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Among lattice configurations of densely packed hard ellipses, Monte Carlo simulations are used to identify the so-called parallel and diagonal lattices as the two favorable states. The free energies of these two states are computed for several system sizes employing the Einstein crystal method. An accurate calculation of the free energy difference between the two states reveals the parallel lattice as the state with the lowest free energy. The origin of the entropic difference between the two states is further elucidated by assessing the roles of the translational and rotational degrees of freedom. [ABSTRACT FROM AUTHOR]

Cloud computing is widely applied in modern industrial areas due to its technological advancement, cost reduction, and applicability. Packets (tasks) belonging to different applications (agents) compete to share the common cloud resource through a series of edge nodes (processors) in pursuit of fast transmission. This paper abstracts the cloud computing system as a multi-agent flow-shop scheduling (MAFS) problem. The objective is to minimise the total completion time of several agents with the restriction that the maximum lateness cannot exceed a given bound. Given the complexity of the considered problem, a branch and bound algorithm combined with several pruning rules and lower bounds is proposed to obtain optimal solutions. Furthermore, the considered problem is generalised to a bi-scenario version, and a bi-population cooperative co-evolutionary (BCCE) algorithm is proposed to solve it. A reinforcement learning-based method is presented to generate the initial population. Several problem-specific intensification strategies are constructed to explore promising solutions. Comprehensive experiments verified the effectiveness of the proposed algorithms. The industrial data from the China Earthquake Network Centre further confirmed the superiority of the BCCE algorithm. Overall, the MAFS model and the proposed algorithms effectively enhance the user experience and reasonably guarantee revenue. [ABSTRACT FROM AUTHOR]

Nonlinear dynamical systems such as coupled oscillators are being actively investigated as Ising machines for solving computationally hard problems in combinatorial optimization. Prior works have established the equivalence between the global minima of the cost function describing the coupled oscillator system and the ground state of the Ising Hamiltonian. However, the properties of the oscillator Ising machine (OIM) from a nonlinear control viewpoint, such as the stability of the OIM solutions, remain unexplored. Therefore, in this work, using nonlinear control-theoretic analysis, we (i) identify the conditions required to ensure the functionality of the coupled oscillators as an Ising machine, (ii) show that all globally optimal phase configurations may not always be stable, resulting in some configurations being more favored over others and, thus, creating a biased OIM, and (iii) elucidate the impact of the stability of locally optimal phase configurations on the quality of the solution computed by the system. Our work, fostered through the unique convergence between nonlinear control theory and analog systems for computing, provides a new toolbox for the design and implementation of dynamical system-based computing platforms. [ABSTRACT FROM AUTHOR]

OBJECTIVE: The study objective was to evaluate intraoperative experience with newly developed high-spatial-resolution microelectrode grids composed of poly(3,4-ethylenedioxythiophene) with polystyrene sulfonate (PEDOT:PSS), and those composed of platinum nanorods (PtNRs). METHODS: A cohort of patients who underwent craniotomy for pathological tissue resection and who had high-spatial-resolution microelectrode grids placed intraoperatively were evaluated. Patient demographic and baseline clinical variables as well as relevant microelectrode grid characteristic data were collected. The primary and secondary outcome measures of interest were successful microelectrode grid utilization with usable resting-state or task-related data, and grid-related adverse intraoperative events and/or grid dysfunction. RESULTS: Included in the analysis were 89 cases of patients who underwent a craniotomy for resection of neoplasms (n = 58) or epileptogenic tissue (n = 31). These cases accounted for 94 grids: 58 PEDOT:PSS and 36 PtNR grids. Of these 94 grids, 86 were functional and used successfully to obtain cortical recordings from 82 patients. The mean cortical grid recording duration was 15.3 ± 1.15 minutes. Most recordings in patients were obtained during experimental tasks (n = 52, 58.4%), involving language and sensorimotor testing paradigms, or were obtained passively during resting state (n = 32, 36.0%). There were no intraoperative adverse events related to grid placement. However, there were instances of PtNR grid dysfunction (n = 8) related to damage incurred by suboptimal preoperative sterilization (n = 7) and improper handling (n = 1); intraoperative recordings were not performed. Vaporized peroxide sterilization was the most optimal sterilization method for PtNR grids, providing a significantly greater number of usable channels poststerilization than did steam-based sterilization techniques (median 905.0 [IQR 650.8-935.5] vs 356.0 [IQR 18.0-597.8], p = 0.0031). CONCLUSIONS: High-spatial-resolution microelectrode grids can be readily incorporated into appropriately selected craniotomy cases for clinical and research purposes. Grids are reliable when preoperative handling and sterilization considerations are accounted for. Future investigations should compare the diagnostic utility of these high-resolution grids to commercially available counterparts and assess whether diagnostic discrepancies relate to clinical outcomes.

Structural biology efforts using cryogenic electron microscopy are frequently stifled by specimens adopting preferred orientations on grids, leading to anisotropic map resolution and impeding structure determination. Tilting the specimen stage during data collection is a generalizable solution but has historically led to substantial resolution attenuation. Here, we develop updated data collection and image processing workflows and demonstrate, using multiple specimens, that resolution attenuation is negligible or significantly reduced across tilt angles. Reconstructions with and without the stage tilted as high as 60° are virtually indistinguishable. These strategies allowed the reconstruction to 3 Å resolution of a bacterial RNA polymerase with preferred orientation, containing an unnatural nucleotide for studying novel base pair recognition. Furthermore, we present a quantitative framework that allows cryo-EM practitioners to define an optimal tilt angle during data acquisition. These results reinforce the utility of employing stage tilt for data collection and provide quantitative metrics to obtain isotropic maps.

The recently developed Projective Quantum Eigensolver (PQE) offers an elegant procedure to evaluate the ground state energies of molecular systems in quantum computers. However, the noise in available quantum hardware can result in significant errors in computed outcomes, limiting the realization of quantum advantage. Although PQE comes equipped with some degree of inherent noise resilience, any practical implementation with apposite accuracy would require additional routines to eliminate or mitigate the errors further. In this work, we propose a way to enhance the efficiency of PQE by developing an optimal framework for introducing Zero Noise Extrapolation (ZNE) in the nonlinear iterative procedure that outlines the PQE, leading to the formulation of ZNE-PQE. Moreover, we perform a detailed analysis of how various components involved in it affect the accuracy and efficiency of the reciprocated energy convergence trajectory. Additionally, we investigate the underlying mechanism that leads to the improvements observed in ZNE-PQE over conventional PQE by performing a comparative analysis of their residue norm landscape. This approach is expected to facilitate practical applications of quantum computing in fields related to molecular sciences, where it is essential to determine molecular energies accurately. [ABSTRACT FROM AUTHOR]

In 2021, the assessment-data management librarian at Lehman College Library decided to conduct a privacy audit of the Library's public computers and networks. This audit comprised one of the Library's two annual formal assessments of resources and services. The American Library Association's (ALA) Library Privacy Checklist for Public Access Computers and Networks was selected to review 17 key items related to protecting user privacy and confidentiality. Faculty and staff from Circulation, Library Technology, and Online Learning identified 10 indicators needing work. Suggestions are provided for collaboratively resolving these issues and future steps are described to continuously maximize the online security of the campus community. [ABSTRACT FROM AUTHOR]

We introduce an approach for calculating perturbative corrections to the ring-polymer instanton approximation to tunneling splittings (RPI+PC) by computing higher-order terms in the asymptotic expansion in ℏ. The resulting method goes beyond standard instanton theory by using information on the third and fourth derivatives of the potential along the tunneling path to include additional anharmonic effects. This leads to significant improvements both in systems with low barriers and in systems with anharmonic modes. We demonstrate the applicability of RPI+PC to molecular systems by computing the tunneling splitting in full-dimensional malonaldehyde and a deuterated derivative. Comparing to both experiment and recent quantum mechanical benchmark results, we find that our perturbative correction reduces the error from −11% to 2% for hydrogen transfer and performs even better for the deuterated case. This makes our approach more accurate than previous calculations using diffusion Monte Carlo and path-integral molecular dynamics while being more computationally efficient. [ABSTRACT FROM AUTHOR]

Micromagnetic simulations have been used to create reversible logic gates that utilize magnetic skyrmions as input and output signals. The geometrical design of the logic devices consists of a two-dimensional assembly of ferromagnetic nanotracks, resembling rectangular grooves. Drawing an analogy to the billiard-ball model, this study takes into account elastic collisions between magnetic skyrmions and introduces the concept of control skyrmion. The application of spin-polarized currents to the system induces the motion of magnetic skyrmions along the nanotracks. The synchronized movement of magnetic skyrmions across the operational logic gates, required for their interaction, is achieved by incorporating multiple triangular magnetic notches along the nanotracks. Short polarized current pulses are then applied through the logic gates to facilitate this process. We have successfully implemented the XOR/AND and Full Adder logic gates as well as the reversible Toffoli and Peres logic gates using skyrmion-based architectures for computing. Our results offer valuable guidelines for leveraging magnetic skyrmions as digital signals in skyrmion-based computing systems. [ABSTRACT FROM AUTHOR]

The flipped classroom enables teachers to accommodate the different needs of their students. With in-class activities, learning gaps and strengths can be addressed individually, allowing a more personalized approach to teaching. This research assessed the potential influence of the Flipped Classroom utilization towards the mathematics achievement in Rational Functions competencies of the Grade 11 Computer Systems Servicing and General Academic Strand students of Talisay City National High School in Talisay City Division for the school year 2023-2024. A descriptivecorrelational research design was utilized in this study using a survey questionnaire and achievement test. There were 134 respondents in the sample, and it was selected using simple random sampling from the population of the students at Talisay City National High School. The data were statistically analyzed through percentage, frequency counts, weighted mean, and Pearson-r correlational test for significant relationships. The findings revealed that the level of acceptability in terms of the pedagogical aspect of the utilization of the flipped classroom significantly influences the student's academic achievement in Mathematics. The researcher highly recommends that the Flipped Classroom Utilization Enhancement Plan be implemented and could serve as a supplementary strategy in addressing the students' varied learning needs, particularly in mathematics. [ABSTRACT FROM AUTHOR]

Implementing algorithms purely on digital computing platforms dramatically halts the performance of conventional computing systems. Revolutionary computing systems with extreme energy efficiency and high accuracy are demanded to handle the growing computing tasks. Here, the research on hybrid analog–digital computing platforms enabled by memristor‐based optimization solvers for achieving ultrafast computations is presented. By utilizing tunable memristors as parameters to solve linear programming (LP) and quadratic programming (QP) problems, a real‐time control algorithm for micro air vehicles (MAVs) and a support vector machine (SVM) algorithm for cancer diagnosis are implemented. These experiments demonstrate over 2000x speed‐up compared to conventional digital platforms, with negligible energy consumption, using a memristor‐based system consisting of six memristors. These findings underscore the vast potential of memristor‐based optimization solvers not only in hybrid analog–digital computing platforms but also as a transformative solution for a wide range of modern computing challenges. This approach promises significant advancements in energy efficiency and ultrafast speed, positioning it as a leading contender for next‐generation computing paradigms. [ABSTRACT FROM AUTHOR]

Energy efficiency and physical-layer security are crucial considerations in the advancement of mobile edge computing systems. This paper addresses the trade-off between secure-reliability and energy consumption in finite blocklength (FBL) communications. Specifically, we examine a three-node scenario involving a user, a legitimate edge computing server, and an eavesdropper, where the user offloads sensitive data to the edge server while facing potential eavesdropping threats. We propose an optimization framework aimed at minimizing energy consumption while ensuring secure-reliability by decomposing the problem into manageable subproblems. By demonstrating the convexity of the objective function concerning the variables, we establish the existence of an optimal parameter selection for the problem. This implies that practical optimization of parameters can significantly enhance system performance. Our numerical results demonstrate that the application of FBL regime and retransmission mechanism can effectively reduce the energy consumption of the system while ensuring secure-reliability. For the quantitative analyses, the retransmission mechanism is 33.1% better than no retransmission, and the FBL regime is 13.1% better than infinite blocklength (IBL) coding. [ABSTRACT FROM AUTHOR]

Nanomechanical oscillators are an alternative platform for computation in harsh environments. However, external perturbations arising from such environments may hinder information processing by introducing errors into the computing system. Here, we simulate the dynamics of three coupled Duffing oscillators whose multiple equilibrium states can be used for information processing and storage. Our analysis reveals that, within experimentally relevant parameters, error correcting dynamics can emerge, wherein the system's state is robust against random external impulses. We find that oscillators in this configuration have several surprising and attractive features, including dynamic isolation of resonators exposed to extreme impulses and the ability to correct simultaneous errors. [ABSTRACT FROM AUTHOR]

Total knee arthroplasty (TKA) is a well‐established treatment for end‐stage knee osteoarthritis. However, in patients with concomitant extra‐articular deformities, conventional TKA techniques may lead to unsatisfactory outcomes and higher complication rates. This review summarizes the application of navigated TKA for treating knee osteoarthritis with extra‐articular deformities. The principles and potential benefits of computer navigation systems, including improved component alignment, soft tissue balancing, and restoration of mechanical axis, are discussed. Research studies demonstrate that navigated TKA can effectively correct deformities, relieve pain, and improve postoperative joint function and quality of life compared with conventional methods. The advantages of navigated TKA in terms of surgical precision, lower complication rates, and superior functional recovery are highlighted. Despite challenges like the learning curve and costs, navigated TKA is an increasingly indispensable tool for achieving satisfactory outcomes in TKA for knee osteoarthritis patients with extra‐articular deformities. [ABSTRACT FROM AUTHOR]

To achieve cost‐effectiveness, researchers are exploring various memristors for their adaptation in neuromorphic computing. Recent studies have focused on developing versatile functioning singular memristors, such as those involved in on‐receptor computing, which integrates sensory functions into current neuromorphic computing paradigms. Additionally, adaptations like reservoir computing are being investigated for computing systems. In this study, a memristor composed of a stack of Ti/NbOx/Pt layers is fabricated to explore multifunctional behaviors within a single memristor. By applying bias toward the top Ti electrode, gradual current changes with volatile features are demonstrated, revealing an ion‐migration‐based nonfilamentary switching memristor. Leveraging this volatile functionality, an artificial nociceptor is first implemented, demonstrating key functions of biological nociceptors including thresholding, relaxation, no‐adaptation, and sensitization. Subsequently, synapse emulation akin to the biological brain is achieved through easy conductance potentiation and depression with diverse synapse functions, enabling the memristor to mimic learning activities with spike firing. Lastly, computational applications are explored by adapting edge computing and multi‐bit reservoir computing, expanding the memristor's applications across diverse fields with versatile behaviors. [ABSTRACT FROM AUTHOR]

Society 4.0 brings new information technology to mass audience accompanied by economic and social developments in the 21st century. The enablers of Society 4.0 such as Blockchain, Artificial Intelligence (AI), and the Internet of Things (IoT) significantly influence current human activities with more efficient information management. Blockchain technology takes a step forward towards innovation and has numerous uses yet to be discovered in developing a smart society. The objective is to extensively review existing literature for the implementation of a smart society to identify different research lacunas. IoT enables real-time data collection by allowing computing systems, humans, and objects to connect for practical purposes and economic gains that call for the highest levels of security, data privacy, authentication, and attack resistance. Blockchain can be a viable solution to satisfy these requirements as it maintains an immutable network transactions log. Applications in volatile, uncertain, and complicated business contexts benefit from the unification of Blockchain with AI in terms of security and efficiency. We conduct a systematic review with explorative analysis to outline the significance of Blockchain integrated with different enablers of Society 4.0 along with different applications. Bibliometric analysis confirms the significance of the integration of Blockchain technology for current and future human activities as an integral part of Society 4.0. This review will provide a lucid research summary for all applications proposed and implemented to fully realize the concept of Society 4.0 using Blockchain technology. This article also enumerates future research avenues for the deployment of the next-generation Blockchain-based Society 4.0 applications. [ABSTRACT FROM AUTHOR]

Online quantitative safety monitoring is the key technology for ensuring the operational safety of the automatic train protection (ATP) system for the future advanced train control system. However, the traditional formal verification method can only deal with the system of small scale since these verification algorithms exhaustively search all executable paths of formal models. Especially for the formal models with uncertain parameters, the problem of memory overflow will occur due to the state space explosion, which makes verification algorithms fail to converge. To solve this problem, an intelligent quantitative safety monitoring approach is proposed by integrating the probabilistic model checking method (PMCM) with neural network in this paper. To begin with, the instantiated continuous-time Markov Chains model is verified by PMCM offline. Then, the neural network is constructed and optimized based on the offline verification results. The quantitative safety boundaries (QSBs) for all quantitative safety levels are also computed by the designed algorithm. Furthermore, hierarchical iterative evaluation method is applied to efficiently evaluate the reliability performance of the ATP online. Finally, the quantitative safety level of ATP or its subsystem will be determined by both reliability performance and the previous QSBs or the neural network online. [ABSTRACT FROM AUTHOR]

Cloud computing and green cloud computing are related concepts, yet they have distinct focuses and objectives. Cloud computing involves the provision of diverse computing services over the internet, such as servers, storage, databases, software, and more. It allows users to access and utilize these services as needed, often from shared and remote computing resources instead of relying on dedicated on-site infrastructure. Cloud computing has become a foundational technology in modern IT, providing businesses and individuals with flexibility, scalability, and cost-effectiveness. Green computing, also known as sustainable or eco-friendly computing, is a technological and operational domain that concentrates on enhancing the environmental friendliness of computing systems and practices. It encompasses various aspects of the IT lifecycle. This paper offers a comparative analysis between cloud computing and green computing, aiming to elucidate their distinctions and similarities, particularly within the context of environmental sustainability and resource efficiency. [ABSTRACT FROM AUTHOR]

Fire is a serious threat to people's lives and property. Be aimed at the low detection accuracy of the current fire detection system, a fire detection system based on one-chip computer is proposed in this work. To achieve fire detection and warning, the system collects these signals such as smoke and temperature from the surrounding environment, and processes them with a one-chip computer. The simulation experiments of the system are conducted with Proteus, and the experimental results indicate that the system can detect accurately. [ABSTRACT FROM AUTHOR]

This study focuses on examining and ascertaining the impact of various elements of industry 4.0, namely the internet of things (IoT) and -physical-cyber systems, on the human factors concerning operators in the Refinery industry. The primary objective of this study is to analyze the relationship between these variables and human factors. In this study, a quantitative approach employing partial least square-structural equation modeling (PLS-SEM) was utilized as the research methodology. The study sample consisted of 70 respondents who were specifically chosen from the Indonesian oil and gas sector operators. The findings of this research demonstrate a positive and significant influence of Industry 4.0 on human factors. Moreover, a significant correlation between the Internet of Things and human factors was identified. Additionally, the study also revealed a noteworthy association between the physical-cyber system and human factors. [ABSTRACT FROM AUTHOR]

Let F \in \mathbb {Z}[x_0, \ldots, x_n] be homogeneous of degree d and assume that F is not a 'nullform', i.e., there is an invariant I of forms of degree d in n+1 variables such that I(F) \neq 0. Equivalently, F is semistable in the sense of Geometric Invariant Theory. Minimizing F at a prime p means to produce T \in Mat(n+1, \mathbb {Z}) \cap GL(n+1, \mathbb {Q}) and e \in \mathbb {Z}_{\ge 0} such that F_1 = p^{-e} F([x_0, \ldots, x_n] \cdot T) has integral coefficients and v_p(I(F_1)) is minimal among all such F_1. Following Kollár [Electron. Res. Announc. Amer. Math. Soc. 3 (1997), pp. 17–27], the minimization process can be described in terms of applying weight vectors w \in \mathbb {Z}_{\ge 0}^{n+1} to F. We show that for any dimension n and degree d, there is a complete set of weight vectors consisting of [0,w_1,w_2,\dots,w_n] with 0 \le w_1 \le w_2 \le \dots \le w_n \le 2 n d^{n-1}. When n = 2, we improve the bound to d. This answers a question raised by Kollár. These results are valid in a more general context, replacing \mathbb {Z} and p by a PID R and a prime element of R. Based on this result and a further study of the minimization process in the planar case n = 2, we devise an efficient minimization algorithm for ternary forms (equivalently, plane curves) of arbitrary degree d. We also describe a similar algorithm that allows to minimize (and reduce) cubic surfaces. These algorithms are available in the computer algebra system Magma. [ABSTRACT FROM AUTHOR]

In order to realise the real-time monitoring of seeding quality of small vegetable seeds, and solve the problem of inconvenience caused by the small particle sizes of vegetable seeds, an intelligent testing bench for air-suction seed metering device for small vegetable seeds with a particle diameter of 0.5–3 mm was designed, and a complementary quality monitoring and controlling system was developed. The photoelectric sensing technology and image recognition technology were used together in the system. Photoelectric sensing technology was used for real-time monitoring of seed counting, while the image recognition technology can dynamically monitor the missed seeding, repeated seeding and the falling trajectory of seed deposition in real time. The system was installed on the air-suction intelligent testing bench to test the monitoring accuracy of the monitoring system. The experimental results demonstrated that the monitoring accuracy of drilling quality monitoring system was over 97%, the monitoring accuracy of miss seeding was over 95%, and the monitoring accuracy of repeated seeding was over 93%. Furthermore, the monitoring system was less affected by the seed particle size, shape, and plant spacing, etc., and the system was stable, which effectively realised the real-time monitoring of the operation of air-suction metering device. • New monitoring system for an air-suction seeder for small vegetable seeds. • Different types of seeds ranging from 0.8 to 3.0 mm were used to evaluate the system. • To realise the monitoring of seeding status of air-suction seeder. • Provide reference for seeding monitoring of small seed air suction seeders. [ABSTRACT FROM AUTHOR]

In this paper, we consider the router buffer modeling and the optimal message transmission problem in computer networks. For the routing networks with multiple source inputs and multiple link outputs, we propose a switched system, in which the switchings depending on both state and control, to describe the change of queue length in router buffers. Taking the weighted sum of the received data at the destination routers and the congestion time of routers as the performance index, we then present an optimal control problem involving the switched system and subject to continuous state inequality constraints. To solve this optimal control problem, we transform the above switched system to the one with time-dependent switchings. Furthermore, by using the control parametrization, time-scaling transformation and constraint transcription techniques, we approximate the resulting optimal control problem by a series of parameter optimization problems, and develop an improved particle swarm optimization algorithm for solving the parameter optimization problems. Finally, we demonstrate the effectiveness of the algorithm by solving three test problems and two example problems. [ABSTRACT FROM AUTHOR]

For the time being, many government or private organizations for recruitment of staff or educational institutions moving towards online based tests. The online examination system is a software application used for conducting examination using computer systems. It helps to the recruitment agency or any govt. or private organizations for conducting any job recruitment examinations transparently. Due to this system results are processed without delay and efficiently evaluated to assess the candidates abilities. But the biggest challenge for online examination system is data integrity, security and privacy. The current system is resolving the privacy issue by providing authentication credentials such as user name, password to the candidates. So that only authorized users with proper credentials can login to the system and attempt the exam. But the data confidentiality and integrity are biggest challenges for the system. As the data stored in system database is in plain text format, hence it may be modified or misused by the internal staff of the organization. This paper presents the frame work for secure storage and management of candidates data using encryption scheme, distributed databases in cloud database system. The proposed framework enhances the data confidentiality, integrity and avoids any cheating by internal staff or third party institutions. This paper conducts experimental work on proposed framework and analyses the results of the system. [ABSTRACT FROM AUTHOR]

With the continuous expansion of network scale, the hierarchical and modular characteristics of network structure are becoming increasingly prominent. The traditional single-layer network research paradigm has certain limitations in characterizing the complex relationships between various network systems. Analyzing and constructing models for multi-layer networks has gradually become an important direction in complex network research. The author proposes a multi-layer network model for the Internet and the significance of constructing multi-layer network models in the field of the Internet, based on the characteristics of the multi-layer network structure presented in the application process of the Internet. By analyzing the characteristics of internet data, a multi-layer network model covering three types of networks, namely the internet infrastructure layer, business application layer, and user account layer, was designed. The experimental results show that the average shortest path lengths of the three networks, namely routing relationship network, web hyperchain network, and email address network, are 3.8, 2.9, and 1.7, respectively. Due to the existence of inter layer edges in multi-layer networks, nodes can reach each other across layers in addition to intra layer edges. Experimental results show that the average shortest path length of the three networks has been shortened to some extent, with values of 3.1, 2.7, and 1.6, respectively. On the basis of the single-layer network generation model, a layer correlation method for multi-layer networks was designed, and the model construction of multi-layer networks was achieved. [ABSTRACT FROM AUTHOR]

In the annals of contemporary innovation, the study of miniature marvels nanoscale hardware implants emerged as a pivotal instrument, scarcely perceptible to the naked eye, embodies a prowess of cutting-edge technologies and clandestine intrigue. The objective of this research is to introduce a time delay nonlinear system for nanoscale hardware implants vulnerabilities that portray the exploitation of a system by utilizing the stupendous knacks of advanced deep bidirectional long short-term memory (LSTM) networks for time series predictions. Firmware-level bugs present the potential for escalating privileges and executing of code remotely beneath the operating system, allowing for infiltration or complete intervention within a computer system. The designed deep bidirectional LSTM is configured to precisely predict and forecast the dynamic states of time delay differential system, offering a robust framework for mimicking delays that commonly manifest in the real cyber-physical systems. To orchestrate the system compromises in real scenarios through the activation of bugged hardware, time delay factors τ1 and τ2 are introduced to account the time delays necessary for exploiting the bugged and patched nodes, respectively. Synthetic data is generated to train the LSTM network for all scenarios of the model for the dynamics of bugged, compromised, patched nodes and these acquired information is used for training, testing and validation purposes regarding exploitation of the bugged hardware. Comparative analysis on exhaustive simulations revealed a minimal difference between the LSTM’s predictions and those from the numerical outcomes with MSE in the range of 10−7, underscoring the network’s effectiveness, robustness and stability in modeling complex system dynamics of hardware vulnerabilities. [ABSTRACT FROM AUTHOR]

Neuromorphic computation draws inspiration from the remarkable features of the human brain including low energy consumption, parallelism, adaptivity, cognitive functions, and learning ability. These qualities hold the promise of unlocking groundbreaking computational techniques that surpass the limitations of traditional computing systems. This paper reports a remarkable photo‐synaptic behavior in the field of rare earth ion‐doped luminescent oxides by using long‐persistent luminescence (LPL). This system utilizes electron trap states to regulate the synaptic behavior, operating through a fundamentally different mechanism from that of electronic‐based synaptic devices. To realize this strategy, Tb3+ doped CaSnO3, which shows a significant LPL property under UV‐light excitation, is prepared. The luminescent system shows key neuromorphic characteristics such as paired‐pulse facilitation, pulse‐number/timing dependent potentiation, and pulse‐number/timing dependent short‐ to long‐term plasticity transition, which are required for realizing synaptic devices. This feature expands the way for advanced neuromorphic technologies employing light stimuli. [ABSTRACT FROM AUTHOR]

Human-computer interaction is a discipline that studies the interaction between computer systems and users. Nowadays, it has become an important research topic in the field of robotics, especially in the field of auxiliary robots. As gestures can express rich information, gesture recognition is widely used in machine control, smart home, etc. Vision-based gesture recognition systems can greatly facilitate the lives of the elderly, the sick and the disabled. However, it is also a challenging task to compress the model while maintaining its accuracy in order to achieve efficient recognition on some inexpensive hardware configurations with limited resources. To this end, a lightweight gesture recognition based on an improved gated recurrent unit (GRU) for skeleton dynamic graphs is proposed in this paper. Firstly, the gesture skeleton data is preprocessed, and the coordinates of key joints are selected as input. Then, the mapped gesture feature data is cut to an appropriate size based on an adjustment and clipping module, and then input into an improved GRU for feature learning, which strengthens the ability of the GRU network to learn spatial features, so as to achieve efficient extraction of temporal and spatial features. Finally, the prediction result is output by the classifier. The extensive experiments are conducted on the public datasets DHG14/28 and SHREC'17. In terms of the model size or number of parameters, the proposed model can get the state-of-the-art performance. The average size of the proposed model decreases by more than 30% compared with the general GRU network. Furthermore, the proposed model can get a competitive accuracy performance of 92.13% and 93.98%, on the dataset DHG14/28 and SHREC'17, respectively. It is proved that our model is a lightweight and efficient model for gesture recognition of skeleton dynamic graphs. [ABSTRACT FROM AUTHOR]

This paper presents a new edge detection process implemented in an embedded IoT device called Bee Smart Detection node to detect catastrophic apiary events. Such events include swarming, queen loss, and the detection of Colony Collapse Disorder (CCD) conditions. Two deep learning sub-processes are used for this purpose. The first uses a fuzzy multi-layered neural network of variable depths called fuzzy-stranded-NN to detect CCD conditions based on temperature and humidity measurements inside the beehive. The second utilizes a deep learning CNN model to detect swarming and queen loss cases based on sound recordings. The proposed processes have been implemented into autonomous Bee Smart Detection IoT devices that transmit their measurements and the detection results to the cloud over Wi-Fi. The BeeSD devices have been tested for easy-to-use functionality, autonomous operation, deep learning model inference accuracy, and inference execution speeds. The author presents the experimental results of the fuzzy-stranded-NN model for detecting critical conditions and deep learning CNN models for detecting swarming and queen loss. From the presented experimental results, the stranded-NN achieved accuracy results up to 95%, while the ResNet-50 model presented accuracy results up to 99% for detecting swarming or queen loss events. The ResNet-18 model is also the fastest inference speed replacement of the ResNet-50 model, achieving up to 93% accuracy results. Finally, cross-comparison of the deep learning models with machine learning ones shows that deep learning models can provide at least 3–5% better accuracy results. [ABSTRACT FROM AUTHOR]

In the era of ubiquitous computing, the challenges imposed by the increasing demand for real-time data processing, security, and energy efficiency call for innovative solutions. The emergence of fog computing has provided a promising paradigm to address these challenges by bringing computational resources closer to data sources. Despite its advantages, the fog computing characteristics pose challenges in heterogeneous environments in terms of resource allocation and management, provisioning, security, and connectivity, among others. This paper introduces COGNIFOG, a novel cognitive fog framework currently under development, which was designed to leverage intelligent, decentralized decision-making processes, machine learning algorithms, and distributed computing principles to enable the autonomous operation, adaptability, and scalability across the IoT–edge–cloud continuum. By integrating cognitive capabilities, COGNIFOG is expected to increase the efficiency and reliability of next-generation computing environments, potentially providing a seamless bridge between the physical and digital worlds. Preliminary experimental results with a limited set of connectivity-related COGNIFOG building blocks show promising improvements in network resource utilization in a real-world-based IoT scenario. Overall, this work paves the way for further developments on the framework, which are aimed at making it more intelligent, resilient, and aligned with the ever-evolving demands of next-generation computing environments. [ABSTRACT FROM AUTHOR]

Task scheduling is a critical challenge in cloud computing systems, greatly impacting their performance. Task scheduling is a nondeterministic polynomial time hard (NP-Hard) problem that complicates the search for nearly optimal solutions. Five major uncertainty parameters, i.e., security, traffic, workload, availability, and price, influence task scheduling decisions. The primary rationale for selecting these uncertainty parameters lies in the challenge of accurately measuring their values, as empirical estimations often diverge from the actual values. The integral-valued Pythagorean fuzzy set (IVPFS) is a promising mathematical framework to deal with parametric uncertainties. The Dyna Q+ algorithm is the updated form of the Dyna Q agent designed specifically for dynamic computing environments by providing bonus rewards to non-exploited states. In this paper, the Dyna Q+ agent is enriched with the IVPFS mathematical framework to make intelligent task scheduling decisions. The performance of the proposed IVPFS Dyna Q+ task scheduler is tested using the CloudSim 3.3 simulator. The execution time is reduced by 90%, the makespan time is also reduced by 90%, the operation cost is below 50%, and the resource utilization rate is improved by 95%, all of these parameters meeting the desired standards or expectations. The results are also further validated using an expected value analysis methodology that confirms the good performance of the task scheduler. A better balance between exploration and exploitation through rigorous action-based learning is achieved by the Dyna Q+ agent. [ABSTRACT FROM AUTHOR]

Car-following models are crucial in adaptive cruise control systems, making them essential for developing intelligent transportation systems. This study investigates the characteristics of high-speed traffic flow by analyzing the relationship between headway distance and dynamic desired distance. Building upon the optimal velocity model theory, this paper proposes a novel traffic car-following computing system in the time domain by incorporating an absolutely safe time headway strategy and a relatively safe time headway strategy to adapt to the dynamic changes in high-speed traffic flow. The interpretable physical law of motion is used to compute and analyze the car-following behavior of the vehicle. Three different types of car-following behaviors are modeled, and the calculation relationship is optimized to reduce the number of parameters required in the model's adjustment. Furthermore, we improved the calculation of dynamic expected distance in the Intelligent Driver Model (IDM) to better suit actual road traffic conditions. The improved model was then calibrated through simulations that replicated changes in traffic flow. The calibration results demonstrate significant advantages of our new model in improving average traffic flow speed and vehicle speed stability. Compared to the classic car-following model IDM, our proposed model increases road capacity by 8.9%. These findings highlight its potential for widespread application within future intelligent transportation systems. This study optimizes the theoretical framework of car-following models and provides robust technical support for enhancing efficiency within high-speed transportation systems. [ABSTRACT FROM AUTHOR]

Cloud computing is a popular, flexible, scalable, and cost-effective technology in the modern world that provides on-demand services dynamically. The dynamic execution of user requests and resource-sharing facilities require proper task scheduling among the available virtual machines, which is a significant issue and plays a crucial role in developing an optimal cloud computing environment. Round Robin is a prevalent scheduling algorithm for fair distribution of resources with a balanced contribution in minimized response time and turnaround time. This paper introduced a new enhanced round-robin approach for task scheduling in cloud computing systems. The proposed algorithm generates and keeps updating a dynamic quantum time for process execution, considering the available number of process in the system and their burst length. Since our method dynamically runs processes, it is appropriate for a real-time environment like cloud computing. The notable part of this approach is the capability of scheduling tasks with asymmetric distribution of burst time, avoiding the convoy effect. The experimental result indicates that the proposed algorithm has outperformed the existing improved round-robin task scheduling approaches in terms of minimized average waiting time, average turnaround time, and number of context switches. Comparing the method against five other enhanced round robin approaches, it reduced average waiting times by 15.77% and context switching by 20.68% on average. After executing the experiment and comparative study, it can be concluded that the proposed enhanced round-robin scheduling algorithm is optimal, acceptable, and relatively better suited for cloud computing environments. [ABSTRACT FROM AUTHOR]

Color characteristics are a crucial indicator of green tea quality, particularly in needle-shaped green tea, and are predominantly evaluated through subjective sensory analysis. Thus, the necessity arises for an objective, precise, and efficient assessment methodology. In this study, 885 images from 157 samples, obtained through computer vision technology, were used to predict sensory evaluation results based on the color features of the images. Three machine learning methods, Random Forest (RF), Support Vector Machine (SVM) and Decision Tree-based AdaBoost (DT-AdaBoost), were carried out to construct the color quality evaluation model. Notably, the DT-Adaboost model shows significant potential for application in evaluating tea quality, with a correct discrimination rate (CDR) of 98.50% and a relative percent deviation (RPD) of 14.827 in the 266 samples used to verify the accuracy of the model. This result indicates that the integration of computer vision with machine learning models presents an effective approach for assessing the color quality of needle-shaped green tea. [ABSTRACT FROM AUTHOR]

The visual system can compute summary statistics of several visual elements at a glance. Numerous studies have shown that an ensemble of different visual features can be perceived over 50-200 ms; however, the time point at which the visual system forms an accurate ensemble representation associated with an individual's perception remains unclear. This is mainly because most previous studies have not fully addressed time-resolved neural representations that occur during ensemble perception, particularly lacking quantification of the representational strength of ensembles and their correlation with behavior. Here, we conducted orientation ensemble discrimination tasks and electroencephalogram (EEG) recordings to decode orientation representations over time while human observers discriminated an average of multiple orientations. We modeled EEG signals as a linear sum of hypothetical orientation channel responses and inverted this model to quantify the representational strength of orientation ensemble. Our analysis using this inverted encoding model revealed stronger representations of the average orientation over 400-700 ms. We also correlated the orientation representation estimated from EEG signals with the perceived average orientation reported in the ensemble discrimination task with adjustment methods. We found that the estimated orientation at approximately 600-700 ms significantly correlated with the individual differences in perceived average orientation. These results suggest that although ensembles can be quickly and roughly computed, the visual system may gradually compute an orientation ensemble over several hundred milliseconds to achieve a more accurate ensemble representation. [ABSTRACT FROM AUTHOR]

Federated learning (FL) can break the problem of data silos and allow multiple data owners to collaboratively train shared machine learning models without disclosing local data in mobile edge computing. However, how to incentivize these clients to actively participate in training and ensure efficient convergence and high test accuracy of the model has become an important issue. Traditional methods often use a reverse auction framework but ignore the consideration of client volatility. This paper proposes a multi-dimensional reverse auction mechanism (MRATR) that considers the uncertainty of client training time and reputation. First, we introduce reputation to objectively reflect the data quality and training stability of the client. Then, we transform the goal of maximizing social welfare into an optimization problem, which is proven to be NP-hard. Then, we propose a multi-dimensional auction mechanism MRATR that can find the optimal client selection and task allocation strategy considering clients' volatility and data quality differences. The computational complexity of this mechanism is polynomial, which can promote the rapid convergence of FL task models while ensuring near-optimal social welfare maximization and achieving high test accuracy. Finally, the effectiveness of this mechanism is verified through simulation experiments. Compared with a series of other mechanisms, the MRATR mechanism has faster convergence speed and higher testing accuracy on both the CIFAR-10 and IMAGE-100 datasets. [ABSTRACT FROM AUTHOR]

Synaptic devices have gained prominence as viable alternatives to conventional complementary metal‐oxide‐semiconductor‐based (CMOS) electronics in the information processing field owing to their inherent advantages in analog and parallel operations. The potential of synaptic devices has not yet been fully utilized for logic operations because only the conventional binary logic structure has been applied to analog synaptic devices, leading to the loss of advantages unique to analog signals. To resolve this issue, an innovative concept is proposed: an analog logic gate that can perform parallel operations at the device level and is coupled with logic reconfigurations, enabling comprehensive analog computation. This logic gate comprises two synaptic devices with different retention characteristics, adjusted by the side‐chain engineering of an organic polymer. The long‐term and short‐term synaptic devices serve as reconfigurable synapse for logic mode selection and parallel processable logic synapse, respectively. In this study, the reconfigurable and parallel processable synaptic logic circuit is effectively implemented in a personalized disease risk diagnostic system. This innovative approach not only allows for the simultaneous computation of analog‐formed diagnostic data, but also enhances both computing efficiency and system complexity in general synaptic systems. [ABSTRACT FROM AUTHOR]

This study reports intrinsic multimodal memristivity of a nonconjugated radical polymer with ambient stability. Organic memristive devices represent powerful candidates for biorealistic data storage and processing. However, there exists a substantial knowledge gap in realizing the synthetic biorealistic systems capable of effectively emulating the cooperative and multimodal activation processes in biological systems. In addition, conventional organic memristive materials are centered on conjugated small and macromolecules, making them synthetically challenging or difficult to process. In this work, we first describe the intrinsic resistive switching of the radical polymer that resulted in an exceptional state retention of >105 s and on/off ratio of >106. Next, we demonstrate its bimodal cooperative switching, in response to the proton accumulation as a biological input. Last, we expand our system toward an advanced in-sensor computing system. Our research demonstrates a nonconjugated radical polymer with intrinsic memristivity, which is directly applicable to future electronics including data storage, neuromorphics, and in-sensor computing. [ABSTRACT FROM AUTHOR]

This paper provides an in-depth discussion on the comprehensive requirements analysis, design implementation, algorithm optimization, and experimental evaluation of an electric power marketing information system, aiming to build a modern information system that is efficient, secure, and user-friendly. In the requirements analysis phase, the importance of business process optimization, data management analysis, security compliance, system integration and scalability is emphasized, while the diversified needs of end customers are considered. For the design and implementation part, the system architecture is based on microservices and cloud-native technologies to ensure high performance and security; and modularized development is achieved through Spring Boot, Vue.js and other technology stacks. For algorithm optimization, LSTM is used for power demand prediction and anomaly detection by combining integrated learning and self-encoder, which improves the prediction accuracy and anomaly identification capability. Experimental evaluation shows that the system demonstrates good performance, security and scalability in cloud computing environment, and the cost-effectiveness is significantly better than traditional deployment. [ABSTRACT FROM AUTHOR]

A complete low-power, low-cost and wireless solution for bridge structural health monitoring is presented. This work includes monitoring nodes with modular hardware design and low power consumption based on a control and resource management board called CoreBoard, and a specific board for sensorization called SensorBoard is presented. The firmware is presented as a design of FreeRTOS parallelised tasks that carry out the management of the hardware resources and implement the Random Decrement Technique to minimize the amount of data to be transmitted over the NB-IoT network in a secure way. The presented solution is validated through the characterization of its energy consumption, which guarantees an autonomy higher than 10 years with a daily 8 min monitoring periodicity, and two deployments in a pilot laboratory structure and the Eduardo Torroja bridge in Posadas (Córdoba, Spain). The results are compared with two different calibrated commercial systems, obtaining an error lower than 1.72% in modal analysis frequencies. The architecture and the results obtained place the presented design as a new solution in the state of the art and, thanks to its autonomy, low cost and the graphical device management interface presented, allow its deployment and integration in the current IoT paradigm. [ABSTRACT FROM AUTHOR]

A new shear-locking free polygonal elements is proposed for static bending and free vibration analysis of thin/thick functionally graded material plates. The domain is discretized with arbitrary polygons and Wachspress interpolants are employed for approximating the unknowns. The plate kinematics is based on Reissner-Mindlin plate theory and shear locking syndrome is suppressed by employing the novel n + 1 integration scheme based on Richardson extrapolation technique. Within this, a two stage sampling technique is introduced with the Weighted Richardson scheme to compute the system matrices. From the systematic study, it is inferred that the proposed scheme passes patch tests and yields accurate results. [ABSTRACT FROM AUTHOR]

In this paper, the authors present a technique for developing a secure messaging service called "CryptoMess" which utilizes the Commutative Supersingular Isogeny Diffie-Hellman (CSIDH) algorithm for secure key exchange and the Advanced Encryption Standard (AES) to protect message content in communication. In addition, the authors have incorporated the Falcon post-quantum digital signature technology to ensure the integrity and authenticity of communications between the sender and the recipient. The novel post-quantum cryptographic algorithms utilized by the authors are still under consideration in the competition to select cryptosystems for use in quantum computing systems organized by the NIST Standards Institute. As a result, the messaging program "CryptoMess" is able to exchange messages between users, providing safety, security, integrity, and authenticity. The authors have included modern post-quantum cryptography techniques, such as the Falcon digital signature system, to guarantee that the product operates safely. The program has a transmitting rate of approximately 209–261 ms and a receiving rate of approximately 168–206 ms. The message signing time is about 260 ms, and the message verification speed is approximately 185 ms. Key generation time is about 741 ms, key encapsulation time is about 1.454 ms, and key decapsulation time is about 1.921 ms. The source code of the "CryptoMess" communications program has been analyzed and tested by the authors using the Fortify Static Code Analyzer tool to ensure that the product has been created safely and securely. [ABSTRACT FROM AUTHOR]

Taking the deictic judgment that is the modernist gesture of declaring something to be art as a starting point, this essay suggests an analogous deixis as a necessary condition for literature. This deixis also can serve as the basis for discussing the expectations of computer‐generated texts. Against the idea that computers or AI systems need only produce sufficiently good output in order to be considered authors, the essay proposes an approach that takes the social recognition of the deictic act within a community of judgment as a precondition for authorship. As an alternative to the Turing test, which is based on the paradigm of deception (people are tricked into considering computer‐written text to be written by humans), the essay favors a version of Susan Leigh Star's "Durkheim test," which is based on the paradigm of co‐sociality (people directly recognize computers as social actors). Only if the gesture of a machine declaring something to be art is recognized as a deictic judgment in the full sense can one plausibly speak of computer authorship. [ABSTRACT FROM AUTHOR]

Computer system resilience refers to the ability of a computer system to continue functioning even in the face of unexpected events or disruptions. These disruptions can be caused by a variety of factors, such as hardware failures, software glitches, cyber attacks, or even natural disasters. Modern computational environments need applications that can recover quickly from major disruptions while also being environmentally sustainable. Balancing system resilience with energy efficiency is challenging, as efforts to improve one can harm the other. This paper presents a method to enhance disaster survivability in microservice architectures, particularly those using Kubernetes in cloud-based environments, focusing on optimizing electrical energy use. Aiming to save energy, our work adopt the consolidation strategy that means grouping multiple microservices on a single host. Our aproach uses a widely adopted analytical model, the Generalized Stochastic Petri Net (GSPN). GSPN are a powerful modeling technique that is widely used in various fields, including engineering, computer science, and operations research. One of the primary advantages of GSPN is its ability to model complex systems with a high degree of accuracy. Additionally, GSPN allows for the modeling of both logical and stochastic behavior, making it ideal for systems that involve a combination of both. Our GSPN models compute a number of metrics such as: recovery time, system availability, reliability, Mean Time to Failure, and the configuration of cloud-based microservices. We compared our approach against others focusing on survivability or efficiency. Our approach aligns with Recovery Time Objectives during sudden disasters and offers the fastest recovery, requiring 9% less warning time to fully recover in cases of disaster with alert when compared to strategies with similar electrical consumption. It also saves about 27% energy compared to low consolidation strategies and 5% against high consolidation under static conditions. [ABSTRACT FROM AUTHOR]