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This paper aims to study the application of high-tolerance and flexible indigenous bacteria and fungi, along with the co-metabolism in recycled paper and cardboard mill (RPCM) wastewater treatment (WWT).

The molecular characterization of isolated indigenous bacteria and fungi was performed by 16S rRNA and 18S rRNA gene sequencing, respectively. Glucose was used as a cometabolic substrate to enhance the bioremediation process.

The highest removal efficiency was achieved for both chemical oxygen demand (COD) and color [78% COD and 45% color removal by Pseudomonas aeruginosa RW-2 (MZ603673), as well as approximately 70% COD and 48% color removal by Geotrichum candidum RW-4 (ON024394)]. The corresponding percentages were higher in comparison with the efficiency obtained from the oxidation ditch unit in the full-scale RPCM WWT plant.

Indigenous P. aeruginosa RW-2 and G. candidum RW-4 demonstrated effective capability in RPCM WWT despite the highly toxic and low biodegradable nature, especially with the assistance of glucose.

Prefabricated building has been widely applied in the construction industry all over the world, which can significantly reduce labor consumption and improve construction efficiency compared with conventional approaches. During the construction of prefabricated buildings, the overall efficiency largely depends on the lifting sequence and path of each prefabricated component. To improve the efficiency and safety of the lifting process, this study proposes a framework for automatically optimizing the lifting path of prefabricated building components using building information modeling (BIM), improved 3D-A* and a physic-informed genetic algorithm (GA).

Firstly, the industry foundation class (IFC) schema for prefabricated buildings is established to enrich the semantic information of BIM. After extracting corresponding component attributes from BIM, the models of typical prefabricated components and their slings are simplified. Further, the slings and elements’ rotations are considered to build a safety bounding box. Secondly, an efficient 3D-A* is proposed for element path planning by integrating both safety factors and variable step size. Finally, an efficient GA is designed to obtain the optimal lifting sequence that satisfies physical constraints.

The proposed optimization framework is validated in a physics engine with a pilot project, which enables better understanding. The results show that the framework can intuitively and automatically generate the optimal lifting path for each type of prefabricated building component. Compared with traditional algorithms, the improved path planning algorithm significantly reduces the number of nodes computed by 91.48%, resulting in a notable decrease in search time by 75.68%.

In this study, a prefabricated component path planning framework based on the improved A* algorithm and GA is proposed for the first time. In addition, this study proposes a safety-bounding box that considers the effects of torsion and slinging of components during lifting. The semantic information of IFC for component lifting is enriched by taking into account lifting data such as binding positions, lifting methods, lifting angles and lifting offsets.

In this final chapter, we explore the ever-evolving 21st century landscape where ethics drive community development toward resilience and progress. Drawing inspiration from the subheadings mapping our journey, we traverse international case studies spanning Canada, Brazil, Sweden, Kenya, China, Australia, Antarctica, and India. Through these global insights, we uncover the impacts of dynamic forces on communities worldwide, navigating ethical dilemmas and opportunities. We present strategies tailored to diverse continent-specific needs, explore inclusive governance models, and highlight the transformative power of ethical engagement. This journey underscores the vital role of resilience and concludes with a global call to embrace ethical approaches for inclusive community development and a sustainable future.

By introducing the mass customization service mode into the cloud logistics environment, this paper studies the joint optimization of service provider selection and customer order decoupling point (CODP) positioning based on the mass customization service mode to provide customers with more diversified and personalized service content with lower total logistics service cost.

This paper addresses the general process of service composition optimization based on the mass customization mode in a cloud logistics service environment and constructs a joint decision model for service provider selection and CODP positioning. In the model, the two objective functions of minimum service cost and most satisfactory delivery time are considered, and the Pareto optimal solution of the model is obtained via the NSGA-II algorithm. Then, a numerical case is used to verify the superiority of the service composition scheme based on the mass customization mode over the general scheme and to verify the significant impact of the scale effect coefficient on the optimal CODP location.

(1) Under the cloud logistics mode, the implementation of the logistics service mode based on mass customization can not only reduce the total cost of logistics services by means of the scale effect of massive orders on the cloud platform but also make more efficient use of a large number of logistics service providers gathered on the cloud platform to provide customers with more customized and diversified service content. (2) The scale effect coefficient directly affects the total cost of logistics services and significantly affects the location of the CODP. Therefore, before implementing the mass customization logistics service mode, the most reasonable clustering of orders on the cloud logistics platform is very important for the follow-up service combination.

The originality of this paper includes two aspects. One is to introduce the mass customization mode in the cloud logistics service environment for the first time and summarize the operation process of implementing the mass customization mode in the cloud logistics environment. Second, in order to solve the joint decision optimization model of provider selection and CODP positioning, this paper designs a method for solving a mixed-integer nonlinear programming model using a multi-layer coding genetic algorithm.

Introduction: Today the ability of nations to develop and implement innovations is core for their international competitiveness. Ukraine is striving for innovation progress; however, its innovation performance is relatively low. The research problem is to find the bottlenecks, affecting Ukraine’s innovation capability.

Purpose: This study aims to research the national innovation capability profiles, based on cluster analysis, to develop an understanding of drivers and threats for the innovation capability of Ukraine.

Need of the study: The knowledge-based economy, which had already turned into one of the most efficient developmental models of the 21st century, became a key driver of international competitiveness for the leading developed countries due to their progressive structural shifts towards the growth of high-technology manufacturing and knowledge-intensive sectors. These trends are significant to capture for the sake of increasing the innovation capability of the economy of Ukraine.

Methodology: The study is based on the K-means clustering method, which is employed for identifying 10 country clusters based on the indicators of their R&D and innovation activities, which allowed us to assess the innovation capability of Ukraine in comparison with 140 countries of the world. Data selection and normalisation were based on the 2019 Global Competitiveness Report indicators.

Findings: The study showed that Ukraine’s innovation capability problems are typical for most developing countries and are prevalently connected to low R&D expenditures, patent applications, and international co-invention activities. Most countries, except for the technologically developed ones, follow the so-called ‘passive technological learning’ strategies, which usually result in low economic productivity.

Practical implications: Several innovation policy implications have been developed for the government of Ukraine based on the cluster analysis results and accounting for the problems of the national innovation system (NIS).

This study aims to conceptualize the dimensions of resilient leadership and develop the resilient leadership scale (RLS) through three studies.

In Study 1, based on interviews with 77 leaders and 8 junior employees, a seven-factor resilient leadership model was constructed. In Study 2, exploratory factor analysis (n = 237) was conducted to refine the initial items. In Study 3, confirmatory factor analysis (n = 610) was performed to validate the dimensional structure identified in Study 2, and different types of validity of the RLS were assessed.

The validated RLS composed of seven dimensions: contingency planning, improvisation, adaptive instructing, contingency control, emergency care, adjustment recovery and mutual growth. The scale showed desirable measurement qualities in terms of reliability and validity. Resilient leadership and its dimensions significantly impact employee turnover intentions and employee resilience.

This research contributes to the literature on the resilience of hospitality and tourism enterprises and enriches the research scope and theoretical framework of resilient leadership.

This research revealed the resilient leadership responses to crisis in hospitality and tourism enterprises with practical implications for tourism enterprise leaders to deal with major crisis.

The aim of this study was to observe how a user’s individual factors in a commercial weight-loss treatment app affect their perceived usefulness of its features and how they relate to each other.

The information was obtained from an online survey with a sample of 412 users from a branded app for a commercial weight-loss treatment using body mass index (BMI), self-efficacy, social support and perceived usefulness as variables.

Users with higher self-efficacy perceived the app’s features as more useful. However, BMI was not a factor except for the psycho-emotional support features, which individuals with obesity perceived as more useful. Likewise, it was found that there weren’t any significant differences in self-efficacy based on their BMI. Lastly, it was found that social support could not be used as a factor to predict self-efficacy.

This study helps understand how individual factors for behavioural change may affect the perceived usefulness of a weight-loss app. Furthermore, the significance of self-efficacy as an influencing factor provides useful information for companies and app developers alike when developing their branded apps.

This study contributes to the body of knowledge on factors affecting user perceptions of weight-loss apps. It also adds to the literature of branded apps as complimentary resources for companies, which has not been studied in detail.

This research paper aims to delve into the perceptions of health susceptibility among Iranian, Afghan and Tajik individuals hailing from asylum-seeking or refused asylum-seeking backgrounds currently residing in Finland, Norway and Sweden.

Semi-structured interviews were conducted between May and October 2022 involving a sample size of 27 participants. An adapted framework based on the health belief model along with previous studies served as a guide for formulating interview questions.

Notably influenced by cultural background, religious beliefs, psychological states and past traumatic experiences during migration journeys – before arrival in these countries till settling down – subjects’ perception of health concerns emerged significantly shaped. Additionally impacting perspectives were social standing, occupational status, personal/family medical history, lifestyle choices and dietary preferences nurtured over time, leading to varying degrees of influence upon individuals’ interpretation about their own wellness or illness.

Insights garnered throughout the authors’ analysis hold paramount significance when it comes to developing targeted strategies catering culturally sensitive health-care provisions, alongside framing policies better aligned with primary care services tailored explicitly around singular demands posed by these specific communities dwelling within respective territories.

This investigation represents one among few pioneering initiatives assessing perceptions regarding both physical and mental well-being within minority groups under examination across Nordic nations, unveiling complexities arising through intersecting factors like individual attributes mingling intricately with socio-cultural environments, thereby forming unique viewpoints towards health-care belief systems prevalent among such population segments.