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This paper aims to develop an algorithm to study the impact of dynamic resource disruption on project makespan and provide a suitable resource disruption ratio for various complex industrial and emergency projects.

This paper addresses the RCPSP in dynamic environments, which assumes resources will be disrupted randomly, that is, the information about resource disruption is not known in advance. To this end, a reactive scheduling model is proposed for the case of random dynamic disruptions of resources. To solve the reactive scheduling model, a hybrid genetic algorithm with a variable neighborhood search is proposed.

The results obtained on the PSLIB instances prove the performance advantage of the algorithm; through sensitivity analysis, it can be obtained, the project makespan increases exponentially as the number of disruptions increase. Furthermore, if more than 50% of the project's resources are randomly disrupted, the project makespan will be significantly impacted.

The paper focuses on the impact of dynamic resource disruptions on project makespan. Few studies have considered stochastic, dynamic resource uncertainty. In addition, this research proposes a reasonable scheduling algorithm for the research problem, and the conclusions drawn from the research provide decision support for project managers.

This study aims to evaluate the health literacy level of the parents of children diagnosed with Down syndrome (DS) within one institution in Turkey.

A cross-sectional survey measuring demographics, information of the child and the parent and health literacy was administered to participants. The health literacy levels in the study were measured with the European Health Literacy Scale (EHLS), which consists of 47 questions.

Of the 65 participants who completed the questionnaire, 56.9% were mothers, 68.1% were diagnosed in the neonatal outpatient clinic examination after birth, and 58.5% stayed in the neonatal intensive care unit after birth. The mean score of the IHLS scale was 25.06 ± 6.59. Of the parents, 63.1% were found to be inadequate, 18.5% problematic-limited, and 18.5% adequate health literate. Any parent with excellent health literacy level was identified. High education level (p < 0.001), high income level (p < 0.001), living in the city center (p < 0.05), planned pregnancy (p < 0.05) and being a health worker (p < 0.001) were found to be statistically significant with a high EHSL score.

The presence of Down syndrome (DS) in a child also necessitates ongoing monitoring for a range of conditions, including eye diseases and heart disease. Some surgical procedures, such as heart or gastrointestinal surgeries, may also be required. Additionally, the child may require the administration of various medications. Finally, due to the potential lifelong need for assistance, the child may require the support of an adult throughout their lifetime. This is because of the child's inability to live independently due to their mental state. Therefore, parent education is the most important issue in the follow-up of the disease.

To the best of the authors’ knowledge, this is the first study to determine that parents of children diagnosed with DS have very limited knowledge of the disease and health literacy. Explanation of current diseases, treatments and training of parents should also be included in genetic counseling.

DS is a chromosomal disease that requires multidisciplinary care. Parents have to know the course of the disease and its complications.

The findings of this study indicate that parents of children with Down syndrome exhibit a profound lack of knowledge regarding the nature of their child's condition and the available healthcare options. It is therefore imperative that genetic counseling incorporates an explanation of the diagnosed diseases, treatments, and educational resources for parents.

Artificial intelligence and augmented reality are two key tools gaining importance in the digital era due to their wide range of applications in different fields and sectors. Industry 4.0 lays emphasis principally on the technology used to help the business remain competitive and sustainable. Sustainable development goals are another important objective of the UN which has laid responsibility for every business to support addressing the global challenges. Purpose: This chapter essentially aims to present the standpoint of artificial intelligence and augmented reality in meeting the sustainability perspective of organizations. Information about the study is gathered through secondary approaches, critically reviewing published literature, scientific reports, and statistical data accessible through business reports, and corporate websites. Further analyzed to present the perspectives of the authors in the study. Globally artificial intelligence market size is predicted to reach $190 billion by 2025, while the funding for startups doubled during the period 2011–2020 globally. The investment in artificial intelligence is going to reach $500 by 2024 resulting in substantial revenue returns. The augmented reality market size could reach $97 billion by 2028. Artificial intelligence today is increasingly used in many fields and is attracting multiple applications in many sectors such as manufacturing, retail, education, IT, and health care and has also contributed to sustainable development the same time by providing energy conservation options, optimization, and reduction of resources, minimizing wastage, offering timely assistance on maintenance schedules, practices which are enabling organizations to reach closer to sustainability and transformation.

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.

The main aim of this study is to characterise and identify specific chemo-sensory profiles of ciders from the Canary Islands (Spain).

Commercial samples of Canary ciders were compared to ciders from the Basque Country and Asturias. In total, 18 samples were studied, six for each region. The analysis comprised their sensory profiling and chemical characterisation of their polyphenolic profile, volatile composition, conventional chemical parameters and CIELAB colour coordinates. In parallel, the sensory profile of the samples from the Canary Islands was first compared with their Basque and Asturian counterparts by labelled sorting task. Then, their specific aroma profile was characterised by flash profile. Further quantification of sensory-active compounds was performed by GC–MS and GC-FID to identify the volatile compounds involved in their aroma profile.

Results show that Canary ciders present a specific chemical profile characterised by higher levels of ethanol, and hydroxycinnamic acids, mainly t-ferulic, t-coumaric and neochologenic acids, and lower levels of volatile and total acidity than their Asturian and Basque counterparts. They also present a specific aroma profile characterised by fruity aroma, mainly fruit in syrup and confectionary, and sweet flavours related to their highest levels of vinylphenols formed by transformation of hydroxycinnamic acids.

An integrated strategy to explore the typicity of the currently existing Canary ciders in the market was developed. The results are important in that they will help other regions to identify specific typical chemo-sensory profiles and to promote the creation of certifications supporting regional typicity.

The main aim of this study is to identify and prioritize the factors that influence the adoption of big data analytics (BDA) within the supply chain (SC) of the food industry in India.

The study is carried out in two distinct phases. In the first phase, barriers hindering BDA adoption in the Indian food industry are identified. Subsequently, the second phase rates/prioritizes these barriers using multicriteria methodologies such as the “analytical hierarchical process” (AHP) and the “fuzzy analytical hierarchical process” (FAHP). Fifteen barriers have been identified, collectively influencing the BDA adoption in the SC of the Indian food industry.

The findings suggest that the lack of data security, availability of skilled IT professionals, and uncertainty about return on investments (ROI) are the top three apprehensions of the consultants and managers regarding the BDA adoption in the Indian food industry SC.

This research has identified several reasons for the adoption of bigdata analytics in the supply chain management of foods in India. This study has also highlighted that big data analytics applications need specific skillsets, and there is a shortage of critical skills in this industry. Therefore, the technical skills of the employees need to be enhanced by their organizations. Also, utilizing similar services offered by other external agencies could help organizations potentially save time and resources for their in-house teams with a faster turnaround.

The present study will provide vital information to companies regarding roadblocks in BDA adoption in the Indian food industry SC and motivate academicians to explore this area further.

The purpose of this paper is to assess the appropriateness of equal weights estimation (sumscores) and the application of the composite equivalence index (CEI) vis-à-vis differentiated indicator weights produced by partial least squares structural equation modeling (PLS-SEM).

The authors rely on prior literature as well as empirical illustrations and a simulation study to assess the efficacy of equal weights estimation and the CEI.

The results show that the CEI lacks discriminatory power, and its use can lead to major differences in structural model estimates, conceals measurement model issues and almost always leads to inferior out-of-sample predictive accuracy compared to differentiated weights produced by PLS-SEM.

In light of its manifold conceptual and empirical limitations, the authors advise against the use of the CEI. Its adoption and the routine use of equal weights estimation could adversely affect the validity of measurement and structural model results and understate structural model predictive accuracy. Although this study shows that the CEI is an unsuitable metric to decide between equal weights and differentiated weights, it does not propose another means for such a comparison.

The results suggest that researchers and practitioners should prefer differentiated indicator weights such as those produced by PLS-SEM over equal weights.

To the best of the authors’ knowledge, this study is the first to provide a comprehensive assessment of the CEI’s usefulness. The results provide guidance for researchers considering using equal indicator weights instead of PLS-SEM-based weighted indicators.