Salt water adversely affects human health and plant growth. In parallel with the increasing interest in non-contact determination of salt concentration in water, a novel approach is proposed in this study. In the proposed approach, S parameter measurements, which show the scattering properties of electromagnetic waves, are used. First, the relationship between salt concentration in water and permittivity values, a distinguishing feature for liquids, is shown. Then, based on the derived correlations from a set of S parameter measurements, it is shown that the salt concentration in water can be predicted. Finally, after exactly determining the relations of permittivity, salt concentration and S parameter, a system that allows non-contact determination of salt concentration is proposed. Since the proposed system makes its prediction using a classifier, decision tree algorithms are employed for this purpose. In order to evaluate the appropriateness and success of the algorithms, a set of classification experiments were held using various water samples with different levels of salt concentration. The results of the classification experiments show that the Hoeffding tree algorithm achieved the best results and is the most suitable decision tree algorithm for determining the salt concentration of liquids. For this reason, the proposed non-contact approach can be used to determine the salt concentration in water reliably and quickly if its hardware and software components can be embedded into a prototype system. HIGHLIGHTS Showing the relationship between salt concentration in water and permittivity values. Predicting salt concentration. Proposing a system that allows non-contact determination of salt concentration.

Shoulder prostheses may need to be maintained or replaced over time for different reasons. These maintenance procedures are also performed by surgeries. There are different types of shoulder prostheses produced by different manufacturers, and different equipment is required to remove and care for each. In cases where sufficient information about the prosthesis type cannot be provided,

Alcohol is widely used in various fields today. Alcohol overdose and its improper use can cause many health problems. Drinking liquids with high alcohol concentration, especially methanol, causes poisoning, and the use of colognes with high alcohol concentration causes various skin conditions and respiratory diseases. Therefore, for public health it is important to determine the type and concentration of alcohol in beverages and liquid products. In this paper, K * algorithm is used to detect and classify liquids containing high concentration of alcohol by measuring the scattering parameter of the liquids in the microwave frequency band. For this purpose, scatter parameter values of aqueous solutions of ethanol, methanol, 1-propanol and 2-propanol at different concentrations have been used as training sets. Commercial liquids with known ethanol concentrations and liquids with other known alcohol concentrations have been tested to prove the accuracy of the proposed approach. As the results show the proposed approach can classify liquids containing alcohol and their alcohol concentrations successfully and with high accuracy.

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Social work aims to promote social change, development, cohesion, and the empowerment of people and communities. Its professionals work with institutions and families and help to provide and advance various aspects of human equity such as civil rights, unemployment insurance, worker's compensation, disability pay, medicaid and medicare, and child abuse and neglect prevention. In recent years, social engineering practices started to threaten not only just a single or group of individuals but also the entire society. Considering the potential threats of social engineering practices on the practices of social work, strategies and good practices should be developed to achieve the sustainable goals of social work. Since the social engineering practices target our fundamental biases, our ignorance, and our lack of cognition, the key to counteracting them is awareness. This chapter covers the potential threats of social engineering practices, challenges related to counteracting them, methodologies used to uncover them, and future research directions in this domain.