Search

The integration of autonomous vehicles into our daily lives is steadily advancing, with researchers and institutions contributing to the realization of fully autonomous commercially available vehicles. Among the critical components of such vehicles is object detection, which draws from various fields like image processing and statistics. This article introduces a novel real-time adaptive object detection method inspired by the principles of real-time computing and control systems, meticulously tailored for use in automated vehicle control systems. In this context, it is essential to recognize that computational resources are limited, and this limitation has a direct impact on the reaction time. To address this challenge, our method leverages the aggregate channel features (ACF) detection algorithm, thoughtfully incorporating considerations of computational resources by integrating feedback from the vehicle motion planner. The proposed model undergoes comprehensive analysis and simulation within the MATLAB and Simulink environment, and the results are indeed promising, showcasing significant enhancements in the reaction time. [ABSTRACT FROM AUTHOR]

AI chatbots have emerged as powerful tools for providing text-based solutions to a wide range of everyday challenges. Selecting the appropriate chatbot is crucial for optimising outcomes. This paper presents a comprehensive comparative analysis of five leading chatbots: ChatGPT, Bard, Llama, Ernie, and Grok. The analysis is based on a systematic review of 28 scholarly articles. The review indicates that ChatGPT, developed by OpenAI, excels in educational, medical, humanities, and writing applications but struggles with real-time data accuracy and lacks open-source flexibility. Bard, powered by Google, leverages real-time internet data for problem solving and shows potential in competitive quiz environments, albeit with performance variability and inconsistencies in responses. Llama, an open-source model from Meta, demonstrates significant promise in medical contexts, natural language processing, and personalised educational tools, yet it requires substantial computational resources. Ernie, developed by Baidu, specialises in Chinese language tasks, thus providing localised advantages that may not extend globally due to restrictive policies. Grok, developed by Xai and still in its early stages, shows promise in providing engaging, real-time interactions, humour, and mathematical reasoning capabilities, but its full potential remains to be evaluated through further development and empirical testing. The findings underscore the context-dependent utility of each model and the absence of a singularly superior chatbot. Future research should expand to include a wider range of fields, explore practical applications, and address concerns related to data privacy, ethics, security, and the responsible deployment of these technologies. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Natural image matting based on pixel pair optimization is commonly employed during image post-processing. However, obtaining high-quality alpha mattes for high-resolution images via existing image matting methods is challenging as it typically requires considerable computational resources. In this paper, we design a novel optimization information transmission strategy that can be applied to images of different resolutions to improve the quality of the transmitted information required for evolutionary optimization. In addition, we propose a micro-scale searching matting algorithm, which allows us to obtain high-quality matting for high-resolution images with limited computational resources. To verify the applicability of the proposed algorithm for high-resolution images, experiments were conducted on the alpha matting benchmark dataset. Experimental results show that the proposed micro-scale searching matting algorithm can estimate high-quality alpha mattes without incurring excessive computational resources. Moreover, the proposed algorithm outperforms the state-of-the-art optimized matting algorithms when applied to high-resolution images. [ABSTRACT FROM AUTHOR]

Quantum Convolutional Neural Network (QCNN) has achieved significant success in solving various complex problems, such as quantum many-body physics and image recognition. In comparison to the classical Convolutional Neural Network (CNN) model, the QCNN model requires excellent numerical performance or efficient computational resources to showcase its potential quantum advantages, particularly in classical data processing tasks. In this paper, we propose a computationally resource-efficient QCNN model referred to as RE-QCNN. Specifically, through a comprehensive analysis of the complexity associated with the forward and backward propagation processes in the quantum convolutional layer, our results demonstrate a significant reduction in computational resources required for this layer compared to the classical CNN model. Furthermore, our model is numerically benchmarked on recognizing images from the MNIST and Fashion-MNIST datasets, achieving high accuracy in these multi-class classification tasks. [ABSTRACT FROM AUTHOR]

ADAR (Adenosine Deaminases Acting on RNA) proteins are a group of enzymes that play a vital role in RNA editing by converting adenosine to inosine in RNAs. This process is a frequent post-transcriptional event observed in metazoan transcripts. Recent studies indicate widespread dysregulation of ADAR-mediated RNA editing across many immune-related diseases, such as human cancer. We comprehensively review ADARs' function as pattern recognizers and their capability to contribute to mediating immune-related pathways. We also highlight the potential role of site-specific RNA editing in maintaining homeostasis and its relationship to various diseases, such as human cancers. More importantly, we summarize the latest cutting-edge computational approaches and data resources for predicting and analyzing RNA editing sites. Lastly, we cover the recent advancement in site-directed ADAR editing tool development. This review presents an up-to-date overview of ADAR-mediated RNA editing, how site-specific RNA editing could potentially impact disease pathology, and how they could be harnessed for therapeutic applications. [ABSTRACT FROM AUTHOR]

The new generation of mobile networks goes beyond classic radio-focus access networks by adopting the softwareisation of functions that typically relied on special-purpose hardware, now to be deployed in as general as possible purpose platforms. The resulting virtualised Radio Access Network (vRAN) follows the Network Function Virtualisation (NFV) paradigm, enabling a distributed and scalable network architecture. However, this approach increases the complexity of management tasks as computing resources now play an essential role in the network provisioning process. In this sense, this work aims to assess the impact of computational resources on the Quality of Experience (QoE) of video streaming services delivered over vRAN-based networks. This is done by performing multiple playback of 4K video while allocating varying numbers of Physical Resource Blocks (PRBs) and virtual CPU to vRAN. The obtained results prove that inadequate resource assignment to vRAN instances leads to degradation of the QoE, even if the allocation of radio resources is adequate for the service.

ADAR (Adenosine Deaminases Acting on RNA) proteins are a group of enzymes that play a vital role in RNA editing by converting adenosine to inosine in RNAs. This process is a frequent post-transcriptional event observed in metazoan transcripts. Recent studies indicate widespread dysregulation of ADAR-mediated RNA editing across many immune-related diseases, such as human cancer. We comprehensively review ADARs’ function as pattern recognizers and their capability to contribute to mediating immune-related pathways. We also highlight the potential role of site-specific RNA editing in maintaining homeostasis and its relationship to various diseases, such as human cancers. More importantly, we summarize the latest cutting-edge computational approaches and data resources for predicting and analyzing RNA editing sites. Lastly, we cover the recent advancement in site-directed ADAR editing tool development. This review presents an up-to-date overview of ADAR-mediated RNA editing, how site-specific RNA editing could potentially impact disease pathology, and how they could be harnessed for therapeutic applications.

The research aims to show the ways to benefit from corpus linguistics in the field of pragmatics, it investigates an important question: What are the possibilities to investing corpus linguistics in conducting pragmatic analysis of Arabic literary texts? "TARAFA ibn al-Abd poetry" was taken as a model for the practical applications Accordingly, the research findings emphasized the necessity of construction of various Arabic, be it literary or others, and investing computer resources to facilitate the research and the analysis in the pragmatic field particularly and in various fields of linguistic research and literary analysis in general. [ABSTRACT FROM AUTHOR]

The object of this study is the relationship between the technical characteristics of a computing system and the duration of modeling the motion of particles of granular materials by the discrete element method. The scheme of the calculation algorithm is presented; its main stages are analyzed. A 3D model of a belt feeder and a mathematical model of particle motion were developed for calculations by the discrete element method in the EDEM 2017 environment. The physical and mechanical properties of bulk material are defined; the structural and technological parameters of equipment are determined. The parameters of the algorithm and computing system are analyzed. Those parameters are defined, the change of which does not affect the accuracy of calculations but can change the volume of computing resources used. These include the number of particles of loose material, the «grid» step, and the number of processor cores. The influence of these parameters on the duration of simulation was determined using a complete factor experiment. Experimental studies have shown that for the duration of the simulation, the determining parameters are the number of particles and the number of processor cores. It was established that there is a linear relation)ship between the duration of the simulation and the number of particles. The regression equation is built, which makes it possible to predict the simulation time. It was also established that the software does not fully use all available computing resources; the maximum load on the processor when utilizing all available cores is 57 %. The use of RAM and disk subsystem almost did not change during simulation. The results reported here make it possible to plan the use of computing resources for research using the discrete element method and to predict the simulation time. [ABSTRACT FROM AUTHOR]

Abstract Background Automated assignment of specific ontology concepts to mentions in text is a critical task in biomedical natural language processing, and the subject of many open shared tasks. Although the current state of the art involves the use of neural network language models as a post-processing step, the very large number of ontology classes to be recognized and the limited amount of gold-standard training data has impeded the creation of end-to-end systems based entirely on machine learning. Recently, Hailu et al. recast the concept recognition problem as a type of machine translation and demonstrated that sequence-to-sequence machine learning models have the potential to outperform multi-class classification approaches. Methods We systematically characterize the factors that contribute to the accuracy and efficiency of several approaches to sequence-to-sequence machine learning through extensive studies of alternative methods and hyperparameter selections. We not only identify the best-performing systems and parameters across a wide variety of ontologies but also provide insights into the widely varying resource requirements and hyperparameter robustness of alternative approaches. Analysis of the strengths and weaknesses of such systems suggest promising avenues for future improvements as well as design choices that can increase computational efficiency with small costs in performance. Results Bidirectional encoder representations from transformers for biomedical text mining (BioBERT) for span detection along with the open-source toolkit for neural machine translation (OpenNMT) for concept normalization achieve state-of-the-art performance for most ontologies annotated in the CRAFT Corpus. This approach uses substantially fewer computational resources, including hardware, memory, and time than several alternative approaches. Conclusions Machine translation is a promising avenue for fully machine-learning-based concept recognition that achieves state-of-the-art results on the CRAFT Corpus, evaluated via a direct comparison to previous results from the 2019 CRAFT shared task. Experiments illuminating the reasons for the surprisingly good performance of sequence-to-sequence methods targeting ontology identifiers suggest that further progress may be possible by mapping to alternative target concept representations. All code and models can be found at: https://github.com/UCDenver-ccp/Concept-Recognition-as-Translation .

With the fast and unstoppable development of technology, the amount of available technological devices and the data they produce is overwhelming. In analyzing the context of a smart home, a diverse group of intelligent devices generating constant reports of its environment information is needed for the proper control of the house. Due to this demand, many possible solutions have been developed in the literature to assess the need for processing power and storage capacity. This work proposes HOsT (home-context-aware fog-computing solution)—a solution that addresses the problems of data heterogeneity and the interoperability of smart objects in the context of a smart home. HOsT was modeled to compose a set of intelligent objects to form a computational infrastructure in fog. A publish/subscribe communication module was implemented to abstract the details of communication between objects to disseminate heterogeneous information. A performance evaluation was carried out to validate HOsT. The results show evidence of efficiency in the communication infrastructure; and in the impact of HOsT compared with a cloud infrastructure. Furthermore, HOsT provides scalability about the number of devices acting simultaneously and demonstrates its ability to work with different devices. [ABSTRACT FROM AUTHOR]

Background: Automated assignment of specific ontology concepts to mentions in text is a critical task in biomedical natural language processing, and the subject of many open shared tasks. Although the current state of the art involves the use of neural network language models as a post-processing step, the very large number of ontology classes to be recognized and the limited amount of gold-standard training data has impeded the creation of end-to-end systems based entirely on machine learning. Recently, Hailu et al. recast the concept recognition problem as a type of machine translation and demonstrated that sequence-to-sequence machine learning models have the potential to outperform multi-class classification approaches. Methods: We systematically characterize the factors that contribute to the accuracy and efficiency of several approaches to sequence-to-sequence machine learning through extensive studies of alternative methods and hyperparameter selections. We not only identify the best-performing systems and parameters across a wide variety of ontologies but also provide insights into the widely varying resource requirements and hyperparameter robustness of alternative approaches. Analysis of the strengths and weaknesses of such systems suggest promising avenues for future improvements as well as design choices that can increase computational efficiency with small costs in performance. Results: Bidirectional encoder representations from transformers for biomedical text mining (BioBERT) for span detection along with the open-source toolkit for neural machine translation (OpenNMT) for concept normalization achieve state-of-the-art performance for most ontologies annotated in the CRAFT Corpus. This approach uses substantially fewer computational resources, including hardware, memory, and time than several alternative approaches. Conclusions: Machine translation is a promising avenue for fully machine-learning-based concept recognition that achieves state-of-the-art results on the CRAFT Corpus, evaluated via a direct comparison to previous results from the 2019 CRAFT shared task. Experiments illuminating the reasons for the surprisingly good performance of sequence-to-sequence methods targeting ontology identifiers suggest that further progress may be possible by mapping to alternative target concept representations. All code and models can be found at: https://github.com/UCDenver-ccp/Concept-Recognition-as-Translation. [ABSTRACT FROM AUTHOR]

The proliferation of artificial intelligence (AI) and natural language processing (NLP) technologies has enabled the generation of realistic and coherent texts, but it also raises concerns regarding the potential misuse of these technologies for generating misleading or malicious content. Automatic detection of generated texts is crucial in addressing this issue. This article provides a comprehensive examination of the relationship between the detection of generated texts and energy consumption, delving into the techniques, challenges, and opportunities for developing energyefficient algorithms for text detection.

We argue that there is a simple, unique, reason for all quantum paradoxes, and that such a reason is not uniquely related to quantum theory. It is rather a mathematical question that arises at the intersection of logic, probability, and computation. We give our ‘weirdness theorem’ that characterises the conditions under which the weirdness will show up. It shows that whenever logic has bounds due to the algorithmic nature of its tasks, then weirdness arises in the special form of negative probabilities or non-classical evaluation functionals. Weirdness is not logical inconsistency, however. It is only the expression of the clash between an unbounded and a bounded view of computation in logic. We discuss the implication of these results for quantum mechanics, arguing in particular that its interpretation should ultimately be computational rather than exclusively physical. We develop in addition a probabilistic theory in the real numbers that exhibits the phenomenon of entanglement, thus concretely showing that the latter is not specific to quantum mechanics. [ABSTRACT FROM AUTHOR]

Genetic simulation programs are used to model data under specified assumptions to facilitate the understanding and study of complex genetic systems. Standardized data sets generated using genetic simulation are essential for the development and application of novel analytical tools in genetic epidemiology studies. With continuing advances in high-throughput genomic technologies and generation and analysis of larger, more complex data sets, there is a need for updating current approaches in genetic simulation modeling. To provide a forum to address current and emerging challenges in this area, the National Cancer Institute (NCI) sponsored a workshop, entitled "Genetic Simulation Tools for Post-Genome Wide Association Studies of Complex Diseases" at the National Institutes of Health (NIH) in Bethesda, Maryland on March 11-12, 2014. The goals of the workshop were to (1) identify opportunities, challenges, and resource needs for the development and application of genetic simulation models; (2) improve the integration of tools for modeling and analysis of simulated data; and (3) foster collaborations to facilitate development and applications of genetic simulation. During the course of the meeting, the group identified challenges and opportunities for the science of simulation, software and methods development, and collaboration. This paper summarizes key discussions at the meeting, and highlights important challenges and opportunities to advance the field of genetic simulation.

In Wireless Sensor Networks (WSN), nodes are vulnerable to security attacks because they are installed in a harsh environment with limited power and memory, low processing power, and medium broadcast transmission; therefore, identifying threats, challenges, and solutions of security and privacy is a hot topic today. This article analyzes the research work that has been carried out on the security mechanisms for the protection of WSN against threats and attacks, as well as the trends that emerge in other countries combined with future research lines. From the methodological point of view, this analysis is shown through the visualization and study of works indexed in databases such as IEEE, ACM, Scopus and Springer, with a range of 7 years as an observation window, from 2013 to 2019. A total of 4,728 publications were obtained, with a high rate of collaboration between China and India. The research raised developments, such as advances in security principles and defense mechanisms, which have led to the design of countermeasures in the intrusion detection. Finally, the results show the interest of the scientific and business community in the use of artificial intelligence and machine learning (ML) to optimize performance measurements.

In current days cloud domain gained a tremendous increase of user's attention by several small and large scale companies including Software, BPO, Medical, Schools and a lot more. Since there was very less security in the primitive clouds for storing and accessing the information from the remote locations, still there was a lot of demand for the data which is to be stored in the cloud. As we know that in primitive clouds, there are no concepts like privacy for the data in terms of encryption and message digest in order to provide data authorization. In current days cloud servers are almost dishonest in nature by omitting intentionally some qualified results to save computational resources and communication overhead. In this paper, we try to design a new protocol and analyze the importance of Cuckoo filter over encrypted cloud data to provide data search accurately by the cloud users. Here we try to encrypt the data using AES algorithm and try to generate MAC key for the data which is uploaded and downloaded by the cloud users. For data authorization we use MD5 algorithm and with the help of MD5 algorithm, we can generate a short signature to find out the data verification and finally we try to apply Cuckoo filter for data integrity. This cuckoo filter will try to store the data in duplicated manner and try to provide end user with original data. [ABSTRACT FROM AUTHOR]

In Wireless Sensor Networks (WSN), nodes are vulnerable to security attacks because they are installed in a harsh environment with limited power and memory, low processing power, and medium broadcast transmission. Therefore, identifying threats, challenges, and solutions of security and privacy is a talking topic today. This article analyzes the research work that has been carried out on the security mechanisms for the protection of WSN against threats and attacks, as well as the trends that emerge in other countries combined with future research lines. From the methodological point of view, this analysis is shown through the visualization and study of works indexed in databases such as IEEE, ACM, Scopus, and Springer, with a range of 7 years as an observation window, from 2013 to 2019. A total of 4,728 publications were obtained, with a high rate of collaboration between China and India. The research raised developments, such as advances in security principles and defense mechanisms, which have led to the design of countermeasures in intrusion detection. Finally, the results show the interest of the scientific and business community in the use of artificial intelligence and machine learning (ML) to optimize performance measurements. [ABSTRACT FROM AUTHOR]

With the fast and unstoppable development of technology, the amount of available technological devices and the data they produce is overwhelming. In analyzing the context of a smart home, a diverse group of intelligent devices generating constant reports of its environment information is needed for the proper control of the house. Due to this demand, many possible solutions have been developed in the literature to assess the need for processing power and storage capacity. This work proposes HOsT (home-context-aware fog-computing solution)—a solution that addresses the problems of data heterogeneity and the interoperability of smart objects in the context of a smart home. HOsT was modeled to compose a set of intelligent objects to form a computational infrastructure in fog. A publish/subscribe communication module was implemented to abstract the details of communication between objects to disseminate heterogeneous information. A performance evaluation was carried out to validate HOsT. The results show evidence of efficiency in the communication infrastructure; and in the impact of HOsT compared with a cloud infrastructure. Furthermore, HOsT provides scalability about the number of devices acting simultaneously and demonstrates its ability to work with different devices.

In South Africa, differential performance in school mathematics with respect to social class remains an enduring concern as reflected in national and international large-scale assessments. The article examines the implications of evaluation for orientations to mathematics in a school populated by learners from upper-middle-class or elite backgrounds and a school populated by learners from working-class backgrounds. The particular focus is on mathematics problems featured in tests used by two Grade 10 teachers in each school and teachers’ marking of learners’ test scripts. A distinction between single-topic and multi-topic mathematics problem types is refracted through an analysis that draws on the adaptation by Davis of Lotman’s distinction between content orientation and expression orientation with respect to the reproduction of texts. The analysis reveals a preponderance of single-topic mathematics problems and the absence of multi-topic mathematics problems employed in the school populated by learners from working-class backgrounds and the presence of both single-topic and multi-topic problem types in the school populated by learners from middle-class or elite backgrounds. Differences in the types of mathematical problems suggest differences in mathematical demand expected of learners and differences in their preparation for examinations in the two social class contexts. The selection of test problems and the marking of test scripts as instances of evaluation construct an orientation to mathematics that is expression oriented in the working-class context whereas both expression and content orientations are evident in the middle-class or elite context. The analysis provides a potential explanation for the persistent disparity in mathematics performance along social class lines in South African secondary schools.

Security and privacy of the Internet of Things (IoT) systems are critical challenges in many data‐sensitive applications. The IoT systems are constrained in terms of execution time, flexibility and computational resources. In recent years, many encryption algorithms have been proposed to provide the security of IoT communication. In this study, flexible and high‐throughput hardware structures of the PRESENT, SIMON, and LED lightweight block ciphers are presented for IoT applications. The proposed flexible structures can perform various configurations of these block ciphers to support variable key sizes. For example, the PRESENT, SIMON, and LED ciphers support key sizes (80, 128 bits), (96, 144, 128, 192, and 256 bits), and (64, 128 bits), respectively. Therefore, these architectures provide versatile implementations that enable adaptive security level using a variable key size. In the proposed structures, sub‐blocks of the ciphers are implemented based on optimised circuits. In the PRESENT and LED ciphers, the S‐boxes are implemented based on area‐optimised logic circuits. The implementation results of proposed flexible architectures are achieved in 180 nm CMOS technology. Area, throughput and throughput/area of the proposed structures have improved compared to other related works. [ABSTRACT FROM AUTHOR]

In South Africa, differential performance in school mathematics with respect to social class remains an enduring concern as reflected in national and international large-scale assessments. The article examines the implications of evaluation for orientations to mathematics in a school populated by learners from upper-middle-class or elite backgrounds and a school populated by learners from working-class backgrounds. The particular focus is on mathematics problems featured in tests used by two Grade 10 teachers in each school and teachers' marking of learners' test scripts. A distinction between single-topic and multi-topic mathematics problem types is refracted through an analysis that draws on the adaptation by Davis of Lotman's distinction between content orientation and expression orientation with respect to the reproduction of texts.The analysis reveals a preponderance of single-topic mathematics problems and the absence of multi-topic mathematics problems employed in the school populated by learners from working-class backgrounds and the presence of both single-topic and multi-topic problem types in the school populated by learners from middle-class or elite backgrounds. Differences in the types of mathematical problems suggest differences in mathematical demand expected of learners and differences in their preparation for examinations in the two social class contexts.The selection of test problems and the marking of test scripts as instances of evaluation construct an orientation to mathematics that is expression oriented in the working-class context whereas both expression and content orientations are evident in the middle-class or elite context. The analysis provides a potential explanation for the persistent disparity in mathematics performance along social class lines in South African secondary schools. [ABSTRACT FROM AUTHOR]

Fractional calculus is finding increased usage in the modeling and control of nonlinear systems with the enhanced robustness. However, from the implementation perspectives, the simultaneous modeling of the systems and the design of controllers with fractional-order operators can bring additional advantages. In this paper, a fractional order model of a nonlinear system along with its controller design and its implementation on a field programmable gate array (FPGA) is undertaken as a case study. Overall, three variants of the controllers are designed, including classical sliding mode controller, fractional controller for an integer model of the plant, and a fractional controller for a fractional model of the plant (FCFP). A high-level synthesis approach is used to map all the variants of the controllers on FPGA. The integro-differential fractional operators are realized with infinite impulse response filters architecturally implemented as cascaded second-order sections to withstand quantization effects introduced by fixed-point computations necessary for FPGA implementations. The experimental results demonstrate that the fractional order sliding mode controller-based on fractional order plant (FCFP) exhibits reduced dynamics in sense of fractional integration and differentials. It is further verified that the FCFP is as robust as the classical sliding mode with comparable performance and computational resources.

ADAR (Adenosine Deaminases Acting on RNA) proteins are a group of enzymes that play a vital role in RNA editing by converting adenosine to inosine in RNAs. This process is a frequent post-transcriptional event observed in metazoan transcripts. Recent studies indicate widespread dysregulation of ADAR-mediated RNA editing across many immune-related diseases, such as human cancer. We comprehensively review ADARs' function as pattern recognizers and their capability to contribute to mediating immune-related pathways. We also highlight the potential role of site-specific RNA editing in maintaining homeostasis and its relationship to various diseases, such as human cancers. More importantly, we summarize the latest cutting-edge computational approaches and data resources for predicting and analyzing RNA editing sites. Lastly, we cover the recent advancement in site-directed ADAR editing tool development. This review presents an up-to-date overview of ADAR-mediated RNA editing, how site-specific RNA editing could potentially impact disease pathology, and how they could be harnessed for therapeutic applications.

Cloud computing and storage have become an integral part of online Internet services. Over the past decade, enterprises have steadily moved their applications and storage to the cloud paradigm. Server virtualisation enables cloud providers to utilise computational resources efficiently by employing virtual machines (VMs) and containers. As more enterprise applications are hosted by VMs in the cloud, a significant amount of network communication occurs among servers within a data centre, increasing so‐called 'east‐west communication'. In addition to scaling to accommodate more east‐west traffic, future data centre networks must support efficient VM migration, which is used to optimise power consumption. This article proposes a new data centre network architecture called DCnet. DCnet changes addressing and routing at layers 2 and 3 completely to increase throughput and support live VM migration throughout an organisation, including across multiple data centres owned by an organisation that spans large geographic areas. DCnet retains compatibility with existing hardware by using the same frame format as standard protocols, specifically Ethernet and Internet protocol (IP). Furthermore, DCnet does not require any changes to applications and host operating systems or libraries. In addition to presenting the architecture, the article describes a test bed used to assess DCnet and reports experimental measurements. [ABSTRACT FROM AUTHOR]

The asynchronous communication always exists in networked control systems that consist of interconnected heterogeneous subsystems. To improve the closed‐loop performance, a networked and distributed predictive controller is developed for such a system in this study. At each time instant, a virtual trajectory generator for the interconnected agents with guaranteed error bounds is proposed to implement the receding horizon optimisation. Then, a dual‐mode strategy is designed for the asynchronous distributed model predictive control to make effective use of computational resources. Conditions for ensuring the feasibility of the proposed algorithm and the stabilisation of the closed‐loop networked control systems are developed, respectively. Finally, a simulation example of a set of coupled Van der Pol oscillators is presented to illustrate the results. [ABSTRACT FROM AUTHOR]

Adoption of wearable assistive technologies relies heavily on improvement of existing control system models. Knowing which models to use and how to improve them is difficult to determine due to the number of proposed solutions with relatively little broad comparisons. One type of these models, muscle activation models, describes the nonlinear relationship between neural inputs and mechanical activation of the muscle. Many muscle activation models can be found in the literature, but no comparison is available to guide the community on limitations and improvements. In this research, an EMG-driven elbow motion model is developed for the purpose of evaluating muscle activation models. Seven muscle activation models are used in an optimization procedure to determine which model has the best performance. Root mean square errors in muscle torque estimation range from 1.67–2.19 Nm on average over varying input trajectories. The computational resource demand was also measured during the optimization procedure, as it is an important aspect for determining if a model is feasible for use in a particular wearable assistive device. This study provides insight into the ability of these models to estimate elbow motion and the trade-off between estimation accuracy and computational demand. [ABSTRACT FROM AUTHOR]

[Display omitted] • The dcGO database provides mappings from protein domains to ontologies. • The resource has evolved to support annotations for SCOP, Pfam and InterPro. • The resource supports ontologies across various knowledge contexts. • The new website offers a more centralised and user-friendly way to mine the resource. Protein structural domains have been less studied than full-length proteins in terms of ontology annotations. The dcGO database has filled this gap by providing mappings from protein domains to ontologies. The dcGO update in 2023 extends annotations for protein domains of multiple definitions (SCOP, Pfam, and InterPro) with commonly used ontologies that are categorised into functions, phenotypes, diseases, drugs, pathways, regulators, and hallmarks. This update adds new dimensions to the utility of both ontology and protein domain resources. A newly designed website at http://www.protdomainonto.pro/dcGO offers a more centralised and user-friendly way to access the dcGO database, with enhanced faceted search returning term- and domain-specific information pages. Users can navigate both ontology terms and annotated domains through improved ontology hierarchy browsing. A newly added facility enables domain-based ontology enrichment analysis. [ABSTRACT FROM AUTHOR]

Genome editing with engineered nucleases (zinc finger nucleases, TAL effector nucleases s and Clustered regularly interspaced short palindromic repeats/CRISPR-associated) has recently been shown to have great promise in a variety of therapeutic and biotechnological applications. However, their exploitation in genetic analysis and clinical settings largely depends on their specificity for the intended genomic target. Large and complex genomes often contain highly homologous/repetitive sequences, which limits the specificity of genome editing tools and could result in off-target activity. Over the past few years, various computational approaches have been developed to assist the design process and predict/reduce the off-target activity of these nucleases. These tools could be efficiently used to guide the design of constructs for engineered nucleases and evaluate results after genome editing. This review provides a comprehensive overview of various databases, tools, web servers and resources for genome editing and compares their features and functionalities. Additionally, it also describes tools that have been developed to analyse post-genome editing results. The article also discusses important design parameters that could be considered while designing these nucleases. This review is intended to be a quick reference guide for experimentalists as well as computational biologists working in the field of genome editing with engineered nucleases. [ABSTRACT FROM AUTHOR]

A distributed control algorithm using a new idea of collaborative processing for networked control systems with limited computational resources is proposed. In a networked control system, the processing unit is required to complete multiple tasks (such as control, sensing, signal processing, etc.) in a timely manner. Due to limited computational resources that are available to the processing unit, the assumption that the control input is computed at every time step may no longer hold. There are multiple remote controllers and each controller controls its own plant is considered. The authors give the controllers within a neighbourhood the ability to compute their neighbours' control inputs. By suitable design of the processor collaboration algorithm, their are able to ensure the stochastic stability of the entire system even if there are random computation interruptions occurring at different time steps. Numerical results verify the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

Reduced-order models (ROMs) based on the proper orthogonal decomposition have been proposed to reduce the computational resources required by the full-order models (FOMs) to approximate partial differential equations. In this paper a Roe-based ROM is developed to solve the shallow water equations in presence of source terms more efficiently than the Roe-based FOM. The well-balanced property and other numerical corrections such as the entropy fix and the wet–dry treatment are taken into account using augmented Riemann solvers to build the Roe-based FOM. In addition to this, a time averaging approach is necessary to develop the Roe-based ROM. This approach is validated by solving some cases and the computed solutions are compared with those ones of Lax–Friedrichs-based ROMs. It is also studied whether the ROM preserves or not the well-balancing, the entropy fix and the wet–dry treatment. [ABSTRACT FROM AUTHOR]

Adoption of wearable assistive technologies relies heavily on improvement of existing control system models. Knowing which models to use and how to improve them is difficult to determine due to the number of proposed solutions with relatively little broad comparisons. One type of these models, muscle activation models, describes the nonlinear relationship between neural inputs and mechanical activation of the muscle. Many muscle activation models can be found in the literature, but no comparison is available to guide the community on limitations and improvements. In this research, an EMG-driven elbow motion model is developed for the purpose of evaluating muscle activation models. Seven muscle activation models are used in an optimization procedure to determine which model has the best performance. Root mean square errors in muscle torque estimation range from 1.67–2.19 Nm on average over varying input trajectories. The computational resource demand was also measured during the optimization procedure, as it is an important aspect for determining if a model is feasible for use in a particular wearable assistive device. This study provides insight into the ability of these models to estimate elbow motion and the trade-off between estimation accuracy and computational demand.