7,415 research outputs found

    Converging organoids and extracellular matrix::New insights into liver cancer biology

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    Analysis and Design of Non-Orthogonal Multiple Access (NOMA) Techniques for Next Generation Wireless Communication Systems

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    The current surge in wireless connectivity, anticipated to amplify significantly in future wireless technologies, brings a new wave of users. Given the impracticality of an endlessly expanding bandwidth, there’s a pressing need for communication techniques that efficiently serve this burgeoning user base with limited resources. Multiple Access (MA) techniques, notably Orthogonal Multiple Access (OMA), have long addressed bandwidth constraints. However, with escalating user numbers, OMA’s orthogonality becomes limiting for emerging wireless technologies. Non-Orthogonal Multiple Access (NOMA), employing superposition coding, serves more users within the same bandwidth as OMA by allocating different power levels to users whose signals can then be detected using the gap between them, thus offering superior spectral efficiency and massive connectivity. This thesis examines the integration of NOMA techniques with cooperative relaying, EXtrinsic Information Transfer (EXIT) chart analysis, and deep learning for enhancing 6G and beyond communication systems. The adopted methodology aims to optimize the systems’ performance, spanning from bit-error rate (BER) versus signal to noise ratio (SNR) to overall system efficiency and data rates. The primary focus of this thesis is the investigation of the integration of NOMA with cooperative relaying, EXIT chart analysis, and deep learning techniques. In the cooperative relaying context, NOMA notably improved diversity gains, thereby proving the superiority of combining NOMA with cooperative relaying over just NOMA. With EXIT chart analysis, NOMA achieved low BER at mid-range SNR as well as achieved optimal user fairness in the power allocation stage. Additionally, employing a trained neural network enhanced signal detection for NOMA in the deep learning scenario, thereby producing a simpler signal detection for NOMA which addresses NOMAs’ complex receiver problem

    An investigation of entorhinal spatial representations in self-localisation behaviours

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    Spatial-modulated cells of the medial entorhinal cortex (MEC) and neighbouring cortices are thought to provide the neural substrate for self-localisation behaviours. These cells include grid cells of the MEC which are thought to compute path integration operations to update self-location estimates. In order to read this grid code, downstream cells are thought to reconstruct a positional estimate as a simple rate-coded representation of space. Here, I show the coding scheme of grid cell and putative readout cells recorded from mice performing a virtual reality (VR) linear location task which engaged mice in both beaconing and path integration behaviours. I found grid cells can encode two unique coding schemes on the linear track, namely a position code which reflects periodic grid fields anchored to salient features of the track and a distance code which reflects periodic grid fields without this anchoring. Grid cells were found to switch between these coding schemes within sessions. When grid cells were encoding position, mice performed better at trials that required path integration but not on trials that required beaconing. This result provides the first mechanistic evidence linking grid cell activity to path integration-dependent behaviour. Putative readout cells were found in the form of ramp cells which fire proportionally as a function of location in defined regions of the linear track. This ramping activity was found to be primarily explained by track position rather than other kinematic variables like speed and acceleration. These representations were found to be maintained across both trial types and outcomes indicating they likely result from recall of the track structure. Together, these results support the functional importance of grid and ramp cells for self-localisation behaviours. Future investigations will look into the coherence between these two neural populations, which may together form a complete neural system for coding and decoding self-location in the brain

    Complexity Science in Human Change

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    This reprint encompasses fourteen contributions that offer avenues towards a better understanding of complex systems in human behavior. The phenomena studied here are generally pattern formation processes that originate in social interaction and psychotherapy. Several accounts are also given of the coordination in body movements and in physiological, neuronal and linguistic processes. A common denominator of such pattern formation is that complexity and entropy of the respective systems become reduced spontaneously, which is the hallmark of self-organization. The various methodological approaches of how to model such processes are presented in some detail. Results from the various methods are systematically compared and discussed. Among these approaches are algorithms for the quantification of synchrony by cross-correlational statistics, surrogate control procedures, recurrence mapping and network models.This volume offers an informative and sophisticated resource for scholars of human change, and as well for students at advanced levels, from graduate to post-doctoral. The reprint is multidisciplinary in nature, binding together the fields of medicine, psychology, physics, and neuroscience

    Specificity of the innate immune responses to different classes of non-tuberculous mycobacteria

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    Mycobacterium avium is the most common nontuberculous mycobacterium (NTM) species causing infectious disease. Here, we characterized a M. avium infection model in zebrafish larvae, and compared it to M. marinum infection, a model of tuberculosis. M. avium bacteria are efficiently phagocytosed and frequently induce granuloma-like structures in zebrafish larvae. Although macrophages can respond to both mycobacterial infections, their migration speed is faster in infections caused by M. marinum. Tlr2 is conservatively involved in most aspects of the defense against both mycobacterial infections. However, Tlr2 has a function in the migration speed of macrophages and neutrophils to infection sites with M. marinum that is not observed with M. avium. Using RNAseq analysis, we found a distinct transcriptome response in cytokine-cytokine receptor interaction for M. avium and M. marinum infection. In addition, we found differences in gene expression in metabolic pathways, phagosome formation, matrix remodeling, and apoptosis in response to these mycobacterial infections. In conclusion, we characterized a new M. avium infection model in zebrafish that can be further used in studying pathological mechanisms for NTM-caused diseases

    Unified System on Chip RESTAPI Service (USOCRS)

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    Abstract. This thesis investigates the development of a Unified System on Chip RESTAPI Service (USOCRS) to enhance the efficiency and effectiveness of SOC verification reporting. The research aims to overcome the challenges associated with the transfer, utilization, and interpretation of SoC verification reports by creating a unified platform that integrates various tools and technologies. The research methodology used in this study follows a design science approach. A thorough literature review was conducted to explore existing approaches and technologies related to SOC verification reporting, automation, data visualization, and API development. The review revealed gaps in the current state of the field, providing a basis for further investigation. Using the insights gained from the literature review, a system design and implementation plan were developed. This plan makes use of cutting-edge technologies such as FASTAPI, SQL and NoSQL databases, Azure Active Directory for authentication, and Cloud services. The Verification Toolbox was employed to validate SoC reports based on the organization’s standards. The system went through manual testing, and user satisfaction was evaluated to ensure its functionality and usability. The results of this study demonstrate the successful design and implementation of the USOCRS, offering SOC engineers a unified and secure platform for uploading, validating, storing, and retrieving verification reports. The USOCRS facilitates seamless communication between users and the API, granting easy access to vital information including successes, failures, and test coverage derived from submitted SoC verification reports. By automating and standardizing the SOC verification reporting process, the USOCRS eliminates manual and repetitive tasks usually done by developers, thereby enhancing productivity, and establishing a robust and reliable framework for report storage and retrieval. Through the integration of diverse tools and technologies, the USOCRS presents a comprehensive solution that adheres to the required specifications of the SOC schema used within the organization. Furthermore, the USOCRS significantly improves the efficiency and effectiveness of SOC verification reporting. It facilitates the submission process, reduces latency through optimized data storage, and enables meaningful extraction and analysis of report data

    Design and Construction of a Longitudinally Polarized Solid Nuclear Target for CLAS12

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    A new polarized nuclear target has been developed, constructed, and deployed at Jefferson Laboratory in Newport News, VA for use with the upgraded 12 GeV CEBAF (Continuous Electron Beam Accelerator Facility) accelerator and the Hall B CLAS12 (12 GeV CEBAF Large Acceptance Spectrometer) detector array. This ‘APOLLO’ (Ammonia POLarized LOngitudinally) target is a longitudinally polarized, solid ammonia, nuclear target which employs DNP (Dynamic Nuclear Polarization) to induce a net polarization in samples of protons (NH3) and deuterons (ND3) cooled to 1K via helium evaporation, held in a 5T polarizing field supplied by the CLAS12 spectrometer, and irradiated with 140 GHz microwave radiation. It was utilized in the RGC (Run Group C) experiment suite through a collaboration of the JLab Target Group, Old Dominion University, Christopher Newport University, the University of Virginia, and the CLAS Collaboration. RGC comprised six experiments which measured multiple spin-dependent observables across a wide kinematic phase space for use in nucleon spin studies. The dimensional constraints necessary for the incorporation of APOLLO into CLAS12, as well as the considerations necessary to utilize the CLAS12 solenoid, introduced unique challenges to the target design. This document presents the innovative solutions developed for these challenges including a novel material transport system, superconducting magnetic correction coils, and an all new bespoke NMR (Nuclear Magnetic Resonance) system. In addition to a detailed description of the complete target system and an initial report of the RGC experimental run, it will also present a study of Quark-Hadron Duality in the g1 spin structure function based on Hall B EG1b data and pQCD fits from the JAM (Jefferson Lab Angular Momentum) Collaboration

    酸性条件下におけるメタン生成古細菌の死滅促進現象とその数理モデルに関する研究

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    The study focused on the effects of low pH on the decay rate of methanogenic microorganisms and their potential VFA inhibition. Results showed that low pH conditions, with a pH below 6.5, significantly enhanced the decay rate of methanogenic microorganisms, leading to irreversible inhibition. Whereas, the acidogenic bacteria were found to be more tolerant to the acidic environment. The biocidal phenomenon was mainly attributed to the proton concentration in the system and was not affected by the presence of phosphate buffer. The impacts of acidic species (C1–C5 VFAs) and the VFA concentration (until 40 mM of un-dissociated VFAs) on the biomass decay were negligibly small comparing to the low pH stress. A modified ADM1 equipped with a lag-phase sub-model could reproduce the deterioration of reactor performance during an acidic failure and resulte in a 37% improvement in the Nash-Sutcliffe efficiency coefficient compared to the calibrated ADM1 model.北九州市立大

    Air Traffic Controller Workload Level Prediction using Conformalized Dynamical Graph Learning

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    Air traffic control (ATC) is a safety-critical service system that demands constant attention from ground air traffic controllers (ATCos) to maintain daily aviation operations. The workload of the ATCos can have negative effects on operational safety and airspace usage. To avoid overloading and ensure an acceptable workload level for the ATCos, it is important to predict the ATCos' workload accurately for mitigation actions. In this paper, we first perform a review of research on ATCo workload, mostly from the air traffic perspective. Then, we briefly introduce the setup of the human-in-the-loop (HITL) simulations with retired ATCos, where the air traffic data and workload labels are obtained. The simulations are conducted under three Phoenix approach scenarios while the human ATCos are requested to self-evaluate their workload ratings (i.e., low-1 to high-7). Preliminary data analysis is conducted. Next, we propose a graph-based deep-learning framework with conformal prediction to identify the ATCo workload levels. The number of aircraft under the controller's control varies both spatially and temporally, resulting in dynamically evolving graphs. The experiment results suggest that (a) besides the traffic density feature, the traffic conflict feature contributes to the workload prediction capabilities (i.e., minimum horizontal/vertical separation distance); (b) directly learning from the spatiotemporal graph layout of airspace with graph neural network can achieve higher prediction accuracy, compare to hand-crafted traffic complexity features; (c) conformal prediction is a valuable tool to further boost model prediction accuracy, resulting a range of predicted workload labels. The code used is available at \href{https://github.com/ymlasu/para-atm-collection/blob/master/air-traffic-prediction/ATC-Workload-Prediction/}{Link\mathsf{Link}}

    Brain Computations and Connectivity [2nd edition]

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    This is an open access title available under the terms of a CC BY-NC-ND 4.0 International licence. It is free to read on the Oxford Academic platform and offered as a free PDF download from OUP and selected open access locations. Brain Computations and Connectivity is about how the brain works. In order to understand this, it is essential to know what is computed by different brain systems; and how the computations are performed. The aim of this book is to elucidate what is computed in different brain systems; and to describe current biologically plausible computational approaches and models of how each of these brain systems computes. Understanding the brain in this way has enormous potential for understanding ourselves better in health and in disease. Potential applications of this understanding are to the treatment of the brain in disease; and to artificial intelligence which will benefit from knowledge of how the brain performs many of its extraordinarily impressive functions. This book is pioneering in taking this approach to brain function: to consider what is computed by many of our brain systems; and how it is computed, and updates by much new evidence including the connectivity of the human brain the earlier book: Rolls (2021) Brain Computations: What and How, Oxford University Press. Brain Computations and Connectivity will be of interest to all scientists interested in brain function and how the brain works, whether they are from neuroscience, or from medical sciences including neurology and psychiatry, or from the area of computational science including machine learning and artificial intelligence, or from areas such as theoretical physics
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