Stochastic and deterministic models for age-structured populations with genetically variable traits

Understanding how stochastic and non-linear deterministic processes interact is a major challenge in population dynamics theory. After a short review, we introduce a stochastic individual-centered particle model to describe the evolution in continuous time of a population with (continuous) age and trait structures. The individuals reproduce asexually, age, interact and die. The 'trait' is an individual heritable property (d-dimensional vector) that may influence birth and death rates and interactions between individuals, and vary by mutation. In a large population limit, the random process converges to the solution of a Gurtin-McCamy type PDE. We show that the random model has a long time behavior that differs from its deterministic limit. However, the results on the limiting PDE and large deviation techniques \textit{\`a la} Freidlin-Wentzell provide estimates of the extinction time and a better understanding of the long time behavior of the stochastic process. This has applications to the theory of adaptive dynamics used in evolutionary biology. We present simulations for two biological problems involving life-history trait evolution when body size is plastic and individual growth is taken into account.Comment: This work is a proceeding of the CANUM 2008 conferenc

Deep Transfer Learning: A Novel Collaborative Learning Model for Cyberattack Detection Systems in IoT Networks

Federated Learning (FL) has recently become an effective approach for cyberattack detection systems, especially in Internet-of-Things (IoT) networks. By distributing the learning process across IoT gateways, FL can improve learning efficiency, reduce communication overheads and enhance privacy for cyberattack detection systems. Challenges in implementation of FL in such systems include unavailability of labeled data and dissimilarity of data features in different IoT networks. In this paper, we propose a novel collaborative learning framework that leverages Transfer Learning (TL) to overcome these challenges. Particularly, we develop a novel collaborative learning approach that enables a target network with unlabeled data to effectively and quickly learn knowledge from a source network that possesses abundant labeled data. It is important that the state-of-the-art studies require the participated datasets of networks to have the same features, thus limiting the efficiency, flexibility as well as scalability of intrusion detection systems. However, our proposed framework can address these problems by exchanging the learning knowledge among various deep learning models, even when their datasets have different features. Extensive experiments on recent real-world cybersecurity datasets show that the proposed framework can improve more than 40% as compared to the state-of-the-art deep learning based approaches.Comment: 12 page

Data-driven structural health monitoring using feature fusion and hybrid deep learning

Smart structural health monitoring (SHM) for large-scale infrastructures is an intriguing subject for engineering communities thanks to its significant advantages such as timely damage detection, optimal maintenance strategy, and reduced resource requirement. Yet, it is a challenging topic as it requires handling a large amount of collected sensors data continuously, which is inevitably contaminated by random noises. Therefore, this study developed a practical end-to-end framework that makes use of physical features embedded in raw data and an elaborated hybrid deep learning model, namely 1DCNN-LSTM, featuring two algorithms - Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM). In order to extract relevant features from sensory data, the method combines various signal processing techniques such as the autoregressive model, discrete wavelet transform, and empirical mode decomposition. The hybrid deep learning 1DCNN-LSTM is designed based on the CNN’s capacity of capturing local information and the LSTM network’s prominent ability to learn long-term dependencies. Through three case studies involving both experimental and synthetic datasets, it is demonstrated that the proposed approach achieves highly accurate damage detection, as accurate as the powerful two-dimensional CNN, but with a lower time and memory complexity, making it suitable for real-time SHM

An investigation of the evidence of benefits from climate compatible development

Climate change is likely to have profound effects on developing countries both through the climate impacts experienced, but also through the policies, programmes and projects adopted to address climate change. Climate change mitigation (actions taken to reduce the extent of climate change), adaptation (actions taken to ameliorate the impacts), and on-going development are all critical to reduce current and future losses associated with climate change, and to harness gains. In the context of limited resources to invest in climate change, policies, programmes, or projects that deliver ‘triple wins’ (i.e. generating climate adaptation, mitigation and development benefits) – also known as climate compatible development – are increasingly discussed by bilateral and multilateral donors. Yet there remains an absence of empirical evidence of the benefits and costs of triple win policies. The purpose of this paper is therefore to assess evidence of ‘triple wins’ on the ground, and the feasibility of triple wins that do not generate negative impacts. We describe the theoretical linkages that exist between adaptation, mitigation and development, as well as the trade-offs and synergies that might exist between them. Using four developing country studies, we make a simple assessment of the extent of climate compatible development policy in practice through the lens of ‘no-regrets’, ‘low regrets’ and ‘with regrets’ decision making. The lack of evidence of either policy or practice of triple wins significantly limits the capacity of donors to identify, monitor or evaluate ‘triple wins at this point in time. We recommend a more strategic assessment of the distributional and financial implications of 'triple wins' policies

Интеллектуальная радиосеть с нечеткой конфигурацией

В статье обсуждаются возможности применения одноранговой радиосети стандарта IEEE 802.15.4 (ZigBee) диапазона 2,4 ГГц для работы системы, состоящей из группы малогабаритных мобильных роботов и одного командного пункта. Основная задача группы роботов – проведение разведки во время спасательных операций после техногенных и природных катастроф и аварий. Для сохранения управляемости отдельными роботами и системой в целом предлагается повысить «интеллект» системы связи за счет гибкой маршрутизации каналов между командным пунктом и конкретным мобильным роботом с тем, чтобы иметь систему с автоматическим, интеллектуальным восстановлением канала обмена данных.У статті обговорюються можливості застосування однорангової радіомережі стандарту ІЕЕ 802.15.4 (ZigBee) діапазону 2,4 Ггу для роботи системи, що складається з групи малогабаритних мобільних роботів та одного командного пункту. Основна задача групи роботів – проведення розвідки під час рятувальних операцій після техногенних та природних катастроф і аварій. Для збереження керованості окремими ротами та системою в цілому пропонується підвищити інтелект системи зв’язку за рахунок гнучкої маршрутитизації каналів між командним пунктом та конкретним мобільним роботом з тим, щоб мати систему з автоматичним, інтелектуальним відновлюванням каналу обміну даних.In the article the possibilities of application peer-to-peer radio networks of standard IEEE 802.15.4 (ZigBee) a range of 2,4 GHz for work of the system consisting of small-sized mobile robots group and one command point are discussed. The primary goal of group of robots – is carrying out of investigation during rescue operations after technogenic and natural accidents and failures. For controllability preservation by separate robots and system as a whole, it is offered to raise “intelligence” of a communication system at the expense of flexible routeing of channels between command point and the concrete mobile robot to have system with automatic, intellectual restoration of the channel of data exchange

Extremely Hot Ambient Temperature and Injury-related Mortality

This pilot study aimed to evaluate the effects of extremely hot ambient temperatures on the total number of fatal injuries. Data were collected from a population-based mortality registry of Thanh Hoa, a province in the North Central region of Vietnam. This study qualified the distributed lag non-linear model and calculated the RR and 95% CI adjusted for long-term trend and absolute humidity. For the entire study population with 3,949 registered deaths due to injuries collected during 2005-2007, after the onset of extremely hot ambient temperatures, an increased risk of death was observed on the 9th day RR (95% CI) = 1.44 (1.06–1.97) and reached the peak on the 12th day RR (95% CI) = 1.58 (1.14–2.17), and at the 15th day RR (95% CI) = 1.49 (1.08–2.06). Men and old adults were identified as the most vulnerable groups. This study confirmed a positive association between hot temperatures and injury-related deaths in the province of 3.6 million people. The findings motivated further investigation into the effect of warm climate changes and the risk of deaths related to other specific causes such as road traffic, work-related injury, and etc

University’s shared vision for research and teaching: an international comparative study

How do universities encourage academics to buy into a shared vision while often setting punitive targets in teaching and research? This article explores possible antecedents of a university’s shared vision and its relationships with academics’ research and teaching performance in the era of managerialism. This cross-country study of two large universities in the UK and Vietnam draws on data from multiple sources to uncover the key components of a university’s shared vision. A survey strategy was adopted. Data were collected from different sources, using a stratified random sampling technique from academics of different schools at those universities. A total of 431 survey responses from academics at these universities were included for analysis, employing structure equation modelling. It provides fresh insights into whether having a shared vision can benefit academics’ research and teaching performance. The findings of this study show that while achieving a high degree of shared vision may enhance research performance, it may do little to improve teaching performance. The study provides empirical evidence indicating that a shared vision emerges as strongly rooted within individual employees rather than managers, challenging the common belief that a shared vision emanates primarily from the top down. This article advances social exchange theory (SET) by showing the interdependence of workplace antecedents, personal attributes, interpersonal connections, and performance. It introduces a framework for the relationship between universities’ shared vision with its possible antecedents and with academics’ teaching performance and research performance. The article also discusses useful implications for higher education leaders, based on the findings of the study

On how religions could accidentally incite lies and violence: folktales as a cultural transmitter

Folklore has a critical role as a cultural transmitter, all the while being a socially accepted medium for the expressions of culturally contradicting wishes and conducts. In this study of Vietnamese folktales, through the use of Bayesian multilevel modeling and the Markov chain Monte Carlo technique, we offer empirical evidence for how the interplay between religious teachings (Confucianism, Buddhism, and Taoism) and deviant behaviors (lying and violence) could affect a folktale’s outcome. The findings indicate that characters who lie and/or commit violent acts tend to have bad endings, as intuition would dictate, but when they are associated with any of the above Three Teachings, the final endings may vary. Positive outcomes are seen in cases where characters associated with Confucianism lie and characters associated with Buddhism act violently. The results supplement the worldwide literature on discrepancies between folklore and real-life conduct, as well as on the contradictory human behaviors vis-à-vis religious teachings. Overall, the study highlights the complexity of human decision-making, especially beyond the folklore realm