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The objective was to evaluate the effect of banana peel into cake (a bakery product that may add bananas) and panettone (a product that never is added to bananas) formulations on consumers' acceptance and perceived attributes and the effect on the technological properties of the product.

Bakery products were produced with banana peel in the formulation and control samples without residue. Volunteers scored their acceptance and checked all attributes applied to the four samples in the blind and informed tests. The texture, color, proximate composition and bake properties were measured.

The acceptance of both cakes, with and without banana peel, was not affected when consumers were informed that there was a residue in both cakes. Relative risk indicated that information had no influence on acceptance. Panettone with banana pulp showed a reduction in acceptance when volunteers were informed of the presence of banana peel. Risk analysis showed a significant relative risk of product rejection when consumers were aware of information. The addition of banana peel enhanced panettone chewiness and crumb hardness, reduced bake loss of cakes, reduced the volume of both products and changed some color parameters of both products.

Banana peel is a source of functional compounds, but it is not clear how consumers react when informed that the food presents it.

The demand for titanium alloys has received massive attention in the aerospace and automotive industry owing to their magnificent electrochemically compatibility and corrosion resistance, high strength at elevated temperatures and high strength-to-weight ratio. Although titanium alloy has impressive mechanical properties, they are challenging to machine or metal form due to its poor heat conductivity, high chemical reactivity, low modulus of elasticity, high friction coefficient and difficult lubricant that limits its application field and increases wear. However, surface treatment coating with the strong metallurgical bond between the titanium alloy matrixes is novel technique to resolve these challenges. This research will illustrate the influence of laser scanning power on the microstructure and tribological behavior of Nickel (NI)-composite claddings fabricated on TC4 titanium alloy to realize the strong metallurgical bond between the titanium alloy and NI-composite coating.

In this research, TiC/TC4 alloy nanocomposites were fabricated based on different laser power and temperatures. TC4 has been selected as a base material instead of TiC for the strong metallurgical bond between the titanium alloy matrixes. Then Ni-composite coating was used as the surface treatment coating on TC4 by laser cladding (LC) technique. The Ni-based alloy coating material powder is good self-fluxing, has high-temperature resistance and is analytically pure with 200 mesh, which can easily overcome the various challenges of titanium alloy. The chemical properties of Ni composite coating include 31.2% Chromium, 8%Titenium and 3.6% Carbon. The prepared surface treatment coating characterization and microstructure behavior are analyzed using optical micrograph, X-ray diffraction, scanning electron microscopes, energy dispersive spectroscopy and electron probe micro analyzer methods.

It is evident that at the beginning of the experiment, if the laser power increased, the quality of the coating increased. An optimal quality of the coating is found when the laser scanning power about 12.55 kJ/cm². Further increased laser power diminished the quality of the coating because the material plasticity had deteriorated. The TiC ceramic particle reinforced phase is dispersed into a two-phase solid solution of β-Ti and γ-Ni. The micro-hardness of the used coating is greater than the base alloy.

This research has practical value in the modern aerospace and automobile industry to increase the application of titanium alloy.

In the context of collaborative research and development (R&D), multi-actor participation and multi-resource integration of technological knowledge has become the mainstream paradigm for the R&D and spillover of industry generic technology (GT). As GT's core characteristics, “fundamentality” and “externality,” make differential requests on knowledge bases regarding the R&D and spillover of GT (SGT). Knowledge breadth can enhance the generality of technology. The purpose of this paper is to integrate “generic technology R&D” and “generic technology spillover” into a single study, and try to solve the theoretical problem of “whether broader mean more general?”

This paper collects and collates the patent data from the two patent databases of Derwent and SooPAT, and then makes an empirical analysis of the patent data collected by the authors with the data analysis software Stata.

Taking 352 strategic emerging firms in China as the sample, this paper examined the effects of general knowledge breadth (GKB) and specific knowledge breadth (SKB) on the R&D and SGT. The authors concluded that both general and SKB have a positive effect on the R&D of GT (RGT), and the latter has a greater effect. There is a significant inverted U-shaped relationship between SKB and SGT.

The theoretical contributions of this paper are as follows. GT can effectively link different technologies and knowledge fields (Gambardella and Giarratana, 2013; Appio et al., 2017a, b). Therefore, existing studies regard the role of knowledge breadth on the R&D and SGT as an existing hypothesis. This paper challenges such hypothesis in two ways. First, this paper divides knowledge breadth into “general knowledge breadth” and “specific knowledge breadth” in response to the insufficient division of knowledge breadth in previous research, although some existing studies have examined the antecedents of the R&D and SGT from the perspective of R&D and SGT. Thus, the authors define GKB as the scope of context-free knowledge and SKB as the scope of context-specific knowledge, both of which shows differential nature, source and application. Second, this paper decomposes the effect of knowledge breadth on RGT, as well as on SGT, basing on distinguishing the SKB from GKB. Existing research reaches a consensus of the positive role of knowledge breadth, no matter on RGT or SGT (e.g. Schmidt et al., 2016; Appio et al., 2017a, b). Yet, such hypothesis ignores the refinement and decomposition of “knowledge breadth” in the research field of R&D and SGT, which is essential in promoting the development of GT theory. In this paper, the authors find that these two types of knowledge breadths play different roles in the RGT, and especially SKB plays a double-edged sword effect on the SGT.

Proponents of micro-credentials envision them as vehicles for upskilling or re-skilling individuals. The study examines how integrating micro-credentials in the higher education ecosystem enhances employability. It aims to offer insights from the perspective of stakeholders who may benefit from these credentials at an institutional or individual level.

Online in-depth interviews are conducted with 65 participants from India, Nigeria, the United Arab Emirates and the United Kingdom to explore how micro-credentials can be a valuable addition to the higher education ecosystem. A multi-stakeholder approach is adopted to collect data.

The analysis highlights two possible methods of integrating micro-credentials into the higher education ecosystem. First, micro-credentials-driven courses can be offered using a blended approach that provides a flexible learning path. Second, there is also the possibility of wide-scale integration of micro-credentials as an outcome of standalone online programs. However, the effectiveness of such programs is driven by enablers like student profiles, standardization and the dynamics of the labor market. Finally, the study stipulates that micro-credentials can enhance employability.

The study's findings suggest that, for successful integration of micro-credentials, an operational understanding of micro-credentials, their enablers and strategic deliberation are critical in higher education. Institutions must identify the determinants, address technological limitations and select a suitable delivery mode to accelerate integration. However, micro-credentials can augment employability, considering the increasing emphasis on lifelong learning. An overview of the findings is presented through a comprehensive framework.

Recently, powerful instruments for biomedical engineering research studies, including disease modeling, drug designing and nano-drug delivering, have been extremely investigated by researchers. Particularly, investigation in various microfluidics techniques and novel biomedical approaches for microfluidic-based substrate have progressed in recent years, and therefore, various cell culture platforms have been manufactured for these types of approaches. These microinstruments, known as tissue chip platforms, mimic in vivo living tissue and exhibit more physiologically similar vitro models of human tissues. Using lab-on-a-chip technologies in vitro cell culturing quickly caused in optimized systems of tissues compared to static culture. These chipsets prepare cell culture media to mimic physiological reactions and behaviors.

The authors used the application of lab chip instruments as a versatile tool for point of health-care (PHC) applications, and the authors applied a current progress in various platforms toward biochip DNA sensors as an alternative to the general bio electrochemical sensors. Basically, optical sensing is related to the intercalation between glass surfaces containing biomolecules with fluorescence and, subsequently, its reflected light that arises from the characteristics of the chemical agents. Recently, various techniques using optical fiber have progressed significantly, and researchers apply highlighted remarks and future perspectives of these kinds of platforms for PHC applications.

The authors assembled several microfluidic chips through cell culture and immune-fluorescent, as well as using microscopy measurement and image analysis for RNA sequencing. By this work, several chip assemblies were fabricated, and the application of the fluidic routing mechanism enables us to provide chip-to-chip communication with a variety of tissue-on-a-chip. By lab-on-a-chip techniques, the authors exhibited that coating the cell membrane via poly-dopamine and collagen was the best cell membrane coating due to the monolayer growth and differentiation of the cell types during the differentiation period. The authors found the artificial membrane, through coating with Collagen-A, has improved the growth of mouse podocytes cells-5 compared with the fibronectin-coated membrane.

The authors could distinguish the differences across the patient cohort when they used a collagen-coated microfluidic chip. For instance, von Willebrand factor, a blood glycoprotein that promotes hemostasis, can be identified and measured through these type-coated microfluidic chips.

Developing a reliable cost estimate at the early stage of construction projects is challenging due to inadequate project information. Most of the information during this stage is qualitative, posing additional challenges to achieving accurate cost estimates. Additionally, there is a lack of tools that use qualitative project information and forecast the budgets required for project completion. This research, therefore, aims to develop a model for setting project budgets (excluding land) during the pre-conceptual stage of residential buildings, where project information is mainly qualitative.

Due to the qualitative nature of project information at the pre-conception stage, a natural language processing model, DistilBERT (Distilled Bidirectional Encoder Representations from Transformers), was trained to predict the cost range of residential buildings at the pre-conception stage. The training and evaluation data included 63,899 building permit activity records (2021–2022) from the Victorian State Building Authority, Australia. The input data comprised the project description of each record, which included project location and basic material types (floor, frame, roofing, and external wall).

This research designed a novel tool for predicting the project budget based on preliminary project information. The model achieved 79% accuracy in classifying residential buildings into three cost_classes ($100,000-$300,000, $300,000-$500,000, $500,000-$1,200,000) and F1-scores of 0.85, 0.73, and 0.74, respectively. Additionally, the results show that the model learnt the contextual relationship between qualitative data like project location and cost.

The current model was developed using data from Victoria state in Australia; hence, it would not return relevant outcomes for other contexts. However, future studies can adopt the methods to develop similar models for their context.

This research is the first to leverage a deep learning model, DistilBERT, for cost estimation at the pre-conception stage using basic project information like location and material types. Therefore, the model would contribute to overcoming data limitations for cost estimation at the pre-conception stage. Residential building stakeholders, like clients, designers, and estimators, can use the model to forecast the project budget at the pre-conception stage to facilitate decision-making.