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CHILDREN'S SOCIOEMOTIONAL SKILLS
No full textMindfulness 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, Y?K
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 ?ncesi,? "mindfulness,? and "bilin?li
fark?ndal?k" 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
AUTOMATED SOLAR AND ELECTRIC COMPOST BINS FOR THE TOURISM AND FOOD & BEVERAGE (HOTEL) INDUSTRIES
Full text linkPENCIRIAN KUBIS FERMENTASI PROBIOTIK DAN POTENSI DALAM MENGURANGKAN DISBIOSIS USUS DAN PERUBAHAN FISIOKIMIA YANG DISEBABKAN OLEH RACUN PEROSAK
No full text
ANALISIS LAPORAN KETIDAKSELAMATAN DALAM SURATKHABAR BERCETAK DAN DALAM TALIAN: KAJIAN TERHADAP EMPAT SURAT KHABAR TERPILIH DI NIGERIA
No full textGENETIC ALGORITHM-ARTIFICIAL NEURAL NETWORK (GA-ANN) AND GIS-BASED WIND MAPPING FOR WIND ENERGY EXPLOITATION: CASE STUDY IN MALAYSIA
Full text linkWind 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
KESAN BERFAEDAH APLIKASI SILIKON TERHADAP PRESTASI PERTUMBUHAN TANAMAN LADA HITAM (Piper nigrum L.)
No full textMODELLING OF SICK BUILDING SYNDROME (SBS) SYMPTOMS AND INDOOR AIR QUALITY (IAQ) ACROSS DOMINANT SUB-ECONOMIES IN TERENGGANU: A STUDY OF MONSOONAL VARIATIONS
Full text linkOptimum indoor air quality (IAQ) is crucial for maintaining a healthy work environment. This study examines the effects of IAQ on Sick Building Syndrome (SBS) symptoms across various economic subsectors during the monsoonal seasons in Terengganu, Malaysia. Four locations representing the education (S1), wholesale or retail trade (S2), manufacturing (S3), and services (S4) subsectors were assessed. IAQ
was measured using ventilation indicators (carbon dioxide, CO2), chemical parameters (formaldehyde (HCHO), total volatile organic compounds (TVOC), and carbon monoxide (CO)), and physical parameters (temperature, relative humidity, air movement) during the Southwest Monsoon (SWM) and Northeast Monsoon (NEM). The objectives included evaluating IAQ compliance, simulating 3D distributions using Computational Fluid Dynamics (CFD), identifying IAQ factors through Principal Component Analysis (PCA), and developing predictive Generalized Linear Models (GLM). Data included SBS symptom feedback and IAQ metrics, analysed using GLM with SBS syptoms as the dependent variable. Results showed seasonal IAQ variations, with temperatures ranging from 23.50?C to 32.91?C and relative humidity from 57.77% to 90.68%. CO2 levels were higher in enclosed spaces, particularly in manufacturing and retail sectors during the SWM. CFD simulations revealed increased turbulence near ventilation systems, with accuracies of up to 91.90% (SWM, S1) and 91.17% (NEM, S4). PCA identified three main IAQ contributors: physical conditions, chemical exposure, and human activities, accounting for up to 45.58% (NEM, S3), 24.17% (SWM, S3), and 31.42% (SWM, S4) of variance. The GLM demonstrated
higher predictive accuracy during the NEM, with an R2 of up to 0.9949. Seasonal
variations in IAQ significantly impacted SBS symptoms across different economic sectors in Terengganu, Malaysia. Poor IAQ, driven by physical conditions, chemical exposures, and human activities, was found to be worse during the SWM. The study recommends improving ventilation in enclosed spaces, regularly monitoring IAQ to address seasonal changes, reducing chemical emissions, controlling indoor activities, and enforcing IAQ compliance to create healthier work environments
PEMBANGUNAN KITARAN PEMANDUAN MENGGUNAKAN K-SHAPE DAN RANGKAIAN NEURAL KONVOLUSI UNTUK ANALISIS PENGUNAAN TENAGA DAN EMISI
No full textOne of the key challenges in the automotive industry is enhancing fuel efficiency and reducing emissions while meeting regulatory requirements. Common issues include the lack of context-specific driving cycles, limitations of conventional clustering methods in capturing non-linear driving behaviours, inefficiencies in real-time system integration with Siemens Totally Integrated Automation (TIA) Portal, and delays from traditional data exchange methods, leading to inaccuracies in fuel consumption and emission analysis. This research focuses on integrating MATLAB scripts for fuel consumption and emission analysis with real-time execution in the Siemens TIA Portal. A major focus is on seamless integration strategies between MATLAB and Siemens environments during execution to enhance analysis efficiency. The study involves collecting driving cycle data for Ipoh City using MATLAB Mobile and DC-TRAD and constructing the Ipoh City driving cycle using the K-shape clustering technique, which identifies complex patterns more accurately than conventional clustering methods. Additionally, a convolutional neural network (CNN) algorithm is applied for effective and precise driving cycle development. The research includes a detailed analysis of execution cycle time, fuel consumption, and emissions across both MATLAB and Siemens environments. A significant improvement in execution performance is achieved, with model cycle times in the Siemens environment reduced by over 90%, reaching a maximum of 100 milliseconds compared to 45 seconds in MATLAB. This substantial reduction in cycle time is accomplished without compromising accuracy, as the results from MATLAB are successfully replicated in the Siemens environment, leading to the selection of route 6 as the optimized route for the Ipoh City driving cycle. Addressing challenges related to computational power and system integration for real-time processing, this research outlines strategies to optimize MATLAB scripts for real-time deployment within Siemens systems. Ultimately, this integration aims to provide efficient and accurate solutions for analysing energy consumption and emissions in automotive applications, contributing valuable advancements to the field
A NEW DIGITAL TRANSFORMATION FRAMEWORK TO ENHANCE ESG PERFORMANCE FOR PUBLIC LISTED COMPANIES IN MALAYSIA
Full text linkThis paper aims to explore whether the trend of digital transformation is driving
companies to engage in environmental, social, and governance (ESG) practices. The
impact of strategic transformation on firms is all-encompassing, making it difficult to
capture the mechanisms of impact on corporate ESG practices. To this end, we con-
struct a new theoretical framework that combines slack resources and stakeholder
theory. This framework attributes the heterogeneity of ESG practices to differences
in the ability and willingness of companies. Using data from Chinese listed companies,
we find that the digital transformation is improving ESG performance