To share or not? Factors influencing word of mouth communication

The objective of this study is to determine the factors that will influence on word of mouth communication among mobile phone users. In this study, five factors which are perceived value, perceived quality, customers’ satisfaction, brand love and brand trust are examined to determine whether these factors influenced word of mouth communication. Hypothesized relationships are tested using survey responses from a sample of 393 respondents. This study was conducted among young adults from Universiti Utara Malaysia (UUM), Sintok Kedah. The data were analysed using Statistical Package for the Social Sciences (SPSS) version 19.0. The methods used in analysing the data are Normality test, Reliability test, Descriptive Analysis, ANOVA, Independent Sample T-Test, Pearson Correlation Analysis and Multiple Regression Analysis. The findings indicated that all the five independent variables have a strong positive relationship with word of mouth. In addition, the results showed that brand love had the strongest significant positive relationship with word of mouth communication with correlation value of 0.802, followed by brand trust with correlation value of 0.793. Pearson correlation analysis that was conducted showed that brand trust and brand love are the strongest factors influencing word of mouth communication

Non-close-packed breath figures via ion-partitioning-mediated self-assembly

We report a one-step method of forming non-close-packed (NCP) pore arrays of micro- and sub-micropores using chloroform-based solutions of polystyrene acidified with hydrogen bromide for breath figure (BF) patterning. As BF patterning takes place, water vapor condenses onto the polystyrene solution, forming water droplets on the solution surface. Concurrently, preferential ion partitioning of hydrogen bromide leads to positively charged water droplets, which experience interdroplet electrostatic repulsion. Self-organization of charged water droplets because of surface flow and subsequent evaporation of the droplet templates result in ordered BF arrays with pore separation/diameter (L/D) ratios of up to 16.5. Evidence from surface potential scans show proof for preferential ion partitioning of HBr. Radial distribution functions and Voronoi polygon analysis of pore arrays show that they possess a high degree of conformational order. Past fabrication methods of NCP structures typically require multi-step processes. In contrast, we have established a new route for facile self-assembly of previously inaccessible patterns, which comprises of only a single operational step

Fatigue and Fracture Behaviour of Laser Powder Bed Fusion Stainless Steel 316L: Influence of Processing Parameters

The laser powder bed fusion (L-PBF) process involves a large number of processing parameters. Extending the intricate relationship between processing and structure to mechanical performance is essential for structural L-PBF materials. The high cycle fatigue properties of L-PBF parts are very sensitive to process-induced porosities which promote premature failure through the crack initiation mechanisms. Results from this work show that for stainless steel 316L, porosity does not impinge on the high cycle fatigue properties when processing is kept within a ±30% tolerance band. In this ‘optimum’ processing region, crack initiation takes place due to defects at the solidification microstructure level. Beyond the ‘optimum’ processing region, over-melting and under-melting can lead to porosity-driven cracking and inferior fatigue resistance. In addition, regardless of the processing condition, fatigue resistance was found to follow a direct linear relationship with ductility and tensile strength in the low and high stress fatigue regimes respectively.Economic Development Board (EDB)Accepted versionThis work was supported by the Singapore Economic Development Board (EDB) Industrial Postgraduate Programme (IPP)

To share or not? Factors influencing word of mouth communication

In the consumer market, loyalty is an essential goal and a key element for a company to build a long-term sustainability and growth.Loyal consumers are more willing to make recommendations, advice and suggestions about a firms products to their friends or relatives through Word of Mouth (WOM) communication.In this relation, 90% of the advertising is viewed by consumers as non-credible while 90% of word of mouth communicated is treated as credible (Lee, Mullen and Fraedrich, 2011).The objective of this study is to determine the factors influencing word of mouth (WOM) communication among mobile phone users. Five factors which are Perceived Value, Perceived Quality, Customer Satisfaction, Brand Love and Brand Trust are examined to determine whether these factors influenced word of mouth communication.This study was conducted among 393 respondents from Universiti Utara Malaysia (UUM), Sintok Kedah.The methods used to analyse the data are Reliability test, Descriptive Analysis, ANOVA, Independent Sample T-Test, Pearson Correlation Analysis and Multiple Regression Analysis.The findings indicated that all these five factors have a strong positive relationship with Word of Mouth communication.The results also showed that Brand Love had the strongest significant positive relationship with Word of Mouth communication with correlation value of 0.802, followed by Brand Trust with correlation value of 0.793.In addition, Pearson correlation analysis results showed that Brand Trust and Brand Love are factors that strongly influenced Word of Mouth communication. This study provides an understanding of the factors that drives word of mouth communication by a consumer.Word of mouth can be a main potential source for business to grow and hence it is very important for marketers to understand it

A Novel Respiratory Rate Estimation Algorithm from Photoplethysmogram Using Deep Learning Model

Respiratory rate (RR) is a critical vital sign that can provide valuable insights into various medical conditions, including pneumonia. Unfortunately, manual RR counting is often unreliable and discontinuous. Current RR estimation algorithms either lack the necessary accuracy or demand extensive window sizes. In response to these challenges, this study introduces a novel method for continuously estimating RR from photoplethysmogram (PPG) with a reduced window size and lower processing requirements. To evaluate and compare classical and deep learning algorithms, this study leverages the BIDMC and CapnoBase datasets, employing the Respiratory Rate Estimation (RRest) toolbox. The optimal classical techniques combination on the BIDMC datasets achieves a mean absolute error (MAE) of 1.9 breaths/min. Additionally, the developed neural network model utilises convolutional and long short-term memory layers to estimate RR effectively. The best-performing model, with a 50% train–test split and a window size of 7 s, achieves an MAE of 2 breaths/min. Furthermore, compared to other deep learning algorithms with window sizes of 16, 32, and 64 s, this study’s model demonstrates superior performance with a smaller window size. The study suggests that further research into more precise signal processing techniques may enhance RR estimation from PPG signals

From Plate to Prevention: A Dietary Nutrient-aided Platform for Health Promotion in Singapore

Singapore has been striving to improve the provision of healthcare services to her people. In this course, the government has taken note of the deficiency in regulating and supervising people's nutrient intake, which is identified as a contributing factor to the development of chronic diseases. Consequently, this issue has garnered significant attention. In this paper, we share our experience in addressing this issue and attaining medical-grade nutrient intake information to benefit Singaporeans in different aspects. To this end, we develop the FoodSG platform to incubate diverse healthcare-oriented applications as a service in Singapore, taking into account their shared requirements. We further identify the profound meaning of localized food datasets and systematically clean and curate a localized Singaporean food dataset FoodSG-233. To overcome the hurdle in recognition performance brought by Singaporean multifarious food dishes, we propose to integrate supervised contrastive learning into our food recognition model FoodSG-SCL for the intrinsic capability to mine hard positive/negative samples and therefore boost the accuracy. Through a comprehensive evaluation, we present performance results of the proposed model and insights on food-related healthcare applications. The FoodSG-233 dataset has been released in https://foodlg.comp.nus.edu.sg/

Security in Process: Detecting Attacks in Industrial Process Data

Due to the fourth industrial revolution, industrial applications make use of the progress in communication and embedded devices. This allows industrial users to increase efficiency and manageability while reducing cost and effort. Furthermore, the fourth industrial revolution, creating the so-called Industry 4.0, opens a variety of novel use and business cases in the industrial environment. However, this progress comes at the cost of an enlarged attack surface of industrial companies. Operational networks that have previously been phyiscally separated from public networks are now connected in order to make use of new communication capabilites. This motivates the need for industrial intrusion detection solutions that are compatible to the long-term operation machines in industry as well as the heterogeneous and fast-changing networks. In this work, process data is analysed. The data is created and monitored on real-world hardware. After a set up phase, attacks are introduced into the systems that influence the process behaviour. A time series-based anomaly detection approach, the Matrix Profiles, are adapted to the specific needs and applied to the intrusion detection. The results indicate an applicability of these methods to detect attacks in the process behaviour. Furthermore, they are easily integrated into existing process environments. Additionally, one-class classifiers One-Class Support Vector Machines and Isolation Forest are applied to the data without a notion of timing. While Matrix Profiles perform well in terms of creating and visualising results, the one-class classifiers perform poorly

Risk association, linkage disequilibrium, and haplotype analyses of ß-like globin gene polymorphisms with malaria risk in the Sabah population of Malaysian Borneo

Single nucleotide polymorphisms (SNPs) in the β-like globin gene of the human hosts to the risk of malaria are unclear. Therefore, this study investigates these associations in the Sabah population, with a high incidence of malaria cases. In brief, DNA was extracted from 188 post-diagnostic blood samples infected with Plasmodium parasites and 170 healthy controls without a history of malaria. Genotyping of the β-like globin C-158T, G79A, C16G, and C-551T SNPs was performed using a polymerase chain reaction-restriction fragment length polymorphism approach. Risk association, linkage disequilibrium (LD), and haplotype analyses of these SNPs were assessed. This study found that the variant allele in the C-158T and C16G SNPs were protective against malaria infections by 0.5-fold, while the variant allele in the G79A SNP had a 6-fold increased risk of malaria infection. No SNP combination was in perfect LD, but several haplotypes (CGCC, CGCT, and CGGC) were identified to link with different correlation levels of malaria risk in the population. In conclusion, the C-158T, G79A, and C16G SNPs in the β-like globin gene are associated with the risk of malaria. The haplotypes (CGCC, CGCT, and CGGC) identified in this study could serve as biomarkers to estimate malaria risk in the population. This study provides essential data for the design of malaria control and management strategies

Cardiovascular Outcomes in Acute Coronary Syndrome and Malnutrition: A Meta-Analysis of Nutritional Assessment Tools

Background: There is emerging evidence that malnutrition is associated with poor prognosis among patients with acute coronary syndrome (ACS). // Objectives: This study seeks to elucidate the prognostic impact of malnutrition in patients with ACS and provide a quantitative review of most commonly used nutritional assessment tools. // Methods: Medline and Embase were searched for studies reporting outcomes in patients with malnutrition and ACS. Nutritional screening tools of interest included the Prognostic Nutrition Index, Geriatric Nutritional Risk Index, and Controlling Nutritional Status. A comparative meta-analysis was used to estimate the risk of all-cause mortality and cardiovascular events based on the presence of malnutrition and stratified according to ACS type, ACS intervention, ethnicity, and income. // Results: Thirty studies comprising 37,303 patients with ACS were included, of whom 33.5% had malnutrition. In the population with malnutrition, the pooled mortality rate was 20.59% (95% CI: 14.95%-27.67%). Malnutrition was significantly associated with all-cause mortality risk after adjusting for confounders including age and left ventricular ejection fraction (adjusted HR: 2.66, 95% CI: 1.78-3.96, P = 0.004). There was excess mortality in the group with malnutrition regardless of ACS type (P = 0.132), ethnicity (P = 0.245), and income status (P = 0.058). Subgroup analysis demonstrated no statistically significant difference in mortality risk between individuals with and without malnutrition (P = 0.499) when using Controlling Nutritional Status (OR: 7.80, 95% CI: 2.17-28.07, P = 0.011), Geriatric Nutritional Risk Index (OR: 4.30, 95% CI: 2.78-6.66, P < 0.001), and Prognostic Nutrition Index (OR: 4.67, 95% CI: 2.38-9.17, P = 0.023). // Conclusions: Malnutrition was significantly associated with all-cause mortality risk following ACS, regardless of ACS type, ethnicity, and income status, underscoring the importance of screening and interventional strategies for patients with malnutrition

Highly Efficient Thermally Co-evaporated Perovskite Solar Cells and Mini-modules

The rapid improvement in the power conversion efficiency (PCE) of perovskite solar cells (PSCs) has prompted interest in bringing the technology toward commercialization. Capitalizing on existing industrial processes facilitates the transition from laboratory to production lines. In this work, we prove the scalability of thermally co-evaporated MAPbI3 layers in PSCs and mini-modules. With a combined strategy of active layer engineering, interfacial optimization, surface treatments, and light management, we demonstrate PSCs (0.16 cm2 active area) and mini-modules (21 cm2 active area) achieving record PCEs of 20.28% and 18.13%, respectively. Un-encapsulated PSCs retained ∼90% of their initial PCE under continuous illumination at 1 sun, without sample cooling, for more than 100 h. Looking toward tandem and building integrated photovoltaic applications, we have demonstrated semi-transparent mini-modules and colored PSCs with consistent PCEs of ∼16% for a set of visible colors. Our work demonstrates the compatibility of perovskite technology with industrial processes and its potential for next-generation photovoltaics