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Jan 13, 2025

Solar photovoltaic adoption among landed residential property in Malaysia: Integration of sustainable energy security dimension

The Malaysian government's efforts to promote solar photovoltaic (PV) usage among households face a challenge due to its low adoption rate. This study delves into the factors influencing the exponential adoption of solar PV electricity generation among landed residential property owners in Malaysia. The research aims to comprehensively examine the predictors influencing the adoption of solar PV systems among Malaysian households. Hence, the study employs an enhanced Theory of Planned Behavior framework, integrating sustainable energy security dimensions such as availability, affordability, efficiency, acceptability, regulation, and governance. The sample comprised 556 Malaysian residents who owned and resided in the landed properties. The home locations where at least one solar PV installation existed within a residential street. Snowball sampling was employed through referrals, leveraging social and community networks. Collected data was analyzed using the partial least squares structural equation modeling. Attitude, affordability, and acceptability emerged as pivotal factors significantly impacting the intention to use solar PV systems among Malaysian households. This research not only enriches academic discourse but also offers practical implications for policymakers, guiding the formulation of targeted strategies to promote sustainable energy practices and facilitate the widespread adoption of solar PV systems in Malaysia.

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Jan 13, 2025

The design and implementation of Intelligent-Electricity Consumption and Billing Information System (IEBCIS)

The increasing demand for electricity and the need to reduce carbon emissions have made optimizing energy usage and promoting sustainability critical in the modern economy. This research paper explores the design and implementation of an Intelligent-Electricity Consumption and Billing Information System (IEBCIS), focusing on its role in addressing electricity sustainability challenges. Using the Design Science Research (DSR) methodology, the system's architecture collects, analyses, and visualizes electricity usage data, providing users with valuable insights into their consumption patterns. The research involved developing and validating the IEBCIS prototype, with results demonstrating enhanced real-time monitoring, load shedding schedules, and billing information. These results were validated through user testing and feedback, contributing to the scientific knowledge of intelligent energy management systems. The contributions of this research include the development of a framework for intelligent energy management and the integration of data-driven insights to optimize electricity consumption, reduce costs, and promote sustainable energy use. This research was conducted over a time scope of two years (24 months) and entails design, development, pilot test implementation and validation phases.

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Jan 13, 2025

Enhancing assessment practices: Incorporating developmental distress in adolescent depression diagnosis

Depression is a prevalent mental health problem, particularly in the middle to late stages of adolescence. There are certain characteristics of depression that make it difficult to diagnose and require a thorough evaluation. Primary care doctors are often the first to see teens and have a critical role in recognizing and treating the symptoms of this condition. In addition, there are groups that advise testing for depression at this time. To summarize, the major objective of this paper is to conduct a comprehensive review of the scientific literature about the screening, diagnosis, and treatment of depression in adolescents, with a particular focus on primary care settings. There are a number of environmental and genetic elements that are related with the development of depression in teenagers, making it a difficult job to comprehend the pathophysiology of this condition. Diagnosing this ailment can be challenging because of its wide range of clinical manifestations, even though there are specific screening tests and diagnostic criteria. Furthermore, it can be mistakenly identified with other mental illnesses, which is why a variety of other differential diagnoses must be used. The feasible options for therapy are determined in part by the severity of the ailment, associated risk factors, and the resources at hand. These substitutes might consist of pharmaceuticals (mostly fluoxetine) and psychotherapies (relationships and cognitive behavioural). Under no circumstances can treatment be completed without psychological education, a supportive attitude, and family involvement. Preventive measures not only have a major impact on reducing the prevalence of this illness, but they also have a major positive impact on community health generally. Because of its unpredictable clinical course and the fact that it is significantly underdiagnosed all over the globe, depression in adolescents is a disorder that is of concern to the medical profession. General practitioners are able to give early diagnosis, the beginning of therapy, and referrals to mental health experts when it is important to do so.

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Jan 13, 2025

Differences in the influence of virtual influencers on different consumer groups

This paper uses quantitative research methods to explore the differences in the impact of virtual influencers on different consumer groups in the context of technological integration and innovation. The study uses DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering technology to segment consumers and combines social media behavior analysis with purchase records to collect data to identify differences in consumer behavior under the influence of virtual influencers. Consumers' emotional resonance and brand awareness information about virtual influencers are extracted through sentiment analysis technology. The study finds that there are significant differences in the influence of virtual influencers on different consumer groups, especially in high-potential purchase groups, where the influence of virtual influencers is strong but short-lived. This paper further explores the deep integration of virtual influencer technology with new generation information technologies such as 5G and artificial intelligence, and emphasizes the importance of such technological integration in enhancing the endogenous and empowering capabilities of virtual influencers. The research results show that technological integration and innovation can not only promote the development of virtual influencers, but also provide new technical support for infrastructure construction, especially in the fields of smart cities and industrial production. This paper provides a new theoretical perspective for the market application of virtual influencers and provides practical support for the application of virtual technology in infrastructure construction.

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Jan 14, 2025

Preparation, characterization of Telfairia occidentalis stem extract-silver nanocomposite and application in remediation of lead (ⅱ) ions from oilfield produced water

Every production day in Nigeria, and in other oil producing countries, millions of barrels of produced water is generated. Being very toxic, remediation of the produced water before discharge into environment or re-use is very essential. An eco-friendly and cost effective approach is hereby reported for remediative pre-treatment of produced water (PW) obtained from Nigerian oilfield. In this approach, Telfairia occidentalis stem extract-silver nanoparticles (TOSE-AgNPs) were synthesized, characterized and applied as bio-based adsorbent for treating the PW in situ. The nanoparticles were of average size 42.8 nm ± 5.3 nm, spherical to round shaped and mainly composed of nitrogen and oxygen as major atoms on the surface. Owing to the effect of addition of TOSE-AgNPs, the initially high levels (mg/L) of Total Dissolved Solids (TDS), Biological Oxygen Demand (BOD) and TSS of 607, 3.78 and 48.4 in the PW were reduced to 381, 1.22 and 19.6, respectively, whereas DO and COD improved from 161 and 48.4 to 276 and 19.6 respectively, most of which fell within WHO and US-EPA safe limits. Particularly, the added TOSE-AgNPs efficiently removed Pb (II) ions from the PW at temperatures between 25 ℃ to 50 ℃. Removal of TOSE-AgNPs occurred through the adsorption mechanism and was dependent contact time, temperature and dose of TOSE-AgNPs added. Optimal remediation was achieved with 0.5 g/L TOSE-AgNPs at 30 ℃ after 5 h contact time. Adsorption of Pb (Ⅱ) ions on TOSE-AgNPs was spontaneous and physical in nature with remediation efficiency of over 82% of the Pb (Ⅱ) ions in solution. Instead of discarding the stem of Telfairia occidentalis, it can be extracted and prepared into a new material and applied in the oilfield as reported here for the first time.

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Jan 14, 2025

An insight from machine learning perspective for COVID-19 survival prediction in Malaysia based on demographic factor

Malaysia reported its first imported COVID-19 case on 23 January 2020, which marked the country's first confirmed positive case. The first case in Malaysia was from eight close contacts in Johor. The global health landscape has been significantly impacted by the COVID-19 pandemic, with mortality or survival being critical outcomes of interest. This study aims to predict COVID-19 survival occurrences in Malaysia by utilizing machine learning approaches based on demographic factors. The dataset used in this study comprises demographic information of 2,151,315 COVID-19 patients, including nationality, regions, age groups, gender, medical history, vaccine brands, and the number of vaccine doses received between 2020 and 2022. Four machine learning algorithms, namely Logistic Regression, Naïve Bayes, Support Vector Machine, and Artificial Neural Network were employed to assess the relationship between demographic factors and COVID-19 survival. To evaluate the model performance, the datasets are categorized into imbalanced and balanced (down-sampling). The results indicate that the balanced dataset (down-sampling) outperforms the imbalanced dataset in terms of overall accuracy, sensitivity, specificity, precision, and Area Under the Curve (AUC). Based on the analysis, the Artificial Neural Network (ANN) classifier exhibited the highest performance with a specificity 95.2% on a balanced dataset. The model excels in accurately identifying survivors, thereby minimizing false mortality predictions and is selected as the best model for predicting COVID-19 survival. Its capacity to process larger sample sizes, combined with numerous interconnected nodes, enables it to identify complex patterns and extract meaningful insights from diverse datasets, such as demographic factors. Additionally, the optimization of parameters, including the number of layers, learning rate, and activation functions, significantly contributed to its superior accuracy. The study identifies that those of chronic diseases, male, and aged 45 and above as the notable factors associated with lower survival rates among COVID-19 patients. The findings underscore the importance of completing the vaccination series by obtaining at least the second dose, as the first dose alone may not offer sufficient protection. In conclusion, this study successfully achieves its objectives by identifying the optimal dataset configuration and predictive model for forecasting COVID-19 survival based on demographic factors. This network could serve as a benchmark model classifier, offering a valuable tool to predict and promote vaccinations, as well as optimize the general healthcare system during the pandemic outbreak. The study not only contributes to the theoretical understanding of effective COVID-19 prediction but also emphasizes the practical implications of integrating advanced machine learning techniques into pandemic management strategies. Future research can build upon these findings by exploring additional machine learning techniques and considering geographical and environmental factors to further enhance the accuracy of long-term predictions.

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