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The purpose of this paper is to purify the wastewater in the garment industry.

The preparation of the calcium alginate (CA)/activated carbon (AC) composite membrane was achieved by vacuum freeze-drying and the cross-linking reaction between sodium alginate and CaCl₂. Effective parameters in the methylene blue (MB) adsorption such as temperature, dose, contact time and pH were discussed. The adsorption properties of the composite membrane were investigated by isotherm, kinetics and thermodynamic analysis. The adsorption equilibrium data were described by the adsorption isotherm Langmuir model and the Freundlich model. The pseudo-first-order, pseudo-second-order and intra-particle diffusion equations were selected to evaluate the kinetics. The thermodynamic study described that the adsorption reaction was spontaneous and exothermic.

The AC/CA membrane is an efficient and powerful adsorbent to remove MB in printing and dyeing wastewater, and provides a new idea for the selection of adsorption materials for industrial printing and dyeing wastewater.

The composite membrane research on CA and AC can provide new ideas for the research of these kinds of materials.

The paper contributes to its wider and convenientapplication in wastewater treatment.

Studies on the combination of CA and AC into adsorption membranes and for the removal of dyes from printing and dyeing wastewater have not been reported. A novel composite material is provided for treatment dyeing wastewater in garment production. The composite membrane research on CA and AC can provide new ideas for the research of these kinds of materials and contribute to its wider and convenient application in wastewater treatment.

In order to discover a new adsorbent that can be used to purify dye wastewater in the textile and apparel industry, a novel type of graphene oxide/gluten composite material using an improved acid bath coagulation method was synthesized, which can remove methylene blue in an aqueous environment.

After experimentally compounding different ratios of graphene oxide and gluten, the graphene oxide/gluten composite material with 20% graphene oxide content and superlative adsorption effect was chosen. The synthesized material was characterized by different techniques such as FT-IR and SEM, indicating the microstructure of the material and the success of the composite. Various factors were considered, namely, the influence of temperature, dosage, pH and contact time. The isotherms, kinetics and thermodynamic parameters were successively discussed.

The qmax value of 214.29 mg/g of the material was higher compared to the general sorbent, thus, the graphene oxide/gluten composite material was a suitable sorbent for methylene blue removal. Overall, the graphene oxide/gluten composite material can be considered as an effectual and prospective adsorbent to remove methylene blue in the textile and apparel industrial effluent.

Graphene oxide is a potentially excellent sorbent. However, the high dispersibility of GO is detrimental to adsorption, it disperses rapidly in an aqueous solution making separation and recovery difficult. The high load capacity and recyclability of gluten as a colloid make it a suitable carrier for fixing GO. Studies on the combination of GO and GT into composite adsorption material and for the removal of dyes from dyeing wastewater have not been reported. The composite material research on GO and GT can provide new ideas for the research of these kinds of materials and contribute to its wider and convenient application in wastewater treatment.

A stylized fact in finance literature is the belief in positive relationship between ex ante return and risk. Hence, a rational investor, by utility preference axiom can only consider committing fund in asset which promises commensurate higher return for higher risk. Questions have been asked as to whether this holds true across securities, sectors and markets. Empirical evidence appears less convincing, especially in developing markets. Accordingly, the author investigates the nature of reward for taking risk in the Nigerian Capital Market within the context of individual assets and markets.

The author employed ex post design to collect weekly stock prices of firms listed on the Premium Board of Nigerian Stock Exchange for period 2014–2022 to attempt to answer research questions. Data were analyzed using a unique M Vec TGarch-in-Mean model considered to be robust in handling many assets, and hence portfolio management.

The study found that idea of risk-expected return trade-off is perhaps more general than as depicted by traditional finance literature. The regression revealed that conditional variance and covariance risks reveal minimal or no differences in sign and sizes of coefficients. However, standard errors were also found to be large suggesting somewhat inconclusive evidence of existence of defined incentive structure for taking additional risk in the market.

In terms of choice of methodology and outcomes, this research adds substantial value to body of knowledge. The adapted multivariate model used in this paper is a rare approach especially for management of portfolios in developing markets. Remarkably, the research found empirical evidence that positive risk-expected return trade-off, as known in mainstream literature, is not supported especially using a typical developing country data.

The purpose of this paper is to summarize the current applications of BIM, the integration of related technologies and the tendencies and challenges systematically.

Using quantitative and qualitative bibliometric statistical methods, the current mode of interaction between BIM and other related technologies is summarized.

This paper identified 24 different BIM applications in the life cycle. From two perspectives, the implementation status of BIM applications and integrated technologies are respectively studied. The future industry development framework is drawn comprehensively. We summarized the challenges of BIM applications from the perspectives of management, technology and promotion, and confirmed that most of the challenges come from the two driving factors of promotion and management.

The technical challenges reviewed in this paper are from the collected literature we have extracted, which is only a part of the practical challenges and not comprehensive enough.

We summarized the current mode of interactive use of BIM and sorted out the challenges faced by BIM applications to provide reference for the risks and challenges faced by the future industry.

There is little literature to integrate BIM applications and to establish BIM related challenges and risk frameworks. In this paper, we provide a review of the current implementation level of BIM and the risks and challenges of stakeholders through three aspects of management, technology and promotion.

This paper aims to examine the day-to-day within-person associations between employees' flow experience and organizational identification within the rarely studied context of construction engineering project organizations.

In this daily diary design, the authors surveyed 204 (M_age = 28.3, SD = 5.69) employees of a state-owned construction engineering project organization in southwest China via the online questionnaires comprising flow and organizational identification scales once daily on each workday for three consecutive weeks, which yielded 3,060 data entries. The authors then tested the temporal directionality between flow and organizational identification with multilevel time-series cross-lagged path analysis using Mplus 8.3.

Daily flow experience was linked positively with same-day organizational identification. Importantly, flow experience on the previous day predicted organizational identification on a subsequent day, but not vice versa.

This study suggests that construction engineering project managers should implement interventions fostering the employees' flow experience to promote organizational identification, with important implications for organizations aiming at flourishing workforces by facilitating organizational identification through implementing flow strategies.

There is a dearth of diary studies on flow and organizational identification specific to construction engineering project employees. The authors’ findings provide concrete evidence of the fluctuant nature of daily flow experience and organizational identification as well as their dynamic predictive pathway relationship.

This paper aims to examine the relationship between ownership type and the likelihood of publication of a corporate social responsibility (CSR) report.

Drawing on stakeholder salience theory, the probit model is used for a sample of 1,839 Chinese listed firms to study how different types of owners influence firm CSR engagement.

The analysis reveals that the Chinese stock exchanges exert a positive influence on the likelihood of a firm producing a CSR report, an effect which is more significant in state-owned enterprises (SOEs). Foreign investors lead to a greater likelihood of publication of a CSR report, though this effect is weaker in SOEs. In contrast, the holdings of state and domestic institutional investors are broadly neutral.

The study helps corporate managers to recognise how particular types of shareholders will value their efforts regarding CSR activities and disclosure and also assists policymakers in improving the level of CSR disclosure through the development of new policy.

Apposite CSR disclosure enhances trust and facilitates the shared values on which to build a more cohesive society.

The novelty of this study is that it addresses the effect of institutional investors on Chinese firm CSR engagement and thus provides an important insight for firms, investors and other stakeholders into the interplay of portfolio investment and CSR.

Under the background of coexistence of information overload and information fragmentation, it is of great significance to identify influencing factors and reveal the evolution logic of public opinion for public opinion governance.

Taking 24 hot social events as research cases, firstly, the evolution process of public opinion was divided into initial stage and response stage. Secondly, eight antecedent variables were extracted for qualitative comparative analysis of fuzzy sets. Finally, the configuration path of public opinion evolution results was summarized.

The research showed that compared with the initial stage, the influencing factors in the reaction stage played a key role in the continuous evolution of public opinion. The influencing factors in the initial stage and response stage played an indispensable role in promoting the evolution of public opinion to calm down.

This research can provide reference for regulators to timely grasp the initiative, discourse power and leadership of public opinion development.

Research on the two-stage configuration path of public opinion evolution is helpful to clarify the key factors affecting the evolution trend of online public opinion of hot events.

This study aims to investigate the effects of graphite-MoS₂ composite solid lubricant on the tribological properties of copper-based bearing materials under dry conditions.

The mixture of Graphite-MoS₂ was inlaid in ZQSn6-6–3 tin bronze and ZQAl9-4 aluminum bronze matrix. These copper-embedded self-lubricating bearing materials were considered in friction pairs with 2Cr13 stainless steel, and their tribological properties were studied by using an MM200 wear test machine.

The results show that the friction coefficients and wear rates of copper-embedded self-lubricating bearing materials are lower than those of the ordinary copper-based bearing materials. The wear performance of the tin bronze inlaid self-lubricating bearing material is better than that of the aluminum bronze inlaid self-lubricating bearing material. The wear mechanism of the tin bronze bearing material is mainly adhesive wear, and that of the aluminum bronze bearing material is mainly grinding wear, oxidation wear and adhesive wear. The copper-embedded self-lubricating bearing materials had no obvious abrasion, whereas the aluminum bronze inlaid self-lubricating bearing material exhibited deep furrows and obvious abrasion under high loads.

These results are helpful for the application of copper-embedded self-lubricating bearing materials.

This study aims to tackle the critical issue of detecting stock market manipulation, which undermines the integrity and stability of financial markets globally. Even enhanced with machine learning, traditional statistical methods often struggle to analyze high-frequency trading data effectively due to inherent noise and the limited availability of publicly known manipulation cases. This leads to poor model generalization and a tendency toward over-fitting. Focusing on China's securities market, our study introduces an innovative approach that employs deep learning-based high-frequency jump tests to overcome these challenges and to develop a more effective method for identifying manipulative activities.

We employed the “Jump Variation – Time-of-Day” (JV-TOD) non-parametric technique for jump tests on high-frequency data, coupled with the synthetic minority over-sampling technique (SMOTE) algorithm for re-balancing sample data. Our approach trains a deep neural network (DNN) on refined data to enhance its ability to identify manipulation patterns accurately.

Our results show that the deep neural network model, calibrated with high-frequency price jump data, identifies manipulation behavior more specifically and accurately than traditional models. The model achieved an accuracy rate of 94.64%, an F1-score of 95.26% and a recall rate of 95.88%, significantly outperforming traditional models. These results demonstrate the effectiveness of our approach in mitigating over-fitting and improving the robustness of market manipulation detection.

The proposed model provides regulatory entities and financial institutions with a more efficient tool to monitor and counteract market manipulation, thereby improving market fairness and investor protection.

By integrating the JV-TOD jump test with deep learning, this study proposed a new approach to market manipulation detection. The innovation is in its capacity to detect subtle manipulation signals that traditional methods typically overlook. Our model, which is trained on jump test data enhanced by the SMOTE algorithm, excels at learning complex manipulation patterns. This enhances both detection accuracy and robustness. In contrast to existing methods that are challenged by the noisy and intricate nature of high-frequency data, our approach shows enhanced performance in identifying nuanced market manipulations, offering a more effective and reliable method for detecting market manipulation.

This paper aims to explore the influence of the reinforcement included either glass beads (GBs) or carbon fiber (CF) on the reinforced polyamide 12 (PA12) composite samples prepared by selective laser sintering (SLS).

In this paper, the microstructure and mechanical properties are investigated, and the results are compared with those obtained for non-reinforced pure PA12 samples prepared by SLS.

The tensile fracture surface of the non-reinforced pure PA12 sample presents strong micro-deformation within the crack origination zone between the melted PA12 matrix and the un-melted PA12 particle cores. As a result, the pure PA12 sample exhibits the greatest maximum elongation. The maximum tensile strength is obtained for the CF reinforced sample because of the strengthening effect of CF and the relatively good bonding between CFs and the PA12 matrix. The minimum tensile strength is obtained for the GB reinforced PA12 sample because of the relatively weak bonding between GBs and the PA12 matrix.

These results demonstrate that the characteristics of the interfaces between the reinforcement and the PA12 matrix have an important influence on the fracture mechanisms and mechanical properties of PA12 composites fabricated by SLS.