first used to obtain a curvature analysis of loaded contact points. An innovative numerical loaded tooth contact analysis (NLTCA) is applied to develop the data-driven relationships of machine tool settings with respect to loaded meshing transmission performance evaluations. Moreover, the MOO function is solved by using an achievement function approach to accurate machine tool settings output, satisfying the prescribed requirements. Finally, numerical examples are given to verify the proposed methodology. The presented approach can serve as a powerful tool to optimize the loaded meshing transmission performances with higher computational accuracy and efficiency than the conventional methods.

Validation of a 3D-printed robot-assisted partial nephrectomy training model.

Objectives: Most renal tumours can be treated with a partial nephrectomy, with robot-assisted partial nephrectomy becoming the new gold standard. This procedure is challenging to learn in a live setting, especially the enucleation and renorraphy phases. In this study, we attempted to evaluate face, content, and preliminary construct validity of a 3D-printed silicone renal tumour model in robotic training for robot-assisted partial nephrectomy. Materials and Methods: We compared the operative results of three groups of surgeons with different experience levels (>20 partial nephrectomies, 1-20 partial nephrectomies and no experience at all) performing a robotic tumour excision of a newly developed silicone model with four embedded 3D-printed renal tumours. We evaluated the participants' performance using surgical margins, excision time, total preserved parenchyma, tumour injury and GEARS score (as assessed by two blinded experts) for construct validity. Postoperatively, the participants were asked to complete a survey to evaluate the usefulness, realism and difficulty of the model as a training and/or evaluation model. NASA-TLX scores were used to evaluate the operative workload. Results: Thirty-six participants were recruited, each group consisting of 10-14 participants. The operative performance was significantly better in the expert group as compared to the beginner group. NASA-TLX scores proved the model to be of an acceptable difficulty level.Expert group survey results showed an average score of 6.3/10 on realism of the model, 8.2/10 on the usefulness as training model and 6.9/10 score on the usefulness as an evaluation tool. GEARS scores showed a non-significant tendency to improve between trials, emphasizing its potential as a training model. Conclusion: Face and content validity of our 3D renal tumour model were demonstrated. The vast majority of participants found the model realistic and useful for training and for evaluation. To evaluate construct and predictive validity, we require further research, aiming to compare the results of 3D-model trained surgeons with those of untrained surgeons in real-life surgery.

A Proof-of-Principle Design for Through-Space Transmission of Unidirectional Rotary Motion by Molecular Photogears.

The construction of molecular photogears that can achieve through-space transmission of the unidirectional double-bond rotary motion of light-driven molecular motors onto a remote single-bond axis is a formidable challenge in the field of artificial molecular machines. Here, we present a proof-of-principle design of such photogears that is based on the possibility of using stereogenic substituents to control both the relative stabilities of two helical forms of the photogear and the double-bond photoisomerization reaction that connects them. The potential of the design was verified by quantum-chemical modeling through which photogearing was found to be a favorable process compared to free-standing single-bond rotation ("slippage"). Overall, our study unveils a surprisingly simple approach to realizing unidirectional photogearing.

Effect of Process Parameters on the Crystallinity and Geometric Quality of Injection Molded Polymer Gears and the Resulting Stress State during Gear Operation.

The quality of gear manufacturing significantly influences the way load is distributed in meshing gears. Despite this being well-known from practical experience, gear quality effects were never systematically characterized for polymer gears in a manner able to account for them in a standard calculation process. The present study employs a novel combination of numerical and experimental methods, leading to a successful determination of these effects. The findings of the study enhance existing gear design models and contribute to a more optimized polymer gear design. The study first explores the effect of injection-molding parameters on the gear quality and secondly the effect of resulting gear quality on the stress conditions in a polymer gear pair. For the gear sample production, different combinations of process parameters were investigated, and a classic injection-molding and the Variotherm process were utilized. Gear quality and crystallinity measurements were conducted for all produced gears, providing insights into the correlation between them. Based on the evaluated gear quality of produced samples, the effect of gear quality was further studied by numerical means within a meaningful range of quality grades and transmitted loads. Special attention was dedicated to lead and pitch deviations, which were found to exert a noteworthy influence on the stress state (both root and flank) of the gear. The effect of lead deviation was most pronounced when improving the gear quality from grade Q12 to grade Q10 (30% to 80% stress reduction, depending on the load). However, enhancing the quality grade from Q10 to Q8 yielded less improvement (5% to 20% stress reduction, depending on the load). A similar pattern was evident also for pitch deviations.

Discrimination, Reliability, Sensitivity, and Specificity of Robotic Surgical Proficiency Assessment With Global Evaluative Assessment of Robotic Skills and Binary Scoring Metrics: Results From a Randomized Controlled Trial.

Objective: To compare binary metrics and Global Evaluative Assessment of Robotic Skills (GEARS) evaluations of training outcome assessments for reliability, sensitivity, and specificity. Background: GEARS-Likert-scale skills assessment are a widely accepted tool for robotic surgical training outcome evaluations. Proficiency-based progression (PBP) training is another methodology but uses binary performance metrics for evaluations. Methods: In a prospective, randomized, and blinded study, we compared conventional with PBP training for a robotic suturing, knot-tying anastomosis task. Thirty-six surgical residents from 16 Belgium residency programs were randomized. In the skills laboratory, the PBP group trained until they demonstrated a quantitatively defined proficiency benchmark. The conventional group were yoked to the same training time but without the proficiency requirement. The final trial was video recorded and assessed with binary metrics and GEARS by robotic surgeons blinded to individual, group, and residency program. Sensitivity and specificity of the two assessment methods were evaluated with area under the curve (AUC) and receiver operating characteristics (ROC) curves. Results: The PBP group made 42% fewer objectively assessed performance errors than the conventional group (P < 0.001) and scored 15% better on the GEARS assessment (P = 0.033). The mean interrater reliability for binary metrics and GEARS was 0.87 and 0.38, respectively. Binary total error metrics AUC was 97% and for GEARS 85%. With a sensitivity threshold of 0.8, false positives rates were 3% and 25% for, respectively, the binary and GEARS assessments. Conclusions: Binary metrics for scoring a robotic VUA task demonstrated better psychometric properties than the GEARS assessment.

Wear Analysis of 3D-Printed Spur and Herringbone Gears Used in Automated Retail Kiosks Based on Computer Vision and Statistical Methods.

This paper focuses on a wear evaluation conducted for prototype spur and herringbone gears made from PET-G filament using additive manufacturing. The main objective of this study is to verify if 3D-printed gears can be considered a reliable choice for long-term exploitation in selected mechanical systems, specifically automated retail kiosks. For this reason, two methods were applied, utilizing: (1) vision-based inspection of the gears' cross-sectional geometry and (2) the statistical characterization of the selected kinematic parameters and torques generated by drives. The former method involves destructive testing and allows for identification of the gears' operation-induced geometric shape evolution, whereas the latter method focuses on searching for nondestructive kinematic and torque-based indicators, which allow tracking of the wear. The novel contribution presented in this paper is the conceptual and experimental application of the identification of the changes of 3D-printed parts' geometric properties resulting from wear. The inspected exploited and non-exploited 3D-printed parts underwent encasing in resin and a curing process, followed by cutting in a specific plane to reveal the desired shapes, before finally being subjected to a vision-based geometric characterization. The authors have experimentally demonstrated, in real industrial conditions, on batch production parts, the usefulness of the presented destructive testing technique providing valid indices for wear identification.

Biomimetics Design of Tooth Root Zone at Cylindrical Gears Profile.

During the last few decades, the requirements for modern machine elements in terms of size reduction, increasing the energy efficiency, and a higher load capacity of standard and non-standard gears have been very prevalent issues. Within these demands, the main goals are the optimization of the gears' tooth profiles, as well as the investigation of new tooth profile designs. The presented design idea is based on the optimal solutions inspired by nature. Special attention is paid to the new design of the tooth root zones of spur gears in order to decrease the stress concentration values and increase the tooth root fatigue resistance. The finite element method is used for stress and strain state calculations, and the particular gear pair is modeled and optimized for these purposes. For tooth root strength analysis, the estimations are based on the theory of critical distances and the stress gradients obtained through finite element analysis. The obtained stress gradients have shown important improvements in the stress distribution in the transition zone optimized by biomimetics. An analysis of the material variation influence is also performed. Based on the investigations of a particular gear pair, a significant stress reduction of about 7% for steel gears and about 10.3% for cast iron gears is obtained for tooth roots optimized by bio-inspired design.

Longitudinal assessment of pharmacy student well-being using the well-being index and 5 gears assessment.

OBJECTIVE: The objective of the study was to assess the level of pharmacy student well-being during the first 2 years of their didactic education utilizing the Well-being Index (WBI) and 5 Gears assessment. METHODS: WBI and 5 Gears data were tracked monthly for first- and second-year students enrolled at the Medical University of South Carolina College of Pharmacy from September 2019 to March 2022. Data were collected through monthly RedCap surveys, then de-identified and separated into 4 study cohorts (A-D). Data were analyzed using descriptive statistics. RESULTS: Responses from 279 students were evaluated. WBI ratings showed variance across the first and second professional years of the program. Students also reported fluctuations in WBI throughout academic years, most often correlating with major events (scheduled breaks, COVID-19 pandemic). Similarly, the 5 Gears assessments results also changed throughout the study period, including variance within and between each academic year. CONCLUSION: Incorporating well-being assessments into the co-curriculum has allowed us to identify when students are struggling with their well-being, provide tools and resources to help enhance their well-being, and opportunities to discuss struggles with their peers. Colleges of Pharmacy must incorporate holistic approaches to address all aspects of well-being, including consideration of how the curriculum is impacting the student experience as well as institutional approaches to well-being.

Abrasive Wear Resistance of Ultrafine Ausferritic Ductile Iron Intended for the Manufacture of Gears for Mining Machinery.

The purpose of this study was to experimentally determine the abrasion wear properties of ausferritic ductile iron austempered at 250 °C in order to obtain cast iron of class EN-GJS-1400-1. It has been found that such a cast iron grade makes it possible to create structures for material conveyors used for short-distance transport purposes, required to perform in terms of abrasion resistance under extreme conditions. The wear tests addressed in the paper were conducted at a ring-on-ring type of test rig. The test samples were examined under the conditions of slide mating, where the main destructive process was surface microcutting via loose corundum grains. The mass loss of the examined samples was measured as a parameter characteristic of the wear. The volume loss values thus obtained were plotted as a function of initial hardness. Based on these results, it has been found that prolonged heat treatment (of more than 6 h) causes only an insignificant increase in the resistance to abrasive wear.

Simulator Fidelity Does Not Affect Training for Robot-Assisted Minimally Invasive Surgery.

This study was undertaken to compare performance using a surgical robot after training with one of three simulators of varying fidelity. METHODS: Eight novice operators and eight expert surgeons were randomly assigned to one of three simulators. Each participant performed two exercises using a simulator and then using a surgical robot. The primary outcome of this study is performance assessed by time and GEARS score. RESULTS: Participants were randomly assigned to one of three simulators. Time to perform the suturing exercise (novices vs. experts) was significantly different for all 3 simulators. Using the da Vinci robot, peg transfer showed no significant difference between novices and experts and all participants combined (mean time novice 2.00, expert 2.21, p = 0.920). The suture exercise had significant differences in each group and all participants combined (novice 3.54, expert 1.90, p = 0.001). ANOVA showed p-Values for suturing (novice 0.523, expert 0.123) and peg transfer (novice 0.742, expert 0.131) are not significantly different. GEARS scores were different (p < 0.05) for novices and experts. CONCLUSION: Training with simulators of varying fidelity result in similar performance using the da Vinci robot. A dry box simulator may be as effective as a virtual reality simulator for training. Further studies are needed to validate these results.

Aerodynamic Evaluation of Flapping Wings with Leading-Edge Twisting.

The purpose of the current study is to emphasize the characteristics and phenomena of leading-edge twisting in flapping wing vehicles. A fused deposition modeling (FDM) 3D printing method is applied to develop the flapping mechanisms with bevel gears to achieve the leading-edge twisting. Three flapping mechanisms were developed, including simple flapping only (type-A1: normal servo mechanism), flapping with continuous leading-edge twisting (type-B: servo-bevel gear mechanism), and flapping with restricted leading-edge twisting via mechanical stoppers (type-B1: servo-bevel gear mechanism with adjustable mechanical stoppers). Utilizing a low-speed wind tunnel, the aerodynamic performances of these mechanisms are examined by extracting their lift and net thrust forces. The wind tunnel testing data showed that the flapping with restricted leading-edge twisting via mechanical stoppers (type-B1) showed better performance than the simple flapping (type-A1) by 32.9%, and also better performance than the flapping with continuous leading-edge twisting (type-B) by 64%. Next, MATLAB software was used to create the 3D wing surfaces from the instantaneous stereophotography Kwon3D trajectories to fully sketch the leading-edge twisting features. The 2D airfoil cut sections at the mean aerodynamic chord at different stroke moments depict the instantaneous angles of attack to justify the aforementioned wind tunnel testing data and it was verified using a theoretical trajectory model. This comprehensive study using the 3D-printed mechanisms is well suited for the quantitative evaluation of the lift contribution from leading-edge twisting.