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In smart cities striving for innovation, development, and prosperity, hydrogen offers a promising path for decarbonization. However, its effective integration into the evolving energy landscape requires understanding regional intricacies and identifying areas for improvement. This chapter examines hydrogen transport from production to utilization, evaluating technologies’ pros, cons, and process equations and using Analytic Hierarchy Process (AHP) as a Multi-Criteria Decision-Making (MCDM) tool to assess these technologies based on multiple criteria. It also explores barriers and opportunities in hydrogen transport within the 21st-century energy transition, providing insights for overcoming challenges. Evaluation criteria for hydrogen transport technologies were ranked by relative importance, with energy efficiency topping the list, followed by energy density, infrastructure requirements, cost, range, and flexibility. Safety, technological maturity, scalability, and compatibility with existing infrastructure received lower weights. Hydrogen transport technologies were categorized into three performance levels: low, medium, and high. Hydrogen tube trailers ranked lowest, while chemical hydrides, hydrail, liquid organic hydrogen carriers, hydrogen pipelines, and hydrogen blending exhibited moderate performance. Compressed hydrogen gas, liquid hydrogen, ammonia carriers, and hydrogen fueling stations demonstrated the highest performance. The proposed framework is crucial for next-gen smart cities, cutting emissions, boosting growth, and speeding up development with a strong hydrogen infrastructure. This makes the region a sustainable tech leader, improving air quality and well-being. Aligned with Gulf Region goals, it is key for smart cities. Policymakers, industries, and researchers can use these insights to overcome barriers and seize hydrogen transport tech opportunities.

In recent decades, advancements in biosensors technology have made fluorescent biosensor pivotal for biomolecular recognition. This paper aims to provide an in-depth analysis of polymerase chain reaction (PCR) fluorescent biosensor detection technology for identifying Escherichia coli (E. coli), setting the stage for future developments in the field.

The review of literature on PCR fluorescent biosensor detection technology for E. coli over the past decades includes discussions on traditional biological fluorescent detection, quantitative PCR fluorescent detection and digital fluorescent detection technology.

Advancements in fluorescent biosensor technology enable precise measurement of fluorescent signals, and when integrated with microfluidic technology, produce compact, reagent-efficient digital sensor devices.

This paper provides a comprehensive review of recent fluorescent detection technology for pathogenic E. coli, assessing method efficiencies and offering insights to advance the field.

This study aims to present the concept of aircraft turbofan engine health status prediction with artificial neural network (ANN) pattern recognition but augmented with automated features engineering (AFE).

The main concept of engine health status prediction was based on three case studies and a validation process. The first two were performed on the engine health status parameters, namely, performance margin and specific fuel consumption margin. The third one was generated and created for the engine performance and safety data, specifically created for the final test. The final validation of the neural network pattern recognition was the validation of the proposed neural network architecture in comparison to the machine learning classification algorithms. All studies were conducted for ANN, which was a two-layer feedforward network architecture with pattern recognition. All case studies and tests were performed for both simple pattern recognition network and network augmented with automated feature engineering (AFE).

The greatest achievement of this elaboration is the presentation of how on the basis of the real-life engine operational data, the entire process of engine status prediction might be conducted with the application of the neural network pattern recognition process augmented with AFE.

This research could be implemented into the engine maintenance strategy and planning. Engine health status prediction based on ANN augmented with AFE is an extremely strong tool in aircraft accident and incident prevention.

Although turbofan engine health status prediction with ANN is not a novel approach, what is absolutely worth emphasizing is the fact that contrary to other publications this research was based on genuine, real engine performance operational data as well as AFE methodology, which makes the entire research very reliable. This is also the reason the prediction results reflect the effect of the real engine wear and deterioration process.

This study aims to examine the cutting performance of a coated carbide tool during the boring of 1Cr17Ni2 martensitic stainless steel, with a focus on how the tool’s structural parameters, particularly the nose radius, affect the wear patterns, wear volume and lifetime of the cutting tool, and related mechanisms.

A full factorial boring experiment with three factors at two levels was conducted to analyze systematically the impact of cutting parameters on the tool wear behavior. The evolution of tool wear over the machining time was recorded, and the influences of the cutting parameters and nose radius on wear behavior of the tool were examined.

The results show that higher cutting parameters lead to significant wear or plastic deformation at the tool nose. When the cutting depth is less than the nose radius, the tool wear tends to be minimized. Larger nose radius tools have weaker chip-breaking but greater strength and wear resistance. Higher cutting parameters reduce wear for the tools with larger nose radius, maintaining their integrity. Wear mechanisms are primarily abrasive, adhesive and diffusion wear. Furthermore, the full-factorial analysis of variance revealed that for the tool with r_ε = 0.4 mm and 0.8 mm, the factors contributing the most to tool wear were cutting speed (38.76%) and cutting depth (86.43%), respectively.

This study is of great significance for selection of cutting tools and cutting parameters for boring 1Cr17Ni2 martensitic stainless-steel parts.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-07-2024-0266/

On the basis of demand survey feedback from individuals with disabilities and caregivers, this study designed two sets of functional garments for long-term bedridden patients, with the primary objective of increasing convenience and reducing the physical workload of caregivers.

Wear trials were conducted by employing 24 subjects to perform 11 different tasks to compare the performance of the two newly developed garments with that of conventional hospital patient apparel. Task operation time, heart rate (HR), electromyography (EMG) signals, and subjective perceptions were evaluated.

The new functional garments reduced the time required to perform tasks by 29–79%, maintained the average HR of caregivers at approximately the resting threshold and resulted in a 37–74% reduction in the root mean square (RMS) of the EMG at the arm muscles in the private and thigh nursing tasks. All the subjective and objective evaluation results of the caregivers demonstrated varying degrees of correlation.

This study has practical implications for the design of functional clothing for long-term bedridden patients and provides guidance for evaluating the ergonomics of garments that can be utilized only with caregiver support.

In contrast to previous studies that focused primarily on individuals with disabilities while overlooking the indispensable role of caregivers in the nursing process, this study shifted its emphasis to long-term bedridden patients who relied exclusively on caregivers for daily activities. Additionally, this study attempted to analyze the correlations between the evaluation parameters to explore the relationships between the evaluation methods.

This paper explores the association between directors’ and officers’ liability insurance (D&O insurance) and management tone manipulation.

This study uses data from A-share listed non-financial companies from 2009 to 2021 as its sample for empirical tests. In addition, the study relies on text analysis and the construction of models to investigate the relationship between D&O insurance and management tone manipulation.

The authors find that the purchase of D&O insurance will lead to management tone manipulation in the “management discussion and analysis” part of companies’ annual reports, and operating risk and agent cost are the two paths for the effect. Further analysis shows that having a male CEO and employing high-quality auditors can weaken the positive impact of D&O insurance on tone manipulation.

This paper provides a new approach for studying the literature related to D&O insurance and management behavior, and the findings enrich our understanding of the influencing factors and the mechanism of management tone manipulation, thus revealing policy implications for further standardization of the terms and system of D&O insurance in China.

This study aims to explore the impact of green inclusive leadership (GIL) on green creativity (GCRY) within the context of higher education institutions (HEIs) in China. Specifically, it aims to examine the mediating roles of green intrinsic motivation (GIM), environmental knowledge (EK) and green thinking (GT) according to the componential theory of creativity (CTC).

The study employed a series of questionnaire surveys to collect data at three different time points from various sources. A total of 583 leader-faculty matched samples were obtained from two universities in China. The hypothesized relationships were tested using PROCESS macro in SPSS.

The findings indicate a beneficial influence of GIL on GCRY, mediated by GIM, EK and GT. Noteworthy interaction effects were observed, with GIM fostering EK and GT, and EK laying the groundwork for GT.

This research contributes to the existing literature by confirming the implementation of GIL and supporting the CTC, offering insights into the motivational processes driving GCRY and with practical implications discussed for the effective management of GIL and GCRY in higher education settings.

The novelty of this research model lies in its operationalization of environmental sustainability within the CTC. This study is the initial investigation highlighting the role of GIL in fostering GCRY within HEIs. The key contribution of the study is the investigation of GIM, EK and GT as potential mediators in the relationship between GIL and GCRY. This expands the theoretical boundaries of the CTC framework.

In developing countries, rapid industrial growth frequently results in companies with high pollution levels, which in turn exhibit characteristics such as elevated emissions, increased energy consumption and overcapacity. In order to promote sustainable development among these heavily polluting firms, it is essential to implement a system of incentives and penalties that encourages environmentally responsible behaviour. China's environmental protection tax has replaced the previous pollution discharge fee (PDF) system. This tax aims to guide enterprises towards continuous adjustments and improvements in their production methods, increased investments in green technology, adoption of environmentally friendly production methods, reduced pollutant emissions and promotion of high-quality development. This chapter analyses how China's Environmental Protection Tax Law, enforced in 2018, affects the sustainable development capabilities of A-share listed companies in China. We utilise a difference-in-differences (DiD) model and measure total factor productivity (TFP) to quantify the impact of the tax law on these enterprises. TFP is a key indicator used to measure the effectiveness of resources utilised by enterprises in the production process. Our empirical analysis provides compelling evidence that the implementation of environmental protection taxes has significantly enhanced the TFP of heavily polluting enterprises. Importantly, the impact of these taxes is more pronounced for state-owned enterprises (SOEs) in comparison to their private counterparts in this sector. These findings offer valuable insights for policymakers in developing countries as they consider the design of environmental protection tax systems and supportive measures to promote sustainable development of companies with significant environmental impacts.

The purpose of this paper is to study the importance of image data in hotel selection-recommendation using different types of cognitive features and to explore whether there are reinforcing effects among these cognitive features.

This study represents user-generated images “cognitive” in a knowledge graph through multidimensional (shallow, middle and deep) analysis. This approach highlights the clustering of hotel destination imagery.

This study develops a novel hotel selection-recommendation model based on image sentiment and attribute representation within the construction of a knowledge graph. Furthermore, the experimental results show an enhanced effect between different types of cognitive features and hotel selection-recommendation.

This study enhances hotel recommendation accuracy and user satisfaction by incorporating cognitive and emotional image attributes into knowledge graphs using advanced machine learning and computer vision techniques.

This study advances the understanding of user-generated images’ impact on hotel selection, helping users make better decisions and enabling marketers to understand users’ preferences and trends.

This research is one of the first to propose a new method for exploring the cognitive dimensions of hotel image data. Furthermore, multi-dimensional cognitive features can effectively enhance the selection-recommendation process, and the authors have proposed a novel hotel selection-recommendation model.

Previous investigations into the connection between strategic orientation (SO) and firm performance (FP) have generated inconsistent outcomes. The authors aim to reconcile and explain these varying results through the application of a mediated moderation model. The purpose of this paper is to explore how SO and strategic flexibility (SF) could create the possibility of improving FP. The indirect effect of SO was also tested in this study. Furthermore, by using the moderated mediation model, the authors also investigated the moderating role of strategic human resource management (SHRM).

This study adopted the empirical research method, collecting data through questionnaires by a two-wave and multi-source data collection approach. The sample consisted of 188 firms from Shandong and Fujian Province in China. SPSS was used to test the research hypotheses.

This study examines how and when SO enhances FP by considering SF as a critical mediator and SHRM as an important contingency of the mediation effect. The results revealed that SO positively affects SF and FP in small and medium-sized enterprises (SMEs). Furthermore, the mediating role of SF between the relationships of SO and FP was also confirmed. Moreover, SHRM strengthens the connection between SO, SR and FP.

This study contributes to the SO literature by revealing how firms translate their SO-based strategies into positive FP through SF and when this indirect effect is most effective.