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The cognitive cell: bacterial behavior reconsidered
Research on how bacteria adapt to changing environments underlies the contemporary biological understanding of signal transduction (ST), and ST provides the foundation of the information-processing approach that is the hallmark of the 'cognitive revolution,' which began in the mid-20th century. Yet cognitive scientists largely remain oblivious to research into microbial behavior that might provide insights into problems in their own domains, while microbiologists seem equally unaware of the potential importance of their work to understanding cognitive capacities in multicellular organisms, including vertebrates. Evidence in bacteria for capacities encompassed by the concept of cognition is reviewed. Parallels exist not only at the heuristic level of functional analogue, but also at the level of molecular mechanism, evolution and ecology, which is where fruitful cross-fertilization among disciplines might be found.
Vestibular pathways involved in cognition
Recent discoveries have emphasized the role of the vestibular system in cognitive processes such as memory, spatial navigation and bodily self-consciousness. A precise understanding of the vestibular pathways involved is essential to understand the consequences of vestibular diseases for cognition, as well as develop therapeutic strategies to facilitate recovery. The knowledge of the "vestibular cortical projection areas", defined as the cortical areas activated by vestibular stimulation, has dramatically increased over the last several years from both anatomical and functional points of view. Four major pathways have been hypothesized to transmit vestibular information to the vestibular cortex: (1) the vestibulo-thalamo-cortical pathway, which probably transmits spatial information about the environment via the parietal, entorhinal and perirhinal cortices to the hippocampus and is associated with spatial representation and self-versus object motion distinctions; (2) the pathway from the dorsal tegmental nucleus via the lateral mammillary nucleus, the anterodorsal nucleus of the thalamus to the entorhinal cortex, which transmits information for estimations of head direction; (3) the pathway via the nucleus reticularis pontis oralis, the supramammillary nucleus and the medial septum to the hippocampus, which transmits information supporting hippocampal theta rhythm and memory; and (4) a possible pathway via the cerebellum, and the ventral lateral nucleus of the thalamus (perhaps to the parietal cortex), which transmits information for spatial learning. Finally a new pathway is hypothesized via the basal ganglia, potentially involved in spatial learning and spatial memory. From these pathways, progressively emerges the anatomical network of vestibular cognition.
The role of language in emotion: predictions from psychological constructionism
Common sense suggests that emotions are physical types that have little to do with the words we use to label them. Yet recent psychological constructionist accounts reveal that language is a fundamental element in emotion that is constitutive of both emotion experiences and perceptions. According to the psychological constructionist Conceptual Act Theory (CAT), an instance of emotion occurs when information from one's body or other people's bodies is made meaningful in light of the present situation using concept knowledge about emotion. The CAT suggests that language plays a role in emotion because language supports the conceptual knowledge used to make meaning of sensations from the body and world in a given context. In the present paper, we review evidence from developmental and cognitive science to reveal that language scaffolds concept knowledge in humans, helping humans to acquire abstract concepts such as emotion categories across the lifespan. Critically, language later helps individuals use concepts to make meaning of on-going sensory perceptions. Building on this evidence, we outline predictions from a psychological constructionist model of emotion in which language serves as the "glue" for emotion concept knowledge, binding concepts to embodied experiences and in turn shaping the ongoing processing of sensory information from the body and world to create emotional experiences and perceptions.
Proximal Remote Sensing Buggies and Potential Applications for Field-Based Phenotyping
The achievements made in genomic technology in recent decades are yet to be matched by fast and accurate crop phenotyping methods. Such crop phenotyping methods are required for crop improvement efforts to meet expected demand for food and fibre in the future. This review evaluates the role of proximal remote sensing buggies for field-based phenotyping with a particular focus on the application of currently available sensor technology for large-scale field phenotyping. To illustrate the potential for the development of high throughput phenotyping techniques, a case study is presented with sample data sets obtained from a ground-based proximal remote sensing buggy mounted with the following sensors: LiDAR, RGB camera, thermal infra-red camera and imaging spectroradiometer. The development of such techniques for routine deployment in commercial-scale breeding and pre-breeding operations will require a multidisciplinary approach to leverage the recent technological advances realised in computer science, image analysis, proximal remote sensing and robotics.
Transformer Network for Remaining Useful Life Prediction of Lithium-Ion Batteries
Accurately predicting the Remaining Useful Life (RUL) of a Li-ion battery plays an important role in managing the health and estimating the state of a battery. With the rapid development of electric vehicles, there is an increasing need to develop and improve the techniques for predicting RUL. To predict RUL, we designed a Transformer-based neural network. First, battery capacity data is always full of noise, especially during battery charge/discharge regeneration. To alleviate this problem, we applied a Denoising Auto-Encoder (DAE) to process raw data. Then, to capture temporal information and learn useful features, a reconstructed sequence was fed into a Transformer network. Finally, to bridge denoising and prediction tasks, we combined these two tasks into a unified framework. Results of extensive experiments conducted on two data sets and a comparison with some existing methods show that our proposed method performs better in predicting RUL. Our projects are all open source and are available athttps://github.com/XiuzeZhou/RUL.
Design and Development of a Deep Learning-Based Model for Anomaly Detection in IoT Networks
The growing development of IoT (Internet of Things) devices creates a large attack surface for cybercriminals to conduct potentially more destructive cyberattacks; as a result, the security industry has seen an exponential increase in cyber-attacks. Many of these attacks have effectively accomplished their malicious goals because intruders conduct cyber-attacks using novel and innovative techniques. An anomaly-based IDS (Intrusion Detection System) uses machine learning techniques to detect and classify attacks in IoT networks. In the presence of unpredictable network technologies and various intrusion methods, traditional machine learning techniques appear inefficient. In many research areas, deep learning methods have shown their ability to identify anomalies accurately. Convolutional neural networks are an excellent alternative for anomaly detection and classification due to their ability to automatically categorize main characteristics in input data and their effectiveness in performing faster computations. In this paper, we design and develop a novel anomaly-based intrusion detection model for IoT networks. First, a convolutional neural network model is used to create a multiclass classification model. The proposed model is then implemented using convolutional neural networks in 1D, 2D, and 3D. The proposed convolutional neural network model is validated using the BoT-IoT, IoT Network Intrusion, MQTT-IoT-IDS2020, and IoT-23 intrusion detection datasets. Transfer learning is used to implement binary and multiclass classification using a convolutional neural network multiclass pre-trained model. Our proposed binary and multiclass classification models have achieved high accuracy, precision, recall, and F1 score compared to existing deep learning implementations.
A Standard Model of the Mind: Toward a Common Computational Framework Across Artificial Intelligence, Cognitive Science, Neuroscience, and Robotics
A standard model captures a community consensus over a coherent region of science, serving as a cumulative reference point for the field that can provide guidance for both research and applications, while also focusing efforts to extend or revise it. Here we propose developing such a model for humanlike minds, computational entities whose structures and processes are substantially similar to those found in human cognition. Our hypothesis is that cognitive architectures provide the appropriate computational abstraction for defining a standard model, although the standard model is not itself such an architecture. The proposed standard model began as an initial consensus at the 2013 AAAI Fall Symposium on Integrated Cognition, but is extended here through a synthesis across three existing cognitive architectures: ACT‐R, Sigma, and Soar. The resulting standard model spans key aspects of structure and processing, memory and content, learning, and perception and motor, and highlights loci of architectural agreement as well as disagreement with the consensus while identifying potential areas of remaining incompleteness. The hope is that this work will provide an important step toward engaging the broader community in further development of the standard model of the mind.
Transcriptome profiling revealed multiple genes and ECM-receptor interaction pathways that may be associated with breast cancer
BACKGROUND: Exploration of the genes with abnormal expression during the development of breast cancer is essential to provide a deeper understanding of the mechanisms involved. Transcriptome sequencing and bioinformatics analysis of invasive ductal carcinoma and paracancerous tissues from the same patient were performed to identify the key genes and signaling pathways related to breast cancer development. METHODS: Samples of breast tumor tissue and paracancerous breast tissue were obtained from 6 patients. Sequencing used the Illumina HiSeq platform. All. Only perfectly matched clean reads were mapped to the reference genome database, further analyzed and annotated based on the reference genome information. Differentially expressed genes (DEGs) were identified using the DESeq R package (1.10.1) and DEGSeq R package (1.12.0). Using KOBAS software to execute the KEGG bioinformatics analyses, enriched signaling pathways of DEGs involved in the occurrence of breast cancer were determined. Subsequently, quantitative real time PCR was used to verify the accuracy of the expression profile of key DEGs from the RNA-seq result and to explore the expression patterns of novel cancer-related genes on 8 different clinical individuals. RESULTS: The transcriptomic sequencing results showed 937 DEGs, including 487 upregulated and 450 downregulated genes in the breast cancer specimens. Further quantitative gene expression analysis was performed and captured 252 DEGs (201 downregulated and 51 upregulated) that showed the same differential expression pattern in all libraries. Finally, 6 upregulated DEGs (CST2, DRP2, CLEC5A, SCD, KIAA1211, DTL) and 6 downregulated DEGs (STAC2, BTNL9, CA4, CD300LG, GPIHBP1 and PIGR), were confirmed in a quantitative real time PCR comparison of breast cancer and paracancerous breast tissues from 8 clinical specimens. KEGG analysis revealed various pathway changes, including 20 upregulated and 21 downregulated gene enrichment pathways. The extracellular matrix-receptor (ECM-receptor) interaction pathway was the most enriched pathway: all genes in this pathway were DEGs, including the THBS family, collagen and fibronectin. These DEGs and the ECM-receptor interaction pathway may perform important roles in breast cancer. CONCLUSION: Several potential breast cancer-related genes and pathways were captured, including 7 novel upregulated genes and 76 novel downregulated genes that were not found in other studies. These genes are related to cell proliferation, movement and adhesion. They may be important for research into breast cancer mechanisms, particularly CST2 and CA4. A key signaling pathway, the ECM-receptor interaction signal pathway, was also identified as possibly involved in the development of breast cancer.
Perfusion CT: a worthwhile enhancement?
Les tumeurs nasales chez le chien sont relativement rares (1-2%). Ce travail propose d'abord une revue bibliographique exhaustive sur ces tumeurs, en particulier les outils diagnostiques, et les approches thérapeutiques actuelles. Il explore ensuite l'utilisation de la séquence de perfusion au scanner pour le suivi de ces tumeurs, une pratique très peu réalisée en oncologie vétérinaire contrairement à la médecine humaine. Les critères de réponse aux traitements de l’OMS et du RECIST sont difficilement applicables aux tumeurs nasales canines en raison de leur forme irrégulière. Cela souligne le besoin de développer des indicateurs pronostiques plus adaptés. L'angiogenèse tumorale, associée à l'évolution de la tumeur, se révèle pertinente dans ce contexte. Les paramètres de perfusion, tels que le débit et le volume sanguin tumoral, servent de marqueurs pour évaluer la vascularisation tumorale. En oncologie humaine, des valeurs élevées de perfusion avant traitement sont associées à une meilleure réponse à la radiothérapie. Une étude prospective menée sur 8 chiens atteints de tumeurs nasales traités par radiothérapie montre que les sarcomes ont une perfusion initiale plus faible et une réduction de volume tumoral moindre par rapport aux carcinomes. Aucune corrélation significative n’a pu être établie entre le taux de réduction tumorale et la survie, ni entre les paramètres initiaux de perfusion et la survie. Ces résultats confirment les tendances observées dans les études antérieures, tout en soulignant la nécessité de cohortes plus larges pour valider pleinement le rôle des paramètres de perfusion comme indicateurs pronostiques et pour affiner les stratégies thérapeutiques.
A Survey on LiDAR Scanning Mechanisms
In recent years, light detection and ranging (LiDAR) technology has gained huge popularity in various applications such as navigation, robotics, remote sensing, and advanced driving assistance systems (ADAS). This popularity is mainly due to the improvements in LiDAR performance in terms of range detection, accuracy, power consumption, as well as physical features such as dimension and weight. Although a number of literatures on LiDAR technology have been published earlier, not many has been reported on the state-of-the-art LiDAR scanning mechanisms. The aim of this article is to review the scanning mechanisms employed in LiDAR technology from past research works to the current commercial products. The review highlights four commonly used mechanisms in LiDAR systems: Opto-mechanical, electromechanical, micro-electromechanical systems (MEMS), and solid-state scanning. The study reveals that electro-mechanical scanning is the most prominent technology in use today. The commercially available 1D time of flight (TOF) LiDAR instrument is currently the most attractive option for conversion from 1D to 3D LiDAR system, provided that low scanning rate is not an issue. As for applications with low size, weight, and power (SWaP) requirements, MEMS scanning is found to be the better alternative. MEMS scanning is by far the more matured technology compared to solid-state scanning and is currently given great emphasis to increase its robustness for fulfilling the requirements of ADAS applications. Finally, solid-state LiDAR systems are expected to fill in the gap in ADAS applications despite the low technology readiness in comparison to MEMS scanners. However, since solid-state scanning is believed to have superior robustness, field of view (FOV), and scanning rate potential, great efforts are given by both academics and industries to further develop this technology.
Hydrogen Fuel Cell Technology for the Sustainable Future of Stationary Applications
The climate changes that are becoming visible today are a challenge for the global research community. The stationary applications sector is one of the most important energy consumers. Harnessing the potential of renewable energy worldwide is currently being considered to find alternatives for obtaining energy by using technologies that offer maximum efficiency and minimum pollution. In this context, new energy generation technologies are needed to both generate low carbon emissions, as well as identifying, planning and implementing the directions for harnessing the potential of renewable energy sources. Hydrogen fuel cell technology represents one of the alternative solutions for future clean energy systems. This article reviews the specific characteristics of hydrogen energy, which recommends it as a clean energy to power stationary applications. The aim of review was to provide an overview of the sustainability elements and the potential of using hydrogen as an alternative energy source for stationary applications, and for identifying the possibilities of increasing the share of hydrogen energy in stationary applications, respectively. As a study method was applied a SWOT analysis, following which a series of strategies that could be adopted in order to increase the degree of use of hydrogen energy as an alternative to the classical energy for stationary applications were recommended. The SWOT analysis conducted in the present study highlights that the implementation of the hydrogen economy depends decisively on the following main factors: legislative framework, energy decision makers, information and interest from the end beneficiaries, potential investors, and existence of specialists in this field.
What Have We Learned from Market Design?
This article discusses some things we have learned about markets, in the process of designing marketplaces to fix market failures. To work well, marketplaces have to provide thickness, i.e. they need to attract a large enough proportion of the potential participants in the market; they have to overcome the congestion that thickness can bring, by making it possible to consider enough alternative transactions to arrive at good ones; and they need to make it safe and sufficiently simple to participate in the market, as opposed to transacting outside of the market, or having to engage in costly and risky strategic behaviour. I will draw on recent examples of market design ranging from labour markets for doctors and new economists, to kidney exchange, and school choice in New York City and Boston.
Accounting, hybrids and the management of risk
[no abstract]
A State-of-the-Art Review on the Integration of Building Information Modeling (BIM) and Geographic Information System (GIS)
The integration of Building Information Modeling (BIM) and Geographic Information System (GIS) has been identified as a promising but challenging topic to transform information towards the generation of knowledge and intelligence. Achievement of integrating these two concepts and enabling technologies will have a significant impact on solving problems in the civil, building and infrastructure sectors. However, since GIS and BIM were originally developed for different purposes, numerous challenges are being encountered for the integration. To better understand these two different domains, this paper reviews the development and dissimilarities of GIS and BIM, the existing integration methods, and investigates their potential in various applications. This study shows that the integration methods are developed for various reasons and aim to solve different problems. The parameters influencing the choice can be summarized and named as “EEEF” criteria: effectiveness, extensibility, effort, and flexibility. Compared with other methods, semantic web technologies provide a promising and generalized integration solution. However, the biggest challenges of this method are the large efforts required at early stage and the isolated development of ontologies within one particular domain. The isolation problem also applies to other methods. Therefore, openness is the key of the success of BIM and GIS integration.
The intense world syndrome – an alternative hypothesis for autism
Autism is a devastating neurodevelopmental disorder with a polygenetic predisposition that seems to be triggered by multiple environmental factors during embryonic and/or early postnatal life. While significant advances have been made in identifying the neuronal structures and cells affected, a unifying theory that could explain the manifold autistic symptoms has still not emerged. Based on recent synaptic, cellular, molecular, microcircuit, and behavioral results obtained with the valproic acid (VPA) rat model of autism, we propose here a unifying hypothesis where the core pathology of the autistic brain is hyper-reactivity and hyper-plasticity of local neuronal circuits. Such excessive neuronal processing in circumscribed circuits is suggested to lead to hyper-perception, hyper-attention, and hyper-memory, which may lie at the heart of most autistic symptoms. In this view, the autistic spectrum are disorders of hyper-functionality, which turns debilitating, as opposed to disorders of hypo-functionality, as is often assumed. We discuss how excessive neuronal processing may render the world painfully intense when the neocortex is affected and even aversive when the amygdala is affected, leading to social and environmental withdrawal. Excessive neuronal learning is also hypothesized to rapidly lock down the individual into a small repertoire of secure behavioral routines that are obsessively repeated. We further discuss the key autistic neuropathologies and several of the main theories of autism and re-interpret them in the light of the hypothesized Intense World Syndrome.
IoT-Equipped and AI-Enabled Next Generation Smart Agriculture: A Critical Review, Current Challenges and Future Trends
Smart agriculture techniques have recently seen widespread interest by farmers. This is driven by several factors, which include the widespread availability of economically-priced, low-powered Internet of Things (IoT) based wireless sensors to remotely monitor and report conditions of the field, climate, and crops. This enables efficient management of resources like minimizing water requirements for irrigation and minimizing the use of toxic pesticides. Furthermore, the recent boom in Artificial Intelligence can enable farmers to deploy autonomous farming machinery and make better predictions of the future based on present and past conditions to minimize crop diseases and pest infestation. Together these two enabling technologies have revolutionized conventional agriculture practices. This survey paper provides: (a) A detailed tutorial on the available advancements in the field of smart agriculture systems through IoT technologies and AI techniques; (b) A critical review of these two available technologies and challenges in their widespread deployment; and (c) An in-depth discussion about the future trends including both technological and social, when smart agriculture systems will be widely adopted by the farmers globally.
A CNN-Assisted Enhanced Audio Signal Processing for Speech Emotion Recognition
Speech is the most significant mode of communication among human beings and a potential method for human-computer interaction (HCI) by using a microphone sensor. Quantifiable emotion recognition using these sensors from speech signals is an emerging area of research in HCI, which applies to multiple applications such as human-reboot interaction, virtual reality, behavior assessment, healthcare, and emergency call centers to determine the speaker's emotional state from an individual's speech. In this paper, we present major contributions for; (i) increasing the accuracy of speech emotion recognition (SER) compared to state of the art and (ii) reducing the computational complexity of the presented SER model. We propose an artificial intelligence-assisted deep stride convolutional neural network (DSCNN) architecture using the plain nets strategy to learn salient and discriminative features from spectrogram of speech signals that are enhanced in prior steps to perform better. Local hidden patterns are learned in convolutional layers with special strides to down-sample the feature maps rather than pooling layer and global discriminative features are learned in fully connected layers. A SoftMax classifier is used for the classification of emotions in speech. The proposed technique is evaluated on Interactive Emotional Dyadic Motion Capture (IEMOCAP) and Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) datasets to improve accuracy by 7.85% and 4.5%, respectively, with the model size reduced by 34.5 MB. It proves the effectiveness and significance of the proposed SER technique and reveals its applicability in real-world applications.
Transport impacts on atmosphere and climate: Land transport
[no abstract]
Automated medical diagnosis of COVID-19 through EfficientNet convolutional neural network
[no abstract]
Variational LSTM Enhanced Anomaly Detection for Industrial Big Data
With the increasing population of Industry 4.0, industrial big data (IBD) has become a hotly discussed topic in digital and intelligent industry field. The security problem existing in the signal processing on large scale of data stream is still a challenge issue in industrial internet of things, especially when dealing with the high-dimensional anomaly detection for intelligent industrial application. In this article, to mitigate the inconsistency between dimensionality reduction and feature retention in imbalanced IBD, we propose a variational long short-term memory (VLSTM) learning model for intelligent anomaly detection based on reconstructed feature representation. An encoder-decoder neural network associated with a variational reparameterization scheme is designed to learn the low-dimensional feature representation from high-dimensional raw data. Three loss functions are defined and quantified to constrain the reconstructed hidden variable into a more explicit and meaningful form. A lightweight estimation network is then fed with the refined feature representation to identify anomalies in IBD. Experiments using a public IBD dataset named UNSW-NB15 demonstrate that the proposed VLSTM model can efficiently cope with imbalance and high-dimensional issues, and significantly improve the accuracy and reduce the false rate in anomaly detection for IBD according to F1, area under curve (AUC), and false alarm rate (FAR).
A Review on Mixed Reality: Current Trends, Challenges and Prospects
Currently, new technologies have enabled the design of smart applications that are used as decision-making tools in the problems of daily life. The key issue in designing such an application is the increasing level of user interaction. Mixed reality (MR) is an emerging technology that deals with maximum user interaction in the real world compared to other similar technologies. Developing an MR application is complicated, and depends on the different components that have been addressed in previous literature. In addition to the extraction of such components, a comprehensive study that presents a generic framework comprising all components required to develop MR applications needs to be performed. This review studies intensive research to obtain a comprehensive framework for MR applications. The suggested framework comprises five layers: the first layer considers system components; the second and third layers focus on architectural issues for component integration; the fourth layer is the application layer that executes the architecture; and the fifth layer is the user interface layer that enables user interaction. The merits of this study are as follows: this review can act as a proper resource for MR basic concepts, and it introduces MR development steps and analytical models, a simulation toolkit, system types, and architecture types, in addition to practical issues for stakeholders such as considering MR different domains.
A Comprehensive Survey on UAV Communication Channel Modeling
Unmanned aerial vehicles (UAVs) have stroke great interested both by the academic community and the industrial community due to their diverse military applications and civilian applications. Furthermore, UAVs are also envisioned to be part of future airspace traffic. The application functions delivery relies on information exchange among UAVs as well as between UAVs and ground stations (GSs), which further closely depends on aeronautical channels. However, there is a paucity of comprehensive surveys on aeronautical channel modeling in line with the specific aeronautical characteristics and scenarios. To fill this gap, this paper focuses on reviewing the air-to-ground (A2G), ground-to-ground (G2G), and air-to-air (A2A) channel measurements and modeling for UAV communications and aeronautical communications under various scenarios. We also provide the design guideline for managing the link budget of UAV communications taking account of link losses and channel fading effects. Moreover, we also analyze the receive/transmit diversity gain and spatial multiplexing gain achieved by multiple-antenna-aided UAV communications. Finally, we discuss the remaining challenge and open issues for the future development of UAV communication channel modeling.