CHILDREN'S SOCIOEMOTIONAL SKILLS

Mindfulness is a skill that enables individuals to accept their thoughts and feelings without judgement and to focus on their immediate experiences. It is important to determine the effects of mindfulness activities on children during the preschool period, as this is critical for their development. In this study, the systematic review method was used to examine the effects of mindfulness practices conducted with preschool children on their development. It is expected that the study will contribute to the scientific literature in terms of understanding the effects of mindfulness on children's different developmental areas and behaviours; it is also expected that it will be serves as a guide on the development of educational programmes and intervention practices to support mindfulness skills at an early age. The research included scientific studies and theses on mindfulness in preschool children (36-72 months) in Google Scholar, YÖK Thesis Centre, Tandfonline, ProQuest, EBSCO, Wiley Online Library, MEDLINE, Sage Journals, DergiPark, and JSTOR databases, which were conducted between 2015-2025 and could be accessed in full text. The databases were searched using the keywords "mindfulness" and "preschool" in English and "okul öncesi,” "mindfulness,” and "bilinçli farkındalık" in Turkish, which were determined by considering the subject title and the relationship with the purpose of the study. As a result of the search, 51 sources, 11 theses, and 40 studies were included in the review. The research results show that mindfulness-based programs have the potential to positively affect children's executive functioning, self-regulation skills, and social-emotional development. This study highlights the importance of early interventions and offers evidence-based guidance for future educational programs

A COST-EFFECTIVE IOT-BASED SOIL MOISTURE MONITORING COST-EFFECTIVE IOT-BASED SOIL MOISTURE MONITORING SYSTEM FOR WILDFIRE-PRONE COASTAL SOILSYSTEM FOR WILDFIRE-PRONE COASTAL SOILS

Abstract: The accuracy and unit cost of sensors are important factors for a continuous soil moisture monitoring system. This study compares the accuracy of four soil moisture sensors differing in unit costs in coarse-, fine- and medium-textured soils. The sensor outputs were recorded for the VWC, ranging from 0% to 50%. Low-cost capacitive and resistive sensors were evaluated with and without the external 16-bit analog-to-digital converter ADS1115 to improve their performances without adding much cost. Without ADS1115, using only Arduino’s built-in analog-to-digital converter, the low-cost sensors had a maximum RMSE of 4.79% (v/v) for resistive sensors and 3.78% for capacitive sensors in medium-textured soil. The addition of ADS1115 showed improved performance of the low-cost sensors, with a maximum RMSE of 2.64% for resistive sensors and 1.87% for capacitive sensors. The higher-end sensors had an RMSE of up to 1.8% for VH400 and up to 0.95% for the 5TM sensor. The RMSE differences between higher-end and low-cost sensors with the use of ADS1115 were not statistically significant

ELUCIDATING THE CALCIUM OXIDE DERIVED FROM THE WASTE SHELLS OF MUD CLAM (GELOINA EXPANSA) AS A CATALYST FOR BIODIESEL PRODUCTION ELUCIDATING THE CALCIUM OXIDE DERIVED FROM THE WASTE SHELLS OF MUD CLAM (GELOINA EXPANSA) AS A CATALYST FOR BIODIESEL PRODUCTION

Biodiesel is one of the alternative forms of diesel fuel and can be obtained using the transesterification process of waste cooking oil with a catalyst to accelerate the reaction. The heterogeneous catalyst from waste scallop shells is used due to its potential for being reused in the subsequent transesterification reactions. Heterogeneous catalysts can also be recycled, contributing to their environmentally friendly nature. This study aims to identify the performance of recycling a calcium oxide (CaO) catalyst from scallop shell waste on synthesis biodiesel. The method used is the transesterification method with the basic ingredients of waste cooking oil using a CaO catalyst. Then, after the transesterification process is complete, the catalyst is separated from the biodiesel and recycled to be reused in the transesterification process up to five times. The biodiesel samples obtained are identified for yield value, physico-chemical properties, thermal properties and performance. X-ray diffraction characterization results for the CaO catalyst show that it has a crystal size of 67.83 nm. Scanning electron microscope characterization shows that it has spherical particle shapes. Fourier transform infrared characterization shows the presence of Ca–O bonds. The highest biodiesel yield value of 74.23% is obtained in biodiesel Cycle 1. The flash point value of biodiesel samples ranges from 141.2°C to 149°C. Further, all of the biodiesel samples exhibit a cetane number of 75. The highest lower heating value of 38.22 MJ/kg is obtained in biodiesel Cycle 1 and the viscosity of the biodiesel samples ranges from 5.65 to 5.88 cSt. The density of the biodiesel samples ranges from 881.23 to 882.92 kg/m3. Besides, ester functional groups (C=O) and methyl functional groups have been successfully formed in all samples, with the methyl oleate compound observed as dominating the biodiesel samples. The cloud point value of the biodiesel samples ranges from 12°C to 13°C, and their pour point value ranges from 10°C to 12°C. The lead content in biodiesel is 0.8826 mg/kg. The lowest sulphur content is obtained from biodiesel Cycles 1 and 2 at 0.005%. Performance tests show that biodiesel has lower torque and brake power values than commercial diesel fuel and higher specific fuel consumption values than commercial diesel fuel

INFLUENCE OF ANTHROPOGENIC POLLUTANTS ON MUD CRAB ECOSYSTEMS TO ENSURE SUSTAINABLE AQUACULTURE AND SAFE CONSUMPTION TO HUMAN HEALTH

Heavy metal pollution from anthropogenic activities can harm aquatic ecosystems. This study aims to determine the concentration of heavy metals (Pb and Cu) in waters, sediments, and mud crabs (Scylla serrata), and to analyze the relationship between environmental parameters and S. serrata which is consumed by humans. Samples were taken in the mangrove ecosystem around the Tanjung Api-Api port area in South Sumatra, Indonesia. Pb and Cu analysis used the Atomic Absorption Spectrophotometer (AAS). Pb and Cu linkages in waters, sediments, and S. serrata analyzed by SigmaPlot V12.5 and Principal Component Analysis (PCA) analyzed by XLSTAT 2022. The limit consumption of S. serrata was calculated using MWI (MaximumWeekly Intake) and MIT (Maximum Intake Tolerance). Based on the results, the heavy metal Pb in water was 0.1055 – 0.1322 mg.L−1, and Cu was not detected. Furthermore, Pb in sediments ranged from 7.0104 - 11.8186 mg.kg−1, Cu 3.7127 - 4.5347 mg.kg−1, and Pb in S. serrata ranged from 0.0001 - 0.0021 mg.kg−1, and Cu ranged from 0.03 – 0.0791 mg.kg−1. The concentration of heavy metals in water, sediment, and S. serrata had not exceeded the specified quality standard, except for Pb in water. The principal component analysis obtained F1 (44.35%), F2 (27.53%) and F3 (17.83%) groups. Based on MWI and MIT values that S. serrata was still safe for human consumption

GENETIC ALGORITHM-ARTIFICIAL NEURAL NETWORK (GA-ANN) AND GIS-BASED WIND MAPPING FOR WIND ENERGY EXPLOITATION: CASE STUDY IN MALAYSIA

Wind maps are required to determine wind resource over a given areas and they are an important component of wind energy exploration and exploitation. The intermittency of wind, geographical, and temporal variability, as well as the complex relationship between wind and their nature, have made accurate spatial wind speed modelling more difficult. The aim of this study was to contribute a novel and original solution to the problem of developing wind maps for wind energy exploitation in Malaysia. The main inputs of this study were 37 Malaysian Meteorological Department stations’ wind data and 3 installed wind masts’ data. The Genetic Algorithm-Artificial Neural Network model was applied in the Measure-Correlate- Predict method to substitute and fill missing data. Spatial modelling was conducted to establish wind maps by interpolating point sources of wind data and extrapolating the wind flow at 10-m and 50-m heights. The Genetic Algorithm-Artificial Neural Network model was also applied to training spatial modelling and to generate a nonlinear wind map. The results revealed that nonlinear wind map had addressed the overprediction issue of the wind maps in mountainous areas at the Cameron Highlands site, where the root mean squared error, and the mean absolute error decreased by 60.39% and 64.01% respectively. Overall, the nonlinear wind map improved simulated wind data by increasing accuracy and decreasing errors, up to 18.39% and 31.42% respectively. In conclusion, the results clearly prove that addressing the complex nonlinear relationship between the input parameters and output wind map decrease errors in the simulation of wind speed