Search results

The textile industry holds immense significance in the economy of any nation, particularly in the production of synthetic yarn and fabrics. Consequently, the pursuit of acquiring high-quality products at a reduced cost has become a significant concern for countries. The primary objective of this research is to leverage data mining and data intelligence techniques to enhance and refine the production process of texturized yarn by developing an intelligent operating guide that enables the adjustment of production process parameters in the texturized yarn manufacturing process, based on the specifications of raw materials.

This research undertook a systematic literature review to explore the various factors that influence yarn quality. Data mining techniques, including deep learning, K-nearest neighbor (KNN), decision tree, Naïve Bayes, support vector machine and VOTE, were employed to identify the most crucial factors. Subsequently, an executive and dynamic guide was developed utilizing data intelligence tools such as Power BI (Business Intelligence). The proposed model was then applied to the production process of a textile company in Iran 2020 to 2021.

The results of this research highlight that the production process parameters exert a more significant influence on texturized yarn quality than the characteristics of raw materials. The executive production guide was designed by selecting the optimal combination of production process parameters, namely draw ratio, D/Y and primary temperature, with the incorporation of limiting indexes derived from the raw material characteristics to predict tenacity and elongation.

This paper contributes by introducing a novel method for creating a dynamic guide. An intelligent and dynamic guide for tenacity and elongation in texturized yarn production was proposed, boasting an approximate accuracy rate of 80%. This developed guide is dynamic and seamlessly integrated with the production database. It undergoes regular updates every three months, incorporating the selected features of the process and raw materials, their respective thresholds, and the predicted levels of elongation and tenacity.

Previous research indicates that vitamin D and omega-3 co-supplementation may benefit overall health, but current evidence regarding its effects on lipid profile remains unclear. The present systematic review and meta-analysis aimed to examine the effects of vitamin D and omega-3 co-supplementation on lipid profile (total cholesterol [TC], low-density lipoprotein [LDL], triglyceride [TG] and high-density lipoprotein [HDL]) in adults.

In this systematic review and meta-analysis, relevant studies were obtained by searching the PubMed, Scopus and Web of Science databases (from inception to January 2022). Weighted mean differences and 95% confidence intervals were estimated via a random-effects model. Heterogeneity, sensitivity analysis and publication bias were reported using standard methods.

Pooled analysis of six randomized controlled trials (RCTs) revealed that vitamin D and omega-3 co-supplementation yielded significant reductions in TG (p = 0.631). A pooled analysis of five trials indicated a significant association between omega-3 and vitamin D treatment and reductions in TC (p = 0.001) and LDL (p = 0.001). Although, pooled analyses of omega-3 and vitamin D did not significantly affect HDL.

The findings suggest that vitamin D and omega-3 co-supplementation lowers TG, TC and LDL in adults. Future, large-scale, RCTs on various populations are needed to elucidate further beneficial effects of vitamin D and omega-3 co-supplementation on lipid profile and establish guidelines for clinical practice.

In this study, a novel framework based on deep learning models is presented to assess energy and environmental performance of a given building space layout, facilitating the decision-making process at the early-stage design.

A methodology using an image-based deep learning model called pix2pix is proposed to predict the overall daylight, energy and ventilation performance of a given residential building space layout. The proposed methodology is then evaluated by being applied to 300 sample apartment units in Tehran, Iran. Four pix2pix models were trained to predict illuminance, spatial daylight autonomy (sDA), primary energy intensity and ventilation maps. The simulation results were considered ground truth.

The results showed an average structural similarity index measure (SSIM) of 0.86 and 0.81 for the predicted illuminance and sDA maps, respectively, and an average score of 88% for the predicted primary energy intensity and ventilation representative maps, each of which is outputted within three seconds.

The proposed framework in this study helps upskilling the design professionals involved with the architecture, engineering and construction (AEC) industry through engaging artificial intelligence in human–computer interactions. The specific novelties of this research are: first, evaluating indoor environmental metrics (daylight and ventilation) alongside the energy performance of space layouts using pix2pix model, second, widening the assessment scope to a group of spaces forming an apartment layout at five different floors and third, incorporating the impact of building context on the intended objectives.

This paper aims to explore the level of hibah knowledge among Muslims in Kedah and investigate its determinants, consisting of education level, education stream, religiosity, social influence and social media.

This study is quantitative in nature. Questionnaires were distributed to collect data from Muslims in Alor Setar, Kedah. In total, 195 questionnaires were collected and data were analyzed using descriptive analysis, correlation analysis and multiple regression analysis.

The study finds that Muslims in Alor Setar, Kedah have good knowledge of hibah. Further, education stream, religiosity, social influence and social media were identified as significant factors that influence their knowledge of hibah.

The first limitation is its narrow focus in surveying Muslims only in Alor Setar, Kedah. The second limitation is the limited number of determinants used in investigating hibah knowledge among Muslims and the techniques used in analyzing the data. Despite these limitations, the study’s findings provide invaluable insights into the factors influencing hibah knowledge among Muslims in Alor Setar, Kedah.

This study provides insights regarding the significant personal factors and environmental factors to increase Muslims’ knowledge of hibah. The link between the Islamic education stream and hibah knowledge provides a clear indication that Islamic education can curb the economic problems caused by the substantial amounts of frozen and unclaimed assets in Malaysia. A significant relationship between the environmental factors (social influence and social media) and hibah knowledge also implies that the government and private agencies related to Islamic estate planning and management may use these significant determinants as part of the marketing strategy to increase the usage of hibah as an alternative tool for estate planning.

This study contributes to a better understanding of Muslims’ knowledge about hibah. The government and related agencies in Islamic estate planning and management can now gain better insights into Muslims’ level of knowledge about hibah and the factors influencing their knowledge of hibah as an effective tool for Islamic estate planning and management. Hence, more effective strategies can be recommended to enhance the knowledge of Muslims on hibah. The findings of this study should be of value to the government in its effort to address the increasing number of frozen estates in Malaysia.