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A novel structural reliability analysis method that combines the improved beluga whale optimization (IBWO) and the arctangent function‐based maximum entropy method (AMEM) is proposed in this paper. It aims to augment the accuracy of failure probability prediction in structural reliability analysis based on the traditional maximum entropy method (MEM). First, the arctangent function is introduced to avoid the effects of truncation error and numerical overflow in the traditional MEM. The arctangent function can nonlinearly transform the structural performance function defined on the infinite interval into a transformed performance function defined on the bounded interval. Subsequently, the undetermined Lagrange multipliers in the maximum entropy probability density function (MEPDF) of the transformed performance function are obtained using IBWO at a swifter convergence speed with heightened convergence accuracy. Finally, the MEPDF of the transformed performance function can be obtained by combining the IBWO and AMEM, and the structural failure probability can be predicted. The analysis of the metro bogie frame as an engineering example reveals that compared with the traditional MEM using the genetic algorithm to solve the Lagrange multipliers, the proposed method diminishes the relative error in failure probability prediction from 20.51% to only 0.09%. This method significantly enhances the prediction accuracy of failure probability. [ABSTRACT FROM AUTHOR]

In this article, we propose a computer-oriented method of construction of optimal group sequential hypothesis tests with variable group sizes. In particular, for independent and identically distributed observations, we obtain the form of optimal group sequential tests which turn to be a particular case of sequentially planned probability ratio tests (SPPRTs, see Schmitz 1993). Formulas are given for computing the numerical characteristics of general SPPRTs, like error probabilities, average sampling cost, etc. A numerical method of designing the optimal tests and evaluation of the performance characteristics is proposed, and computer algorithms of its implementation are developed. For a particular case of sampling from a Bernoulli population, the proposed method is implemented in R programming language, the code is available in a public GitHub repository. The proposed method is compared numerically with other known sampling plans. [ABSTRACT FROM AUTHOR]

Summary Perfect detection of unused/underutilized frequency band of the primary user (PU) in the cognitive radio communication systems is an essential component for its efficient utilization by the secondary/cognitive user (CU) without any interference. In the cooperative spectrum sensing scenario, the presence or absence of PU is determined by analyzing the decisions of each CU engaged in the collaboration. In this paper, we have emphasized on the significance of spectrum sensing duration in the optimal threshold selection approach in the cooperative spectrum sensing scenarios for a range of the received PU signal signal‐to‐noise ratio (SNR) from low to high values. The sensing duration parameter is taken as ts = 2.5 ms in the higher SNR environment, whereas in the lower SNR environment when the optimal threshold condition fails, ts is kept dynamic to satisfy minimum target values of sensing performance parameters. We have computed the spectrum sensing performance parameters and throughput using the same approach and compared the results with those obtained from the constant false‐alarm rate (CFAR), constant detection rate (CDR), and minimized error probability (MEP) threshold selection approaches. The spectrum sensing performance computed by the proposed approach is significantly improved as compared with the other reported approaches. At SNR = −24 dB, global probability of false alarm Qf = 0.002 and global probability of detection Qd = 0.999 are achieved. However, the throughput is relatively less for low SNR values. [ABSTRACT FROM AUTHOR]

The paper proposes an innovative methodology of Human Cognitive Reliability-Cognitive Reliability and Error Analysis Method (HCR-CREAM) coupled with A search and genetic algorithm (GA) to tackle ship cabin equipment layout considering human factor reliability optimization with the goal of minimizing human error probability (HEP) subjected to practical requirements. After establishing the mathematical model of cabin equipment inspection tasks in ship cabin equipment layout problem through HCR-CREAM and equipment geometric simplification, a method of the horizontal movement based on minimum distance is presented to avoid the equipment overlapping, then A search is used for planning inspection paths and GA with selection, crossover, and mutation operators is applied to solve equipment layout results. A case of equipment layout in a certain ship engine room has been taken to carry out parameter sampling experiments by Latin Hypercube for GA. The results show the solution effect of GA is less affected by its parameter variation. And through the comparison with the initial equipment layout, the indicators influencing the HEP of the optimized result have been improved, thus significantly reducing HEP. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the positioning accuracy of traditional SLAM algorithm decreases due to the influence of dynamic feature points in dynamic environment, this paper proposed SF-VINS which fused semantic information. Firstly, it incorporated the PP-LiteSeg semantic segmentation network into the front-end of system based on the VINS-Mono algorithm and removed the dynamic feature points according to the results of semantic segmentation. Then, it constructed the semantic probability error constraint with the pixel semantic probabilities and employed adaptive weights for feature points in the backend. Based on these modifications, this paper proposed a new BA cost function and an optimization strategy for camera extrinsic parameters to improve the accuracy of state estimation. Finally, this paper conducted experiments on the VIODE and NTU VIRAL datasets to validate its effectiveness. The experimental results show that the proposed algorithm has advantages in terms of positioning accuracy and robustness in both dynamic and static scenes compared with state-of-the-art visual-inertial SLAM algorithms. [ABSTRACT FROM AUTHOR]

Summary In this paper, a non‐orthogonal multiple access (NOMA)‐based precoded quadrature spatial modulation (PQSM) technique (NOMA‐PQSM) has been proposed for the downlink scenario. In NOMA‐PQSM, two intended receiving antennas are activated at any time instant. One antenna is activated for the in‐phase component of the transmitted signal, and another one is activated for the quadrature phase component, on the basis of data bits. NOMA‐PQSM provides benefits like improved spatial diversity and spectral efficiency in comparison with spatial modulation. This work uses zero forcing (ZF) precoding over downlink flat fading Rayleigh multiple input multiple output (MIMO) channels, to limit the channel's deteriorating effect on transmitted signal, assuming perfect channel state information (CSI) at the transmitter. A low complexity receiver based on the successive interference cancellation is used. An expression for the upper bound of average bit error probability is derived. Moreover, the expressions for the sum mutual information of users and its lower bound are also derived. The proposed scheme is compared with the preprocessing aided spatial modulation (PSM)‐based counterpart. Monte Carlo simulations reveal that the NOMA‐PQSM scheme outperforms its orthogonal counterpart and the PSM scheme. [ABSTRACT FROM AUTHOR]

Human reliability analysis (HRA) is approaching nearly 60 years of reliance on key aspects of the original HRA method Technique for Human Error Rate Prediction (THERP), including its process for analyzing dependency. Despite advances in computational abilities and HRA-relevant techniques, the conceptualization, modeling, and quantification of dependency have remained largely unchanged since the introduction of THERP. As a result, current HRA methods do not consider dependency in a realistic manner, and there remain foundational gaps related to the definition, lack of causality, and quantification for HRA dependency. In this paper, we review the current conceptualization of dependency and demonstrate that current research in dependency is not addressing all of the technical gaps. To address the outstanding technical gaps in HRA dependency, we propose a set of fundamental dependency structures (HRA dependency idioms) that capture the spectrum of relationships possible between HRA variables. The idioms provide a robust logical structure for HRA dependency that emphasizes causality and is based on a causal Bayesian network modeling architecture. The idioms conceptualize and model HRA dependency in an objective, traceable, and causally informed manner that facilitates data-based quantification of HRA dependency. [ABSTRACT FROM AUTHOR]

The single-letter characterization of the entanglement-assisted capacity of a quantum channel is one of the seminal results of quantum information theory. In this paper, we consider a modified communication scenario in which the receiver is allowed an additional, “inconclusive” measurement outcome, and we employ an error metric given by the error probability in decoding the transmitted message conditioned on a conclusive measurement result. We call this setting postselected communication and the ensuing highest achievable rates the postselected capacities. Here, we provide a precise single-letter characterization of postselected capacities in the setting of entanglement assistance as well as the more general nonsignaling assistance, establishing that they are both equal to the channel’s projective mutual information — a variant of mutual information based on the Hilbert projective metric. We do so by establishing bounds on the one-shot postselected capacities, with a lower bound that makes use of a postselected teleportation-based protocol and an upper bound in terms of the postselected hypothesis testing relative entropy. As such, we obtain fundamental limits on a channel’s ability to communicate even when this strong resource of postselection is allowed, implying limitations on communication even when the receiver has access to postselected closed timelike curves. [ABSTRACT FROM AUTHOR]

The integration of large intelligent surfaces (LIS) with non-orthogonal multiple access (NOMA) networks has emerged as a promising solution to enhance the capacity and coverage of wireless communication systems. In this study, we analyse the performance of a NOMA network with the assistance of LIS. We propose a system model where a base station (BS) equipped with a LIS serves multiple users. The LIS consists of many passive elements that can influence the wireless channel by adjusting the reflection coefficients. We consider a downlink scenario where the BS transmits to multiple users simultaneously using NOMA, and the LIS helps to improve the signal quality and coverage. We additionally evaluate the efficiency of the suggested LIS-assisted NOMA network. In addition, we evaluate the efficiency of the LIS-assisted NOMA network in comparison to conventional NOMA systems that do not utilize LISs. The findings indicate that the LIS has a notable impact on enhancing the system's performance in terms of diversity gain, probability of error, and pairwise error probability (PEP). Moreover, the suggested LIS-assisted NOMA network is shown to be superior to conventional NOMA systems through comparison. These findings offer useful insights into the performance analysis of LIS-assisted NOMA networks. They also serve as inspiration and motivation for future research and development in this new subject, with the potential to revolutionize wireless communication systems. [ABSTRACT FROM AUTHOR]

Lifeboats are life-saving equipment used when it is necessary to abandon a ship or, in some ships, for man-overboard situations (to collect persons from water). Every seafarer onboard a ship has a task related to lifeboat operation in an emergency. In order to master and practise the assigned tasks, be ready to react at any moment, and efficiently use life-saving equipment and appliances, seafarers on ships perform drills at prescribed intervals. Effective drill performance is of paramount importance, as it improves safety and enables crew members to practise lifeboat operations. However, although their primary role is life-saving, lifeboat drills have resulted in numerous accidents, causing injuries and fatalities, besides equipment damage. Therefore, it is necessary to prevent such unwanted events and discover their root causes. As the human factor is considered a significant cause of marine accidents, this paper aims to quantify human error probability (HEP) during lifeboat drills. In addition, because lifeboat drill accident data are scarce, this study adopted the Success Likelihood Index Method (SLIM) for human reliability analysis (HRA). Based on expert judgments, the tasks with the highest probability of human error and factors significantly influencing human performance during lifeboat drills are identified. According to the study results, the recovery of the lifeboat is the most hazardous phase with the highest HEP. In addition, the BN-SLIM is adopted to estimate the probability of human error during the recovery of the lifeboat. The task with the largest HEP is confirming the release lever is properly rested and hooks locked (HEP = 4.5%). Furthermore, the design and condition of equipment and Crew Competence are identified as the most important Performance-Shaping Factors (PSFs) that affect crew members' performance. The BN-SLIM model was verified utilising a sensitivity analysis and validated by analysing real-life lifeboat drill accidents that occurred during lifeboat recovery. The results confirmed that the model could be used to analyse lifeboat accidents and for proactive preventive measures because most influencing factors are recognised, and acting on them can significantly reduce the HEP of the overall task, improve lifeboat safety, and save lives at sea. [ABSTRACT FROM AUTHOR]

Internal quality control in clinical chemistry laboratories are based on analyzing samples of stable control materials among the patient samples. The control results are interpreted by using quality control rules that usually are designed to detect systematic errors. The best rules have a high probability of error detection (Ped), i.e. to detect the maximal allowable (critical) systematic error and a low probability of false rejection (Pfr, false alarm). In this work we show that quality control rules can be represented by points on a ROC curve which appears when Ped is plotted against Pfrand only the control limit is varied. Further, we introduce a new method for choosing the optimal control limit, analogous to choosing the optimal operating point on the ROC curve of a diagnostic test. This decision needs knowledge of the pretest probability of a critical systematic error, the benefit of detecting it when it occurs and the cost of false alarm. The ROC curve analysis showed that if rules based on N = 2 are used, mean rules outperform Westgard rules because the ROC curve of the mean rules was lying above the ROC curves of the Westgard rules. A mean rule also had a lower maximum expected increase in the number of unacceptable patient results reported during the presence of an out-of-control error condition (Max E(NUF)) than comparable Westgard rules. [ABSTRACT FROM AUTHOR]

A central challenge in hypothesis testing (HT) lies in determining the optimal balance between Type I (false positive) and Type II (non-detection or false negative) error probabilities. Analyzing these errors' exponential rate of convergence, known as error exponents, provides crucial insights into system performance. Error exponents offer a lens through which we can understand how operational restrictions, such as resource constraints and impairments in communications, affect the accuracy of distributed inference in networked systems. This survey presents a comprehensive review of key results in HT, from the foundational Stein's Lemma to recent advancements in distributed HT, all unified through the framework of error exponents. We explore asymptotic and non-asymptotic results, highlighting their implications for designing robust and efficient networked systems, such as event detection through lossy wireless sensor monitoring networks, collective perception-based object detection in vehicular environments, and clock synchronization in distributed environments, among others. We show that understanding the role of error exponents provides a valuable tool for optimizing decision-making and improving the reliability of networked systems. [ABSTRACT FROM AUTHOR]

The occurrence of probabilistic common cause errors (PCCEs) in man‐machine systems can lead to multiple human errors being affected by common causes and will contribute greatly to the reliability of the system. In this paper, A reliability modeling method based on event tree (ET)‐fault tree (FT) model for man‐machine systems subjected to PCCEs is proposed. Under the influence of PCCEs, risk factors in the system are not independent, and human errors affected by the same common cause will occur with different probabilities. To describe the dependencies among risk factors, a PCCE gate is proposed to extend FTs in the ET‐FT model. To analyze the impact of common causes on the human error probabilities and quantify the extended FT, an explicit method and an implicit method based on the Cognitive Reliability and Error Analysis Method are proposed. The proposed quantification methods consider the different relationships among multiple common causes in a single model. Finally, the proposed methods are validated in the reliability assessment of the emergency response system under malfunction of the solar wing mechanism. [ABSTRACT FROM AUTHOR]

Summary: In this paper, an event‐triggered adaptive tracking control strategy is proposed for strict‐feedback stochastic nonlinear systems with predetermined finite‐time performance. Firstly, a finite‐time performance function (FTPF) is introduced to describe the predetermined tracking performance. With the help of the error transformation technique, the original constrained tracking error is transformed into an equivalent unconstrained variable. Then, the unknown nonlinear functions are approximated by using the multi‐dimensional Taylor networks (MTNs) in the backstepping design process. Meanwhile, an event‐triggered mechanism with a relative threshold is introduced to reduce the communication burden between actuators and controllers. Furthermore, the proposed control strategy can ensure that all signals of the closed‐loop system are bounded in probability and the tracking error is within a predefined range in a finite time. In the end, the effectiveness of the proposed control strategy is verified by two simulation examples. [ABSTRACT FROM AUTHOR]

Two-wave with diffuse power (TWDP) is one of the most promising distributions for describing multipath fading in the emerging millimeter-wave (mmWave) band. However, traditional error performance analysis in these channels is mostly based on the approximate expressions, accurate only for specific combinations of TWDP parameters that describe particular channel conditions. Considering the aforementioned, this paper begins with an extensive literature overview of the existing average bit/symbol error probability (ABEP/ASEP) expressions in TWDP channels, revealing the absence of the exact expressions that could be used to accurately evaluate error performance in all channel conditions, for most modulation and diversity techniques. Next, the exact ASEP expressions are derived for M-ary rectangular quadrature amplitude modulation with coherent detection and for M-ary differential phase shift keying modulation. These expressions are then used to derive corresponding computationally simple closed-form asymptotic ASEPs, which accurately evaluate error performance for signal-to-noise ratios (SNR) greater than 30 dB. All the results are verified through Monte-Carlo simulation. [ABSTRACT FROM AUTHOR]

The evolution of the mechanism of human behavior formation analysis has significantly influenced the development of human reliability analysis (HRA), which aims to calculate human error probability (HEP) with performance shaping factors (PSFs). This paper reviews the typical HRA methods in different generations, the role of PSFs, and their interrelation‐ships in human risk modeling, with the background of nuclear power plants (NPPs). In a retrospective of typical HRA methods, PSF plays a fundamental role in assessing human performance during task operation. However, the subjectivity in defining and evaluating PSFs often leads to a partial representation of human behavior characteristics and human risk evolution, resulting in the neglect of PSF inter‐relationships and conservative HEP estimation. Recent studies have emphasized employing simulation platforms to simulate the task process and obtain data relevant to PSFs that can enable the exploration of the mutual effects to support the calculation of HEP more accurately. Compared to certain previous methods involving over‐simplification and inappropriate assumptions resulting in inaccurate results, current HRA methods are prone to the construction of HEP models based on objective data acquisition and dynamic calculations with process models. This shift enables a better illustration of the intricate relationships among PSFs. Reflecting on the current trend of HRA methodology, this paper proposes a possible PSF quantification based on physiological measurement providing accessible and objective data. It improves the shortcomings in data scarcity and time‐invariance of HEP calculation, thus more accurately and realistically responds to the accumulation and fluctuation of human risks throughout a task. [ABSTRACT FROM AUTHOR]

Summary This article proposes a novel angle representation method for floating‐point number, which eliminates the need for DSP (Digital Signal Processor) resources and reduces the resource usage when performing floating‐point multiplication. Compared with the implementation of floating‐point multiplier using IP cores, the implementation of approximate multiplication based on lookup tables can achieve an average reduction of 58.2% in LUTs (look‐up tables) and an average increase of 20.4% in frequency for mantissa widths ranging from 3 to 12 bits. Additionally, it can also save an average of 23.2% in registers. Analysis of PDP (power‐delay product)/LUT to MRED (mean relative error distance)/PRED (probability of relative error distance) among other approximate multipliers shows that the proposed design extends the Pareto front. At last, simulation of a three‐level inverter is implemented to verify the effectiveness of the multiplier. [ABSTRACT FROM AUTHOR]

• The moment similarity is explained qualitatively and quantitatively. • The positional accuracy reliability at any position is calculated based on the moment similarity. • An optimal position with the lowest failure probability can be searched in a cubic space. A new positional accuracy reliability analysis method of industrial robots is proposed based on the statistical moment similarity of positional error. The first-two order statistical moments of positional error at some positions are accurately obtained through the differential kinematics method to reduce the computational cost of the proposed method. In practical engineering, the statistical moments of positional error can also be obtained from the limited experimental data. Then, the statistical moment similarity of positional error is quantified by the semivariance function. Besides, the first-two order moments of positional error at any position can be calculated according to the statistical moment similarity. Thus, the positional accuracy reliability analysis of any position can be realized by the first-two order moments according to the saddlepoint approximation method. Besides, an optimal position with the lowest failure probability of positional error can be searched based on the statistical moment similarity. The effectiveness of the proposed method is manifest through calculating the positional accuracy reliability and the optimal position with the lowest failure probability of a 6-degree-of-freedom (6-DoF) industrial robot in a cubic space. [ABSTRACT FROM AUTHOR]

This paper first determines the generalized optical orthogonal code (GOOC) parameters to minimize the bit error probability in fiber-optic code division multiple access systems. The systems use on-off keying as the modulator and the optical AND logic gate as the receiver. In situations where the problem of finding the lowest bit error probability has more than one answer, we introduce and examine four approaches to choose the optimal answer. Finally, by employing a complete search of code parameters, we determine GOOC parameters (if there are any) to achieve the maximum bandwidth efficiency considering the design constraints, i.e., the number of network users and the maximum acceptable probability of error. [ABSTRACT FROM AUTHOR]

Human reliability analysis (HRA) is widely used to evaluate the impact of human errors on various complex human–machine systems for enhancing their safety and reliability. Nevertheless, it is hard to estimate the human error probability (HEP) in reality due to the uncertainty of state assessment information and the complex relations among common performance conditions (CPCs). In this paper, we aim to present a new integrated cognitive reliability and error analysis method (CREAM) to solve the HRA problems under probabilistic linguistic environment. First, the probabilistic linguistic term sets (PLTSs) are utilized to handle the uncertain task state assessments provided by experts. Second, the minimum conflict consensus model (MCCM) is employed to deal with conflict task state assessment information to assist experts reach consensus. Third, the entropy weighting method is used to determine the relative objective weights of CPCs. Additionally, the CPC effect indexes are introduced to assess the overall effect of CPCs on performance reliability and obtain the HEP estimation. Finally, the reliability of the proposed CREAM is demonstrated via a healthcare practical case. The result shows that the new integrated CREAM can not only effectively represent experts' uncertain task state assessments but also determine more reliable HEP estimation in HRA. [ABSTRACT FROM AUTHOR]

In recent years, the application prospect of urban logistics unmanned aerial vehicles has attracted extensive attention. The high-density operation of UAVs requires autonomous separation maintenance capability. To achieve autonomous separation maintenance, it is necessary to conduct autonomous track prediction and formulate the required separation accordingly. Based on the target level of safety requirements for UAV operation, aiming at the autonomous separation maintenance ability of UAVs and considering the accuracy of track prediction, a method to calculate the required separation between UAVs is proposed. This study consists of two parts. Firstly, based on historical data, the position prediction error of the flight track is investigated. Using a machine learning model, a two-stage track prediction method, which involves classification followed by prediction, is proposed for urban logistics UAV track data. Subsequently, based on the track prediction error distribution, by designing a gas model and a position error probability model, a separation-formulating model for urban logistics UAVs in free flight is proposed in which UAV maneuverability is considered. By applying this model, the required separation is formulated for UAVs. When the required separation is set to 48.5 m, the overall collision risk meets the TLS requirements. The research provides a feasible method for establishing autonomous separation for urban logistics UAVs. [ABSTRACT FROM AUTHOR]

Summary The present research work aims to investigate the reliability of the mixed free space optical (FSO)/radio frequency (RF) decode‐and‐forward (DF) relaying system where the satellite intends to communicate with the ground station through the unmanned aerial vehicles (UAV) as the relay node. Moreover, it has been considered that a second UAV interferes with the intended UAV. The operation of the UAVs has been represented considering the small scale fading, path loss, 3‐D location, and probability of maintaining line‐of‐sight (LoS) and non line of sight (NLoS) links, while FSO link undergoes Malaga distributed turbulence. Using the aforementioned model, the closed form expressions for the outage probability and bit error rate (BER) have been derived. The exact expressions have been extended to obtain the high signal‐to‐noise ratio (SNR) results for the outage probability and BER. The analytical expressions have been numerically evaluated and the results obtained through these expressions have been verified using the Monte Carlo simulations. More importantly, long short‐term memory (LSTM)‐based deep learning model has been trained for prediction of outage probability. The model is trained offline and then utilized to predict the values of the outage probability in online mode with mean square error (MSE) and root mean square error (RMSE) of MSE = −45.02 and RMSE = −18.34 dB. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of the BP neural network in practical applications, such as easy to fall into local extremum and slow convergence speed, we optimized the initial weights and thresholds of the BP neural network using the particle swarm optimization (PSO). Additionally, cloud computing service, web technology, cloud database and numerical simulation were integrated to construct an intelligent feedback analysis cloud program for underground engineering safety monitoring based on the PSO-BP algorithm. The program could conveniently, quickly, and intelligently carry out numerical analysis of underground engineering and dynamic feedback analysis of surrounding rock parameters. The program was applied to the cloud inversion analysis of the surrounding rock parameters for the underground powerhouse of the Shuangjiangkou Hydropower Station. The calculated displacement simulated with the back-analyzed parameters matches the measured displacement very well. The posterior variance evaluation shows that the posterior error ratio is 0.045 and the small error probability is 0.999. The evaluation results indicate that the intelligent feedback analysis cloud program has high accuracy and can be applied to engineering practice. [ABSTRACT FROM AUTHOR]

The problem of soft errors in the hardware implementation of Spiking Neural Networks (SNN) has always been a challenge. SNNs, unlike traditional deep learning networks, simulate the temporal dynamic behavior of biological neurons and emit spikes when a specific threshold is reached. In recent years, the hardware implementation of SNNs has shown great potential in performing efficient and low-power tasks, but as technology nodes shrink and integrated circuit complexity increases, soft errors become a key challenge. To this end, we propose a novel approach based on input and weight analysis, through specific algorithms at the hardware level, to significantly reduce the probability of soft errors affecting the results. In terms of training accuracy, our method maintains a high accuracy under various voltage fluctuation conditions, demonstrating its superior robustness. [ABSTRACT FROM AUTHOR]

To perform the calculations of the coulombic efficiency, areal and volumetric power densities generated by microbial fuel cells (MFCs), several procedures should be accomplished; this necessitates long period and efforts, beside the probability of committing errors. This article aims at developing a tool to support users, engineers, and specialists in performing these computations by developing a desktop software that can run on laptops and computers. A mathematical model was developed to perform the calculations. Next, a flowchart was generated, and the mathematical model was incorporated in the flowchart. Then, Microsoft Visual FoxPro 9.0 Service Pack 2.0 was implemented to configure the software by combining the flowchart, mathematical model, and interface creation. Data were obtained from government associations, wastewater treatment plants, non-governmental organizations (NGOs), and literature. The data obtained for performing the calculations in the traditional way were utilized to generate output data which were matched with the output data obtained from the software. The results of both the traditional method and the program were congruous. The software is able to calculate the coulombic efficiency, area and volumetric energy density of MFCs by processing various feedstocks such as livestock manure and wastewater. [ABSTRACT FROM AUTHOR]

We propose a universal classifier for binary Neyman–Pearson classification where the null distribution is known, while only a training sequence is available for the alternative distribution. The proposed classifier interpolates between Hoeffding's classifier and the likelihood ratio test and attains the same error probability prefactor as the likelihood ratio test, i.e. the same prefactor as if both distributions were known. In addition, such as Hoeffding's universal hypothesis test, the proposed classifier is shown to attain the optimal error exponent tradeoff attained by the likelihood ratio test whenever the ratio of training to observation samples exceeds a certain value. We propose a lower bound and an upper bound to the optimal training to observation ratio. In addition, we propose a sequential classifier that attains the optimal error exponent tradeoff. [ABSTRACT FROM AUTHOR]

Summary: We propose a new class of priors for Bayesian hypothesis testing, which we name 'cake priors'. These priors circumvent the Jeffreys–Lindley paradox (also called Bartlett's paradox) a problem associated with the use of diffuse priors leading to nonsensical statistical inferences. Cake priors allow the use of diffuse priors (having one's cake) while achieving theoretically justified inferences (eating it too). We demonstrate this methodology for Bayesian hypotheses tests for various common scenarios. The resulting Bayesian test statistic takes the form of a penalised likelihood ratio test statistic. Under typical regularity conditions, we show that Bayesian hypothesis tests based on cake priors are Chernoff consistent, that is, achieve zero type I and II error probabilities asymptotically. We also discuss Lindley's paradox and argue that the paradox occurs with small and vanishing probability as sample size increases. [ABSTRACT FROM AUTHOR]

In recent years, deep metric learning and its probabilistic extensions claimed state-of-the-art results in the face verification task. Despite improvements in face verification, probabilistic methods received little attention in the research community and practical applications. Previous metric learning benchmarks avoided comparison with probabilistic methods, and it is still unclear whether they generalize well to image retrieval tasks beyond faces. In this paper, we, for the first time, perform an in-depth analysis and unified comparison of known probabilistic methods in verification and retrieval tasks. We study different design choices and argue that many of them have limited impact, while only a few probabilistic methods surpass modern metric learning approaches. Combining the best ideas of earlier works, we propose a simple modification of the top-performing method, achieving new state-of-the-art results among probabilistic methods. Finally, we study confidence prediction and show that it correlates with data quality, but contains little information about prediction error probability. We thus provide a new confidence evaluation benchmark and establish a baseline for future confidence prediction research. PyTorch implementation is publicly released. [ABSTRACT FROM AUTHOR]

In order to resist the security risks caused by quantum computing, post-quantum cryptography (PQC) has been a research focus. Constructing a key encapsulation mechanism (KEM) based on lattices is one of the promising PQC routines. The algebraically structured learning with errors (LWE) problem over power-of-two cyclotomics has been one of the most widely used hardness assumptions for lattice-based cryptographic schemes. However, power-of-two cyclotomic rings may be exploited in the inflexibility of selecting parameters. Recently, trinomial cyclotomic rings of the form Z q [ x ] / (x n − x n / 2 + 1) , where n = 2 k 3 l , k ≥ 1 , l ≥ 0 , have received widespread attention due to their flexible parameter selection. In this paper, we propose Tyber, a variant scheme of the NIST-standardized KEM candidate Kyber over trinomial cyclotomic rings. We provide three parameter sets, aiming at the quantum security of 128, 192, and 256 bits (actually achieving 129, 197, and 276 bits) with matching and negligible error probabilities. When compared to Kyber, our Tyber exhibits stronger quantum security, by 22, 31, and 44 bits, than Kyber for three security levels. [ABSTRACT FROM AUTHOR]

Simulating the probability of type I error is a powerful statistical tool that allows confirming if the statistical test achieves the established nominal level. However, its computational implementation has the drawback of significantly long execution times. Therefore, this article analyzes the performance of two parallel implementations (parRapply and boot) which significantly reduce the execution time of simulations of type I error probability for a goodness-of-fit test for the bivariate Poisson distribution. The results obtained demonstrate how the parallelization strategies accelerate the simulations, reducing the time by 50% to 90% when using 2 to 12 processors running in parallel. This reduction is graphically evidenced as the execution time of the analyzed parallel versions fits almost perfectly ( R 2 ≈ 0.999 ) to the power model y = a p b , where p is the number of processors used, and a > 0 and b < 0 are the constants of the model. Furthermore, it is shown that the parallelization strategies used scale with an increasing number of processors. All algorithms were implemented in the R programming language, and their code is included at the end of this article. [ABSTRACT FROM AUTHOR]

In this study, we investigated reconfigurable intelligent surface (RIS)-assisted device-to-device (D2D) communication systems over Nakagami-m fading channels. To enhance the reliability of RIS-assisted D2D communications, we utilized the rate-splitting multiple access (RSMA) technique to maximize the achievable ergodic rate for our considered systems. Specifically, both devices decoded the common symbol by treating private symbols as interference, and then each private symbol was decoded by treating the other as interference. In order to maximize the achievable ergodic rate at the destination, we analyzed the achievable ergodic rate of the RIS link and the D2D link, and the destination jointly decoded both symbols transmitted from the source and device by involving the maximum ratio combination (MRC). We obtained a closed-form expression for the achievable ergodic rate of the proposed RIS-assisted D2D communication system. Finally, we investigated the influence of power allocation factors and the number of reflective elements on the achievable ergodic rate. As seen by the numerical results, there was a good match between the analysis and simulation results, as well as significant superiority compared with existing works. [ABSTRACT FROM AUTHOR]

This study evaluates the performance of large intelligent surface (LIS) technology in the context of a multi-user MIMO mobile communication system (Mu-MIMO) proposed for the sixth generation (6G). LIS employs digitally controlled reflectors to enhance Signal-to-Interference plus Noise Ratio (SINR) and establish line of sight (LoS) connectivity in non-LoS environments, improving transmission security. Analytical expressions are derived to assess LIS performance metrics, including distribution parameters, bit error probability, and secrecy outage probability, considering the presence of eavesdroppers and environmental fading. The study highlights the potential of LIS technology to enhance the confidentiality and reliability of digital communication systems in next-generation networks. [ABSTRACT FROM AUTHOR]

Even though generalized frequency division multiplexing is an alternative waveform method expected to replace the orthogonal frequency division multiplexing in the future, its implementation must alleviate channel effects. Least‐squares (LS), a low‐complexity channel estimation technique, could be improved by using the discrete Fourier transform (DFT) without increasing complexity. Unlike the usage of the LS method, the DFT‐based method requires the receiver to know the channel impulse response (CIR) length, which is unknown. This study introduces a simple, yet effective, CIR length estimator by utilizing LS estimation. As the cyclic prefix (CP) length is commonly set to be longer than the CIR length, it is possible to search through the first samples if CP is larger than a threshold set using the remaining samples. An adaptive scale is also designed to lower the error probability of the estimation, and a simple signal‐to‐interference‐noise ratio estimation is also proposed by utilizing a sparse preamble to support the use of the scale. A software simulation is used to show the ability of the proposed system to estimate the CIR length. Due to shorter CIR length of rural area, the performance is slightly poorer compared to urban environment. Nevertheless, satisfactory performance is shown for both environments. [ABSTRACT FROM AUTHOR]

This letter proposes novel approximate 4:2 compressors developed using input reordering circuits and input combination probabilities. The input reordering circuit is used to reduce the hardware complexity of the proposed designs. This letter proposes two designs of approximate 4:2 compressors. This compressor is used in designing an approximate multiplier. The proposed multiplier designs utilize less energy than the already published ones due to acceptable inaccurate output/precision, which are best suitable for image processing applications. The proposed multiplier designs MUL1, MUL2, MUL3, and MUL4 saves 22.75%, 21.95%, 11.57%, and 8.95% energy than the best of the existing design (Kong and Li, 2021). [ABSTRACT FROM AUTHOR]

With increasing needs for high-bitrate, ultra-reliability, spectral efficiency, power efficiency, and reducing latency in the wireless network, global studies on the sixth generation of this network began in 2020. In this paper, we will look at intelligent reconfigurable surface structure and its application in new promising physical layer technologies, such as terahertz communications and UM-MIMO systems, to support very high-bitrate and superior network capacity in the 6G wireless communications. However, terahertz communications and UM-MIMO systems are the primary research points and confront many challenges for practical implementation. They require many RF chains and create problems in terms of cost and hardware complexity which RIS can simplify hardware and reduce cost. Therefore, we will present different modeling of wireless communication systems based on RIS for different phase information. Simulation results obtained by examining SNR performance and the error probability that shows the improvement of the received signal quality. According to results, RIS-based wireless communications can become an optimized model for future wireless communication systems. [ABSTRACT FROM AUTHOR]

Channel modeling is a first step towards the successful projecting of any wireless communication system. Hence, in this paper, we analyze the performance at the output of a multi-branch selection combining (SC) diversity receiver in a wireless environment that has been distracted by fading and co-channel interference (CCI), whereby the fading is modelled by newer Beaulieu-Xie (BX) distribution, and the CCI is modelled by the κ-µ distribution. The BX distribution provides the ability to include in consideration any number of line-of-sight (LOS) useful signal components and non-LOS (NLOS) useful signal components. This distribution contains characteristics of some other fading models thanks to its flexible fading parameters, which also applies to the κ-µ distribution. We derived here the expressions for the probability density function (PDF) and cumulative distribution function (CDF) for the output signal-to-co-channel interference ratio (SIR). After that, other performances are obtained, namely: outage probability (Pout), channel capacity (CC), moment-generating function (MGF), average bit error probability (ABEP), level crossing rate (LCR), and average fade duration (AFD). Numerical results are presented in several graphs versus the SIR for different values of fading and CCI parameters, as well as the number of input branches in the SC receiver. Then, the impact of parameters on all performance is checked. From our numerical results, it is possible to directly obtain the performance for all derived and displayed quantities for cases of previously known distributions of fading and CCI by inserting the appropriate parameter values. In the second part of the paper, a workflow for automated network experimentation relying on the synergy of Large Language Models (LLMs) and model-driven engineering (MDE) is presented, while the previously derived expressions are used for evaluation. Due to the aforementioned, the biggest value of the obtained results is the applicability to the cases of a large number of other distributions for fading and CCI by replacing the corresponding parameters in the formulas for the respective performances. [ABSTRACT FROM AUTHOR]

Corn is among the main agricultural products produced in Brazil, mainly due to its importance in human and animal food. Corn productivity is mainly related to climatic conditions and nutritional management, especially concerning nitrogen. The study's objective was to evaluate the response of corn hybrids subjected to nitrogen doses in a low-altitude subtropical environment. The experiment was conducted in the agricultural year of 2019/2020 in the municipality of Santa Maria-RS. The experimental design was of completely randomized blocks in a subdivided plot. Four corn hybrids, DKB 240 PRO 3, DKB 290 PRO 3, DKB 345 PRO 3, and Ag 9025 were used as the main plot, and six doses of nitrogen, 0, 50, 100, 150, 200, and 250 kg ha-1 in subplots. We evaluated the number of grain rows (NR) per cob, number of grains per row (NGR), cob length (CL, cm), average grain length (GL, cm), total grain weight per cob (GWC, g), thousand-grain mass (TGM, in g), and grain yield (YIE, in kg ha-1). The data were submitted to the analysis of variance (F test), and the means were compared by the Scott-Knott test at 5% of error probability and submitted to regression analysis. Cultivar Ag 9025 had the highest grain yield, associated with the highest CL, GL, GWC, TGM. The higher GL associated with high TGM compensated for the lower averages of NR and NGR for this cultivar. The maximum technical efficiency for the cultivars was 196.22 kg ha-1 of nitrogen. The mass economic efficiency with commercial values of the 2019/2020 harvest for the cultivars was 185.84 kg ha-1. [ABSTRACT FROM AUTHOR]

This paper addresses the output feedback adaptive resilient control problem for a class of stochastic nonlinear systems under sensor and actuator attacks. By applying a linear state transformation, the original system is transformed into a new one for which output feedback control becomes possible. The Nussbaum function is exploited to overcome the unknown control direction problem generated by uncertain control coefficients and adversary attacks. Based on the available compromised output signal and the states reconstructed by a new extended state observer, an output feedback adaptive resilient control scheme is proposed for such systems by combining the neural-networks-based approximation approach, backstepping technique and some special methods. The proposed controller can guarantee that all signals in the closed-loop system are semiglobally bounded in probability and the stabilisation error variables remain in an adjustable vicinity of origin. Finally, the effectiveness and applicability of the proposed control methodology is verified by two illustrative examples. [ABSTRACT FROM AUTHOR]

One of the main important features of the noisy intermediate-scale quantum (NISQ) era is the correct evaluation and consideration of errors. In this paper, we analyse the main sources of errors in current (IBM) quantum computers and we present a useful tool (TED-qc) designed to facilitate the total error probability expected for any quantum circuit. We propose this total error probability as the best way to estimate a lower bound for the fidelity in the NISQ era, avoiding the necessity of comparing the quantum calculations with any classical one. In order to contrast the robustness of our tool we compute the total error probability that may occur in three different quantum models: 1) the Ising model, 2) the Quantum-Phase Estimation (QPE), and 3) the Grover's algorithm. For each model, the main quantities of interest are computed and benchmarked against the reference simulator's results as a function of the error probability for a representative and statistically significant sample size. The analysis is satisfactory in more than the 99 % of the cases. In addition, we study how error mitigation techniques are able to eliminate the noise induced during the measurement. These results have been calculated for the IBM quantum computers, but both the tool and the analysis can be easily extended to any other quantum computer. [ABSTRACT FROM AUTHOR]

In recent years, few-shot remote sensing scene classification has become an important foundational research topic, aiming to achieve better classification performance with limited labeled data. Due to the large intra-class variances and inter-class similarity of remote sensing scenes, the scene classification task for remote sensing is much more challenging than general few-shot image classification. In this letter, we propose a class distribution learning method under limited samples by fully using the support samples, which consists of intra-class feature aggregation (IFA) module, intra-class feature homogenization module (IFH) and inter-class cross calibration (ICC) module. IFA and IFH modules are proposed to enable the aggregation and even distribution of intra-class features around the prototypes generated by the model, reducing intra-class distances and mitigating the generation of extreme samples near decision boundaries. ICC module is designed to encourage the features generated by the model for inter-class to be further apart, thereby increasing the inter-class distance and reducing the probability of classification errors. To demonstrate the effectiveness of our proposed method, we conduct comprehensive experiments on three benchmark remote sensing scene datasets: NWPU-RESISC45, WHU-RS19, and UC Merced, achieving extremely advanced performance. Our source codes have been available at . [ABSTRACT FROM AUTHOR]

In this paper, aiming at the spectrum coexistence scenario between non-geostationary satellite orbit (NGSO) and geostationary satellite orbit (GSO) satellite systems, the NGSO network, a secondary system, reuses the spectrum resources of the GSO primary system through spectrum sensing and sharing technology. Combined with the influence of factors such as the radio wave propagation model and channel characteristics of the sensing links, as well as the difference of channel environment between sensing nodes, a weighted cooperative spectrum sensing strategy named optimal fusion decision for time-invariant channel link (OFD-TIC) method was proposed for NGSO–GSO downlink sensing communication. The OFD-TIC method is designed to minimize the global error detection probability while considering the constraints of spectrum conflict probability and false alarm probability. To achieve this, OFD-TIC method transforms the optimization problem into a Lagrange function and utilizes Newton's iterative method to obtain the weights of sensing node and the parameter thresholds for fusion decision. In the six different spectrum sensing scenarios constructed using satellite network data registered by the International Telecommunication Union (ITU), the performance evaluation of the proposed OFD-TIC method is carried out and the simulation results demonstrate that the proposed method outperforms existing typical algorithms. Under the same signal-to-noise ratios (SNR) and sampling number, the OFD-TIC strategy achieves a higher global detection probability than others. Furthermore, the proposed method exhibits fast convergence to 1 of the global detection probability even at low SNR. [ABSTRACT FROM AUTHOR]

During the implementation of the Internet of Things (IoT), the performance of communication and sensing antennas that are embedded in smart surfaces or smart devices can be affected by objects in their reactive near field due to detuning and antenna mismatch. Matching networks have been proposed to re-establish impedance matching when antennas become detuned due to environmental factors. In this work, the change in the reflection coefficient at the antenna, due to the presence of objects, is first characterized as a function of the frequency and object distance by applying Gaussian process regression on experimental data. Based on this characterization, for random object positions, it is shown through simulation that a dynamic environment can lower the reliability of a matching network by up to 90%, depending on the type of object, the probability distribution of the object distance, and the required bandwidth. As an alternative to complex and power-consuming real-time adaptive matching, a new, resilient network tuning strategy is proposed that takes into account these random variations. This new approach increases the reliability of the system by 10% to 40% in these dynamic environment scenarios. [ABSTRACT FROM AUTHOR]

It is challenging to enable fast channel switching and packet-loss repair in low-delay live video distribution without negatively influencing the steady-state viewing performance. For example, regularly breaking the inter-frame dependency by introducing intra-predicted keyframes enables random access, but is costly in terms of rate-distortion performance. For this reason, the keyframe-injection method minimizes the impact by sending a compression-efficient normal video stream to all end-users. As accompaniment, a companion stream that solely consists of keyframes is sporadically used for only those users that switch channels or experience packet loss. This paper describes the requirements to implement keyframe injection in three video coding standard generations (H.264/AVC, H.265/HEVC, and H.266/VVC). We evaluated the impact that keyframe injection has on the quality of the video in terms of a decrease in VMAF, PSNR and SSIM. We demonstrate that the quality reduction caused by keyframe insertion is generally low, meaning that keyframe injection typically is imperceptible. However, drift-error artifacts become perceptible over time for rare outliers. Moreover, we pinpointed the cause of this worst-case artifact type to be halfpel interpolation. As a solution, codecs can disable subpel motion estimation, and future standards could design their filters more carefully. Lastly, it should be noted that keyframe injection will only be applied sporadically when users require a random access or experience packet loss, and only to those users. Most interestingly, all other users receive a compression-efficient stream wherein the inter-frame dependency is not artificially broken at regular short intervals. As such, our proposed solution makes low-latency video distribution efficient and viable in multiple coding standards. [ABSTRACT FROM AUTHOR]

Building maintenance is a pivotal determinant for ensuring uninterrupted and flawless operations for facilities. From the moment construction is completed until the demolition, maintenance activities must be diligently performed to establish a secure working environment. Building maintenance costs hold the considerable cost for an organization. Consequently, the effective execution of building maintenance procedures becomes imperative to reduce maintenance expenses and enhance workplace safety. Maintenance errors arise due to a range of factors, including human, management, building design, and machinery errors. Among these causative factors, human errors were more likely to occur. Accordingly, this study aims to identify the causal relationships of human errors in building maintenance. Mixed-method research approach was used with a survey strategy, where primary data was collected from a questionnaire survey and subsequent expert interviews. Data were analyzed under both qualitative and quantitative analytical approaches. 39 causes of human errors were identified through literature and among which, 13 most critical causes of human errors were identified. These critical causes were classified into four category groups, viz. information and communication, resource, management, and design, construction, and installation related. Critical human error probabilities were computed and used to develop a Bayesian Belief Network (BBN) to describe the causal relationships. [ABSTRACT FROM AUTHOR]

In case-control studies, odds ratios (OR) are calculated from 2 × 2 tables and in some instances, we observe small cell counts or zero counts in one of the cells. The corrections to calculate the ORs in the presence of empty cells are available in literature. Some of these include Yates continuity correction and Agresti and Coull correction. However, the available methods provided different corrections and the situations where each could be applied are not very apparent. Therefore, the current research proposes an iterative algorithm of estimating an exact (optimum) correction factor for the respective sample size. This was evaluated by simulating data with varying proportions and sample sizes. The estimated correction factor was considered after obtaining the bias, standard error of odds ratio, root mean square error and the coverage probability. Also, we have presented a linear function to identify the exact correction factor using sample size and proportion. [ABSTRACT FROM AUTHOR]

In risk and safety assessments of aviation systems, engineers generally pay more attention to the risks of hardware or software failure and focus less on the risks caused by human errors. In this paper, a (FRAHE) method is proposed for identifying this critical error type and determining the risk severity of human errors. This method accounts for the human error probability as well as the impacts of human errors on the system. The fuzzy inference approach is employed in this paper to address the uncertainty and issues of imprecision that arise from insufficient information and scarce error data and a risk assessment model of human error is developed. The model can be used to precisely describe the relationship between the output risk severity and the input risk indicators, including the human error probability, the error impact probability, and the human error consequence. A case study of the approach task is presented to demonstrate the availability and reasonability of the model. The risk-based modeling method can not only provide valuable information for reducing the occurrence of critical errors but also be used to conduct prospective analyses to prevent unsafe incidents or aviation accidents. [ABSTRACT FROM AUTHOR]

Reputations can foster cooperation by indirect reciprocity: if I am good to you then others will be good to me. But this mechanism for cooperation in one-shot interactions only works when people agree on who is good and who is bad. Errors in actions or assessments can produce disagreements about reputations, which can unravel the positive feedback loop between social standing and pro-social behaviour. Cooperators can end up punished and defectors rewarded. Public reputation systems and empathy are two possible mechanisms to promote agreement about reputations. Here we suggest an alternative: Bayesian reasoning by observers. By taking into account the probabilities of errors in action and observation and their prior beliefs about the prevalence of good people in the population, observers can use Bayesian reasoning to determine whether or not someone is good. To study this scenario, we develop an evolutionary game theoretical model in which players use Bayesian reasoning to assess reputations, either publicly or privately. We explore this model analytically and numerically for five social norms (Scoring, Shunning, Simple Standing, Staying, and Stern Judging). We systematically compare results to the case when agents do not use reasoning in determining reputations. We find that Bayesian reasoning reduces cooperation relative to non-reasoning, except in the case of the Scoring norm. Under Scoring, Bayesian reasoning can promote coexistence of three strategic types. Additionally, we study the effects of optimistic or pessimistic biases in individual beliefs about the degree of cooperation in the population. We find that optimism generally undermines cooperation whereas pessimism can, in some cases, promote cooperation. Author summary: Cooperation is an important part of our social lives. However, selfish incentives can undermine it: I need not reciprocate with someone who has cooperated with me. One mechanism to promote cooperation is indirect reciprocity, wherein cooperation is rewarded by good reputations and defection punished by bad ones. This reward is provided indirectly, when other individuals cooperate with those of good reputation and defect against those with a bad reputation. Maintaining accurate reputations is a key part of this mechanism, since mistakes in evaluating others' behaviours and disagreements about reputations can undermine the feedback loop between reputations and cooperation. Here we develop a model of individual reasoning that can correct for such mistakes and disagreements, and we explore how this impacts the level of cooperation. We find that individual reasoning to determine accurate reputations can often undermine cooperation. [ABSTRACT FROM AUTHOR]

Previous studies that inferred the observation error statistics from the innovation statistics can only provide the second moment of the error probability density function (pdf). However, the observation errors are sometimes non‐Gaussian, for example, for observation operators with unknown representation errors, or for bounded observations. In this study, we propose a new method, the Deconvolution‐based Observation Error Estimation (DOEE), to infer the full observation error pdf. DOEE does not rely on linear assumptions on the observation operator, the optimality of the data assimilation algorithm, or implicit Gaussian assumptions on the error pdf. The main assumption of DOEE is the availability of an ensemble of background forecasts following the independent and identically distributed (i.i.d.) assumption. We conduct idealized experiments to demonstrate the ability of the DOEE to accurately retrieve a non‐Gaussian (bimodal, skewed, or bounded) observation error pdf. We then apply the DOEE to construct a state‐dependent observation error model for satellite radiances by stratifying the observation errors based on cloud amount. In general, we find that the observation error pdfs in many categories are skewed. By adding a new predictor, total column water vapor (TCWV), into the state‐dependent model, we find that for cloudy pixels, when TCWV is small, the observation error pdfs are quite similar and Gaussian‐like, whereas when TCWV is large, the observation error pdfs differ for different cloud amount, while all of them are positively biased. This result suggests that exploring other predictors, like cloud type, might improve the stratification of the observation error model. We also discuss ways to include a non‐parametric observation error pdf into modern data assimilation schemes, including a consideration of the strong‐constraint 4D‐Var perspective, as well as the implications for other observation types including observations with bounded range. [ABSTRACT FROM AUTHOR]