Recent warming of Tonle Sap Lake,Cambodia: Implications for one of the world’s most productive inland fisheries

Tonle Sap Lake in Cambodia is arguably the world's most productive freshwater ecosystems, as well as the dominant source of animal protein for the country. The rapid rise of hydropower schemes, deforestation, land development and climate change impacts in the Mekong River Basin, however, now represent serious concerns in regard to Tonle Sap Lake's ecological health and its role in future food security. To this end, the present study identifies significant recent warming of lake temperature and discusses how each of these anthropogenic perturbations in Tonle Sap's floodplain and the Mekong River Basin may be influencing this trend. The lake's dry season monthly average temperature increased by 0.03°C/year between 1988 and 2018, being largely in synchrony with warming trends of the local air temperature and upstream rivers. The impacts of deforestation and agriculture development in the lake's floodplain also exhibited a high correlation with an increased number of warm days observed in the lake, particularly in its southeast region (agriculture R² = .61; deforestation R² = .39). A total of 79 dams, resulting in 72 km³ of volumetric water capacity, were constructed between 2003 and 2018 in the Mekong River Basin. This dam development coincided with a decreasing trend in the number of dry season warm days per year in the lower Mekong River, while Tonle Sap Lake's number of dry season warm days continued to increase during this same period. The present study revealed that Tonle Sap Lake's temperature trends are highly influenced by temperature trends in the local climate, agriculture development and deforestation of the lake's watershed. Although there were no noticeable impacts observed from upstream dam development in the Mekong River Basin, local‐to‐regional agricultural and land management of the lake's watershed appear to be effective strategies for maintaining a stable thermal regime in the lake in order to facilitate maximum ecosystem health.     

Enhancement of electromechanical properties of natural rubber by adding barium titanate filler: An electro-mechanical study

Natural rubber is one of the most potential electro-active polymers for sensors, actuators, and energy harvesting applications. Enhancing the characteristic properties of polymers by reinforcing with fillers that possess multifunctional attributes have attracted considerable attention. In the present study, barium titanate reinforced natural rubber composite is prepared by using two-roll mill mixing. Afterwards, mechanical, electrical, and electromechanical properties of the composites are extensively analyzed by reinforcing different amounts of barium titanate into the matrix of natural rubber. The fabricated dielectric composite shows excellent properties such as high dielectric constant, low dielectric losses, high dielectric breakdown strength, and extreme stretchability. It is observed that as the filler loading reaches the value of 11 parts per hundred rubber (phr), maximum agglomeration of the particles occurs. Maximum stretchability and highest ratio of dielectric constant to elastic modulus are obtained at 8 phr of barium titanate fillers and at the loading, a maximum actuation strain of 11.24% is achieved. This study provides a simple, economical, and effective method for preparing enhanced mechanical, electrical, and electromechanical properties of natural rubber composites, facilitating the wide applications of dielectric materials as actuators and generators.     

Ant-based service selection framework for a smart home monitoring environment

Selecting ambient media services in a smart home monitoring environment is challenging. Services in such an environment should be ubiquitous, adaptive, and robust with respect to access and delivery. Many different techniques exist for selecting services in smart environments, for example, dynamic programming, genetic algorithms, and fuzzy logic. However, existing approaches to service selection fail to address the dynamic nature of the services and the requirement of considering the user context and user satisfaction. We address this issue by proposing an ant-inspired service selection framework based on dynamic user preferences and satisfaction. This ant-inspired approach is robust to failures and adaptive to dynamic context. The proposed framework enables different categories of residents (e.g., elderly people, fathers with children, mothers, and so on) to access various media services in such a way that their experiences are optimized with regard to their surrounding environment. Experimental results demonstrate the viability of the proposed framework.     

How to “alternatize” a clustering algorithm

Given a clustering algorithm, how can we adapt it to find multiple, nonredundant, high-quality clusterings? We focus on algorithms based on vector quantization and describe a framework for automatic ‘alternatization’ of such algorithms. Our framework works in both simultaneous and sequential learning formulations and can mine an arbitrary number of alternative clusterings. We demonstrate its applicability to various clustering algorithms—k-means, spectral clustering, constrained clustering, and co-clustering—and effectiveness in mining a variety of datasets.     

An efficient hybrid multicast transport protocol for collaborative virtual environment with networked haptic

In recent years, we have witnessed a growing interest in the synchronous collaboration based class of applications. Several techniques for collaborative virtual environments (CVE), haptic, audio and visual environments (C-HAVE) have been designed. However, several challenging issues remain to be resolved before CVE and C-HAVE become a common place. In this paper, we focus on applications that are based on closely coupled and highly synchronized haptic tasks that require a high-level of coordination among the participants. Four main protocols have been designed to resolve the synchronization issues in such environments: the synchronous collaboration transport protocol, the selective reliable transmission protocol, the reliable multicast transport protocol and the scalable reliable multicast. While these four protocols have shown good performance for CVE and C-HAVE class of applications, none of these protocols has been able to meet all of the basic CVE requirements, i.e., scalability, reliability, synchronization, and minimum delay. In this paper, we present a hybrid protocol that is able to satisfy all of the CVE and C-HAVE requirements and discuss its implementation and results in two tele-surgery applications. This work is partially supported by Grants from Canada Research Chair Program, NSERC, OIT/Ontario Distinguished Researcher Award, Early Research Award and ORNEC Research Grant.     

Observer-invariant histopathology using genetics-based machine learning

Prostate cancer accounts for one-third of noncutaneous cancers diagnosed in US men and is a leading cause of cancer-related death. Advances in Fourier transform infrared spectroscopic imaging now provide very large data sets describing both the structural and local chemical properties of cells within prostate tissue. Uniting spectroscopic imaging data and computer-aided diagnoses (CADx), our long term goal is to provide a new approach to pathology by automating the recognition of cancer in complex tissue. The first step toward the creation of such CADx tools requires mechanisms for automatically learning to classify tissue types—a key step on the diagnosis process. Here we demonstrate that genetics-based machine learning (GBML) can be used to approach such a problem. However, to efficiently analyze this problem there is a need to develop efficient and scalable GBML implementations that are able to process very large data sets. In this paper, we propose and validate an efficient GBML technique——based on an incremental genetics-based rule learner. exploits massive parallelisms via the message passing interface (MPI) and efficient rule-matching using hardware-implemented operations. Results demonstrate that is capable of performing prostate tissue classification efficiently, making a compelling case for using GBML implementations as efficient and powerful tools for biomedical image processing.     

Molecular structures and conformational studies of triarylcyclopropyl and related nonsteroidal antiestrogens

Molecular structures and conformational characteristics of a series of 1,1-dichloro-2,2,3-triarylcyclopropanes (DTACs), which were reported previously to be distinctly antiestrogenic and inhibitors of the estrogen-receptor-positive MCF-7 human breast cancer cells in culture, are reported. In addition, structural and conformational features of the DTACs were compared to the first-known nonsteroidal antiestrogen, MER25, and the clinically useful antiestrogen Tamoxifen. The molecular structures of four DTAC compounds were determined by X-ray diffraction. Crystallographic structures show that the DTAC molecules have nearly the same relative conformation for the three aryl rings which is designated as a "nonpropeller" conformation in contrast to the observed "propeller" conformation for the three rings in all known triarylethylenes. Systematic conformational searches were performed to find the conformational preferences of DTACs, MER25, and Tamoxifen using idealized model compounds built from their respective crystal structure. Energy-minimization and conformational-search studies demonstrated that all DTAC molecules have a common, single global minimum energy conformer for their central core containing the dichlorotriarylcyclopropyl system, which is similar to that found in their crystal structures. Conformational search of MER25 showed that the molecule can assume a number of low-energy conformers of which two, one anti (A1) and one gauche (G1A), have about the same energy. The anti conformation is similar to the one observed in its crystal structure and resembles the estrogenic E-isomer of Tamoxifen, while the lowest energy gauche conformer of MER25 resembles more closely the antiestrogenic Z-isomer of Tamoxifen. NMR spectroscopic analysis of MER25 showed that the molecule exists predominantly in the anti conformation in solution. A comparative review of the structural features and bioactivities of Tamoxifen, DTACs, and MER25 provides a possible explanation for their low estrogen receptor binding affinity which is common to these compounds together with their antiestrogenic activity.     

A framework of genetic algorithm-based CNN on multi-access edge computing for automated detection of COVID-19

The Journal of Supercomputing - This paper designs and develops a computational intelligence-based framework using convolutional neural network (CNN) and genetic algorithm (GA) to detect COVID-19...     

Summarizing consumer reviews

Journal of Intelligent Information Systems - E-commerce giants like Amazon rely on consumer reviews to allow buyers to inform other potential buyers about a product’s pros and cons. While...