POIBERT: A Transformer-based Model for the Tour Recommendation Problem

Tour itinerary planning and recommendation are challenging problems for tourists visiting unfamiliar cities. Many tour recommendation algorithms only consider factors such as the location and popularity of Points of Interest (POIs) but their solutions may not align well with the user's own preferences and other location constraints. Additionally, these solutions do not take into consideration of the users' preference based on their past POIs selection. In this paper, we propose POIBERT, an algorithm for recommending personalized itineraries using the BERT language model on POIs. POIBERT builds upon the highly successful BERT language model with the novel adaptation of a language model to our itinerary recommendation task, alongside an iterative approach to generate consecutive POIs. Our recommendation algorithm is able to generate a sequence of POIs that optimizes time and users' preference in POI categories based on past trajectories from similar tourists. Our tour recommendation algorithm is modeled by adapting the itinerary recommendation problem to the sentence completion problem in natural language processing (NLP). We also innovate an iterative algorithm to generate travel itineraries that satisfies the time constraints which is most likely from past trajectories. Using a Flickr dataset of seven cities, experimental results show that our algorithm out-performs many sequence prediction algorithms based on measures in recall, precision and F1-scores.Comment: Accepted to the 2022 IEEE International Conference on Big Data (BigData2022

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

Nanostructured Bimetallic Block Copolymers as Precursors to Magnetic FePt Nanoparticles

Phase-separated block copolymers (BCPs) that function as precursors to arrays of FePt nanoparticles (NPs) are of potential interest for the creation of media for the next-generation high-density magnetic data storage devices. A series of bimetallic BCPs has been synthesized by incorporating a complex containing Fe and Pt centers into the coordinating block of four different poly­(styrene-<i>b</i>-4-vinylpyridine)­s (PS-<i>b</i>-P4VPs, <b>P1–P4</b>). To facilitate phase separation for the resulting metalated BCPs (<b>PM1–PM4</b>), a loading of the FePt-bimetallic complex corresponding to ca. 20% was used. The bulk and thin-film self-assembly of these BCPs was studied by transmission electron microscopy (TEM) and atomic force microscopy, respectively. The spherical and cylindrical morphologies observed for the metalated BCPs corresponded to those observed for the metal-free BCPs. The products from the pyrolysis of the BCPs in bulk were also characterized by TEM, powder X-ray diffraction, and energy-dispersive X-ray spectroscopy, which indicated that the FePt NPs formed exist in an fct phase with average particle sizes of ca. 4–8 nm within a carbonaceous matrix. A comparison of the pyrolysis behavior of the metalated BCP (<b>PM3</b>), the metalated <b>P4VP</b> homopolymer (<b>PM5</b>), and the molecular model organometallic complex revealed the importance of using a nanostructured BCP approach for the synthesis of ferromagnetic FePt NPs with a smaller average NP size and a close to 1:1 Fe/Pt stoichiometric ratio

Sustainable supply chain management towards disruption and organizational ambidexterity:A data driven analysis

Balancing sustainability and disruption of supply chains requires organizational ambidexterity. Sustainable supply chains prioritize efficiency and economies of scale and may not have sufficient redundancy to withstand disruptive events. There is a developing body of literature that attempts to reconcile these two aspects. This study gives a data-driven literature review of sustainable supply chain management trends toward ambidexterity and disruption. The critical review reveals temporal trends and geographic distribution of literature. A hybrid of data-driven analysis approach based on content and bibliometric analyses, fuzzy Delphi method, entropy weight method, and fuzzy decision-making trial and evaluation laboratory is used on 273 keywords and 22 indicators obtained based on the experts’ evaluation. The most important indicators are identified as supply chain agility, supply chain coordination, supply chain finance, supply chain flexibility, supply chain resilience, and sustainability. The regions show different tendencies compared with others. Asia and Oceania, Latin America and the Caribbean, and Africa are the regions needs improvement, while Europe and North America show distinct apprehensions on supply chain network design. The main contribution of this review is the identification of the knowledge frontier, which then leads to a discussion of prospects for future studies and practical industry implementation

Canagliflozin and renal outcomes in type 2 diabetes and nephropathy

BACKGROUND Type 2 diabetes mellitus is the leading cause of kidney failure worldwide, but few effective long-term treatments are available. In cardiovascular trials of inhibitors of sodium–glucose cotransporter 2 (SGLT2), exploratory results have suggested that such drugs may improve renal outcomes in patients with type 2 diabetes. METHODS In this double-blind, randomized trial, we assigned patients with type 2 diabetes and albuminuric chronic kidney disease to receive canagliflozin, an oral SGLT2 inhibitor, at a dose of 100 mg daily or placebo. All the patients had an estimated glomerular filtration rate (GFR) of 30 to &lt;90 ml per minute per 1.73 m2 of body-surface area and albuminuria (ratio of albumin [mg] to creatinine [g], &gt;300 to 5000) and were treated with renin–angiotensin system blockade. The primary outcome was a composite of end-stage kidney disease (dialysis, transplantation, or a sustained estimated GFR of &lt;15 ml per minute per 1.73 m2), a doubling of the serum creatinine level, or death from renal or cardiovascular causes. Prespecified secondary outcomes were tested hierarchically. RESULTS The trial was stopped early after a planned interim analysis on the recommendation of the data and safety monitoring committee. At that time, 4401 patients had undergone randomization, with a median follow-up of 2.62 years. The relative risk of the primary outcome was 30% lower in the canagliflozin group than in the placebo group, with event rates of 43.2 and 61.2 per 1000 patient-years, respectively (hazard ratio, 0.70; 95% confidence interval [CI], 0.59 to 0.82; P=0.00001). The relative risk of the renal-specific composite of end-stage kidney disease, a doubling of the creatinine level, or death from renal causes was lower by 34% (hazard ratio, 0.66; 95% CI, 0.53 to 0.81; P&lt;0.001), and the relative risk of end-stage kidney disease was lower by 32% (hazard ratio, 0.68; 95% CI, 0.54 to 0.86; P=0.002). The canagliflozin group also had a lower risk of cardiovascular death, myocardial infarction, or stroke (hazard ratio, 0.80; 95% CI, 0.67 to 0.95; P=0.01) and hospitalization for heart failure (hazard ratio, 0.61; 95% CI, 0.47 to 0.80; P&lt;0.001). There were no significant differences in rates of amputation or fracture. CONCLUSIONS In patients with type 2 diabetes and kidney disease, the risk of kidney failure and cardiovascular events was lower in the canagliflozin group than in the placebo group at a median follow-up of 2.62 years

Algorithms on Constrained Sequence Alignment

One of the fundamental issues that arises in computational biology is Multiple Sequence Alignment (MSA), which needs to be addressed in many applications of Bioinformatics (e.g. study of the SARS Coronavirus and the Human Genome Project). Many algorithms have been proposed to solve the MSA problem, but often cannot incorporate users&apos; (biologists&apos;) knowledge of the functionalities or structures of these sequences into their solutions. This kind of information is very useful for an accurate and biologically meaningful alignment. The Constrained Multiple Sequence Alignment (CMSA) was proposed by Tang et al. (2002) to rectify the shortcomings of MSA by introducing a constrained sequence to represent more important residues in the sequences. Every character of the constrained sequence has to appear in an entire column in the alignment of the multiple sequences, and in the same order as in the constrained sequence

Algorithms on constrained sequence alignment

tocabstractpublished_or_final_versionComputer Science and Information SystemsMasterMaster of Philosoph