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This paper aims to offer a roadmap for a strengths-based approach to leadership.

A review and synthesis of various concepts relating to strengths in the workplace.

A strengths-based approach to leadership can lead to many positive outcomes, including increased employee engagement, productivity, satisfaction, performance and a more positive work environment.

This is a synthesis of a variety of ideas about how to lead with strengths and the positive implications of a strengths-oriented workplace culture.

The purpose of this study was to examine how well a strength-based program grounded in positive psychology principles can advance the practical critical thinking skills of those pursuing the teacher training course.

This study used a single-group pre-test post-test design with 35 teacher-trainees from the Bachelor of Education course. The two-and-a-half-week strength-based program used the values in action survey to identify strengths. Pre- and post-test scores, measured with the Cornell Critical Thinking Test – Level Z, underwent Statistical Package for Social Sciences analysis including paired samples t-test for subcomponent and overall composite analysis.

Analysis of the pre- and post-test scores demonstrated a statistical significance in the critical thinking scores obtained by the teacher-trainees. Post-test scores were consistently significant. Out of the elements of critical thinking, induction, meaning, observation and credibility were more prominent. Deduction and assumption identification were also having a significant effect.

Most critical thinking programs focus on evaluating specific teaching methods for improving critical thinking skills. In education, positive psychology studies often center on students’ well-being, attention spans and academic success, aligning with wellness programs. Despite the importance of strengths in positive psychology, there is a lack of research on using a strength-based approach to boost critical thinking skills. This study aims to enhance teacher-trainees’ critical thinking by leveraging their individual strengths, moving away from traditional instructional strategies.

From the factors that affect shear strength of reinforced concrete (RC) beams, the study examines the effect of controversial parameters, width-to-depth (b/d) and effective length-to-depth (l_eff/d) ratio on shear strength of RC slender beams.

The researchers utilized a database of 676 experimental test results from ACI-DAfStb database, Conducted regression analysis to examine relationship between b/d and l_eff/d ratios and shear strength, compare and analyze sensitivity to changes in b/d and l_eff/d ratios for the selected 12 shear models for RC beams.

Increasing b/d ratio enhanced shear strength until b/d ˜ 3, but further increases had limited impact and increasing l_eff/d ratio resulted in decreased shear strength. From comparative analysis, the models provided by various design standards were found to be safe, with EC-2 and JSCE models being conservative. From considered research models, Campione and Arslan models were conservative, while Kim and White model were observed to be unsafe. Sensitivity analysis indicated ACI318-19, JSCE, CEB-FIP-90 and Arslan models were sensitive to changes in b/d and l_eff/d ratios. National code models generally captured shear strength characteristics well. Certain models suggested a constant/decreasing b/d effect despite observed shear strength enhancement. Most models indicated improved shear strength with an increasing l_eff/d ratio, contrary to experimental findings while TS500 and Hwang models aligned with experimental results.

The study's limitations include the dependence on the available database, which may not encompass all possible experimental scenarios. Further research should aim to expand the database and investigate additional parameters that may influence shear strength in RC beams.

The findings of this study have practical implications for the design and analysis of RC beams by suggesting that the width-to-depth and length-to-depth ratios should be carefully considered to optimize shear strength. The identified models can assist engineers in selecting appropriate shear strength prediction models based on specific design scenarios.

The study contributes to the advancement of knowledge in the field of reinforced concrete beam design, which has implications for the safety and reliability of structural systems. By understanding the factors influencing shear strength, engineers can design more efficient and robust structures, ensuring the safety of buildings and infrastructure.

This study provides valuable insights into the influence of the width-to-depth and effective length-to-depth ratios on shear strength in reinforced concrete beams. It contributes to the understanding of these factors and their impact on shear strength, addressing the lack of consensus among researchers. The comparative analysis of shear models and the sensitivity analyses add value by identifying the models that align better with experimental observations. The study emphasizes the need for accurate models that account for these factors and highlights the importance of further research to refine and develop improved predictive models.

The coal-based synthetic natural gas slag (CSNGS) is a solid waste remaining from the incomplete combustion of raw coal to produce gas. With the continuous promotion of efficient and clean utilization of coal in recent years, the stockpiling of CSNGS would increase gradually, and it would have significant social and environmental benefits with reasonable utilization of CSNGS. This study prepared a new geopolymer by mixing CSNGS with PC42.5 cement in a certain mass ratio as the precursor, with sodium hydroxide and sodium silicate solution as the alkali activators.

The formulation of coal-based synthetic natural gas slag geopolymer (CSNGSG) was determined by an orthogonal test, and then the strength mechanism and microstructure of CSNGSG were characterized by multi-scale tests.

The results show that the optimum ratio of CSNGSG was a sodium silicate modulus of 1.3, an alkali dosage of 21% and a water cement ratio of 0.36 and the maximum unconfined compressive strength of CSNGSG at 7 d was 26.88 MPa. The increase of curing temperature could significantly improve the compressive strength of CSNGSG, and the curing humidity had little effect on the compressive strength of CSNGSG. The development of the internal strength of CSNSG at high temperatures consumed SiO₂, Al₂O₃ and CaO and the intensity of corresponding crystalline peaks decreased.

Moreover, the vibration of chemical bonds in different wavenumbers also revealed the reaction mechanism of CSNSG from another perspective. Finally, the relevant test results indicated that CSNGS had practical application value as a raw material for the preparation of geopolymer cementing materials.

Three-dimensional (3D) printing is highly dependent on printing process parameters for achieving high mechanical strength. It is a time-consuming and expensive operation to experiment with different printing settings. The current study aims to propose a regression-based machine learning model to predict the mechanical behavior of ulna bone plates.

The bone plates were formed using fused deposition modeling (FDM) technique, with printing attributes being varied. The machine learning models such as linear regression, AdaBoost regression, gradient boosting regression (GBR), random forest, decision trees and k-nearest neighbors were trained for predicting tensile strength and flexural strength. Model performance was assessed using root mean square error (RMSE), coefficient of determination (R²) and mean absolute error (MAE).

Traditional experimentation with various settings is both time-consuming and expensive, emphasizing the need for alternative approaches. Among the models tested, GBR model demonstrated the best performance in predicting both tensile and flexural strength and achieved the lowest RMSE, highest R2 and lowest MAE, which are 1.4778 ± 0.4336 MPa, 0.9213 ± 0.0589 and 1.2555 ± 0.3799 MPa, respectively, and 3.0337 ± 0.3725 MPa, 0.9269 ± 0.0293 and 2.3815 ± 0.2915 MPa, respectively. The findings open up opportunities for doctors and surgeons to use GBR as a reliable tool for fabricating patient-specific bone plates, without the need for extensive trial experiments.

The current study is limited to the usage of a few models. Other machine learning-based models can be used for prediction-based study.

This study uses machine learning to predict the mechanical properties of FDM-based distal ulna bone plate, replacing traditional design of experiments methods with machine learning to streamline the production of orthopedic implants. It helps medical professionals, such as physicians and surgeons, make informed decisions when fabricating customized bone plates for their patients while reducing the need for time-consuming experimentation, thereby addressing a common limitation of 3D printing medical implants.

Attention deficit hyperactivity disorder (ADHD) is a neuro-developmental condition that has frequently been pathologised in career research and broader society to date. The study seeks to reframe such assumptions through a qualitative positive-focused exploration of career stories of ADHD adults, elicited through a strength-focused technique with wide applicability for coaching and other career-based development activities.

Situated in a strength-focused coaching psychology paradigm, the authors undertook semi-structured interviews with 17 participants, using an adapted feedforward interview technique (FFI) rooted in positive psychology (PP), to investigate individuals' strengths and successful career experiences.

Narrative thematic analysis of the transcripts identified two core themes: “the paradoxical nature of strengths” and “career success as an evolving narrative”. The participants described how they have achieved career success both “in spite of” and “because of” ADHD. The use of the FFI demonstrated a helpful and easily taught method for eliciting personal narratives of success and strengths, an essential foundation to any coaching process.

This research provides a nuanced overview, and an associated conceptual model, of how adults with ADHD perceive their career-based strengths and experiences of success. Further, the research shows the value of using a positive psychological coaching approach when working with neurominority individuals, using a successful adaptation of the FFI. The authors hope that the documentation of this technique and the resulting insights will offer important guidance for managers as coaches and internal and external career coaches, as well as providing positive and relatable narrative resources for ADHD adults.

This paper presents an experimental investigation in establishing the relationship between FDM process parameters and tensile strength of polycarbonate (PC) samples using the I-Optimal design.

I-optimal design methodology is used to plan the experiments by means of Minitab-17.1 software. Samples are manufactured using Stratsys FDM 400mc and tested as per ISO standards. Additionally, an artificial neural network model was developed and compared to the regression model in order to select an appropriate model for optimisation. Finally, the genetic algorithm (GA) solver is executed for improvement of tensile strength of FDM built PC components.

This study demonstrates that the selected process parameters (raster angle, raster to raster air gap, build orientation about Y axis and the number of contours) had significant effect on tensile strength with raster angle being the most influential factor. Increasing the build orientation about Y axis produced specimens with compact structures that resulted in improved fracture resistance.

The fitted regression model has a p-value less than 0.05 which suggests that the model terms significantly represent the tensile strength of PC samples. Further, from the normal probability plot it was found that the residuals follow a straight line, thus the developed model provides adequate predictions. Furthermore, from the validation runs, a close agreement between the predicted and actual values was seen along the reference line which further supports satisfactory model predictions.

This study successfully investigated the effects of the selected process parameters - raster angle, raster to raster air gap, build orientation about Y axis and the number of contours - on tensile strength of PC samples utilising the I-optimal design and ANOVA. In addition, for prediction of the part strength, regression and ANN models were developed. The selected ANN model was optimised using the GA-solver for determination of optimal parameter settings.

The proposed ANN-GA approach is more appropriate to establish the non-linear relationship between the selected process parameters and tensile strength. Further, the proposed ANN-GA methodology can assist in manufacture of various industrial products with Nylon, polyethylene terephthalate glycol (PETG) and PET as new 3DP materials.

This paper aims to evaluate the mechanical behaviour of polycarbonate/acrylonitrile butadiene styrene (PC/ABS) filaments for fusion filament fabrication (FFF). PC/ABS have emerged as a promising material for FFF due to their excellent mechanical properties. However, the optimal processing conditions and the effect of the blending ratio on the mechanical properties of the resulting workpieces are still unclear.

A statistical factorial matrix was designed, including infill pattern, printing speed, nozzle size, layer height and printing temperature as factors (with three levels). A total of 810 workpieces were printed using PC/ABS blends filament with the FFF. The workpieces’ finishing and mass were evaluated. Tensile tests were performed. Analysis of variance was performed to determine the main effects of the processing conditions on the mechanical properties.

The results showed that the PC/ABS (70/30) exhibited higher tensile. Tensile rupture corresponded to 30% of the tensile strength. The infill pattern showed the highest contribution to the responses. The concentric pattern showed higher tensile strength. Tensile strength and mass ratio demonstrated the influence of mass on tensile strength. The influence of printing parameters on deformation depended on the blend proportions. Higher printing speed and lower layer height provided better quality workpieces.

This study has implications for the design and manufacturing of three-dimensional printed parts using PC/ABS filaments. An extensive experimental matrix was applied, aiming at a complete understanding of mechanical behavior, considering the main printing parameters and combinations not explored by literature.

Material extrusion (MEX) 3D printers suffer from an intrinsic limitation of small size of the prints due to its restricted bed dimension. On the other hand, friction stir spot welding (FSSW) is gaining wide interest from automobile, airplane, off-road equipment manufacturers and even consumer electronics. This paper aims to explore the possibility of FSSW on Acrylonitrile Butadiene Styrene/Polylactic acid 3D-printed components to overcome the bed size limitation of MEX 3D printers.

Four different tool geometries (tapered cylindrical pin with/without concavity, pinless with/without concavity) were used to produce the joints. Three critical process parameters related to FSSW (tool rotational speed, plunge depth and dwell time) and two related to 3D printing (material combination and infill percentages) were investigated and optimized using the Taguchi L27 design of experiments. The influence of each welding parameter on the shear strength was evaluated by analysis of variance.

Results revealed that the infill percentage, a 3D printing parameter, had the maximum effect on the joint strength. The joints displayed pull nugget, cross nugget and substrate failure morphologies. The outcome resulted in the joint efficiency reaching up to 100.3%, better than that obtained by other competitive processes for 3D-printed thermoplastics. The results, when applied to weld a UAV wing, showed good strength and integrity. Further, grafting the joints with nylon micro-particles was also investigated, resulting in a detrimental effect on the strength.

To the best of the authors’ knowledge, this is the first study to demonstrate that the welding of dissimilar 3D-printed thermoplastics with/without microparticles is possible by FSSW, whilst the process parameters have a considerable consequence on the bond strength.

The present study has been carried out to study whether and how different aspects of brand management (brand identity, brand image and brand meaning) are instrumental in maintaining and enhancing attachment strength of students with higher education institutes (HEIs). Further, to understand what brand management aspect channels the impact of what branding driver on attachment strength in most effective manner.

The study analysed combined mediating effects as well as specific mediating effects to test the mediating role of brand management aspects.

The study reveals that brand image plays highest mediating role among all aspects of brand management. HEIs need to enhance service quality because brand image carries the highest influence of service quality on attachment strength. Similarly, brand identity carries the highest influence of heritage on attachment strength. Brand meaning carries the highest influence of competence and reputation on attachment strength.

The present study, based on empirical research, has built the framework and mechanism for creating attachment strength utilising the intangible resources of HEIs through brand management. The present study examines how specific intangible resources exhibit varying influences on attachment strength via distinct brand management mediation effects.

The present study provides framework for designing branding strategies to build and channelise necessary intangible resources of branding for nourishing and nurturing attachment strength.

The present study contributes to scarce branding literature in context of HEIs. The study proposes role of HEI branding in developing students' attachment strength with their HEIs.