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The net power delivered to the surface of parts (i.e. the actual heat flux) is a key parameter in the laser melting process and its exact control has a great impact on the numerical solutions. In this paper, the impact of laser additive manufacturing parameters including laser power, scanning speed and powder injection rate on thermal efficiency, net power delivered to the part and power loss due to powder flow has been investigated.

The response surface method was applied to measure the net laser power in laser deposited Inconel 718 using k-type thermocouples. The temperature history obtained by thermocouples was used to calculate the net power delivered by inverse analysis method. The applied model is Rosenthal's optimized model, in which all the thermal properties of the material are considered to vary with temperature.

The results indicated that the thermal efficiency, power delivered to the part and power loss can be optimized simultaneously at laser power of 400 W, scanning speed of 2 mm/s and powder injection rate of 200 mg/s. The microstructure analysis indicated that a high-quality sample without microstructural defects was formed under optimal condition of parameters. Moreover, the primary dendrite arm spacing for the optimal sample was higher than that obtained for other samples.

The novelty of this research summarized as follows: Prediction of the thermal efficiency and power loss during the laser metal deposition of Inconel 718 superalloy using the inverse analysis. Finding the optimal values of thermal efficiency, power delivered to the surface and power loss in the laser metal deposition of Inconel 718 superalloy. Investigating the effect of laser power, powder injection rate and scanning speed on the thermal efficiency and power loss of Inconel 718 superalloy during the laser metal deposition.

Assessing inputs and outputs is a significant aspect of taking decisions while there are complex and multistage processes in many examinations. Due to the presence of interval performance measures in various real-world studies, the purpose of this study is to address the changes of interval inputs of two-stage processes for the perturbations of interval outputs of two-stage systems, given that the overall efficiency scores are maintained.

Actually, an interval inverse two-stage data envelopment analysis (DEA) model is proposed to plan resources. To illustrate, an interval two-stage network DEA model with external interval inputs and outputs and also its inverse problem are suggested to estimate the upper and lower bounds of the entire efficiency and the stages efficiency along with the variations of interval inputs.

An example from the literature and a real case study of the banking industry are applied to demonstrate the introduced approach. The results show the proposed approach is suitable to estimate the resources of two-stage systems when interval measures are presented.

To the best of the authors’ knowledge, there is no study to estimate the fluctuation of imprecise inputs related to network structures for the changes of imprecise outputs while the interval efficiency of network processes is maintained. Accordingly, this paper considers the resource planning problem when there are imprecise and interval measures in two-stage networks.

In order to include the non-negligible lag relaxation time feature that is characteristic of heat transfer in biological bodies, the classical Fourier's law of heat conduction has to be generalized as the Maxwell–Cattaneo law resulting in the thermal-wave model of bio-heat transfer. The purpose of the paper is to retrieve the unknown time-dependent blood perfusion coefficient in such a thermal-wave model of bio-heat transfer from (non-intrusive) measurements of the temperature on an accessible sub-portion of the boundary that may be taken with an infrared scanner.

The nonlinear and ill-posed problem is reformulated as a nonlinear minimization problem of a Tikhonov regularization functional subject to lower and upper simple bounds on the unknown coefficient. For the numerical discretization, an unconditionally stable direct solver based on the Crank–Nicolson finite-difference scheme is developed. The Tikhonov regularization functional is minimized iteratively by the built-in routine lsqnonlin from the MATLAB optimization toolbox. Numerical results for a benchmark test example are presented and thoroughly discussed, shedding light on the performance and effectiveness of the proposed methodology.

The inverse problem of obtaining the time-dependent blood perfusion coefficient and the temperature in the thermal-wave model of bio-heat transfer from extra boundary temperature measurement has been solved. In particular, the uniqueness of the solution to this inverse problem has been established. Furthermore, our proposed computational method demonstrated successful attainment of the perfusion coefficient and temperature, even when dealing with noisy data.

The originalities of the present paper are to account for such a more representative thermal-wave model of heat transfer in biological bodies and to investigate the possibility of determining its time-dependent blood perfusion coefficient from non-intrusive boundary temperature measurements.

The study aims to test prospect theory (PT) predictions in the cryptocurrency (CC) market. It proposes a new asset pricing model that explores the potential of prospect theory value (PTV) as a significant predictor of CC returns.

The study comprehensively analyses a large sample set of 1,629 CCs, representing more than 95% of the CC market. The study uses a portfolio analysis approach, employing univariate and bivariate sorting techniques with equal-weighted and value-weighted portfolios. The study also employs ordinary least squares (OLS) regression, panel data methods and quantile regression (QR) to estimate the models.

This study demonstrates an average inverse relationship between PTV and CC returns. However, this relationship exhibits asymmetry across different quantiles, indicating that investor reactions vary based on market conditions. Moreover, PTV provides more robust predictions for smaller CCs characterized by high volatility and illiquidity. Notably, the findings highlight the dominant role of the probability weighting (PW) component in PT for predicting CC behaviors, suggesting a preference for lottery-like characteristics among CC investors.

The study is one of the early studies on CC price dynamics from the PT perspective. The study is the first to apply a QR approach to analyze the cross-section of CCs using a PT-based asset pricing model. The results shed light on CC investors' decision-making processes and risk perception, offering valuable insights to regulators, policymakers and market participants. From a practical perspective, a trading strategy centered around the PTV effect can be implemented.

Iron deficiency anemia (IDA) is the most common form of anemia in the world, affecting children, women and the elderly, while also being a common comorbidity in several medical conditions. Several studies have suggested a possible association between IDA and neurological dysfunction. Epilepsy, one of the common neurological disorders, has an unknown association with IDA. This pa per aims to investigate whether there is a causal relationship between IDA and epilepsy using a two-sample Mendelian randomization (MR) design.

This paper obtained summary data on IDA and epilepsy from the FinnGen consortium. Genetic variants significantly associated with IDA were used as instrumental variables (IVs). Epilepsy, focal epilepsy and generalized epilepsy were the outcomes. This paper used inverse variance weighted (IVW) as the primary estimate, and other MR methods were used as supplementary measures. Sensitivity analysis was also performed to assess heterogeneity and pleiotropy.

IVW estimates genetically predicted a causal relationship between IDA and the risk of epilepsy [odds ratio (OR), 1.15; 95% confidence interval (95% CI), 1.05–1.26; p = 0.002] and focal epilepsy (OR, 1.978, 95% CI, 1.58–2.48, p ≤ 0.0001), while no significant causal relationship was found with generalized epilepsy (OR, 1.1, 95% CI, 0.94–1.3, p = 0.24). There was no evidence of horizontal pleiotropy and heterogeneity in the sensitivity analysis.

This two-sample MR study found that IDA has a negative effect on the development of epilepsy. Clinical control of IDA may be helpful in the prevention of epilepsy. There is a need for further studies to explain the underlying mechanisms of this association.

This study aims to quantify the influence of urbanization on housing prices at the district-based level, while also investigating the heterogeneous impacts across different quantiles of housing prices.

The study uses remote-sensed spectral images from the Landsat 7 ETM+ satellite to measure urbanization, replacing prior reliance solely on urban population metrics. Subsequently, the two-step system generalized method of moments is used to evaluate how urbanization influences district-based housing prices through three spectrometries: Urban Index (UI), Normalized Difference Built-up Index (NDBI) and Built-Up Index (BUI). Finally, this study examines the heterogeneous impacts across various housing price quantiles through Dynamic Panel Quantile Regression with non-additive fixed effects under Markov Chain Monte Carlo simulation.

The study demonstrates that urbanization leads to an increase in regional housing prices. However, these impact magnitudes vary across housing price quantiles. Specifically, the impact exhibits an inverse V-shaped curve, with urbanization exerting a more pronounced influence on the 60th percentile of housing prices, while its effect on the 10th and 90th percentiles is comparatively weaker.

This study uses a novel method of remote sensing to measure urbanization and investigates its effects on housing prices. Furthermore, it provides an empirical application of non-additive fixed effect quantile regression for analyzing heterogeneity.

This study aims to develop a conceptual model of positive employee experience using sentiment analysis within algorithm-based human resource (HR) strategies. Its goal is to enhance HR professionals’ understanding of employee experiences and enable data-driven decision-making to create a positive work environment, thereby contributing to the originality of HR research.

The study conducts sentiment analysis – a text mining technique – to assess employee reviews and extract distinct positive experience factors. The employed data-driven methodology serves to fortify the reliability and objectivity of the analysis, ultimately resulting in a more refined depiction of the conveyed sentiment.

Utilizing sentiment analysis, the authors identified 135 keywords that signify positive employee experiences. These keywords were then categorized into four clusters aligned with factors influencing employee experience: work, relationships, organizational system and organizational culture, employing an inductive approach. The framework outlines the process of nurturing positive employee experiences throughout the employee life cycle, incorporating insights from the affective events theory and cognitive appraisal theory.

Data-driven insights empower HR professionals to enhance employee satisfaction, engagement and productivity. HR managers implementing AI-assisted HR ecosystems need digital and data science skills. Additionally, these insights can offer practical support in accentuating diversity and ethical considerations within the organizational culture. Candid employee data can enhance leadership and support diversity in organizational culture. Managers play a crucial communication role, ensuring flexible access to personalized HR solutions.

Applying sentiment analysis through opinion mining allows for the collection of unstructured data, reflecting authentic employee perceptions. This innovative approach expedites issue identification and targeted actions, enhancing employee satisfaction. Textual reviews, integral to employee feedback, offer comprehensive insights. Additionally, considering subjectivity and review length in online employee reviews adds value to understanding experiences (Zhao et al., 2019). This study surpasses prior research by directly identifying key factors of employee experience through the analysis of actual employee review texts, addressing a gap in understanding beyond previous attempts.

Cataract, or lens opacification, is a major public health burden accounting for more than half of all blindness worldwide. Plant-based dietary indices provide a unique approach to investigating a modifiable risk for age-related cataracts (ARC). The purpose of this study was to investigate the association between plant-based diet indices and risk of ARC.

This case-control study was conducted on 97 patients with newly diagnosed ARC and 198 healthy people (as a control group) in Iran. Convenience sampling and a food frequency questionnaire were used. Three plant-based dietary indices were used based on the health promoting qualities of food items, the overall plant-based diet index (PDI), healthful plant-based diet index (H-PDI) and unhealthful plant-based diet index (U-PDI) which comprised refined carbohydrates and highly processed foods. The plant-based dietary indices were used to investigate relationships with risk of ARC.

After adjusting for potential covariates, no significant association between a higher adherence to O-PDI and risk of ARC. As well, a higher adherence to H-PDI was inversely associated with the risk of ARC (OR = 0.35,95%CI:0.16–0.78). In contrast, there was a significant positive association between a higher adherence to U-PDI and the risk of ARC (OR = 3.67,95%CI:1.66 – 8.15).

The findings of this study have underscored the potential impact of the quality of plant-based food selections on the likelihood of developing ARC. Therefore, adopting a plant-based diet that is rich in nutrient-dense plant-based foods while being low in unhealthy options may have the potential to reduce the risk of ARC.

The purpose of this paper is to develop a decision-making protocol to meet the new requirements in an atypical panorama, such as the economic instability, in the textile industry.

The methodology consists of analyzing technical criteria, costing parameters and efficiency scores of knitted fabrics using the data envelopment analysis (DEA) and classification and regression (C&R) trees models, together with statistical techniques. From these tools, it is possible to guide the portfolio management of these products in a textile company, identifying those that are inefficient and require immediate management measures. The results are expected to be replicated in other companies because the DEA and C&R trees analytical procedures are applicable to different portfolios, whether in the same industry or not.

The results allowed identifying inefficient textile products regarding the input-output relationship and the main technical reasons related to the most significant inefficiencies, such as fiber composition and knitted fabrics rapports used in manufacturing.

DEA and C&R trees, in combination with the study of textile technical parameters, can be fundamental to investigating the efficiency and profitability of industries in periods of economic instability or other adverse situations. In addition, it is noteworthy that there are practically no studies in the literature on DEA applied in the textile industry, indicating excellent development potential.

Indirect knowledge leakage to rivals located near alliance partners represents a significant risk that has received limited scholarly attention. Hence, the question of how to manage this risk – which the authors term “partner-rival co-location risk” – in nonequity alliances remains unanswered, and this study aims to suggest establishing a steering committee to oversee the partnership.

Drawing on the agglomeration economies and alliance governance literatures, the authors develop a set of hypotheses and perform a series of empirical tests on 470 nonequity alliances in the US biopharmaceutical industry.

The authors propose that there is a positive linkage between partner-rival co-location risk and the formation of a steering committee in a nonequity alliance, which receives strong empirical support. Further, this relationship is significantly moderated by the breadth (alliance scope) but not the depth (reciprocal interdependence) of interaction between the partnering firms.

This paper is a pioneer to shed light on “partner-rival co-location risk” and how partner-rival co-location risk affects the governance decision of whether to establish a steering committee in a nonequity alliance, thus offering important theoretical and practical insights into competition and cooperation in alliance management.