Search results

This study aims to propose and evaluate the progress in the basic-pixel (a strategy to generate continuous trajectories that fill out the entire surface) algorithm towards performance gain. The objective is also to investigate the operational efficiency and effectiveness of an enhanced version compared with conventional strategies.

For the first objective, the proposed methodology is to apply the improvements proposed in the basic-pixel strategy, test it on three demonstrative parts and statistically evaluate the performance using the distance trajectory criterion. For the second objective, the enhanced-pixel strategy is compared with conventional strategies in terms of trajectory distance, build time and the number of arcs starts and stops (operational efficiency) and targeting the nominal geometry of a part (operational effectiveness).

The results showed that the improvements proposed to the basic-pixel strategy could generate continuous trajectories with shorter distances and comparable building times (operational efficiency). Regarding operational effectiveness, the parts built by the enhanced-pixel strategy presented lower dimensional deviation than the other strategies studied. Therefore, the enhanced-pixel strategy appears to be a good candidate for building more complex printable parts and delivering operational efficiency and effectiveness.

This paper presents an evolution of the basic-pixel strategy (a space-filling strategy) with the introduction of new elements in the algorithm and proves the improvement of the strategy’s performance with this. An interesting comparison is also presented in terms of operational efficiency and effectiveness between the enhanced-pixel strategy and conventional strategies.

In this study, the authors propose a novel digital twinning approach specifically designed for controlling transient thermal systems. The purpose of this study is to harness the combined power of deep learning (DL) and physics-based methods (PBM) to create an active virtual replica of the physical system.

To achieve this goal, we introduce a deep neural network (DNN) as the digital twin and a Finite Element (FE) model as the physical system. This integrated approach is used to address the challenges of controlling an unsteady heat transfer problem with an integrated feedback loop.

The results of our study demonstrate the effectiveness of the proposed digital twinning approach in regulating the maximum temperature within the system under varying and unsteady heat flux conditions. The DNN, trained on stationary data, plays a crucial role in determining the heat transfer coefficients necessary to maintain temperatures below a defined threshold value, such as the material’s melting point. The system is successfully controlled in 1D, 2D and 3D case studies. However, careful evaluations should be conducted if such a training approach, based on steady-state data, is applied to completely different transient heat transfer problems.

The present work represents one of the first examples of a comprehensive digital twinning approach to transient thermal systems, driven by data. One of the noteworthy features of this approach is its robustness. Adopting a training based on dimensionless data, the approach can seamlessly accommodate changes in thermal capacity and thermal conductivity without the need for retraining.

The major objective of this research work was to evaluate various physico-chemical characteristics, such as, chemical composition, antioxidant capacity, objective color and texture profile analysis (TPA) of the wheat flour/chickpea flour (CF) blends, so that nutritious baked products could be consumed by the type-2 diabetic persons.

Wholegrain wheat flour (WGF) and white wheat flour (WWF) were substituted with CF at 0 to 40% levels. These wheat flour/CF blends were analyzed for proximate composition, the prepared dough and baked breads were tested for objective color, antioxidant capacity as trolox equivalent antioxidant capacity (TEAC), malondialdehyde (MDA) and total phenolic content (TPC) and TPA.

WGF had the highest TEAC (117.42 mM/100g) value, followed by WWF (73.98 mM/100g) and CF (60.67 mM/100g). TEAC, MDA and TPC values varied significantly among all the three flour samples.

Inclusion of whole chickpea (without dehulling) flour in such type of blends would be another interesting investigation during the future research studies.

These research findings have a great potential for the production of these baked products for human consumption on an industrial scale.

Production of breads using wheat flour and CF blends would benefits the consumers.

Production of Arabic and pan breads using wheat flour and CF blends would, therefore, combine the benefits of both the needed proteins of plant origin and the health-promoting bioactive compounds, in a most sustainable way for the consumers.

In this paper, the authors examine the strategies used to reduce labour costs in three public hospitals in South Africa, which were effective and why. In the democratic era, after the revelations of large-scale corruption, the authors ask whether their case studies provide lessons for how public service institutions might re-make themselves, under circumstances of austerity.

A comparative qualitative case study approach, collecting data using a combination of interviews with managers, focus group discussions and interviews with shop stewards and staff was used.

Management in two hospitals relied on their financial power, divisions between unions and employees' loyalty. They lacked the insight to manage different actors, and their efforts to outsource services and draw on the Extended Public Works Program failed. They failed to support staff when working beyond their scope of practice, reducing employees' willingness to take on extra responsibilities. In the remaining hospital, while previous management had been removed due to protests by the unions, the new CEO provided stability and union–management relations were collaborative. Her legitimate power enabled unions and management to agree on appropriate cost cutting strategies.

Finding an appropriate balance between the new reality of reduced financial resources and the needs of staff and patients, requires competent unions and management, transparency and trust to develop legitimate power; managing in an authoritarian manner, without legitimate power, reduces organisational capacity. Ensuring a fair and orderly process to replace ineffective management is key, while South Africa grows cohorts of competent managers and builds managerial experience.

This study aims to explore the relationship between risk governance characteristics (chief risk officer [CRO], chief financial officer [CFO] and senior directors [SENIOR]) and regulatory adjustments (RAs) in Organization for Economic Cooperation and Development public commercial banks.

Using principal component analysis (PCA) and regression models, the research analyzes a representative data set of these banks.

A significant negative correlation between risk governance characteristics and RAs is found. Sensitivity analysis on the regulatory Tier 1 capital ratio and the total capital ratio indicates mixed outcomes, suggesting a complex relationship that warrants further exploration.

The study’s limited sample size calls for further research to confirm findings and explore risk governance’s impact on banks’ capital structures.

Enhanced risk governance could reduce RAs, influencing banking policy.

The study advocates for improved banking regulatory practices, potentially increasing sector stability and public trust.

This study contributes to understanding risk governance’s role in regulatory compliance, offering insights for policymaking in banking.

This study aims to explore and empirically test variables influencing material delivery schedule inaccuracies?

A mixed-method case approach is applied. Explanatory variables are identified from the literature and explored in a qualitative analysis at an automotive original equipment manufacturer. Using logistic regression and random forest classification models, quantitative data (historical schedule transactions and internal data) enables the testing of the predictive difference of variables under various planning horizons and inaccuracy levels.

The effects on delivery schedule inaccuracies are contingent on a decoupling point, and a variable may have a combined amplifying (complexity generating) and stabilizing (complexity absorbing) moderating effect. Product complexity variables are significant regardless of the time horizon, and the item’s order life cycle is a significant variable with predictive differences that vary. Decoupling management is identified as a mechanism for generating complexity absorption capabilities contributing to delivery schedule accuracy.

The findings provide guidelines for exploring and finding patterns in specific variables to improve material delivery schedule inaccuracies and input into predictive forecasting models.

The findings contribute to explaining material delivery schedule variations, identifying potential root causes and moderators, empirically testing and validating effects and conceptualizing features that cause and moderate inaccuracies in relation to decoupling management and complexity theory literature?

This study relied on the job demands and resource model to understand employees’ turnover intentions. Recent studies have consistently lent support for the significant association between role ambiguity and turnover intentions; however, only a handful of studies focused on examining the potential mediators in this association. The authors argued that role ambiguity positively influences turnover intentions through affective mechanisms: job involvement and satisfaction.

To test the model, a large sample of working adults participated (N = 505).

Structural equation modeling results showed that role ambiguity, job involvement and job satisfaction were significantly associated with turnover intentions. Moreover, a serial mediation was found among the variables: employees with low levels of role ambiguity tended to report higher job involvement, which further increased their satisfaction with the job and subsequently decreased their turnover intentions.

The cross-sectional design is a limitation.

Practical suggestions regarding how organizations can reduce employee turnover are discussed.

The findings provide support for theory-driven interventions to address developing the intention to stay at work among working adults.

With the growing need for employees to be innovative, public-sector organizations are investing in employee training. This study aims to examine the effects of a combined Lean Six Sigma and innovation training, using action learning, on public-sector employees’ creative role identity and innovative work behavior.

The authors studied a public service agency in Singapore in which a five-day Lean Innovation Training was implemented, using a combination of Lean Six Sigma and Creative Problem-Solving tools, with a simulation on day one and subsequent team-based project coaching, spread over six months. The authors administered pre- and postintervention surveys among all the employees, and initiated group interviews and observations before, during and after the intervention.

Creative role identity and innovative work behavior had significantly improved six months after the intervention, enabled through senior management’s transformational leadership. The training induced managers to role-model innovative work behaviors while cocreating, with their employees, a renewal of their agency’s core processes. The three completed improvement projects contributed to an innovative work culture and reduced service turnaround time.

Starting with a role-playing simulation on the first day, during which leaders and followers swapped roles, the action-learning type training taught all the organizational members to use various Lean Six Sigma and Creative Problem-Solving tools. This nimble Lean Innovation Training, and subsequent team-based project coaching, exemplifies how advancing the staff’s creative role identity can have a positive impact.

Nearly 75% of EU buildings are not energy-efficient enough to meet the international climate goals, which triggers the need to develop sustainable construction techniques with high degree of resilience against climate change. In this context, a promising construction technique is represented by ventilated façades (VFs). This paper aims to propose three different VFs and the authors define a novel machine learning-based approach to evaluate and predict their energy performance under different boundary conditions, without the need for expensive on-site experimentations

The approach is based on the use of machine learning algorithms for the evaluation of different VF configurations and allows for the prediction of the temperatures in the cavities and of the heat fluxes. The authors trained different regression algorithms and obtained low prediction errors, in particular for temperatures. The authors used such models to simulate the thermo-physical behavior of the VFs and determined the most energy-efficient design variant.

The authors found that regression trees allow for an accurate simulation of the thermal behavior of VFs. The authors also studied feature weights to determine the most relevant thermo-physical parameters. Finally, the authors determined the best design variant and the optimal air velocity in the cavity.

This study is unique in four main aspects: the thermo-dynamic analysis is performed under different thermal masses, positions of the cavity and geometries; the VFs are mated with a controlled ventilation system, used to parameterize the thermodynamic behavior under stepwise variations of the air inflow; temperatures and heat fluxes are predicted through machine learning models; the best configuration is determined through simulations, with no onerous in situ experimentations needed.

In the developing field of nano-materials synthesis, copper oxide nanoparticles (NPs) are deemed to be one of the most significant transition metal oxides because of their intriguing characteristics. Its synthesis employing green chemistry principles has become a key source for next-generation antibiotics attributed to its features such as environmental friendliness, ease of use and affordability. Because they are more environmentally benign, plants have been employed to create metallic NPs. These plant extracts serve as capping, stabilising or hydrolytic agents and enable a regulated synthesis as well.

Organic chemical solvents are harmful and entail intense conditions during nanoparticle synthesis. The copper oxide NPs (CuO-NPs) synthesised by employing the green chemistry principle showed potential antitumor properties. Green synthesised CuO-NPs are regarded to be a strong contender for applications in the pharmacological, biomedical and environmental fields.

The aim of this study is to evaluate the anticancer potential of CuO-NPs plant extracts to isolate and characterise the active anticancer principles as well as to yield more effective, affordable, and safer cancer therapies.

This review article highlights the copper oxide nanoparticle's biomedical applications such as anticancer, antimicrobial, dental and drug delivery properties, future research perspectives and direction are also discussed.