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The purpose of this paper is to present the author’s method of conservative load spectrum (LS) derivation and close-proximity LS extrapolation applying a correction for measurement uncertainty caused by too low sampling frequency or signal noise, which may affect the load histories collected during the flying session and cause some recorded load increments to be lower than the actual values.

Having in mind that the recorded load signal is burdened with some measurement error, a conservative approach was applied during qualification of the recorded values into 32 discrete load-level intervals and derivation of 32 × 32 half-cycle arrays. A part of each cell value of the half-cycle array was dispersed into the neighboring cells placed above by using a random number generator. It resulted in an increase in the number of load increments, which were one or two intervals higher than those resulting from direct data processing. Such an array was termed a conservative clone of the actual LS. The close-proximity approximation consisted of multiplication of the LSs clones and their aggregation. This way, the LS for extended time of operation was obtained. The whole process was conducted in the MS Excel environment.

Fatigue life calculated for a chosen element of aircraft structure using conservative LS is about 20%–60% lower than for the actual LS (depending on the applied value of dispersion coefficients used in the procedure of LSs clones generation). It means that such a result gives a bigger safety margin when operational life of the aircraft is estimated or when the fatigue test for an extended operational period is programed based on a limited quantity of data from a flying session.

This paper presents a proposal for a novel, conservative approach to fatigue life estimation based on the short-term LS derived from the load signal recorded during the flying session.

The purpose of this research is to conduct a theoretical prediction study exploring the effectiveness of different content marketing strategies in expanding the second-hand market for fashion brands, comparing the costs and risks involved in these strategies in practice.

First, the expert interview method is employed to extract the content marketing strategies of the fashion second-hand market. Then, a descriptive space that is able to identify various fashion brand images is established. Then, experts' perceptions of the relationships between content marketing strategies and fashion brand image dimensions are obtained through a subjective evaluation procedure. Data of semantic evaluation were quantified and analyzed using the fuzzy logic method.

When fashion brands expand to the second-hand market, they not only need to focus on improving the individual differentiation of products but also give priority to the quality of products and services and the overall customer experience. Exploring the “social impact strategy” will become an important direction for the development of fashion brands in the future.

The research methodology employed herein exhibits a noteworthy degree of novelty. This study introduces a pioneering theoretical prediction approach utilizing fuzzy logic, marking the inaugural exploration of this emerging and captivating dimension within the context of the study. Simultaneously, the study provides comparative results among content marketing strategies for expanding the fashion second-hand market, offering guidance for market expansion.

The purpose of this study is to examine the existence of greenwashing and sustainable development goal (SDG)-washing processes by comparing ex ante (SDG Compass) and ex post (SDG Compliance) indicators and investigating whether the limitations associated with these indicators encourage companies to engage in washing processes.

The authors use a sample of 1,154 companies included in the S&P Sustainability Yearbook (formerly the RobecoSAM Yearbook). The authors test for the presence of greenwashing by comparing ex ante and ex post indicators for each SDG, whereas to test for SDG-washing, the authors compare the two ex ante and ex post approaches considering the full set of SDGs.

The results show that there is no consistency between the two types of indicators to measure the level of SDG implementation in organisations. This lack of consistency may facilitate both greenwashing and SDG-washing processes, which is due to the design and limitations of these measurement tools.

Companies may choose those indicators that paint their commitment to the SDGs in the best light, but they may also select indicators based on the SDGs they want to report on. These two options would combine greenwashing and SDG-washing.

The shift towards improved standards and regulations for measuring SDG achievement is the result of several social factors such as investor scrutiny, regulatory reform, consumer awareness and increased corporate accountability.

Few previous studies have analysed in detail the interaction between greenwashing and SDG-washing. They focus on the use of ex ante or ex post indicators separately, with samples composed of local companies, and without considering the whole set of SDGs.

This chapter of the book aims to achieve sustainability and productivity in light of the interaction between managers and engineers in a lean and agile supply chain management system in today’s organizations. The main innovation of this chapter is the use of the balanced scorecard (BSC) model and fuzzy analysis network process (FANP) to create a suitable platform for the realization of this interaction between managers and engineers and to identify exactly which expert system is ideal for the main purpose. Indeed, this chapter introduces its readers to the application of strategic management tools such as the BSC accompanied by FANP in the elements of supply chain management where data analysis of lean and agile networks in supply chain management can create a competitive advantage in the organization.

Football exists and evolves in a dynamic ecosystem, displaying a massive and multidimensional influence on most contemporary societies, and football has grown into a significant industry with a plethora of stakeholders. This research is the first to comprehensively identify the key industry stakeholders and their distinct value, from the individual club perspective, and to conceptualise and test their interrelationship toward the development of a corresponding framework of club benefits.

The study applied a multilevel approach to collect and verify qualitative data. It initially developed a preliminary conceptual framework, which was first validated by an expert panel and was subsequently extensively tested in the Cyprus-specific context, which offered fertile ground for such a study. The empirical stage rested on 41 semi-structured, face-to-face interviews with very high-ranking individuals from the top nine football clubs, as well as with key industry stakeholders.

Though the examined industry is partly in line with international norms, it is also highly affected by unique characteristics that alter the various stakeholders' role, producing (even negative) value of varied typologies that is directly linked with the industry's financial, sporting, cultural and social conditions.

The research ultimately presents scholars, practitioners and policymakers with a systemic and comprehensive understanding of the individual club stakeholder value offerings, delivers a tested framework as a tool for social and business management and prescribes future avenues for research, governance and practice.

Extant studies on the subject are either partial or focus on individual stakeholders and evidently lack requisite scientific comprehensiveness. The current research bridges this significant gap in knowledge by exhaustively identifying the key industry stakeholders, explicating their relative social, economic or other value in the individual club perspective and developing a value-based stakeholder framework.

Previous studies have rarely integrated the financing modes of a capital-constrained manufacturer with the choices of online sales strategies. To address this gap, the authors study how a manufacturer selects optimal financing modes under different sales strategies in three dual-channel supply chains.

This paper considers three sales strategies, namely, combining a traditional retailer channel with one of the direct selling, reselling and agency selling channels, and two common financing modes, namely, bank financing and retailer financing. The authors obtain equilibrium outcomes of the manufacturer and traditional retailer and then provide the conditions for them to select optimal financing modes under three sales strategies.

The results indicate that the manufacturer’s financing decisions rely on the initial capital and interest rates, and the manufacturer selects retailer financing only if the initial capital is relatively larger. In terms of financing mode options, the retailer financing mode is more beneficial for the manufacturer under the three sales strategies. From the perspective of sales strategies, the direct selling model is more beneficial. In addition, the higher the consumer acceptance of the online channel, the more profits the manufacturer obtains.

This paper provides suggestions on how the capital-constrained manufacturer chooses financing modes and sales strategies.

This paper integrates the financing mode and different sales strategies to investigate the manufacturer’s optimal operational decisions. These sales strategies allow us to investigate the manufacturer’s optimal financing modes in the presence of both different financing modes and sales strategies.

In the presence of informal sector in the country, designing an energy policy and the pursuit of higher economic growth become challenging for emerging economies. These economies are usually resource starved, and the presence of underground economy leads to faulty estimates of energy demand. The authors explore the energy–growth nexus in the presence of underground economy for Pakistan, an emerging economy host to large informal sector and facing recurring energy crises.

The authors evaluate the impact of underground economy on energy demand in the presence of explanatory variables, including official gross domestic product (GDP), foreign direct investment and financial development. The authors first assess the influence of official economy on the consumption of energy. The authors investigate how energy consumption is influenced solely by underground economy. Finally, the authors evaluate the impact of true GDP on the energy consumption. The authors employ combined cointegration method of Bayer and Hanck (2013) and then apply vector error correction model.

The results reveal that official GDP, underground economy and true GDP positively and significantly affect energy consumption in both short and long run. Similarly, financial development as well as foreign direct investment enhance energy consumption. The authors find unidirectional causality between energy consumption and official GDP variables (OGDP → EC), underground economy (UE → EC) and true GDP variables (TGDP → EC) in the long run. The authors observe bidirectional causality in the short run between energy consumption and official GDP (OGDP ↔ EC) and true GDP (TGDP ↔ EC).

To the best of the authors' knowledge, no study examines the causal relationship of energy consumption and underground economy. Overall, the findings assist policymakers to consider and implement different energy-related policies considering the significant role of underground economy for energy consumption in Pakistan.

An integrated intuitionistic fuzzy (IF) modelling-based framework for examining the performance analysis of a packaging unit (PU) in three different stages has been proposed.

For the series and parallel configuration of PU, a mathematical model based on the intuitionistic fuzzy Lambda–Tau (IFLT) approach was developed in order to calculate various reliability parameters at various spreads. For determining membership and non-membership function-based reliability parameters for the top event, AND/OR gate transitions expression was employed.

For 15%–30% spread, unit’s availability for the membership function falls by 0.006442%, and it falls even more by 0.014907% with an increase in spread from 30% to 45%. In contrast, for 15%–30% spread, the availability of non-membership function-based systems reduces by 0.007491% and further diminishes. Risk analysis has presented applying an emerging approach called intuitionistic fuzzy failure mode and effect analysis (IFFMEA). For each of the stated failure causes, the output values of the intuitionistic fuzzy hybrid weighted Euclidean distance (IFHWED)-based IFFMEA have been tabulated. Failure causes like HP1, MT6, FB9, EL16, DR23, GR27, categorized under subsystems, namely hopper, motor, fluidized bed dryer, distributor, grader and bin, respectively, with corresponding IFFMEA output scores 1.0975, 1.0190, 0.8543, 1.0228, 0.9026, 1.0021, were the most critical one to contribute in the system’s failure.

The limitation of the proposed framework lies in the fact that the results obtained for both reliability and risk aspects mainly depend on the correctness of raw data provided by the experts. Also, an approximate model of PU is obtained from plant experts to carry performance analysis, and hence more attention is required in constructing the model. Under IFLT, reliability parameters of PU have been calculated at various spreads to study and analyse the failure behaviour of the unit for both membership and non-membership function in the IFS of [0.6,0.8]. For both membership- and non-membership-based results, availability of the considered system shows decreasing trend. To improve the performance of the considered system, risk assessment was carried using IFFMEA technique, ranking all the critical failure causes against IFHWED score value, on which more attention should be paid so as to avoid sudden failure of unit.

The livelihood of millions of farmers and workers depends on sugar industries. So perpetual running of these industries is very important from this viewpoint. On the basis of findings of reliability parameters, the maintenance manager could frame a correct maintenance policy for long-run availability of the sugar mills. This long-run availability will generate revenue, which, in turn, will ensure the livelihood of the farmers.

Mathematical modelling of the considered unit has been done applying basic expressions of AND/OR gate. IFTOPSIS approach has been implemented for ranking result comparison obtained under IFFMEA approach. Eventually, sensitivity analysis was also presented to demonstrate the stability of ranking of failure causes of PU.

Denial-of-service (DoS) attacks develop unauthorized entry to various network services and user information by building traffic that creates multiple requests simultaneously making the system unavailable to users. Protection of internet services requires effective DoS attack detection to keep an eye on traffic passing across protected networks, freeing the protected internet servers from surveillance threats and ensuring they can focus on offering high-quality services with the fewest response times possible.

This paper aims to develop a hybrid optimization-based deep learning model to precisely detect DoS attacks.

The designed Aquila deer hunting optimization-enabled deep belief network technique achieved improved performance with an accuracy of 92.8%, a true positive rate of 92.8% and a true negative rate of 93.6.

The introduced detection approach effectively detects DoS attacks available on the internet.