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The purpose of this study is to synthesize and characterize the mesoporous nanostructure of Hydroxyapatite (HA), evaluate the impact of the addition of sulphate ions (SO₄2-) on the microstructure properties of HA, and assess the ability of the prepared materials to be used as a carrier for Bovine serum albumin (BSA) protein. This study successfully synthesized 0.07 mole of SO₄2- substituted HA using the reflux microwave-assisted wet precipitation method and fully characterized it using modern techniques. In particular, XRD, XPS, Raman, FESEM, and BET techniques were used to explore the microstructure properties of materials. The results shows that the addition of SO₄2- ions revealed increasing the lattice parameters and surface area of HA, and the particles shape was changed from semi-spherical to rod like shape. Adsorption and desorption of protein molecules were evaluated using BSA protein for 24h at 37°C. BSA loading capacity and release behaviours were significantly improved with the incorporation of SO₄2- ions. The SO₄-HA materials hold a promising future for being used as a carrier for protein molecules.

This work demonstrates a method to design photonic surfaces by combining femtosecond laser processing with the inverse design capabilities of tandem neural networks that directly link laser fabrication parameters to their resulting textured substrate optical properties. High throughput fabrication and characterization platforms are developed that generate a dataset comprising 35280 unique microtextured surfaces on stainless steel with corresponding measured spectral emissivities. The trained model utilizes the nonlinear one-to-many mapping between spectral emissivity and laser parameters. Consequently, it generates predominantly novel designs, which reproduce the full range of spectral emissivities (average root-mean-squared-error < 2.5%) using only a compact region of laser parameter space 25 times smaller than what is represented in the training data. Finally, the inverse design model is experimentally validated on a thermophotovoltaic emitter design application. By synergizing laser-matter interactions with neural network capabilities, the approach offers insights into accelerating the discovery of photonic surfaces, advancing energy harvesting technologies.

Abstract This work demonstrates a method to design photonic surfaces by combining femtosecond laser processing with the inverse design capabilities of tandem neural networks that directly link laser fabrication parameters to their resulting textured substrate optical properties. High throughput fabrication and characterization platforms are developed that generate a dataset comprising 35280 unique microtextured surfaces on stainless steel with corresponding measured spectral emissivities. The trained model utilizes the nonlinear one‐to‐many mapping between spectral emissivity and laser parameters. Consequently, it generates predominantly novel designs, which reproduce the full range of spectral emissivities (average root‐mean‐squared‐error < 2.5%) using only a compact region of laser parameter space 25 times smaller than what is represented in the training data. Finally, the inverse design model is experimentally validated on a thermophotovoltaic emitter design application. By synergizing laser‐matter interactions with neural network capabilities, the approach offers insights into accelerating the discovery of photonic surfaces, advancing energy harvesting technologies.

In the present unstable business environment, supply chains are considerably impacted by disruptions, necessitating the implementation of resilience strategies. These strategies, however, pose significant financial challenges for companies. Therefore, financing is essential in developing resilient supply chain networks. In addition to utilizing existing capital, options such as bank loans and trade credit can be employed to alleviate the financial burden and enhance working capital. The present scholarship has failed to address the issue of financial strain resulting from the adoption of resilience strategies. Additionally, the significance of trade credit and repayment scheduling in all levels of the supply chain network also left under-researched. To fill this research gap, this paper proposes a three-tiered supply chain network consisting of main/support suppliers, factories, and distribution centers under uncertain demand conditions. The network is developed to effectively handle demand uncertainty and achieve optimal net present value and demand estimation. To solve the bi-objective model of the study, a preemptive fuzzy ideal programming approach accompanied by the implementation of the CPLEX solver is utilized. The findings lend support to the importance of securing financial support for support suppliers and establishing effective trade credit agreements across all levels of the supply chain.

Considering the underground economy and the dependence of Iran's economy on oil and the influence of the tax system on oil revenues, the present study attempts to investigate the effect of oil rent on taxes, emphasizing the role of the underground economy for the period 1973-2021 in the form symmetrical and asymmetrical model. For this purpose, the size of the underground economy was calculated using the MIMIC method, representing an average of 15.2% of Iran’s economy. Then the research model was estimated by linear (symmetric) and non-linear (asymmetric) autoregressive distributed lag. The result indicates that in the symmetrical estimation, the tax is inversely affected by the oil rent, and the increase in the size of the underground economy aggravates this negative relationship. The asymmetric estimate shows that the effect of oil rent on taxation is asymmetric. First, the effect of positive oil rent shocks on taxes is much greater (more than twice) than the effect of negative oil rent shocks. Second, the expansion of the underground economy intensifies the adverse effect of negative oil rent shocks and the positive effect of negative oil rent shocks on taxes. Accordingly, it is suggested that the economic policymaker, regardless of the ups and downs of oil and the resulting rent, should diligently work to improve the efficiency of the tax system, and attempt as much as possible to control and reduce the size of the underground economy.

During the process of cutting andesite stones, the waste mud is kept in powder form once fully dried. It is difficult to store the waste that is produced as a consequence of the extensive utilization area and consumption of andesite. Thus, eliminating waste storage challenges and incorporating these wastes into the economy are crucial. For this reason, this study examined the effects of waste andesite dust (WAD) on the flexural behavior of reinforced-concrete beams (RCBs) using experimental testing and 3D finite-element modeling (FEM) via ANSYS. Thus, different rates of WAD up to 40% were used to investigate the influence of the WAD rate on the fracture and bending behavior of RCBs. While the RCB with 10% WAD had a slightly lower load-bearing and ductility capacities, ductility capacities significantly drop after 10% WAD. At 40% WAD, both the load-bearing capacity and ductility significantly reduced. Based on the experimental findings, using 10% WAD as a replacement for cement is a reasonable choice to obtain eco-friendly concrete. Moreover, the outcomes of 3D FEM were also compared with those of experiments conducted using ANSYS v19 software. The displacement values between the test and FEM findings are quite similar.

BackgroundDespite the widespread institution of modern massive transfusion protocols with balanced blood product ratios, survival for patients with traumatic hemorrhage receiving ultramassive transfusion (UMT) (defined as ≥20 U of packed red blood cells [RBCs]) in 24 hours) remains low and resource consumption remains high. Therefore, we aimed to identify factors associated with mortality in trauma patients receiving UMT in the modern resuscitation era.MethodsAn Eastern Association for the Surgery of Trauma multicenter retrospective study of 461 trauma patients from 17 trauma centers who received ≥20 U of RBCs in 24 hours was performed (2014-2019). Multivariable logistic regression and Classification and Regression Tree analysis were used to identify clinical characteristics associated with mortality.ResultsThe 461 patients were young (median age, 35 years), male (82%), severely injured (median Injury Severity Score, 33), in shock (median shock index, 1.2; base excess, -9), and transfused a median of 29 U of RBCs, 22 U of fresh frozen plasma (FFP), and 24 U of platelets (PLT). Mortality was 46% at 24 hours and 65% at discharge. Transfusion of RBC/FFP ≥1.5:1 or RBC/PLT ≥1.5:1 was significantly associated with mortality, most pronounced for the 18% of patients who received both RBC/PLT and RBC/FFP ≥1.5:1 (odds ratios, 3.11 and 2.81 for mortality at 24 hours and discharge; both p < 0.01). Classification and Regression Tree identified that age older than 50 years, low initial Glasgow Coma Scale, thrombocytopenia, and resuscitative thoracotomy were associated with low likelihood of survival (14-26%), while absence of these factors was associated with the highest survival (71%).ConclusionDespite modern massive transfusion protocols, one half of trauma patients receiving UMT are transfused with either RBC/FFP or RBC/PLT in unbalanced ratios ≥1.5:1, with increased associated mortality. Maintaining focus on balanced ratios during UMT is critical, and consideration of advanced age, poor initial mental status, thrombocytopenia, and resuscitative thoracotomy can aid in prognostication.Level of evidencePrognostic, level III.

In today's rapidly advancing electronics industry, the widespread use of electrical and electronic equipment has made sustainable management of end-of-life (EOL) equipment a pressing need. A global system is required to facilitate informed decision-making regarding e-waste. Manufacturers must therefore devise strategies for effectively managing products during the EOL phase. This article introduces a novel framework for managing e-waste during the EOL phase to identify the most appropriate EOL options to minimize e-waste. The proposed framework harnesses the power of Twitter as a data source, utilizing data mining techniques to extract consumer opinions on e-waste. The article also outlines the development of a Fuzzy Analytic Hierarchy Process method and a multi-objective mathematical framework, enabling efficient decision-making in EOL processes based on e-waste information obtained from Twitter data. The study's findings have the potential to assist equipment manufacturers in identifying flaws in electrical and electronic equipment by analyzing customer opinions shared on social media platforms. Furthermore, by analyzing consumer sentiment towards e-waste, manufacturers can gain a deeper understanding of customer perspectives and develop appropriate strategic plans for selecting suitable EOL processes for returned products. This study underscores the significance of social media data in addressing e-waste management challenges, showcasing the profound impact of customer opinions.

Purpose of the Study: The overall purpose of this study was to investigate whether or not the incorporation of combined flipped classroom, and collaborative learning pedagogical schemes in two restructured sections of principles of marketing course, had any positive impact on students' learning of 100 key marketing concepts, processes, and strategies. Results: By comparing the students' total assessment scores in Experimental and Control Group groups, it was concluded that the re-structured course had significantly improved students' learning of key marketing concepts, processes, and strategies.

The evolution of information technology, artificial intelligence, and large volumes of data in web2, led to the formation of a new approach from the convergence of two scientific fields of business intelligence (BI) and social media analysis (SMA), which is called social business intelligence (SBI) with some researchers. Growing the number of studies in BI and SMA and the explosion of information, required coherence, integration and summary to knowledge extraction. The purpose of this paper is to recognize the capabilities that are the result of the two scientific field convergence. The bibliometric methods have been used to analyze publications tile 2022 and map the topics trend, historical graph, co-occurrence network and knowledge map of social business intelligence capabilities. The results indicate that the nature of business intelligence studies changes toward the analysis of big social media data and integration of analytical and managerial capabilities in BI with the power of marketing, communication and networking in SM. Also, five clusters of social marketing capability, data analytic capability, knowledge capability, communication capability, and transformational capability have been identified for SBI. About the role of SBI in empowering organizations in the digital era, especially in business related to marketing and innovation goals, it is recommended to equip organizations with this technology and its capabilities.