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The purpose of this research is to enhance the Laser Powder Bed Fusion (LPBF) additive manufacturing technique by addressing its susceptibility to defects, specifically lack of fusion. The primary goal is to optimize the LPBF process using a digital twin (DT) approach, integrating physics-based modeling and machine learning to predict the lack of fusion.

This research uses finite element modeling to simulate the physics of LPBF for an AISI 316L stainless steel alloy. Various process parameters are systematically varied to generate a comprehensive data set that captures the relationship between factors such as power and scan speed and the quality of fusion. A novel DT architecture is proposed, combining a classification model (recurrent neural network) with reinforcement learning. This DT model leverages real-time sensor data to predict the lack of fusion and adjusts process parameters through the reinforcement learning system, ensuring the system remains within a controllable zone.

This study's findings reveal that the proposed DT approach successfully predicts and mitigates the lack of fusion in the LPBF process. By using a combination of physics-based modeling and machine learning, the research establishes an efficient framework for optimizing fusion in metal LPBF processes. The DT's ability to adapt and control parameters in real time, guided by machine learning predictions, provides a promising solution to the challenges associated with lack of fusion, potentially overcoming the traditional and costly trial-and-error experimental approach.

Originality lies in the development of a novel DT architecture that integrates physics-based modeling with machine learning techniques, specifically a recurrent neural network and reinforcement learning.

Face is the most intuitive and representative feature at the individual level. Many studies show that beautiful faces help individuals and enterprises obtain economic benefits and form a high economic premium, but the discussion of their potential social value is insufficient. This study aims to focus on the impact of the personal characteristics of executives. It mainly analyzes the impact mechanism of CEO facial attractiveness on corporate social responsibility (CSR) decision-making, clarifying the social value of beauty from the perspective of CSR.

The authors use the regression model to analyze the panel data set, which was conducted by a sample of Chinese publicly listed firms from 2016 to 2018.

The study found that CEOs with high facial attractiveness are more active in fulfilling CSR, which can usually bring higher social benefits. CEOs with beautiful faces are prone to overconfidence, are optimistic about their ability and the future development of the enterprise and are more willing to increase their investment in CSR. CEO duality can positively regulate the positive correlation between a CEO’s facial attractiveness and CSR.

Based on the perspective of upper echelons theory, this paper explores the mechanism of CEO facial attractiveness on CSR. This study enriches the perspective of the upper echelon’s theoretical research and has essential enlightenment for CEO selection and training practice.

This paper investigates the role of nexus supplier transparency, which involves the collective information disclosure to the public by second-tier nexus suppliers, as an alternative mechanism for mitigating buyer environmental, social and governance (ESG) risk exposure. We also examine buyer supply network accessibility as a moderating factor that facilitates collecting detailed information and undertaking corrective actions accordingly.

We collected a sample of 428 focal buyer firms and their supply networks up to third-tier suppliers. Data were obtained from Bloomberg and RepRisk databases. We identified critical nexus suppliers using data envelopment analysis (DEA) and tested hypotheses using regression analysis.

The results show that the benefits of nexus supplier transparency, such as reducing buyer ESG risk exposure, differ depending on the type of nexus supplier disclosing information and buyer supply network accessibility. Informational nexus supplier transparency was found to be beneficial. However, the results revealed the double-edged sword of monopolistic nexus supplier transparency, which benefits buyers with higher levels of accessibility but increases risk exposure for buyers with lower accessibility.

This study demonstrates that the transparency of critical second-tier suppliers mitigates buyer ESG risk exposure by providing information about lower tiers in the supply network. Challenging the notion of the focal buyer as the main orchestrator of supply chain initiatives, our alternative perspective opens a new avenue for risk management in multi-tier supply chains.

This review provides an overview of recent advances in electrochemical sensors for analyte detection in saliva, highlighting their potential applications in diagnostics and health care. The purpose of this paper is to summarize the current state of the field, identify challenges and limitations and discuss future prospects for the development of saliva-based electrochemical sensors.

The paper reviews relevant literature and research articles to examine the latest developments in electrochemical sensing technologies for saliva analysis. It explores the use of various electrode materials, including carbon nanomaterial, metal nanoparticles and conducting polymers, as well as the integration of microfluidics, lab-on-a-chip (LOC) devices and wearable/implantable technologies. The design and fabrication methodologies used in these sensors are discussed, along with sample preparation techniques and biorecognition elements for enhancing sensor performance.

Electrochemical sensors for salivary analyte detection have demonstrated excellent potential for noninvasive, rapid and cost-effective diagnostics. Recent advancements have resulted in improved sensor selectivity, stability, sensitivity and compatibility with complex saliva samples. Integration with microfluidics and LOC technologies has shown promise in enhancing sensor efficiency and accuracy. In addition, wearable and implantable sensors enable continuous, real-time monitoring of salivary analytes, opening new avenues for personalized health care and disease management.

This review presents an up-to-date overview of electrochemical sensors for analyte detection in saliva, offering insights into their design, fabrication and performance. It highlights the originality and value of integrating electrochemical sensing with microfluidics, wearable/implantable technologies and point-of-care testing platforms. The review also identifies challenges and limitations, such as interference from other saliva components and the need for improved stability and reproducibility. Future prospects include the development of novel microfluidic devices, advanced materials and user-friendly diagnostic devices to unlock the full potential of saliva-based electrochemical sensing in clinical practice.

This study investigates the behavior of viscous hybrid ferromagnetic fluids flowing through plain elastic sheets with the magnetic polarization effect. It examines flow in a porous medium using Stefan blowing and utilizes a versatile hybrid ferrofluid containing MnZnFe₂O₄ and Fe₃O₄ nanoparticles in the C2H2F4 base fluid, offering potential real-world applications. The study focuses on steady, laminar and viscous incompressible flow, analyzing heat and mass transfer aspects, including thermal radiation, Brownian motion, thermophoresis and viscous dissipation with convective boundary condition.

The governing expression of the flow model is addressed with pertinent non-dimensional transformations, and the finite element method solves the obtained system of ordinary differential equations.

The variations in fluid velocity, temperature and concentration profiles against all the physical parameters are analyzed through their graphical view. The association of these parameters with local surface friction coefficient, Nusselt number and Sherwood number is examined with the numerical data in a table.

This work extends previous research on ferrofluid flow, investigating unexplored parameters and offering valuable insights with potential engineering, industrial and medical implications. It introduces a novel approach that uses mathematical simplification techniques and the finite element method for the solution.

This paper aims to focuses on by investigate the heat transmission and free convective flow of a suspension of nano encapsulated phase change materials (NEPCMs) within an enclosure. Particles of NEPCM have a core-shell structure, with phase change material (PCM) serving as the core.

The enclosure consists of a square chamber with an insulated wall on top and bottom and vertical walls that are differently heated. The governing equations are investigated using the finite element technique. A grid inspection and validation test are done to confirm the precision of the results.

The effects of fusion temperature (varying from 0.1 to 0.9), Stefan number (changing from 0.2 to 0.7), Rayleigh number (varying from 10³ to 10⁶) and volume fraction of NEPCM nanoparticles (changing from 0 to 0.05) on the streamlines, isotherms, heat capacity ratio and average Nusselt number are investigated using graphs and tables. From this investigation, it is found that using a NEPCM nano suspension results in a significant enhancement in heat transfer compared to pure fluid. This augmentation becomes more important for the low Stefan number, which is around 16.57% approximately at 0.2. Secondary recirculation is formed near the upper left corner as a result of non-uniform heating of the left vertical border. This eddy expands notably as the Rayleigh number rises. The study findings indicate that the NEPCM nanosuspension has the potential to act as a smart working fluid, significantly enhancing average Nusselt numbers in enclosed chambers.

The NEPCM particle consists of a core (n-octadecane, a phase-change material) and a shell (PMMA, an encapsulation material). The host fluid water and the NEPCM particles are considered to form a dilute suspension.

Using NEPCMs in energy storage thermal systems show potential for improving heat transfer efficiency in several engineering applications. NEPCMs merge the beneficial characteristics of PCMs with the enhanced thermal conductivity of nanoparticles, providing a flexible alternative for effective thermal energy storage and control.

This paper aims to explore the free convective flow and heat transmission of NEPCM water-type nanofluid in a square chamber with an insulated top boundary, a uniformly heated bottom boundary, a cooled right boundary and a non-uniformly heated left boundary.

This paper aims to highlight unconventional or underused theories that can be considered for the study of hospitality and tourism consumers. The authors discuss how these theories can be applied.

This research paper is conceptual and descriptive in nature. The authors address the proposed theories by applying the Delphi method.

Theories such as dramaturgical theory, persuasion theory, script theory, customer inspiration theory and segmented assimilation theory are underused but can be applied to studies of hospitality and tourism consumers’ behavior. They can be helpful for understanding various aspects of consumer behavior, such as their decision-making, motivations, attitudes and perceptions, in hospitality and tourism.

Consumer behavior is more diverse and complex in the post-pandemic era. The authors draw attention to theories that are underused but have explanatory power with regards to hospitality and tourism consumers’ behaviors. These non-conventional theories can provide new theoretical perspectives and offer new insights.

The aim of this research is to underscore the pivotal role of warehouse management in the current turbulent global landscape exacerbated by the confluence of a health crisis and geopolitical instability in Europe. In today's interconnected global economy, the turbulence of the global supply chain causes a lack of its resilience among companies. Facing this critical crisis context, companies are refocusing on business processes and outsourcing support processes such as logistics. In this paper we have empirical and methodological objectives. Methodologically, we employ a qualitative research approach utilizing action research in a collaborative framework that involves academics and practitioners. The purpose of this methodology is to empirically investigate warehouse outsourcing as a solution for enhancing a company's performance and agility within the crisis context.

The authors’ action research based on case study approach is conducted through an immersion within the ALCL French multinational company located in Morocco. The authors mobilize the theory of constraints, which allows us to set up a process of identification and optimization of managerial constraints (Goldratt, 1990). The approach allows to set up a retroactive loop to increase the performance of the constraint.

The study shows that ALCL has a storage over-dimension constraint due to the decrease of physical flows caused by the global crisis. The results of action research protocol show that the optimization of warehousing constraint is achieved by the total outsourcing of the process.

The study provides new insights into how action research can improve management practices within companies and explore concrete solutions to the logistical challenges faced by businesses.

The current study has a two-fold purpose. Firstly, it aims to analyze the extent to which knowledge management (KM) affects the performance of individuals (task and contextual) on the one hand and that of organizations (product or service, perceived and financial) on the other hand. Secondly, it proposes to investigate the mediating effect of motivation and innovation in the relationship between KM and individual and organizational performance.

Partial least squares structural equation modeling (PLS-SEM) was employed in this study, with mediation analysis performed using advanced PLS-SEM techniques. A total of 1,284 respondents from organizations in both the public and private sectors were included in the sample.

The findings emphasize that KM has a more significant direct effect on individual performance compared to organizational performance. Concurrently, in terms of indirect influence, it is found that KM, through motivation and innovation, has a positive and significant effect on both individual and organizational performances, with a higher influence on the organizational one.

The originality of the work can be noted in designing two different structural models to represent the proposed relationships at the individual and organizational levels. These findings could provide organizational decision makers with empirical evidence, helping them (1) internalize the significance of the KM process in organizations as well as its subsequent effects on individual and organizational performance and (2) identify factors that mediate variable relationships.

This paper aims to examine the responses of cryptocurrency markets to the U.S. Securities and Exchange Commission’s (SEC) announcement on June 5, 2023, concerning the charges against Binance. This paper investigates the intraday market reactions and volatilities of a set of cryptocurrencies (Bitcoin, Ethereum, Ripple, Cardano and Litecoin) to this announcement as an event and explore if these reactions are consistent with the prediction of overreaction hypothesis or uncertain information hypothesis.

Considering the day when the SEC filed the lawsuit against Binance as an unexpected event, we classify the price movements of a set of cryptos on the event day as either unexpected favorable news or unexpected unfavorable events. We examine whether the behavior of the prices of the crypto is consistent with the predictions of the overreaction hypothesis (OH) proposed by De Bondt and Thaler (1985) or the uncertain information hypothesis (UIH) suggested by Brown et al. (1988).

The results suggest that the cryptocurrency markets faced a return volatility surge, no matter if investors regarded this event as favorable or unfavorable, and the markets’ responses are mixed. The results of supremum augmented Dickey−Fuller (SADF) and generalized SADF (GSADF) do not support the bubble behavior in selected cryptocurrency series.

The essential implication is that the action of the SEC had an evident impact on the volatility of cryptocurrency markets. This consequence should be seriously considered, as the role of the SEC in regulating the digital asset markets becomes more critical following the approval of the Bitcoin ETFs, early 2024.

To the best of the authors’ knowledge, this is the first paper investigating the cryptocurrencies’ markets reactions to the SEC lawsuit against Binance. It sheds light to the market reactions using intraday data.