Directed Evolution of a Fluorinase for Improved Fluorination Efficiency with a Non-native Substrate.

Fluorinases offer an environmentally friendly alternative for selective fluorination under mild conditions. However, their diversity is limited in nature and they have yet to be engineered through directed evolution. Herein, we report the directed evolution of the fluorinase FlA1 for improved conversion of the non-native substrate 5'-chloro-5'-deoxyadenosine (5'-ClDA) into 5'-fluoro-5'-deoxyadenosine (5'-FDA). The evolved variants, fah2081 (A279Y) and fah2114 (F213Y, A279L), were successfully applied in the radiosynthesis of 5'-[18 F]FDA, with overall radiochemical conversion (RCC) more than 3-fold higher than wild-type FlA1. Kinetic studies of the two-step reaction revealed that the variants show a significantly improved kcat value in the conversion of 5'-ClDA into S-adenosyl-l-methionine (SAM) but a reduced kcat value in the conversion of SAM into 5'-FDA.

Palladium-meta-terarylphosphine catalyst for the Mizoroki-Heck reaction of (hetero)aryl bromides and functional olefins.

The evolutionary meta-terarylphosphine ligand architecture of Cy*Phine was recently shown to be a key feature that imposed outstanding performance in palladium-catalyzed copper-free Sonogashira applications. Herein, the Pd-Cy*Phine combination has similarly proven to be a powerful catalyst system for the Mizoroki-Heck reaction. Using high-throughput screening (HTS) methodology, DMF and NaHCO3 were rapidly identified as the most effective solvent and base pair for the cross-coupling catalysis of challenging and industrially valuable substrates including highly electron-rich heteroaryl bromides and unactivated olefins. Unprotected functional groups were well tolerated using low catalyst loadings, and the simple protocol produced excellent yields (up to 99%) with unprecedented substrate diversity. The Pd-Cy*Phine system broadly outperformed many state-of-the-art commercial alternatives, which demonstrated its potential as a next-generation cross-coupling catalyst.

Benchmarking of circular economy behaviors for Iraqi energy companies based on engagement modes with green technology and environmental, social, and governance rating.

Greenhouse gas emissions and global warming are recent issues of upward trend. This study sought to underline the causal relationships between engagement modes with green technology, environmental, social, and governance (ESG) ratio, and circular economy. Our investigation also captured benchmarking of energy companies' circular economy behaviors. A hybrid-stage partial least squares structural equation modeling (PLS-SEM) and multi-criteria decision-making (MCDM) analysis have been adopted. This study collected 713 questionnaires from heads of departments and managers of energy companies. The findings of this study claimed that engagement modes with green technology affect the circular economy and sustainability. The findings revealed that ESG ratings have a mediating role in the nexus among engagement modes with green technology and circular economy. The results of the MCDM application revealed the identification of the best and worst energy companies of circular economy behaviours. This study is exceptional because it is among the first to address the issues of greenhouse gas emissions by providing decisive evidence about the level of circular economy behaviors in energy companies.

The Influence of the Barriers of Hybrid Strategy on Strategic Competitive Priorities: Evidence from Oil Companies.

This study examines the impact of the barrier of adopting hybrid strategy on strategic performance using the oil sector in Iraq as a case. International oil companies consider various strategies in order to achieve superior performance. The procedure needs to overcome certain essential barriers for the adoption of the hybrid strategy that combines the cost leadership and differentiation strategy. The questionnaire was distributed online due to the COVID-19 pandemic that led to the closure of companies in the country. Out of the 537 questionnaires answered, 483 were used for further analysis which yielded usable response rate of 90%. The structural equation modeling results confirmed that the high costs of technologies, the priority of other external matters, inadequate industry regulation, insufficient supply, organizational capabilities, strategic capabilities, and financial capabilities are significantly related to strategic performance. The researchers recommend conducting an in-depth study of the phenomenon based on theoretical and empirical foundations, especially considering the relationship between the barriers of a hybrid strategy and strategic performance based on linear and non-compensatory relationships. This research sheds light on the barriers to adopting the hybrid strategy required by the oil sector as it relies on continuous production.

Circular economy of medical waste: novel intelligent medical waste management framework based on extension linear Diophantine fuzzy FDOSM and neural network approach.

Environmental pollution has been a major concern for researchers and policymakers. A number of studies have been conducted to enquire the causes of environmental pollution which suggested numerous policies and techniques as remedial measures. One such major source of environmental pollution, as reported by previous studies, has been the garbage resulting from disposed hospital wastes. The recent outbreak of the COVID-19 pandemic has resulted into mass generation of medical waste which seems to have further deteriorated the issue of environmental pollution. This necessitates active attention from both the researchers and policymakers for effective management of medical waste to prevent the harm to environment and human health. The issue of medical waste management is more important for countries lacking sophisticated medical infrastructure. Accordingly, the purpose of this study is to propose a novel application for identification and classification of 10 hospitals in Iraq which generated more medical waste during the COVID-19 pandemic than others in order to address the issue more effectively. We used the Multi-Criteria Decision Making (MCDM) method to this end. We integrated MCDM with other techniques including the Analytic Hierarchy Process (AHP), linear Diophantine fuzzy set decision by opinion score method (LDFN-FDOSM), and Artificial Neural Network (ANN) analysis to generate more robust results. We classified medical waste into five categories, i.e., general waste, sharp waste, pharmaceutical waste, infectious waste, and pathological waste. We consulted 313 experts to help in identifying the best and the worst medical waste management technique within the perspectives of circular economy using the neural network approach. The findings revealed that incineration technique, microwave technique, pyrolysis technique, autoclave chemical technique, vaporized hydrogen peroxide, dry heat, ozone, and ultraviolet light were the most effective methods to dispose of medical waste during the pandemic. Additionally, ozone was identified as the most suitable technique among all to serve the purpose of circular economy of medical waste. We conclude by discussing the practical implications to guide governments and policy makers to benefit from the circular economy of medical waste to turn pollutant hospitals into sustainable ones.

The efficiency of run rules schemes for the multivariate coefficient of variation: a Markov chain approach.

Control charts are one of the important tools to monitor quality. The coefficient of variation (CV) is a common measure of dispersion in many real-life applications. Recently, CV control charts are proposed to monitor processes which do not have a constant mean and a standard deviation which changes with the mean. These processes cannot be monitored by standard control charts which monitor the mean and/or standard deviation. This research proposes the monitoring of the multivariate coefficient of variation (MCV) by means of run rules (RR MCV) control charts, which is not available in the existing literature. The design of these charts is obtained using a Markov-chain approach. The proposed charts are simple to implement. The performance of the RR MCV and Shewhart MCV (SH MCV) charts are compared in terms of the average run length (ARL) and the expected average run length (EARL). An example is illustrated based on a real dataset. The findings revealed that the performance of the proposed charts surpasses the SH MCV chart for detecting small and moderate MCV shifts.

Economic-statistical design of synthetic np chart with estimated process parameter.

The economic-statistical design of the synthetic np chart with estimated process parameter is presented in this study. The effect of process parameter estimation on the expected cost of the synthetic np chart is investigated with the imposed statistical constraints. The minimum number of preliminary subgroups is determined where an almost similar expected cost to the known process parameter case is desired for the given cost model parameters. However, the available number of preliminary subgroups in practice is usually limited, especially when the number of preliminary subgroups is large. Consequently, the optimal chart parameters of the synthetic np chart are computed by considering the practical number of preliminary subgroups in which the cost function is minimized. This leads to a lower expected cost compared to that of adopting the optimal chart parameter corresponding to the known process parameter case.