Dense molecular clumps in the envelope of the yellow hypergiant IRC+10420

The circumstellar envelope of the hypergiant star IRC+10420 has been traced as far out in SiO J=2-1 as in CO J = 1-0 and CO J = 2-1, in dramatic contrast with the centrally condensed (thermal) SiO- but extended CO-emitting envelopes of giant and supergiant stars. Here, we present an observation of the circumstellar envelope in SiO J=1-0 that, when combined with the previous observation in {\sioii}, provide more stringent constraints on the density of the SiO-emitting gas than hitherto possible. The emission in SiO peaks at a radius of $\sim$2\arcsec\ whereas that in SiO J=2-1 emission peaks at a smaller radius of $\sim$1\arcsec, giving rise to their ring-like appearances. The ratio in brightness temperature between SiO J=1-0 and SiO J=2-1 decreases from a value well above unity at the innermost measurable radius to about unity at radius of $\sim$2\arcsec, beyond which this ratio remains approximately constant. Dividing the envelope into three zones as in models for the CO J = 1-0 and CO J = 2-1 emission, we show that the density of the SiO-emitting gas is comparable with that of the CO-emitting gas in the inner zone, but at least an order of magnitude higher by comparison in both the middle and outer zones. The SiO-emitting gas therefore originates from dense clumps, likely associated with the dust clumps seen in scattered optical light, surrounded by more diffuse CO-emitting interclump gas. We suggest that SiO molecules are released from dust grains due to shock interactions between the dense SiO-emitting clumps and the diffuse CO-emitting interclump gas.Comment: Accepted for publication in Ap

Determinants of dividend policy in Malaysian banking sector

The purpose of this study is to examine the factors that influence dividend policy in the Malaysian banking sector. For this purpose, a sample of 19 commercial banks in Malaysia were selected including eight domestic banks and eleven foreign banks. Ordinary Least Squares (OLS) Regression was used to examine the impact of leverage, profitability, liquidity, past dividend, size of firm, sales growth, corporate tax, and business risk on dividend policy in the banking sector over a period of five years from 2007 to 2011. The empirical results of this study show that profitability, liquidity, past dividend, size of firm, and sales growth have a positive relationship with dividend payout. Meanwhile, leverage, corporate tax and business risk have a negative relationship with dividend payout. The results of the analysis indicated that profitability, past dividend, size of firm and corporate tax were the major factors that affected dividend payment decision. Furthermore, we found that domestic banks had higher dividend payout than foreign bank

Disseminated eruptive giant mollusca contagiosa in an adult psoriasis patient during efalizumab therapy

Molluscum contagiosum is a common viral skin infection in children with atopic diathesis and not rare in HIV patients. We report a 45-year-old psoriasis patient who developed eruptive mollusca contagiosa during an antipsoriatic treatment with efalizumab. Copyright (C) 2008 S. Karger AG, Basel

So near and yet so far: Harmonic radar reveals reduced homing ability of nosema infected honeybees

Pathogens may gain a fitness advantage through manipulation of the behaviour of their hosts. Likewise, host behavioural changes can be a defence mechanism, counteracting the impact of pathogens on host fitness. We apply harmonic radar technology to characterize the impact of an emerging pathogen - Nosema ceranae (Microsporidia) - on honeybee (Apis mellifera) flight and orientation performance in the field. Honeybees are the most important commercial pollinators. Emerging diseases have been proposed to play a prominent role in colony decline, partly through sub-lethal behavioural manipulation of their hosts. We found that homing success was significantly reduced in diseased (65.8%) versus healthy foragers (92.5%). Although lost bees had significantly reduced continuous flight times and prolonged resting times, other flight characteristics and navigational abilities showed no significant difference between infected and non-infected bees. Our results suggest that infected bees express normal flight characteristics but are constrained in their homing ability, potentially compromising the colony by reducing its resource inputs, but also counteracting the intra-colony spread of infection. We provide the first high-resolution analysis of sub-lethal effects of an emerging disease on insect flight behaviour. The potential causes and the implications for both host and parasite are discussed

So near and yet so far: Harmonic radar reveals reduced homing ability of nosema infected honeybees

Pathogens may gain a fitness advantage through manipulation of the behaviour of their hosts. Likewise, host behavioural changes can be a defence mechanism, counteracting the impact of pathogens on host fitness. We apply harmonic radar technology to characterize the impact of an emerging pathogen - Nosema ceranae (Microsporidia) - on honeybee (Apis mellifera) flight and orientation performance in the field. Honeybees are the most important commercial pollinators. Emerging diseases have been proposed to play a prominent role in colony decline, partly through sub-lethal behavioural manipulation of their hosts. We found that homing success was significantly reduced in diseased (65.8%) versus healthy foragers (92.5%). Although lost bees had significantly reduced continuous flight times and prolonged resting times, other flight characteristics and navigational abilities showed no significant difference between infected and non-infected bees. Our results suggest that infected bees express normal flight characteristics but are constrained in their homing ability, potentially compromising the colony by reducing its resource inputs, but also counteracting the intra-colony spread of infection. We provide the first high-resolution analysis of sub-lethal effects of an emerging disease on insect flight behaviour. The potential causes and the implications for both host and parasite are discussed

SBTRec- A Transformer Framework for Personalized Tour Recommendation Problem with Sentiment Analysis

When traveling to an unfamiliar city for holidays, tourists often rely on guidebooks, travel websites, or recommendation systems to plan their daily itineraries and explore popular points of interest (POIs). However, these approaches may lack optimization in terms of time feasibility, localities, and user preferences. In this paper, we propose the SBTRec algorithm: a BERT-based Trajectory Recommendation with sentiment analysis, for recommending personalized sequences of POIs as itineraries. The key contributions of this work include analyzing users' check-ins and uploaded photos to understand the relationship between POI visits and distance. We introduce SBTRec, which encompasses sentiment analysis to improve recommendation accuracy by understanding users' preferences and satisfaction levels from reviews and comments about different POIs. Our proposed algorithms are evaluated against other sequence prediction methods using datasets from 8 cities. The results demonstrate that SBTRec achieves an average F1 score of 61.45%, outperforming baseline algorithms. The paper further discusses the flexibility of the SBTRec algorithm, its ability to adapt to different scenarios and cities without modification, and its potential for extension by incorporating additional information for more reliable predictions. Overall, SBTRec provides personalized and relevant POI recommendations, enhancing tourists' overall trip experiences. Future work includes fine-tuning personalized embeddings for users, with evaluation of users' comments on POIs,~to further enhance prediction accuracy

BTRec: BERT-Based Trajectory Recommendation for Personalized Tours

An essential task for tourists having a pleasant holiday is to have a well-planned itinerary with relevant recommendations, especially when visiting unfamiliar cities. Many tour recommendation tools only take into account a limited number of factors, such as popular Points of Interest (POIs) and routing constraints. Consequently, the solutions they provide may not always align with the individual users of the system. We propose an iterative algorithm in this paper, namely: BTREC (BERT-based Trajectory Recommendation), that extends from the POIBERT embedding algorithm to recommend personalized itineraries on POIs using the BERT framework. Our BTREC algorithm incorporates users' demographic information alongside past POI visits into a modified BERT language model to recommend a personalized POI itinerary prediction given a pair of source and destination POIs. Our recommendation system can create a travel itinerary that maximizes POIs visited, while also taking into account user preferences for categories of POIs and time availability. Our recommendation algorithm is largely inspired by the problem of sentence completion in natural language processing (NLP). Using a dataset of eight cities of different sizes, our experimental results demonstrate that our proposed algorithm is stable and outperforms many other sequence prediction algorithms, measured by recall, precision, and F1-scores.Comment: RecSys 2023, Workshop on Recommenders in Touris

AD-Link: An adaptive approach for user identity linkage

National Research Foundation (NRF) Singapore under its International Research Centres in Singapore Funding Initiativ