A Simple Queuing Model for Molecular Communications Receivers

The complexity of molecular communications system, involving a massive number of interacting entities, makes scalability a fundamental property of simulators and modeling tools. A typical scenario is that of targeted drug delivery systems, which makes use of biological nanomachines close to a biological target, able to release molecules in the diseased area. In this paper, we propose a simple although reliable receiver model for diffusion-based molecular communication systems tackling the time needed for analyzing such a system. The proposed model consists of using an equivalent markovian queuing model, which reproduces the aggregate behavior of thousands of receptors spread over the receiver surface. It takes into account not only the fact that the absorption of molecules can occur only through receptors, but also that absorption is not an instantaneous process, and may require a significant time during which the receptor is not available to bind to other molecules. Our results, expressed in terms of number of absorbed molecules and average number of busy receptors, demonstrate that the proposed approach is in good agreement with results obtained through particle-based simulations of a large number of receptors, although the time taken for obtaining the results with the proposed model is order of magnitudes lower than the simulation time. We believe that this model can be the precursor of novel class of models based on similar principles that allow realizing reliable simulations of much larger systems.

JUMRv1: A Sentiment Analysis Dataset for Movie Recommendation

Nowadays, we can observe the applications of machine learning in every field, ranging from the quality testing of materials to the building of powerful computer vision tools. One such recent application is the recommendation system, which is a method that suggests products to users based on their preferences. In this paper, our focus is on a specific recommendation system called movie recommendation. Here, we make use of user reviews of movies in order to establish a general outlook about the movie and then use that outlook to recommend that movie to other users. However, a huge number of available reviews has baffled sophisticated review systems. Consequently, there is a need to find a method of extracting meaningful information from the available reviews and use that in classifying a movie review and predicting the sentiment in each one. In a typical scenario, a review can either be positive, negative, or indifferent about a movie. However, the available research articles in the field mainly consider this as a two-class classification problem—positive and negative. The most popular work in this field was performed on Stanford and Rotten Tomatoes datasets, which are somewhat outdated. Our work is based on self-scraped reviews from the IMDB website, and we have annotated the reviews into one of the three classes—positive, negative, and neutral. Our dataset is called JUMRv1—Jadavpur University Movie Recommendation dataset version 1. For the evaluation of JUMRv1, we took an exhaustive approach by testing various combinations of word embeddings, feature selection methods, and classifiers. We also analysed the performance trends, if there were any, and attempted to explain them. Our work sets a benchmark for movie recommendation systems that is based on the newly developed dataset using a three-class sentiment classification.

A Survey on Multi Objective Optimization Challenges in Swarm Intelligence

Various challenges in real life are multi-objective and conflicting (i.e., alter concurrent optimization). This implies that a single objective is optimized based on another’s cost. The Multi-Objective Optimization (MOO) issues are challenging but potentially realistic, and due to their wide-range application, optimization challenges have widely been analyzed by research with distinct scholarly bases. Resultantly, this has yielded distinct approaches for mitigating these challenges. There is a wide-range literature concerning the approaches used to handle MOO challenges. It is important to keep in mind that each technique has its pros and limitations, and there is no optimum alternative for cure searchers in a typical scenario. The MOO challenges can be identified in various segments e.g., path optimization, airplane design, automobile design and finance, among others. This contribution presents a survey of prevailing MOO challenges and swarm intelligence approaches to mitigate these challenges. The main purpose of this contribution is to present a basis of understanding on MOO challenges.

Real-time object detection technology in railway operations

Development of new technology to enhance train operability, in particular during manual driving by real-time object detection on track, is one of the rising trends in the railway industry. The function of object detection can provide train operators with reminder alerts whenever there is an object detected close to a train, e.g. a defined distance from the train. In this paper, a two-stage vision-based method is proposed to achieve this goal. At first, the Targets Generation Stage focuses on extracting all potential targets by identifying the centre points of targets. Meanwhile, the Targets Reconfirmation Stage is further adopted to re-analyse the potential targets from the previous stage to filter out incorrect potential targets in the output. The experiment and evaluation result shows that the proposed method achieved an Average Precision (AP) of 0.876 and 0.526 respectively under typical scenario sub-groups and extreme scenario sub-groups of the data set collected from a real railway environment at the methodological level. Furthermore, at the application level, high performance with the False Alarm Rate (FAR) of 0.01% and Missed Detection Rate (MDR) of 0.94%, which is capable of practical application, was achieved during the operation in the Tsuen Wan Line (TWL) in Hong Kong.

THE RISK OF HAVING TO PAY DOUBLE ESTATE AGENT’S COMMISSION

This note addresses the question whether a seller who mandates more than one estate agent to find a buyer faces the risk of having to pay more than one commission in circumstances where a sale materialises and (a) it is not entirely clear which estate agent engaged by the seller was the effective cause of the sale; or (b) the sale agreement signed by the seller stipulates that commission is payable to one of the estate agents but another estate agent was in fact the effective cause of the transaction. The typical scenario is where the seller gives two estate agents (A and B) an identical mandate, and each agent subsequently shows the property to the same prospective purchaser. They both explain the property’s features, the finance available, their assessment of the market value of the property, and so on. The buyer and seller are keen to buy and sell. One of the following now occurs: (a) A sale transaction is negotiated between the seller and buyer directly, without any further intervention on the part of any of the two estate agents. The seller’s input is minimal, but the facts are such that one cannot determine which estate agent was the effective cause since the value of their efforts towards the sale was more or less identical. (b) A sale is effected by one of the estate agents (say A), using that agent’s standard pre-printed sale agreement containing the usual commission clause whereby the seller agrees to pay commission to agent A. On the facts, however, the input of estate agent B was the effective cause of the sale. Can the seller in either of these situations face the risk of a double commission claim?

PERCOVID: A Model to Describe COVID Percolation on a Network of Social Relationships

We develop a site-bond percolation model, called PERCOVID, in order to describe the time evolution of COVID epidemics and more generally all epidemics propagating through respiratory tract in human populations. This model is based on a network of social relationships representing interconnected households experiencing governmental non-pharmaceutical interventions. The model successfully accounts for the COVID-19 epidemiological data in metropolitan France from December 2019 up to July 2021. Our model shows the impact of lockdowns and curfews, as well as the influence of the progressive vaccination campaign in order to keep COVID-19 pandemic under the percolation threshold. We illustrate the role played by the social interactions by comparing a typical scenario for the epidemic evolution in France, Germany and Italy during the first wave from January to May 2020. We investigate finally the role played by the alpha and delta variants in the evolution of the epidemic in France till autumn 2021, paying particular attention to the essential role played by the vaccination. Our model predicts that the rise of the epidemic observed in July 2021 will not result in a fourth major epidemic wave in France.

Fragmentation in trader preferences among multiple markets: market coexistence versus single market dominance

Technological advancement has led to an increase in the number and type of trading venues and a diversification of goods traded. These changes have re-emphasized the importance of understanding the effects of market competition: does proliferation of trading venues and increased competition lead to dominance of a single market or coexistence of multiple markets? In this paper, we address these questions in a stylized model of zero-intelligence traders who make repeated decisions at which of three available markets to trade. We analyse the model numerically and analytically and find that the traders’ decision parameters—memory length and how strongly decisions are based on past success—make the key difference between consolidated and fragmented steady states of the population of traders. All three markets coexist with equal shares of traders only when either learning is too weak and traders choose randomly, or when markets are identical. In the latter case, the population of traders fragments across the markets. With different markets, we note that market dominance is the more typical scenario. Overall we show that, contrary to previous research emphasizing the role of traders’ heterogeneity, market coexistence can emerge simply as a consequence of co-adaptation of an initially homogeneous population of traders.

Possibilities of Using Building Information Model Data in Reinforcement Processing Plant

While the AEC industry is moving towards digitalization off-site rebar prefabrication became a common practice. Now most companies use a long-established standard order processing method, where the customer submits 2D paper or PDF-based drawings. Subsequently, the manufacturers are obligated to make additional detailing, redrawing, calculations, and preparation of other required information for manufacturing. Thus, in this typical scenario, there is a great repetition of the same tasks, with the obvious loss of time and increased likelihood of human error. However, improvements can be made by the application of advanced digital production workflow and the use of open BIM standards (e.g., IFC, XML, BVBS). Therefore, this paper presents the typical data flow algorithm in contrast to the automated data flow for reinforcement manufacturing. Further, the two approaches are compared and analyzed based on Multi-Criteria Decision Making (MCDM) methods. The results have shown promising prospects for companies willing to automate their data flow processes by the use of 3D drawings and digital data from the BIM model in their plants.

Optimization of Multiobjective Configuration of Multienergy Microgrid Based on HMM and Typical Scenarios: A Case Study of Smart Park

Decentralized regional multienergy system is one of the important development directions of energy and power systems, and researching on the optimization method of multienergy microgrid configuration could provide important support for the investment income guarantee and orderly development of regional multienergy systems. Based on a park-level multienergy microgrid, this paper proposed a multiobjective optimization model for a multienergy microgrid configuration based on the typical scenario set which was constructed by HMM. Besides, based on the actual historical data, the capacity configuration-oriented planning model and component configuration-oriented planning model were analysed and compared under different external environments. The results show that HMM has a good effect on the reduction and extraction of historical scenarios of the system. Compared with the traditional microgrid, the multienergy microgrid has better economic and emission reduction advantages. In addition, the capacity configuration-oriented planning model could reduce the investment cost by up to 62.4% compared with the component configuration-oriented planning model.