This study proposes an adaptive control synthesis for a class of second-degree fractional order systems with different eigenvalues in the state-space domain. The proposed fractional order adaptive controller is a generalization of the MRAC controller for the class of scalar fractional order systems. In order to control the fractional order plant, an adaptive state space feedback controller is applied based on the error between the system output and a chosen reference model using a fractional adaptation law to make the fractional order plant track the fractional order reference model. We show that the resulting adaptive regulator is able to stabilize the fractional order second degree system with a satisfying performance. A simulation example illustrating these performance properties is provided along with a comparison with a fractional order sliding mode control (FOSMC) to demonstrate the superiority of the proposed control scheme.

The performance of photovoltaic models depends significantly on the accuracy of their parameters, which are determined by the chosen method and objective function. Extracting these parameters accurately under different environmental conditions is essential to enhance reliability, accuracy, and minimize system costs. In this research, a novel technique is proposed for extracting the electrical parameters of the solar cell single diode model, including saturation current, serial resistance, parallel resistance, and ideality factor. To overcome the challenges posed by the chaotic behavior of the I-V curve equation, an improved Iterative Root-Finding algorithm is introduced. This algorithm acts as an optimization tool, increasing the likelihood of obtaining highly accurate solutions by minimizing the quadratic error between experimental and theoretical characteristics in a shorter time frame. The numerical and experimental results demonstrate the effectiveness of this approach in solar module modeling, showing squared errors approaching zero. This study opens new possibilities for improving the accuracy and reliability of photovoltaic models, leading to more efficient solar energy systems.

This study aims to determine the level of smartphone security awareness of associate degree students and to reveal whether smartphone security awareness differs significantly according to some demographic variables. In addition, it is aimed to reveal the relationship between smartphone security awareness and digital literacy. In this direction, firstly, the Smartphone Security Awareness Scale was developed. The scale consists of nine items collected in a single dimension and explains 39.6% of the total variance. The reliability coefficient of the scale was determined as 0.78. The study sample consisted of a total of 612 associate degree students continuing their education in two vocational colleges. The data were collected online and on a voluntary basis. According to the findings, the level of smartphone security awareness of associate degree students was high. There was no significant difference between smartphone security awareness and gender, using security software, receiving information security training, and being concerned about the privacy and protection of personal data. No significant relationship was found between smartphone security awareness and the duration of owning a smartphone. However, significant differences were found according to experiencing security problems and competence in using information technologies. A positive, significant and moderate relationship was found between smartphone security awareness and digital literacy. The scale developed within the scope of the research can be used in different studies in the future. According to the results obtained in this direction, training and seminars can be organised to increase smartphone security awareness.

Fuel is essential in everyday life to be used as a means of transportation, which generally uses fuel oil. In early April 2022, there was an issue that caused an uproar in Indonesian society: rising fuel prices. Based on the problems described, this research uses emotion classification with the BERT and LSTM methods using social media data, such as YouTube, to classify types of emotions based on community responses to government decisions. The highest results were obtained when oversampling was used, with an accuracy of 95%, while without oversampling, the accuracy was only 47%. In the experiments that have been carried out, it turns out that using the oversampling method and the BERT method for emotion classification will cause a reduction in accuracy for the test section.

The purpose of this study is to develop a controlled Fluconazole and Naproxen releasing system for cartilage repair. Interpenetrating polymer network (IPN) type of hydrogels were prepared by using different ratios of 2-Hydroxyethyl methacrylate (HEMA) and gelatin. The hydrogels were synthesized by using ammonium persulfate (APS) and sodium metabisulfite (SBS) as initiator pair and ethylene glycol dimethacrylate (EGDMA) and glutaraldehyde (GA) as cross linkers. The prepared hydrogels were characterized via hydrogel formation and swelling/degradation measurements, Fourier Transform Infrared Spectroscopy (FT-IR) and Scanning Electron Microscope (SEM) analysis. From swelling tests, it is observed that semi-IPN hydrogels swell much more than full-IPNs which crosslinked by two agents, EGDMA and GA. The higher ratios of HEMA/gelatin negatively affect swelling values. In general, the IPN hydrogel discs were not affected by the variation of temperature. The release studies of Fluconazole and Naproxen were performed at 37 ⁰C and it is found that the swelling and releasing profiles were similar to each other. The releases of drugs increase rapidly at first and then complies nearly 36 h-48 h. Because of the looser and porous structure, semi-IPN hydrogels have higher release values than full-IPNs.

The batch adsorptive sequestration of methylene blue from an aqueous solution using unripe Roystonea regia fruit pericarp biomass was investigated in this study. The characteristic nature of the biosorbent was studied using various analytical instruments including Fourier Transform Infra-red spectrophotometer, Scanning Electron Microscope, Energy Dispersive X-ray, X-ray diffractometer, and the Brunauer-Emmett Teller. The adsorption study was perfomed at different experimental conditions including pH, contact time, initial dye concentration, temperature, agitation speed, and biosobent dose. From the results of this study, the optimum biosorption of MB was achieved at 120 min contact time, pH 10, room temperature (298 K), 150 rpm agitation speed and dosage of 100 mg/150 mL dye solution. With 132.30 mgg-1 maximum sorption capacity, the Langmuir isotherm best describes the biosorption equilibrium data. At all initial concentrations, the biosorption kinetics of methylene blue onto the biosorbent fitted best to the pseudo-second order kinetics model, with R2 values ≥ 0.999 and qcal being similar to the qexp. The kinetics study also showed the involvement of intra-particle diffusion in the rate-determining step; although not as the sole limiting step of the sorption process. The results of the thermodynamics study showed the high feasibility, spontaneity, and exothermic nature of the biosorption of methylene blue onto the biosorbent. This study concludes that Roystnea regia fruit pericarp would make an economically viable and renewable biosorbent for practical eco-friendly sequestration of MB dye from wastewaters.

Reflective polarization conversions with a simplistic design of an ultrathin, single-layered, and multi-functional anisotropic metasurface as a polarization converter is utilized for Ku- and K-band applications. The designs with two substrate thicknesses (0.095λ0 and 0.069λ0, respectively) are capable of a cross-polarization converter (CPC) and a linear-to-circular (LTC) polarization conversion. The design with 0.095λ0 thickness achieves a CPC between 17.96 and 26.90GHz with the efficiency of more than 90% and a relative bandwidth of 40% under normal incidence. It maintains angular stability by altering the oblique incidence angles up to 300 with greater than 80% of the PCR in the K-band. Meanwhile, an LTC in two frequency bands, 10.30-10.53GHz and 28.65-29.70GHz, is also numerically demonstrated. The second design with 0.069 λ0 thickness provides a CPC above the PCR value of 87% in the frequency range from 10.46-23.05GHz (covering the entire Ku- and part of the K-band) with angular stability of 40 above the PCR value of 80%. In the meantime, an LTC with relative bandwidth of 75% in the frequency range from 9.53-9.79&24.74-25.27GHz is numerically revealed. These polarization converters exhibit relatively good performances of facile structure and multi-functional properties, which can be useful in Ku- and K-band applications.

The literature in the field of Lean Six Sigma (LSS) is developing in Türkiye as well as over the world. However, theses and papers written in Turkish are waiting to be disseminated. This paper aims to investigate LSS in the Turkish context with the purpose of identifying the current state, gaining insight and exploring future directions. This paper implements the systematic literature review methodology to identify and review all relevant studies in Turkish literature. This paper covers the literature on LSS in Türkiye from 2004 to 2021. As a result, a total of 32 theses and 29 papers were investigated in detail. This paper not only focuses on years, sectors, areas etc. of the studies, but also analyses the contents of papers in relation to enterprise size, project prioritization, and quality tools used in DMAIC phases, and improvements after implementation in terms of descriptive and content analysis. The results show that, although there are many LSS implementations in Turkish context, they could not be shared with global academic area due to the national language usage. LSS implementations have an increasing trend in last years and highly focused on manufacturing sector and large companies. The lack of structured way of applying LSS, especially not using project prioritization and selection methodology takes attention. This paper shows the gaps and future directions in Türkiye for researchers and provides a groundwork to develop a roadmap for Turkish organizations.

The quality of food products can start to deteriorate over time before reaching the consumer. Fast-moving food products and frozen products are involved in cold chain logistics. To ensure food safety, these products must be kept refrigerated or frozen throughout the supply. A cold chain is required in the display and storage processes for the products to reach from the field to the table. Refrigerated display cabinets, which are widely used in supermarkets, are one of the parts of the cold chain. These refrigerators are used for both cold storage and frozen storage of products at specific temperature ranges by standards. In refrigerated display cabinets, the air refrigerated by the heat drawn by the evaporator is blown to the food products. Due to negative interactions with hot ambient air, open-type refrigerated display cabinets consume large amounts of electrical energy. Therefore, the refrigerated display cabinet must have high energy efficiency. On March 1, 2021, energy labeling became mandatory for refrigerated display cabinets placed on the market in the European Union, and energy labeling started to be classified from A to G. With this energy label application, refrigerators are classified according to their efficiency. This study presents a remote monitoring model for a refrigerated display cabinet and aims to determine whether it operates in its energy class value. As a result of the study, the effect of the remote monitoring method on energy efficiency was revealed, and it was concluded that it could contribute to operational efficiency by reducing energy costs and carbon emissions.

Reducing food waste is paramount for a sustainable future as its implications are important to achieve sustainable development goals set by the United Nations. In many industry groups, the public awareness of reducing food waste that may potentially emerge along firms’ operations has grown. In the era of Big Data, one of the most pursued exercises of this escalating attention on reducing food waste is to utilize artificial intelligence techniques to incorporate sustainability concerns into the decision framework. Many firms embrace machine learning methods to build effective decision mechanisms that help make efficient and sustainable decisions. In this study, we analyze the impact of blending machine learning approaches with demand forecasting and order quantity decisions for a firm operating in a setting where the market demand is random, and the demand structure is not observable to the firm. The performance of the methodology is evaluated on sunflower seed demand data taken from Tadım company. Our results suggest that the joint consideration of forecasting and ordering decisions using the quantile regression approach can lead the firm to decrease its operational cost by 6% on average.