Effects of different wall materials on the physicochemical properties and oxidative stability of spray-dried microencapsulated red-fleshed Pitaya (Hylocereus polyrhizus) seed oil.

The aim of this research was to investigate the influence of the composition of the wall material on the encapsulation and stability of microencapsulated red-fleshed pitaya seed oil. Hylocereus polyrhizus seed oil was homogenized with various wall material solutions at a core/wall material ratio of 0.33 and was microencapsulated by spray-drying. The microstructure and morphology of pitaya seed oil powder (PSOP) were observed using a scanning electron microscope (SEM). PSOP encapsulated with gum Arabic exhibited a lower degree of microencapsulation efficiency (MEE; 77.61–85.3%) compared to PSOP encapsulated with proteinaceous bases (90.12–98.06%). The study on oil retention revealed that sodium caseinate > whey protein > gum Arabic as effective wall materials for pitaya seed oil encapsulation. The effects of different wall systems on the oxidation stability of PSOP were studied under accelerated storage conditions; the peroxide value (POV) was determined throughout the test interval at several storage times. This study indicates that the use of lactose as wall material is able to increase the oxidation stability of PSOP; however, further research is needed to evaluate its antioxidative retention toward the oxidative stability of PSOP

LingML: Linguistic-Informed Machine Learning for Enhanced Fake News Detection

Nowadays, Information spreads at an unprecedented pace in social media and discerning truth from misinformation and fake news has become an acute societal challenge. Machine learning (ML) models have been employed to identify fake news but are far from perfect with challenging problems like limited accuracy, interpretability, and generalizability. In this paper, we enhance ML-based solutions with linguistics input and we propose LingML, linguistic-informed ML, for fake news detection. We conducted an experimental study with a popular dataset on fake news during the pandemic. The experiment results show that our proposed solution is highly effective. There are fewer than two errors out of every ten attempts with only linguistic input used in ML and the knowledge is highly explainable. When linguistics input is integrated with advanced large-scale ML models for natural language processing, our solution outperforms existing ones with 1.8% average error rate. LingML creates a new path with linguistics to push the frontier of effective and efficient fake news detection. It also sheds light on real-world multi-disciplinary applications requiring both ML and domain expertise to achieve optimal performance.Comment: 7 page

Written and spoken corpus of real and fake social media postings about COVID-19

This study investigates the linguistic traits of fake news and real news. There are two parts to this study: text data and speech data. The text data for this study consisted of 6420 COVID-19 related tweets re-filtered from Patwa et al. (2021). After cleaning, the dataset contained 3049 tweets, with 2161 labeled as 'real' and 888 as 'fake'. The speech data for this study was collected from TikTok, focusing on COVID-19 related videos. Research assistants fact-checked each video's content using credible sources and labeled them as 'Real', 'Fake', or 'Questionable', resulting in a dataset of 91 real entries and 109 fake entries from 200 TikTok videos with a total word count of 53,710 words. The data was analysed using the Linguistic Inquiry and Word Count (LIWC) software to detect patterns in linguistic data. The results indicate a set of linguistic features that distinguish fake news from real news in both written and speech data. This offers valuable insights into the role of language in shaping trust, social media interactions, and the propagation of fake news.Comment: 9 pages, 3 table

Compositional and thermal characteristics of palm olein-based diacylglycerol in blends with palm super olein

Palm olein-based diacylglycerol (POL-DAG) was blended with palm super olein (POoo) in various concentrations (10–90%), with increments of 10% (wt/wt) POL-DAG. The physical and chemical characteristics, i.e., iodine value, acylglycerol content, fatty acid composition, melting and crystallization profiles and solid fat content, for POL-DAG, POoo and their binary blends were evaluated. The mid-infrared FTIR was used to determine the absorption bands of the different concentrations of the oil blends. Only slight differences of FAC and IV were observed. POL-DAG:POoo blends showed significant changes (p < 0.05) in DAG content and decreases in TAG content with increasing POL-DAG content. The DSC thermograms showed that the addition of different concentrations of POL-DAG changed the melting and crystallization behavior of the oil blends (POL-DAG:POoo). The crystallization onset point increased (p < 0.05) with an increasing POL-DAG concentration (10–90%). POL-DAG has the same absorption bands as POoo, with the exception of several minor peaks that appeared at (I) 2954 cm− 1, (II) 1267 cm− 1, (III) 1199 cm− 1, (IV) 1222 cm− 1 and (V) 966 cm− 1. This study will provide essential information for the palm oil industry to identify the most suitable POL-DAG blends with desirable physicochemical properties for food application purposes

Stakeholder’s Perception on Malaysia’s Edu-Tourism Sustainability Performance

This study shows that Edu-Tourism is manageable by considering various sustainability indicators that are identified from the evaluation of international students’ satisfaction. Differ from past sustainable tourism researches which usually investigate the tourism supply side of stakeholders (economics, social and environmental), this study adds value to the sustainable tourism literature by examining the tourism demand side of stakeholders (tourist satisfaction). Generally, Malaysia Edu-Tourism is operating within “potentially sustainable” category, improvements are needed to achieve “sustainable” status. Six indicators (reputation of university, perceived faculty academic competence, student-student interactions, perceived quality of faculty communications, climate and study environment, information availability) are moderately performed. Best performer being perceived quality of electronic communications and student-admin interaction. Worst performer being social links and geographic proximity. A quantitative research approach was used in this study where questionnaires were distributed for data collection. The sample size of the study consisted of 264 international students of different public and private sector universities. Structural Equation Model using SMARTPLS was used to identify significant indicators and then descriptive analysis was performed to evaluate sustainability of Malaysia Edu-Tourism sector. The article concludes with a discussion of the research and implications of the study along with suggestions for future research

Coherent deglacial changes in western Atlantic Ocean circulation

Abrupt climate changes in the past have been attributed to variations in Atlantic Meridional Overturning Circulation (AMOC) strength. However, the exact timing and magnitude of past AMOC shifts remain elusive, which continues to limit our understanding of the driving mechanisms of such climate variability. Here we show a consistent signal of the 231Pa/230Th proxy that reveals a spatially coherent picture of western Atlantic circulation changes over the last deglaciation, during abrupt millennial-scale climate transitions. At the onset of deglaciation, we observe an early slowdown of circulation in the western Atlantic from around 19 to 16.5 thousand years ago (ka), consistent with the timing of accelerated Eurasian ice melting. The subsequent weakened AMOC state persists for over a millennium (~16.5–15 ka), during which time there is substantial ice rafting from the Laurentide ice sheet. This timing indicates a role for melting ice in driving a two-step AMOC slowdown, with a positive feedback sustaining continued iceberg calving and climate change during Heinrich Stadial 1NERC | Ref. NE/K008536/

Ontology reuse for multiagent systems development through pattern classification

Ontologies play a crucial role in multiagent systems (MASs) development, especially for domain knowledge modeling, interaction specifications, and behavioral aspect representation. Domain‐specific ontologies can be developed in an ad hoc or systematic manner through the incorporation of ontology development steps on the basis of agent‐oriented methodologies. Developing such ontologies, however, is challenging because of the extensive amounts of knowledge and experience required. Moreover, since many ontologies cater for very specific domains, the question arises of whether some can be reused for faster systems development. This paper attempts to answer this question by proposing an ontology pattern classification scheme to allow the reuse of existing ontology knowledge for MAS development. Specifically, ontology patterns relevant to the design problem at hand are identified through the pattern classification scheme. These patterns are then reused and shared among agent software communities during the system development phase. The effectiveness of the proposed approach is validated using a restaurant‐finder MAS case study. Our findings suggest that utilization of the classified ontology patterns reduces development time and complexity when dealing with domain‐specific applications. The scheme also seems useful for software practitioners, where searching and reusing the patterns can easily be done during the analysis, design, and implementation of MAS development

Corrigendum to “Integrated miniature fluorescent probe to leverage the sensing potential of ZnO quantum dots for the detection of copper (II) ions”

Quantum dots are fluorescent semiconductor nanoparticles that can be utilised for sensing applications. This paper evaluates the ability to leverage their analytical potential using an integrated fluorescent sensing probe that is portable, cost effective and simple to handle. ZnO quantum dots were prepared using the simple sol-gel hydrolysis method at ambient conditions and found to be significantly and specifically quenched by copper (II) ions. This ZnO quantum dots system has been incorporated into an in-house developed miniature fluorescent probe for the detection of copper (II) ions in aqueous medium. The probe was developed using a low power handheld black light as excitation source and three photo-detectors as sensor. The sensing chamber placed between the light source and detectors was made of 4-sided clear quartz windows. The chamber was housed within a dark compartment to avoid stray light interference. The probe was operated using a microcontroller (Arduino Uno Revision 3) that has been programmed with the analytical response and the working algorithm of the electronics. The probe was sourced with a 12V rechargeable battery pack and the analytical readouts were given directly using a LCD display panel. Analytical optimisations of the ZnO quantum dots system and the probe have been performed and further described. The probe was found to have a linear response range up to 0.45mM (R(2)=0.9930) towards copper (II) ion with a limit of detection of 7.68×10(-7)M. The probe has high repeatable and reliable performance