Development and validation of a deep eutectic solvent-assisted liquid-liquid extraction method for simultaneous quantification of six steroid hormones in serum by liquid chromatography-tandem mass spectrometry.

Steroid hormones have been reported to be associated with endocrine system diseases. This paper proposes a novel procedure of deep eutectic solvent (DES)-assisted liquid-liquid extraction (LLE) to extract six steroid hormones (including cortisone, cortisol, androstenedione, testosterone, 17-hydroxyprogesterone, and progesterone) from serum coupled with liquid chromatography-tandem mass spectrometry (LC-MS/MS). A total of five types of L-proline, choline chloride, and citric acid-based DESs were tailored; the DES from L-proline and ethylene glycol at a molar ratio of 1:4 with 20 % acetonitrile was selected as the best-fit assisted solvent for the six steroid hormones compared with other DESs. The parameters for extraction by selected DES were optimized using Box-Behnken design (BBD), and the optimal extraction conditions are 200 µL of acetonitrile, 100 µL of the sample, and 80 µL of DES. Under optimum conditions, the method has good linear calibration ranges (between 0.07 ng mL-1 and 600 ng mL-1), correlation coefficients of determination (r2>0.99), and low limits of quantification (between 0.02 and 0.60 ng mL-1). The extraction recoveries were in the range of 81.84-114.43 %, and the intra-day and inter-day relative standard deviations (RSDs) were less than 10 %.In general, the DES-LC-MS/MS method is a simple and environmentally-friendly method, which can be complementary to the presently available methods for determining steroid hormones in serum.

A new method based on dispersive liquid-liquid microextraction for fat-soluble vitamin determination in serum by LC-MS/MS.

A green and inexpensive pretreatment known as dispersive liquid-liquid microextraction (DLLME) was developed in this assay coupled with the LC-MS/MS method for routine analysis of fat soluble vitamins (FSVs). The technique was performed with methanol as the dispersive solvent and dichloromethane as the extraction solvent. The extraction phase containing FSVs was evaporated to dryness and reconstituted in a mixture of acetonitrile and water. The influence variables concerning the DLLME procedure were optimized. After that, the method was investigated for its applicability in LC-MS/MS analysis. As a result, the parameters were settled for the optimal conditions during the DLLME process. A cheap and lipid-free substance was found as an alternative to serum to eliminate the matrix effect while preparing the calibrators. The method validation indicated that it was suitable for determining FSVs in serum. Moreover, this method was applied successfully to determine serum samples, which was consistent with the literature. In summary, the DLLME method developed in this report was reliable and more cost-effective than the traditional LC-MS/MS method, and could be applied in the future.

Effect of introducing virtual community and community group buying on customer's perceived value and loyalty behavior: A convenience store-based perspective.

Customers' declining receptivity to conventional marketing tools has been a challenge for convenience stores. To overcome this, retailers are turning to social media as a new, potent marketing tool for creating business prospects and encouraging direct customer interaction. However, it is still unknown how social media marketing affects the shifts in customer behavior. This paper expands on the relationship of "loyalty program (LP) + virtual community experience → perceived value → customer loyalty" in the traditional convenience store scenario, refining the variables of virtual community experience, perceived value, and customer loyalty. It also compares the effectiveness of different LP design structures (reward amounts × reward time limits) and explores the mediation impact of program loyalty and the moderation effect of alternative attractiveness. The results demonstrate the superior performance of LPs with an expiry policy and differential returns and highlight the importance of enhancing members' virtual community experiences in fostering customer perceived value and loyalty. The results also show the minor negative moderation impact of community group buying and prove that emotional value significantly impacts customer loyalty. Still, the social value does not affect program loyalty. The recommendations are offered, such as designing growing-oriented and periodical zeroing LPs, as well as using new social media marketing tools (virtual community-based marketing) to empower traditional marketing techniques (LP-based relationship marketing) and constructing a "convenience store + community group buying" model. The findings have substantial theoretical and practical implications for traditional convenience stores in properly implementing loyalty and social media marketing tactics to maximize customer value and loyalty with a limited budget.

Is It Necessary to Launch a School-Based Financial Literacy Curriculum? Evidence From China.

As starting university is a critical independence milestone for many young people, it would also be the best time to provide them with some financial education (FE). Although there have been many initiatives aimed at enhancing individual financial literacy (FL) and/or financial decision-making, meta-analyses have shown that the effectiveness of FE has been mixed. This study examined the driving forces behind the decision by college students to enroll in a targeted financial literacy curriculum (FLC) and the impact of this attendance on their FL. An endogenous switching model (ESM) was employed to account for the heterogeneity in the decision to attend or not attend the FLC and to counteract any unobservable characteristics. It was found that students with higher self-perceived FL did not prefer to attend the FLC; however, for others, FLC attendance was found to significantly boost their FL in areas such as financial knowledge (FK), financial attitude (FA), and financial behavior (FB), especially for the non-attendees under the counterfactual framework. These "non-attendees" were observed to have some characteristics (e.g., prior knowledge) that made them more financially literate regardless of attendance; however, if they had attended the FLC, they would have gained a greater FL than the attendees. As the FL of the attendees would have been much lower if they had not attended, the FLC appeared to be particularly important for the attendees, which strengthened the case for making the FLC a compulsory part of a general college education.

Unleashing the role of top management and government support in green supply chain management and sustainable development goals.

Achievement of Sustainable Development Goals (SDGs) has become one of the most important goals across the globe due to their significant role in the economic prosperity and human well-being. Recently, the business industry has become a dominant concentration for SDGs. However, the importance of small- and medium-sized industries has been rarely debated in this perspective. In particular, no attempt has yet been made to estimate the commitment of small and medium Enterprises (SMEs) towards SDGs in the emerging market in Pakistan. This study fills the gap by examining the influence of top management support on SDGs (e.g., environment and community practices) with a mediating role of green supply chain management and government support as a moderator. The model of this study is tested through structural equation modeling on the cross-sectional data collected through a structured questionnaire from 313 Pakistani SMEs. The results show that top management support significantly influences green supply chain management, environment, and community-based SDGs. Green supply chain management plays a partial mediating role between top management support and environment practices as well as between top management support and community practices. Government support significantly strengthens the relationship between top management support and green supply chain management. This research recommends SMEs to encourage participation in green supply chain practices and SDGs. Additionally, this research strongly suggests the government to facilitate SMEs in the adoption of green practices that can resultantly contribute to SDGs. Further implications have stated.

Evaluating the oil production and wastewater treatment efficiency by an extended two-stage network structure model with feedback variables.

Oil is an indispensable and important energy source in modern society, and oil production plays a vital role in economic development. However, there is no denying that oil production has a very bad impact on the environment. To realize the sustainable development of oil production, the environmental problems caused by oil production need to be controlled and managed strictly. Aiming at the practical problems of insufficient recoverable reserves of high quality oil and aggravating environmental pollution, efficient oil production and wastewater treatment become more and more important. Therefore, the whole system is divided into two stages. The stage 1 is oil development, and the stage 2 is wastewater treatment. Considering that the model needs to solve the undesirable output, an extended two-stage Slacks-Based Measure (SBM) Data Envelopment Analysis (DEA) network structure model with a feedback variable is established. The efficiency value of each stage can be obtained, and the weakness of each stage can be identified, so that the efficiency value of the whole system is more accurate. And then thirteen oilfields are selected for the numerical analysis to verify the validity and accuracy of the proposed model, the results demonstrate that the overall system is efficient only if the two stages are efficient; the oil production has higher efficiencies than the oilfield wastewater treatment; There is a stronger relationship between the efficiency of oilfield wastewater treatment and the whole system. A comparison with a traditional model demonstrated that the proposed model has a more scientific, stable and practical evaluation methods.

Semiconductor Metal Oxides as Chemoresistive Sensors for Detecting Volatile Organic Compounds.

Volatile organic compounds (VOCs), which originate from painting, oil refining and vehicle exhaust emissions, are hazardous gases that have significant effects on air quality and human health. The detection of VOCs is of special importance to environmental safety. Among the various detection methods, chemoresistive semiconductor metal oxide gas sensors are considered to be the most promising technique due to their easy production, low cost and good portability. Sensitivity is an important parameter of gas sensors and is greatly affected by the microstructure, defects, catalyst, heterojunction and humidity. By adjusting the aforementioned factors, the sensitivity of gas sensors can be improved further. In this review, attention will be focused on how to improve the sensitivity of chemoresistive gas sensors towards certain common VOCs with respect to the five factors mentioned above.

A two-stage method to determine optimal product sampling considering dynamic potential market.

This paper develops an optimization model for the diffusion effects of free samples under dynamic changes in potential market based on the characteristics of independent product and presents a two-stage method to figure out the sampling level. The impact analysis of the key factors on the sampling level shows that the increase of the external coefficient or internal coefficient has a negative influence on the sampling level. And the changing rate of the potential market has no significant influence on the sampling level whereas the repeat purchase has a positive one. Using logistic analysis and regression analysis, the global sensitivity analysis gives a whole analysis of the interaction of all parameters, which provides a two-stage method to estimate the impact of the relevant parameters in the case of inaccuracy of the parameters and to be able to construct a 95% confidence interval for the predicted sampling level. Finally, the paper provides the operational steps to improve the accuracy of the parameter estimation and an innovational way to estimate the sampling level.