This study presents a simple flow-based system for the determination of the preservative agent sulfite in food and beverages. The standard method of conversion of sulfite ions into SO2 gas by acidification is employed to separate the sulfite from sample matrices. The sample is aspirated into a donor stream of sulfuric acid. A membrane gas-liquid separation unit, also called a ‘gas-diffusion (GD)’ unit, incorporating a polytetrafluoroethylene (PTFE) hydrophobic membrane allows the generated gas to diffuse into a stream of deionized water in the acceptor line. The dissolution of the SO2 gas leads to a change in the conductivity of water which is monitored by an in-line capacitively coupled contactless conductivity detector (C4D). The conductivity change is proportional to the concentration of sulfite in the sample. In this work, both clear (wine) and turbid (fruit juice and extracts of dried fruit) were selected to demonstrate the versatility of the developed method. learn more The method can tolerate turbidity up to 60 Nephelometric Turbidity Units (NTUs). The linear range is 5-25 mg L-1 SO32- with precision less then 2% RSD. The flow system employs a peristaltic pump for propelling all liquid lines. Quantitative results of sulfite were statistically comparable to those obtained from iodimetric titration for the wine samples.In recent years, site-specific antibody drug conjugates (ADC)s have been in great demand because they have an expanded therapeutic index compared with conventional ADCs. AJICAP™ technology is a chemical conjugation platform to obtain site-specific ADCs through the use of a class of Fc-affinity compounds. Promising results from early technology development studies led to further investigation of AJICAP™ ADC materials to obtain site-specific and homogeneous drug antibody ratio (DAR) ADCs. Here we report site-specific conjugation followed by a preparative hydrophobic interaction chromatography (HIC) purification strategy to obtain purified “DAR = 1.0” and “DAR = 2.0” AJICAP™ ADC materials. Optimization of the mobile phase conditions and resin achieved a high recovery rate. In vitro biological assay demonstrated the target selective activity for purified homogeneous DAR ADCs. These results indicate the ability of a HIC purification strategy to provide “DAR = 1.0” and “DAR = 2.0” AJICAP™ ADCs with considerable potency and target selectivity.Direct C‒H arylation coupling is potentially a more economical and sustainable process than conventional cross-coupling. However, this method has found limited application in the synthesis of organic dyes for dye‒sensitized solar cells. Although direct C‒H arylation is not an universal solution to any cross-coupling reactions, it efficiently complements conventional sp2‒sp2 bond formation and can provide shorter and more efficient routes to diketopyrrolopyrrole dyes. Here, we have applied palladium catalyzed direct C‒H arylation in the synthesis of five new 3,6-dithienyl diketopyrrolopyrrole dyes. All prepared sensitizers display broad absorption from 350 nm up to 800 nm with high molar extinction coefficients. The dye‒sensitized solar cells based on these dyes exhibit a power conversion efficiency in the range of 2.9 to 3.4%.Water resources sustainability is a worldwide concern because of climate variability, growing population, and excessive groundwater exploitation in order to meet freshwater demand. Addressing these conflicting challenges sometimes can be aided by using both simulation and mathematical optimization tools. This study combines a groundwater-flow simulation model and two optimization models to develop optimal reconnaissance-level water management strategies. For a given set of hydrologic and management constraints, both of the optimization models are applied to part of the Mahanadi River basin groundwater system, which is an important source of water supply in Odisha State, India. The first optimization model employs a calibrated groundwater simulation model (MODFLOW-2005, the U.S. Geological Survey modular ground-water model) within the Simulation-Optimization MOdeling System (SOMOS) module number 1 (SOMO1) to estimate maximum permissible groundwater extraction, subject to suitable constraints that protect the aquifer from seawater intrusion. The second optimization model uses linear programming optimization to (a) optimize conjunctive allocation of surface water and groundwater and (b) to determine a cropping pattern that maximizes net annual returns from crop yields, without causing seawater intrusion. Together, the optimization models consider the weather seasons, and the suitability and variability of existing cultivable land, crops, and the hydrogeologic system better than the models that do not employ the distributed maximum groundwater pumping rates that will not induce seawater intrusion. The optimization outcomes suggest that minimizing agricultural rice cultivation (especially during the non-monsoon season) and increasing crop diversification would improve farmers’ livelihoods and aid sustainable use of water resources.The current pandemic of the new coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), or COVID-19, has received wide attention by scholars and researchers. The vast increase in infected people is a significant challenge for each country and the international community in general. The prediction and forecasting of the number of infected people (so-called confirmed cases) is a critical issue that helps in understanding the fast spread of COVID-19. Therefore, in this article, we present an improved version of the ANFIS (adaptive neuro-fuzzy inference system) model to forecast the number of infected people in four countries, Italy, Iran, Korea, and the USA. The improved version of ANFIS is based on a new nature-inspired optimizer, called the marine predators algorithm (MPA). The MPA is utilized to optimize the ANFIS parameters, enhancing its forecasting performance. Official datasets of the four countries are used to evaluate the proposed MPA-ANFIS. Moreover, we compare MPA-ANFIS to several previous methods to evaluate its forecasting performance. Overall, the outcomes show that MPA-ANFIS outperforms all compared methods in almost all performance measures, such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Relative Error (RMSRE), and Coefficient of Determination( R 2 ). For instance, according to the results of the testing set, the R 2 of the proposed model is 96.48%, 98.59%, 98.74%, and 95.95% for Korea, Italy, Iran, and the USA, respectively. More so, the MAE is 60.31, 3951.94, 217.27, and 12,979, for Korea, Italy, Iran, and the USA, respectively.