The development of the Turkish Craving for Internet Gaming Scale (CIGS): a validation study

As the use of digital technology has increased, abuse and addiction to technology have been identified among a minority of users. In the mid-1990s, the concept of internet addiction was first used. Today, almost every digital technology use has been claimed to have a minority of disordered users. One key aspect of addictive substance behaviors is craving. Craving is also an important component of behavioral addictions including digital technology disorders such as Internet Gaming Disorder. The aim of the present study was to develop the Turkish version of the Craving for Internet Gaming Scale (CIGS) via an adaptation of the Penn Alcohol Craving Scale (PACS). The present study comprised 368 adolescents from four different samples. The measures used included the Craving for Internet Gaming Scale, Digital Game Addiction Scale, and Brief Self-Control Scale. The structural validity of CIGS was investigated with Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), and criterion validity. The reliability of CIGS was evaluated using Cronbach α internal consistency reliability coefficient and corrected item total correlation coefficients. As a result of EFA, it was found that the five-item CIGS had a single-factor structure. The unidimensional CIGS obtained as a result of EFA was tested with CFA. As a result of CFA, the unidimensional structure of CIGS was confirmed in two different samples. Criterion validity of CIGS was assessed via digital gaming addiction, self-discipline, impulsiveness, daily internet gaming duration, and internet gaming history. As a result of criterion analysis, CIGS was associated with these variables in the expected direction. Finally, according to reliability analysis, the CIGS was found to be a reliable scale. When validity and reliability analysis of the CIGS are considered as a whole, it is concluded that the CIGS is a valid and reliable scale that assesses craving for internet gaming

Preparation and use of maize tassels’ activated carbon for the adsorption of phenolic compounds in environmental waste water samples

The determination and remediation of three phenolic compounds bisphenol A (BPA), ortho-nitrophenol (o-NTP), parachlorophenol (PCP) in wastewater is reported. The analysis of these molecules in wastewater was done using gas chromatography (GC) × GC time-of-flight mass spectrometry while activated carbon derived from maize tassel was used as an adsorbent. During the experimental procedures, the effect of various parameters such as initial concentration, pH of sample solution, eluent volume, and sample volume on the removal efficiency with respect to the three phenolic compounds was studied. The results showed that maize tassel produced activated carbon (MTAC) cartridge packed solid-phase extraction (SPE) system was able to remove the phenolic compounds effectively (90.84–98.49 %, 80.75–97.11 %, and 78.27–97.08 % for BPA, o-NTP, and PCP, respectively) . The MTAC cartridge packed SPE sorbent performance was compared to commercially produced C18 SPE cartridges and found to be comparable. All the parameters investigated were found to have a notable influence on the adsorption efficiency of the phenolic compounds from wastewaters at different magnitudes

Glossokinetic potential based tongue-machine interface for 1-D extraction

The tongue is an aesthetically useful organ located in the oral cavity. It can move in complex ways with very little fatigue. Many studies on assistive technologies operated by tongue are called tongue-human computer interface or tongue-machine interface (TMI) for paralyzed individuals. However, many of them are obtrusive systems consisting of hardware such as sensors and magnetic tracer placed in the mouth and on the tongue. Hence these approaches could be annoying, aesthetically unappealing and unhygienic. In this study, we aimed to develop a natural and reliable tongue-machine interface using solely glossokinetic potentials via investigation of the success of machine learning algorithms for 1-D tongue-based control or communication on assistive technologies. Glossokinetic potential responses are generated by touching the buccal walls with the tip of the tongue. In this study, eight male and two female naive healthy subjects, aged 22-34 years, participated. Linear discriminant analysis, support vector machine, and the k-nearest neighbor were used as machine learning algorithms. Then the greatest success rate was achieved an accuracy of 99% for the best participant in support vector machine. This study may serve disabled people to control assistive devices in natural, unobtrusive, speedy and reliable manner. Moreover, it is expected that GKP-based TMI could be alternative control and communication channel for traditional electroencephalography (EEG)-based brain-computer interfaces which have significant inadequacies arisen from the EEG signals