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With rapidly changing marketing landscape, nowadays, the formulation of various marketing strategies is increasingly focused on how consumers tend to make decisions. To meet the highly demanding consumer expectations, market segmentation can be used as an important marketing strategy. Due to gender marketing concept familiarity in the contemporary world, gender difference is one of the reference features in the process of market segmentation for marketers. This research is aimed to examine various determining factors that foster consumer purchase decision-making and the differences between consumers of different genders while making shopping and purchase decisions with special reference to an emerging economy, i.e. Pakistan.

Based on a cross-sectional sample of 367 consumers, the study adapted Sproles and Kendall's (1986) Consumer Style Inventory (CSI) to scrutinize the decision-making of both genders in Pakistan. For data analysis, the exploratory and confirmatory factor analysis in addition to the structural equation modeling has been used.

The study emphasized that, with the exception of quality awareness, brand consciousness, fashion consciousness, option overload and price consciousness greatly affect buying decisions. In addition, when it comes to consumer purchase decision-making, significant gender variations were discovered for both fashion consciousness and price consciousness.

Drawing upon the distinctive cultural characteristics of Pakistan and its people, in-depth research was conducted on purchasing behaviors of Pakistani consumers and the decision-making characteristics of customers of different genders were summarized. The outcomes are expected to make a significant contribution to the field of gender marketing by organizations.

The product family assembly line (PFAL) is a mixed model-assembly line, which is widely used in mass customization and intelligent manufacturing. The purpose of this paper is to study the problem of PFAL, a flexible (evolution) planning method to respond to product evolution for PFAL, to focus on product data analysis and evolution planning method.

The evolution balancing model for PFAL is established and an improved NSGA_II (INSGA_II) is proposed. From the perspective of data analysis, dynamic characteristics of PFAL are researched and analyzed. Especially the tasks, which stability is considered, can be divided into a platform and individual task. In INSGA_II algorithm, a new density selection and a decoding method based on sorting algorithms are proposed to compensate for the lack of traditional algorithms.

The effectiveness and feasibility of the method are validated by an example of PFAL evolution planning for a family of similar mechanical products. The optimized efficiency is significantly improved using INSGA_II proposed in this paper and the evolution planning model proposed has a stronger ability to respond to product evolution, which maximizes business performance over an effective period of time.

The assembly line designers and managers in discrete manufacturing companies can obtain an optimal solution for PFAL planning through the evolution planning model and INSGA-II proposed in this paper. Then, this planning model and optimization method have been successfully applied in the production of small wheel loaders.

The purpose of this paper is to investigate the tribological properties of a textured lubricating wear-resistant coating modified by nano-SiC at a high temperature. Its aim is to explore the influence of a new composite method on the organisation and structure of sprayed coatings as well as the evolution rules governing their high-temperature tribological properties.

A KF301/WS2 lubricating, wear-resisting, coating was prepared on matrix material GCr15 by applying supersonic plasma spraying technology. On the basis of this sample, using nano-SiC particles as a filler, the KF301/WS2 nano-modified coating with its round, pit-type texture was prepared by laser re-melting technology and a surface texturing technique. Two kinds of coating micro-organisations and structures were examined by scanning electron microscopy, and the tribological properties of both the modified and conventional coatings were studied at a high temperature.

Results showed that nano-particles could effectively improve the coating micro-structure, and make the structure denser and more uniform, thus significantly increasing the wear resistance of the coating. When the friction and wear processes were stable, the friction coefficient decreased by 13 per cent, while the wear loss decreased by 45.9 per cent.

This research concentrating on the study of the process and performance of coatings doped with nano-particles by laser re-melting incorporating simultaneous surface texturing, and studies of their high-temperature tribological properties. That is because applying nano-particle modification technology to the development of wear-resistant coatings, and by applying the nano-particles to such coatings by thermal spraying technology, they can achieve a modification of the coating which makes the structure denser and more uniform.

To further provide some insight into mobile library (m-library) applications (apps) user needs and help libraries or app providers improve the service quality, the purpose of this paper is to explore all the types of user improvement needs and to discover which need is the most important based on user results.

Data were collected from more than 27,000 m-library app users from 16 provinces and autonomous regions in China. Text analysis using latent Dirichlet allocation and Word2Vec was carried out by text preprocessing. Furthermore, a visual presentation was conducted through pyLDAvis and word cloud. Finally, combined with expert opinions, the results were summarized to find the different types of needs.

There are three different types of needs for improvement: needs of function, needs of technology and needs of experience. These types can be further divided into six subtypes: richness of function, feasibility of function, easiness of technology, stableness of technology, optimization of experience and customization of experience. Besides the richness of function, the feasibility of function has received the most attention from users.

Most studies on m-library user needs have only focused on a method of quantitative research based on questionnaire surveys. This study, however, is the first to apply text mining methods for large-scale user opinion texts, which place more focus on user needs and inspire libraries and app providers to further improve their services.

This paper aims to prepare third-order nonlinear optical (NLO) organic materials with large nonlinear optimization value, high damage threshold and ultrafast response time.

A series of novel symmetric and asymmetric compounds possessing third-order NLO properties were synthesized using 1,3,5-tribromobenzene as the basis. The photophysical and electrochemical properties, as well as the click reactions, were characterized by means of UV–VIS–NIR absorption spectroscopy and cyclic voltammetry.

The donor–acceptor chromophores were inserted into compound, making the molecule to have a broader absorption in the near-infrared regions and a narrower optical and electrochemical band gap. It also formed an electron-delocalized organic system, which has larger effects on achieving a third-order NLO response. The third-order NLO phenomenon of benzene ring complexes was experimentally studied at 532 nm using Z-scan technology, and some compounds showed the expected NLO properties.

The click products exhibit more NLO phenomena by performing different click combinations to the side groups, opening new perspectives on using the system in a variety of photoelectric applications.

Food safety risk brought by environmental pollution seriously threatens human health and affects national economic and social development. In particular, heavy metal pollution and nutrient deficiency have caused regional diseases. Thus, the purpose of this paper is to present a risk early warning method of food safety considering environmental and nutritional factors.

A novel risk early warning modelling method based on the long short-term memory (LSTM) neural network integrating sum product based analytic hierarchy process (AHP-SP) is proposed. The data fuzzification method is adopted to overcome the uncertainty of food safety detection data and the processed data are viewed as the input of the LSTM. The AHP-SP method is used to fuse the risk of detection data and the obtained risk values are viewed as the expected output of the LSTM. Finally, the proposed method is applied on one group of sterilized milk data from a food detection agency in China.

The experimental results show that compared with the back propagation and the radial basis function neural networks, the proposed method has higher accuracy in predicting the development trend of food safety risk. Moreover, the causal factors of the risk can be figured out through the predicted results.

The proposed modelling method can achieve accurate prediction and early warning of food safety risk, and provide decision-making basis for the relevant departments to formulate targeted risk prevention and control measures, thereby avoiding food safety incidents caused by environmental pollution or nutritional deficiency.

The purpose of this paper is to investigate the effects of parallel texturing coating on antifriction mechanism of lubricating wear-resistant coating.

A KF301/WS2 lubricating wear-resisting coating was prepared on matrix material GCr15 by applying supersonic plasma spraying technology. On the basis of this sample, the KF301/WS2 modified coating with parallel pit-type texture was prepared by laser re-melting technology and a surface texturing technique. Their friction and wear behaviors were evaluated under ambient temperature, and the antifriction mechanism of two kinds of coatings were discussed.

Results showed that parallel texture has a certain impact on the tribological properties of the coating. When friction and wear reach stable state, the value of the friction coefficient of conventional coating was 0.115, while that of parallel texturing coating was 0.09, the latter decreased by 21 per cent. When the friction and wear time was up to 4 hours, the wear loss of the conventional coating was 0.29 mg, while that of the parallel texturing coating was 0.13 mg, the latter decreased by 55 per cent.

The tribological properties of parallel texturing coating were higher than conventional coating. That is because the change of three-body layer reduces the friction coefficient and the abrasive particles were collected by parallel texture, reducing the effects of debris.

The aim of this study is to estimate the relationship between technological innovation and corporate environmental performance among energy companies working in Egypt.

The study extended the aim with the intention to assess the role of green financing in enhancing corporate environmental performance. Partial least squares (PLS)-based structural equation modeling (SEM) is applied to estimate the nexus among study variables.

The results indicated that technological innovation influenced environmental performance and has a positive impact on company performance. The role of green financing for environmental performance is also significant and positive. Moreover, corporate social responsibility (CSR) has insignificant role in environmental performance of the energy companies in the study context.

The study offers a valuable model for general managers of manufacturing organizations and policymakers to manage CSR, environmental strategy and green innovation in examining environmental performance. It can help to assist general managers of large manufacturing organizations to strengthen their internal resources like CSR, environmental strategy and green innovation to enhance environmental performance.

The findings of this article will help the practitioners to design policies regarding sustainable energy systems and green finance in the presence of any natural calamity.

This study primarily complements the existing literature by establishing how green financing and CSR can augment and/or interact between technological innovation and corporate environmental performance under COVID-19 crises, in a developing country.

The purpose of this study is to develop a new information and communication technology (ICT)-based approach for optimising safety transportation according to the needs of the current industrialised building system (IBS) building construction schemes. The improper handling and information management of road transport workers appears to be a major problem in the safety of the IBS building construction industry. Transportation activity is particularly problematic for IBS building construction projects in which traffic incident and safety management level are not in good condition to match with construction specification.

A new ICT-based approach is suggested for optimising safety transportation in accordance with the needs of the current IBS building construction schemes. As a precursor to this work, the concept of road transport workers practices is reviewed and the main features of ICT tools and techniques currently being used on such projects are presented.

The sophisticated road transport workers system solutions is described as an essential component of this optimisation to promote long-term safety and quality improvements of IBS building construction projects.

Finally, the potential for a research framework for developing such a system in the future is presented.

Automated dust monitoring in workplaces helps provide timely alerts to over-exposed workers and effective mitigation measures for proactive dust control. However, the cluttered nature of construction sites poses a practical challenge to obtain enough high-quality images in the real world. The study aims to establish a framework that overcomes the challenges of lacking sufficient imagery data (“data-hungry problem”) for training computer vision algorithms to monitor construction dust.

This study develops a synthetic image generation method that incorporates virtual environments of construction dust for producing training samples. Three state-of-the-art object detection algorithms, including Faster-RCNN, you only look once (YOLO) and single shot detection (SSD), are trained using solely synthetic images. Finally, this research provides a comparative analysis of object detection algorithms for real-world dust monitoring regarding the accuracy and computational efficiency.

This study creates a construction dust emission (CDE) dataset consisting of 3,860 synthetic dust images as the training dataset and 1,015 real-world images as the testing dataset. The YOLO-v3 model achieves the best performance with a 0.93 F1 score and 31.44 fps among all three object detection models. The experimental results indicate that training dust detection algorithms with only synthetic images can achieve acceptable performance on real-world images.

This study provides insights into two questions: (1) how synthetic images could help train dust detection models to overcome data-hungry problems and (2) how well state-of-the-art deep learning algorithms can detect nonrigid construction dust.