Solventless Microextration Techniques for Pharmaceutical Analysis: The Greener Solution

Extensive efforts have been made in the last decades to simplify the holistic sample preparation process. The idea of maximizing the extraction efficiency along with the reduction of extraction time, minimization/elimination of hazardous solvents, and miniaturization of the extraction device, eliminating sample pre- and posttreatment steps and reducing the sample volume requirement is always the goal for an analyst as it ensures the method’s congruency with the green analytical chemistry (GAC) principles and steps toward sustainability. In this context, the microextraction techniques such as solid-phase microextraction (SPME), stir bar sorptive extraction (SBSE), microextraction by packed sorbent (MEPS), fabric phase sorptive extraction (FPSE), in-tube extraction dynamic headspace (ITEX-DHS), and PAL SPME Arrow are being very active areas of research. To help transition into wider applications, the new solventless microextraction techniques have to be commercialized, automated, and validated, and their operating principles to be anchored to theory. In this work, the benefits and drawbacks of the advanced microextraction techniques will be discussed and compared, together with their applicability to the analysis of pharmaceuticals in different matrices.

Resource Allocation for Intelligent Reflecting Surfaces Assisted Federated Learning System with Imperfect CSI

Due to its ability to significantly improve the wireless communication efficiency, the intelligent reflective surface (IRS) has aroused widespread research interest. However, it is a challenge to obtain perfect channel state information (CSI) for IRS-related channels due to the lack of the ability to send, receive, and process signals at IRS. Since most of the existing channel estimation methods are developed to obtain cascaded base station (BS)-IRS-user devices (UDs) channel, this paper studies the problem of computation and communication resource allocation of the IRS-assisted federated learning (FL) system based on the imperfect CSI. Specifically, we take the statistical CSI error model into consideration and formulate the training time minimization problem subject to the rate outage probability constraints. In order to solve this issue, the semi-definite relaxation (SDR) and the constrained concave convex procedure (CCCP) are invoked to transform it into a convex problem. Subsequently, a low-complexity algorithm is proposed to minimize the delay of the FL system. Numerical results show that the proposed algorithm effectively reduces the training time of the FL system base on imperfect CSI.

Circuit Optimization Study According to the Maximum Principle

The minimization of the processor time of designing can be formulated as a problem of time minimization for transitional process of dynamic system. A special control vector that changes the internal structure of the equations of optimization procedure serves as a principal tool for searching the best strategies with the minimal CPU time. In this case a well-known maximum principle of Pontryagin is the best theoretical approach for finding of the optimum structure of control vector. Practical approach for realization of the maximum principle is based on the analysis of behavior of a Hamiltonian for various strategies of optimization. The possibility of applying the maximum principle to the problem of optimization of electronic circuits is analyzed. It is shown that in spite of the fact that the problem of optimization is formulated as a nonlinear task, and the maximum principle in this case isn't a sufficient condition for obtaining a minimum of the functional, it is possible to obtain the decision in the form of local minima. The relative acceleration of the CPU time for the best strategy found by means of maximum principle compared with the traditional approach is equal two to three orders of magnitude.

Data acquisition time minimization in FANET-based IoT networks

Unmanned aerial vehicles (UAVs) is widely used in many military, and civilian applications. UAVs communicate in a Flying Ad hoc Network (FANET) environment where UAVs communicate with each other through an ad hoc network without infrastructure. FANET provide a flexible platform for internet of things (IoT) applications by playing different roles in IoT such as mobile data collector. In fact, in deadline based IoT applications, the deadline is restricted to the critical application level. And as a result, this deadline for data acquisition is not adequate, and FANET cannot collect data from the sensed area with the predetermined deadline. In this paper, a novel efficient data gathering approach for IoT using FANET is proposed. The main objective of this approach is to solve the problem of insufficient deadlines given by FANET in IoT-based deadline applications. Authors will first provide a multi-objective optimization model as a MILP optimization model to solve this problem, and then normalize and add two weighing coefficients to solve the MILP model. The results obtained in the simulation show that the proposed approach can provide efficient data acquisition while guaranteeing the deadline time.

UTILITY 1 SERVER ON QUEUE SERVICE (STUDY: BANK ACCOUNT NUMBER CONVERSION)

Fast-paced, precise demands and time minimization are dominant to support the service business. Service activities are always expected to be the best by customers. Especially ABC bank customers. Since the information about account conversion, many customers have come to the Bank. The server utility of the queue system plays an important role. One of them is about measuring the usefulness of the queue system, average expectations of waiting times, and expectations of the number of customers in the system. Services that focus on providing services always experience long lines. Therefore, the queue theory is used to assess utilities, waiting for time expectations, and expectations of customer numbers. Research methods using system performance. First, calculate the value of the speed, average service, service level, and performance of the queue system. The result is a queue system of 1 server on average - working with a utility value of 83.5% and the highest in the 4th week, with an average expectation - average waiting time of 0.428 or 25.6 minutes and an expectation of the number of customers in the system of 4.8 or 5 customers. The role of 1 server has not been practical to minimize waiting time expectations.