Brandon University

We consider 27 population and community terms used frequently by parasitologists when describing the ecology of parasites. We provide suggestions for various terms in an attempt to foster consistent use and to make terms used in parasite ecology easier to interpret for those who study free-living organisms. We suggest strongly that authors, whether they agree or disagree with us, provide complete and unambiguous definitions for all parameters of their studies.

Six DNA regions were evaluated as potential DNA barcodes for Fungi, the second largest kingdom of eukaryotic life, by a multinational, multilaboratory consortium. The region of the mitochondrial cytochrome c oxidase subunit 1 used as the animal barcode was excluded as a potential marker, because it is difficult to amplify in fungi, often includes large introns, and can be insufficiently variable. Three subunits from the nuclear ribosomal RNA cistron were compared together with regions of three representative protein-coding genes (largest subunit of RNA polymerase II, second largest subunit of RNA polymerase II, and minichromosome maintenance protein). Although the protein-coding gene regions often had a higher percent of correct identification compared with ribosomal markers, low PCR amplification and sequencing success eliminated them as candidates for a universal fungal barcode. Among the regions of the ribosomal cistron, the internal transcribed spacer (ITS) region has the highest probability of successful identification for the broadest range of fungi, with the most clearly defined barcode gap between inter- and intraspecific variation. The nuclear ribosomal large subunit, a popular phylogenetic marker in certain groups, had superior species resolution in some taxonomic groups, such as the early diverging lineages and the ascomycete yeasts, but was otherwise slightly inferior to the ITS. The nuclear ribosomal small subunit has poor species-level resolution in fungi. ITS will be formally proposed for adoption as the primary fungal barcode marker to the Consortium for the Barcode of Life, with the possibility that supplementary barcodes may be developed for particular narrowly circumscribed taxonomic groups.

Heart disease is one of the most significant causes of mortality in the world today. Prediction of cardiovascular disease is a critical challenge in the area of clinical data analysis. Machine learning (ML) has been shown to be effective in assisting in making decisions and predictions from the large quantity of data produced by the healthcare industry. We have also seen ML techniques being used in recent developments in different areas of the Internet of Things (IoT). Various studies give only a glimpse into predicting heart disease with ML techniques. In this paper, we propose a novel method that aims at finding significant features by applying machine learning techniques resulting in improving the accuracy in the prediction of cardiovascular disease. The prediction model is introduced with different combinations of features and several known classification techniques. We produce an enhanced performance level with an accuracy level of 88.7% through the prediction model for heart disease with the hybrid random forest with a linear model (HRFLM).

Medical care has become one of the most indispensable parts of human lives, leading to a dramatic increase in medical big data. To streamline the diagnosis and treatment process, healthcare professionals are now adopting Internet of Things (IoT)-based wearable technology. Recent years have witnessed billions of sensors, devices, and vehicles being connected through the Internet. One such technology-remote patient monitoring-is common nowadays for the treatment and care of patients. However, these technologies also pose grave privacy risks and security concerns about the data transfer and the logging of data transactions. These security and privacy problems of medical data could result from a delay in treatment progress, even endangering the patient's life. We propose the use of a blockchain to provide secure management and analysis of healthcare big data. However, blockchains are computationally expensive, demand high bandwidth and extra computational power, and are therefore not completely suitable for most resource-constrained IoT devices meant for smart cities. In this work, we try to resolve the above-mentioned issues of using blockchain with IoT devices. We propose a novel framework of modified blockchain models suitable for IoT devices that rely on their distributed nature and other additional privacy and security properties of the network. These additional privacy and security properties in our model are based on advanced cryptographic primitives. The solutions given here make IoT application data and transactions more secure and anonymous over a blockchain-based network.

Due to digitization, a huge volume of data is being generated across several sectors such as healthcare, production, sales, IoT devices, Web, organizations. Machine learning algorithms are used to uncover patterns among the attributes of this data. Hence, they can be used to make predictions that can be used by medical practitioners and people at managerial level to make executive decisions. Not all the attributes in the datasets generated are important for training the machine learning algorithms. Some attributes might be irrelevant and some might not affect the outcome of the prediction. Ignoring or removing these irrelevant or less important attributes reduces the burden on machine learning algorithms. In this work two of the prominent dimensionality reduction techniques, Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) are investigated on four popular Machine Learning (ML) algorithms, Decision Tree Induction, Support Vector Machine (SVM), Naive Bayes Classifier and Random Forest Classifier using publicly available Cardiotocography (CTG) dataset from University of California and Irvine Machine Learning Repository. The experimentation results prove that PCA outperforms LDA in all the measures. Also, the performance of the classifiers, Decision Tree, Random Forest examined is not affected much by using PCA and LDA.To further analyze the performance of PCA and LDA the eperimentation is carried out on Diabetic Retinopathy (DR) and Intrusion Detection System (IDS) datasets. Experimentation results prove that ML algorithms with PCA produce better results when dimensionality of the datasets is high. When dimensionality of datasets is low it is observed that the ML algorithms without dimensionality reduction yields better results.

The Internet of Things (IoT) is made up of billions of physical devices connected to the Internet via networks that perform tasks independently with less human intervention. Such brilliant automation of mundane tasks requires a considerable amount of user data in digital format, which, in turn, makes IoT networks an open source of personally identifiable information data for malicious attackers to steal, manipulate, and perform nefarious activities. A huge interest has been developed over the past years in applying machine learning (ML)-assisted approaches in the IoT security space. However, the assumption in many current works is that big training data are widely available and transferable to the main server because data are born at the edge and are generated continuously by IoT devices. This is to say that classic ML works on the legacy set of entire data located on a central server, which makes it the least preferred option for domains with privacy concerns on user data. To address this issue, we propose the federated-learning (FL)-based anomaly detection approach to proactively recognize intrusion in IoT networks using decentralized on-device data. Our approach uses federated training rounds on gated recurrent units (GRUs) models and keeps the data intact on local IoT devices by sharing only the learned weights with the central server of FL. Also, the approach’s ensembler part aggregates the updates from multiple sources to optimize the global ML model’s accuracy. Our experimental results demonstrate that our approach outperforms the classic/centralized machine learning (non-FL) versions in securing the privacy of user data and provides an optimal accuracy rate in attack detection.

We present a 6-gene, 420-species maximum-likelihood phylogeny of Ascomycota, the largest phylum of Fungi. This analysis is the most taxonomically complete to date with species sampled from all 15 currently circumscribed classes. A number of superclass-level nodes that have previously evaded resolution and were unnamed in classifications of the Fungi are resolved for the first time. Based on the 6-gene phylogeny we conducted a phylogenetic informativeness analysis of all 6 genes and a series of ancestral character state reconstructions that focused on morphology of sporocarps, ascus dehiscence, and evolution of nutritional modes and ecologies. A gene-by-gene assessment of phylogenetic informativeness yielded higher levels of informativeness for protein genes (RPB1, RPB2, and TEF1) as compared with the ribosomal genes, which have been the standard bearer in fungal systematics. Our reconstruction of sporocarp characters is consistent with 2 origins for multicellular sexual reproductive structures in Ascomycota, once in the common ancestor of Pezizomycotina and once in the common ancestor of Neolectomycetes. This first report of dual origins of ascomycete sporocarps highlights the complicated nature of assessing homology of morphological traits across Fungi. Furthermore, ancestral reconstruction supports an open sporocarp with an exposed hymenium (apothecium) as the primitive morphology for Pezizomycotina with multiple derivations of the partially (perithecia) or completely enclosed (cleistothecia) sporocarps. Ascus dehiscence is most informative at the class level within Pezizomycotina with most superclass nodes reconstructed equivocally. Character-state reconstructions support a terrestrial, saprobic ecology as ancestral. In contrast to previous studies, these analyses support multiple origins of lichenization events with the loss of lichenization as less frequent and limited to terminal, closely related species.

The 21st century has seen rapid changes in technology, industry, and social patterns. Most industries have moved towards automation, and human intervention has decreased, which has led to a revolution in industries, named the fourth industrial revolution (Industry 4.0). Industry 4.0 or the fourth industrial revolution (IR 4.0) relies heavily on the Internet of Things (IoT) and wireless sensor networks (WSN). IoT and WSN are used in various control systems, including environmental monitoring, home automation, and chemical/biological attack detection. IoT devices and applications are used to process extracted data from WSN devices and transmit them to remote locations. This systematic literature review offers a wide range of information on Industry 4.0, finds research gaps, and recommends future directions. Seven research questions are addressed in this article: (i) What are the contributions of WSN in IR 4.0? (ii) What are the contributions of IoT in IR 4.0? (iii) What are the types of WSN coverage areas for IR 4.0? (iv) What are the major types of network intruders in WSN and IoT systems? (v) What are the prominent network security attacks in WSN and IoT? (vi) What are the significant issues in IoT and WSN frameworks? and (vii) What are the limitations and research gaps in the existing work? This study mainly focuses on research solutions and new techniques to automate Industry 4.0. In this research, we analyzed over 130 articles from 2014 until 2021. This paper covers several aspects of Industry 4.0, from the designing phase to security needs, from the deployment stage to the classification of the network, the difficulties, challenges, and future directions.

The Generative Pre-trained Transformer (GPT) represents a notable breakthrough in the domain of natural language processing, which is propelling us toward the development of machines that can understand and communicate using language in a manner that closely resembles that of humans. GPT is based on the transformer architecture, a deep neural network designed for natural language processing tasks. Due to their impressive performance on natural language processing tasks and ability to effectively converse, GPT have gained significant popularity among researchers and industrial communities, making them one of the most widely used and effective models in natural language processing and related fields, which motivated to conduct this review. This review provides a detailed overview of the GPT, including its architecture, working process, training procedures, enabling technologies, and its impact on various applications. In this review, we also explored the potential challenges and limitations of a GPT. Furthermore, we discuss potential solutions and future directions. Overall, this paper aims to provide a comprehensive understanding of GPT, its enabling technologies, their impact on various applications, emerging challenges, and potential solutions.

In recent years, new-found interest in the hydrogen economy from both industry and academia has helped to shed light on its potential. Hydrogen can enable an energy revolution by providing much needed flexibility in renewable energy systems. As a clean energy carrier, hydrogen offers a range of benefits for simultaneously decarbonizing the transport, residential, commercial and industrial sectors. Hydrogen is shown here to have synergies with other low-carbon alternatives, and can enable a more cost-effective transition to de-carbonized and cleaner energy systems. This paper presents the opportunities for the use of hydrogen in key sectors of the economy and identifies the benefits and challenges within the hydrogen supply chain for power-to-gas, power-to-power and gas-to-gas supply pathways. While industry players have already started the market introduction of hydrogen fuel cell systems, including fuel cell electric vehicles and micro-combined heat and power devices, the use of hydrogen at grid scale requires the challenges of clean hydrogen production, bulk storage and distribution to be resolved. Ultimately, greater government support, in partnership with industry and academia, is still needed to realize hydrogen's potential across all economic sectors.This article is part of the themed issue 'The challenges of hydrogen and metals'.

The authors investigate the link between the color of a Web page's background screen while the page is downloading and the perceived quickness of the download. They draw on research that supports links between color and feelings of relaxation and between feelings of relaxation and time perception. The authors predict that the background screen color influences how quickly a page is perceived to download and that feelings of relaxation mediate this influence. In a series of experiments, they manipulate the hue, value, and chroma dimensions of the color to induce more or less relaxed feeling states. The findings suggest that for each dimension, colors that induce more relaxed feeling states lead to greater perceived quickness. The authors provide triangulating evidence with an alternative manipulation: the number of times subjects wait for a download. As does color, this also leads to variation in levels of relaxation and perceived quickness. A final experiment reveals that color not only affects perceived download quickness but also has consequences for users' evaluations of the Web site and their likelihood of recommending it to others.

With the evolution of the Internet of Things (IoT), smart cities have become the mainstream of urbanization. IoT networks allow distributed smart devices to collect and process data within smart city infrastructure using an open channel, the Internet. Thus, challenges such as centralization, security, privacy (e.g., performing data poisoning and inference attacks), transparency, scalability, and verifiability limits faster adaptations of smart cities. Motivated by the aforementioned discussions, we present a Privacy-Preserving and Secure Framework (PPSF) for IoT-driven smart cities. The proposed PPSF is based on two key mechanisms: a two-level privacy scheme and an intrusion detection scheme. First, in a two-level privacy scheme, a blockchain module is designed to securely transmit the IoT data and Principal Component Analysis (PCA) technique is applied to transform raw IoT information into a new shape. In the intrusion detection scheme, a Gradient Boosting Anomaly Detector (GBAD) is applied for training and evaluating the proposed two-level privacy scheme based on two IoT network datasets, namely ToN-IoT and BoT-IoT. We also suggest a blockchain-InterPlanetary File System (IPFS) integrated Fog-Cloud architecture to deploy the proposed PPSF framework. Experimental results demonstrate the superiority of the PPSF framework over some recent approaches in blockchain and non-blockchain systems.

Invisible gold in natural and synthetic arsenian pyrite and marcasite correlates with anomalous As content and Fe deficiency, and high contents of invisible gold in most natural and all synthetic arsenopyrite correlate with excess As and Fe deficiency. As-rich, Fe-deficient arsenopyrite synthesized hydrothermally contains up to 3.0 wt% Au uniformly distributed in growth zones of light backscattered electron contrast. At the Deep Star gold deposit, Carlin Trend, Nevada, the sulfide compositions apparently span the full range of metastability from FeSz to near FeAsS (40 at% S); arsenian pyrite contains up to 0.37 wt% Au, but arsenopyrite has excess S and is relatively Au poor. Observed minimum Fe contents are 29.1 at% in arsenian pyrite and marcasite from the Deep Star deposit and 31.3 at% in synthetic arsenopyrite. We suggest that invisible gold in arsenian pyrite and marcasite and arsenopyrite from sediment-hosted gold deposits represents Au removed from ore fluids by chemisorption at As-rich, Fe-deficient surface sites and incorporated into the solids in metastable solid solution. However, the oxidation state of invisible gold (Aua, AU1+) remains uncertain because the chemisorption process is intrinsically nonsystematic in terms of crystal-chemical parameters and does not result in definitive atomic substitution trends.

Pezizomycotina is the largest subphylum of Ascomycota and includes the vast majority of filamentous, ascoma-producing species. Here we report the results from weighted parsimony, maximum likelihood and Bayesian phylogenetic analyses of five nuclear loci (SSU rDNA, LSU rDNA, RPB1, RPB2 and EF-lalpha) from 191 taxa. Nine of the 10 Pezizomycotina classes currently recognized were represented in the sampling. These data strongly supported the monophyly of Pezizomycotina, Arthoniomycetes, Eurotiomycetes, Orbiliomycetes and Sordariomycetes. Pezizomycetes and Dothideomycetes also were resolved as monophyletic but not strongly supported by the data. Lecanoromycetes was resolved as paraphyletic in parsimony analyses but monophyletic in maximum likelihood and Bayesian analyses. Leotiomycetes was polyphyletic due to exclusion of Geoglossaceae. The two most basal classes of Pezizomycotina were Orbiliomycetes and Pezizomycetes, both of which comprise species that produce apothecial ascomata. The seven remaining classes formed a monophyletic group that corresponds to Leotiomyceta. Within Leotiomyceta, the supraclass clades of Leotiomycetes s.s. plus Sordariomycetes and Arthoniomycetes plus Dothideomycetes were resolved with moderate support.

Although federated learning offers a level of privacy by aggregating user data without direct access, it remains inherently vulnerable to various attacks, including poisoning attacks where malicious actors submit gradients that reduce model accuracy. In addressing model poisoning attacks, existing defense strategies primarily concentrate on detecting suspicious local gradients over plaintext. However, detecting non-independent and identically distributed encrypted gradients poses significant challenges for existing methods. Moreover, tackling computational complexity and communication overhead becomes crucial in privacy-preserving federated learning, particularly in the context of encrypted gradients. To address these concerns, we propose a robust privacy-preserving federated learning model resilient against model poisoning attacks without sacrificing accuracy. Our approach introduces an internal auditor that evaluates encrypted gradient similarity and distribution to differentiate between benign and malicious gradients, employing a Gaussian Mixture Model and Mahalanobis Distance for byzantine-tolerant aggregation. The proposed model utilizes Additive Homomorphic Encryption to ensure confidentiality while minimizing computational and communication overhead. Our model demonstrates superior performance in accuracy and privacy compared to existing strategies and encryption techniques, such as Fully Homomorphic Encryption and Two-Trapdoor Homomorphic Encryption. The proposed model effectively addresses the challenge of detecting maliciously encrypted non-independent and identically distributed gradients with low computational and communication overhead.

Abstract An important area of computer vision is real-time object tracking, which is now widely used in intelligent transportation and smart industry technologies. Although the correlation filter object tracking methods have a good real-time tracking effect, it still faces many challenges such as scale variation, occlusion, and boundary effects. Many scholars have continuously improved existing methods for better efficiency and tracking performance in some aspects. To provide a comprehensive understanding of the background, key technologies and algorithms of single object tracking, this article focuses on the correlation filter-based object tracking algorithms. Specifically, the background and current advancement of the object tracking methodologies, as well as the presentation of the main datasets are introduced. All kinds of methods are summarized to present tracking results in various vision problems, and a visual tracking method based on reliability is observed.

SUMMARY Recently, some authors (Kennedy, 1981; Price & Clancy, 1983) have argued that there are fundamental differences between the communities of helminths in fish and bird hosts. Such differences are foreshadowed by the work of Dogiel (1964) and are apparent from survey data (e.g. Threlfall, 1967; Bakke, 1972; Hair & Holmes, 1975 on birds, and compare Chubb, 1963; Mishra & Chubb, 1969; Wootten, 1973; Ingham & Dronen, 1980 on fish). Questions still remain, however, as to whether the distinctions are truly justified and whether the differences are really fundamental. In this paper, we address these questions by examining helminth diversity in a series of hosts. We then discuss and provide explanations for the observed differences.

In any interconnected healthcare system (e.g., those that are part of a smart city), interactions between patients, medical doctors, nurses and other healthcare practitioners need to be secure and efficient. For example, all members must be authenticated and securely interconnected to minimize security and privacy breaches from within a given network. However, introducing security and privacy-preserving solutions can also incur delays in processing and other related services, potentially threatening patients lives in critical situations. A considerable number of authentication and security systems presented in the literature are centralized, and frequently need to rely on some secure and trusted third-party entity to facilitate secure communications. This, in turn, increases the time required for authentication and decreases throughput due to known overhead, for patients and inter-hospital communications. In this paper, we propose a novel decentralized authentication of patients in a distributed hospital network, by leveraging blockchain. Our notion of a healthcare setting includes patients and allied health professionals (medical doctors, nurses, technicians, etc), and the health information of patients. Findings from our in-depth simulations demonstrate the potential utility of the proposed architecture. For example, it is shown that the proposed architecture's decentralized authentication among a distributed affiliated hospital network does not require re-authentication. This improvement will have a considerable impact on increasing throughput, reducing overhead, improving response time, and decreasing energy consumption in the network. We also provide a comparative analysis of our model in relation to a base model of the network without blockchain to show the overall effectiveness of our proposed solution.

Due to the emergence of heterogeneous Internet of Medical Things (IoMT) (e.g., wearable health devices, smartwatch monitoring, and automated insulin delivery systems), large volumes of patient data are dispatched to central cloud servers for disease analysis and diagnosis. Although this direct mode brings a lot of convenience for both patients and medical professionals (MPs), the open communication channel between them also incurs several security and privacy issues, such as man-in-the-middle attacks, eavesdropping attacks, and tracking attacks. Based on the unsolved challenges in wireless medical sensor networks (WMSNs), several researchers have proposed various authentication and key agreement (AKA) protocols for this type of healthcare system recently. However, most of these protocols do not perceive physical-layer security and over-centralized server problem in WMSN. In this article, to address these two open problems, we propose a lightweight and reliable authentication protocol for WMSN, which is composed of cutting-edge blockchain technology and physically unclonable functions (PUFs). In addition, a fuzzy extractor scheme is introduced to deal with biometric information. Subsequently, two security evaluation methods are used to prove the high reliability of our proposed scheme. Finally, performance evaluation experiments illustrate that the proposed mutual authentication protocol requires the least computation and communication cost among the compared schemes.

Annual crop production in the Canadian prairies is undergoing significant change. Traditional monoculture cereal cropping systems, which rely on frequent summer‐fallowing and use of mechanical tillage, are being replaced by extended and diversified crop rotations together with the use of conservation tillage (minimum and zero‐tillage) practices. This paper reviews the findings of western Canadian empirical studies that have examined the economic forces behind these land use and soil tillage changes. The evidence suggests that including oilseed and pulse crops in the rotation with cereal grains contributes to higher and more stable net farm income in most soil–climatic regions, despite a requirement for increased expenditures on purchased inputs. In the very dry Brown soil zone and drier regions of the Dark Brown soil zone where the production risk with stubble cropping is high, the elimination of summer fallow from the cropping system may not be economically feasible under present and near‐future economic conditions. The use of conservation tillage practices in the management of mixed cropping systems is highly profitable in the more moist Black and Gray soil zones (compared with conventional mechanical tillage methods) because of significant yield advantages and substantial resource savings that can be obtained by substituting herbicides for the large amount of tillage that is normally used. However, in the Brown soil zone and parts of the Dark Brown soil zone, the short‐term economic benefits of using conservation tillage practices are more marginal and often less profitable than comparable conventional tillage practices.