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Although it was claimed that the MKR-α method can improve the overshoot and nonlinear stability characteristics of the KR-α method, it seems that it still has a high frequency overshoot in steady-state responses and a weak instability. Three examples are applied to numerically illustrate the two adverse properties. A loading-correction term is introduced into the displacement difference equation to remove the adverse overshoot in high frequency steady-state responses. Besides, it is analytically verified that the MKR-α method has an adverse weak instability. Although the problem of high frequency overshoot in steady-state responses can be overcome, there is no way to eliminate the adverse weak instability for both the KR-α method and MKR-α method. Thus, the applications of the two families of integration methods are very limited. It is demonstrated that a high frequency numerical damping is incapable of mitigating the overshoot caused by a weak instability. [ABSTRACT FROM AUTHOR]

Our study in this paper is focused on the split equality fixed-point problem with firmly quasi-non-expansive operators in infinite-dimensional Hilbert spaces. A self-adaptive simultaneous scheme is introduced, and its weak convergence is established under mild and standard assumptions. The new proposed scheme generalizes and extends some related works in the literature, and its simple structure makes it easy for implementation and numerical testing. Primary experiments presented in this paper, in finite- and infinite-dimensional spaces, emphasize their practical advantages over existing results. [ABSTRACT FROM AUTHOR]

This paper introduces the Composite Rating Method (CRM), a novel approach for the integrated evaluation of basketball player and team performances across multiple leagues. Utilizing data from Euroleague, EuroCup, and Basketball Champions League, the presented method provides comprehensive and accurate rankings, including accounting for actions not included in personal statistics. Drawing inspiration from established methodologies such as ELO, PER, Offensive and Defensive ratings, CRM offers a balanced assessment of player and team capabilities. The paper delineates the data collection and preprocessing procedures, details the algorithmic framework of CRM, and showcases its predictive capacity. By presenting a well-rounded approach to ranking, this paper aims to contribute to the advancement of performance evaluation methods in basketball and sports in general. [ABSTRACT FROM AUTHOR]

In this paper, we develop a nonmonotone line search strategy for minimization of the locally Lipschitz functions. First, the descent direction (DD) is defined based on ∂ϵf(⋅) where ϵ>0 . Next, we introduce a minimization algorithm to find a step length along the DD satisfying the nonsmooth nonmonotone Armijo condition. Choosing an adequate step length is the main purpose of the classic nonmonotone line search methods for a given DD, while in this paper both a search direction and step length are simultaneously computed. The global convergence of the minimization algorithm is proved by some assumptions on the DD. Finally, the proposed algorithm is implemented in the MATLAB environment and compared with another existing nonsmooth algorithm on some nonconvex nonsmooth optimization test problems. The efficiency of the proposed algorithm is shown by numerical results in solving some small-scale and large-scale nonsmooth optimization test problems. [ABSTRACT FROM AUTHOR]

The paper aims at creating a bridge between media and migration studies and critical algorithm studies. By adopting a media ecological approach and a mutual shaping of technology and society perspective, in this paper, we explore the factors that lead, especially in Italy, to discriminant and stigmatizing image search results, related to specific groups of immigrants living in the country. We performed a content analysis of Google-Images search results with regard to the largest immigrant communities hosted in France, Germany, Italy, and the United Kingdom. Results show that the depiction of Romanian, Albanian, Moroccan, and Algerian immigrant communities on Google.it is flattened on a univocal stigmatized representation that shows them as criminals, which is not the case in other countries. Most of these stigmatizing images derive from local online newspapers, which questions the interplay between newsmaking choices and routines, and algorithms logics. [ABSTRACT FROM AUTHOR]

In this paper, I present contextualizing factors, dimensions, and key markers of algorithm literacies, paying attention to the context of parenting and parenthood amidst datafication. Analyzing data from "think-aloud" interviews with 30 parents of children aged between 0 and 18, across England, I draw upon media and digital literacies scholarship to focus, first, in this paper, on the competencies, conversations, and events which contextualize parents' literacies with algorithmic interfaces. Next, I draw out four dimensions of parents' algorithm literacies including algorithm awareness, technical competencies, critical capacities, and championing their and their children's best interests, identifying practical markers for each dimension. I reflect on the broader implications of these for parenting and parenthood in datafied societies, and note that algorithm literacies are, forever, a work in progress, in fluidity and flux across the diverse courses of parenting journeys, deeply contextualized in the resources and restraints that parents encounter in their daily lives. [ABSTRACT FROM AUTHOR]

This paper aims to build a machine learning and a neural network model to detect the probability of money laundering in banks. The paper's data came from a simulation of actual transactions flagged for money laundering in Middle Eastern banks. The main findings highlight that criminal networks mainly use the integration stage to integrate money into the financial system. Fraudsters prefer to launder funds in the early hours, morning followed by the business day's afternoon time intervals. Additionally, the Naïve Bayes and Random Forest classifiers were identified as the two best-performing models to predict bank money laundering transactions. [ABSTRACT FROM AUTHOR]

Emotion detection and Sentiment analysis techniques are used to understand polarity or emotions expressed by people in many cases, especially during interactive systems use. Recognizing users' emotions is an important topic for human–computer interaction. Computers that recognize emotions would provide more natural interactions. Also, emotion detection helps design human-centred systems that provide adaptable behaviour change interventions based on users' emotions. The growing capability of machine learning to analyze big data and extract emotions therein has led to a surge in research in this domain. With this increased attention, it becomes essential to investigate this research area and provide a comprehensive review of the current state. In this paper, we conduct a systematic review of 123 papers on machine learning-based emotion detection to investigate research trends along many themes, including machine learning approaches, application domain, data, evaluation, and outcome. The results demonstrate: 1) increasing interest in this domain, 2) supervised machine learning (namely, SVM and Naïve Bayes) are the most popular algorithms, 3) Text datasets in the English language are the most common data source, and 4) most research use Accuracy to evaluate performance. Based on the findings, we suggest future directions and recommendations for developing human-centred systems. [ABSTRACT FROM AUTHOR]

In this paper, a new method based on the Tufts–Kumaresan (TK) algorithm is used to process nonstationary signals and extract system modes. This new algorithm for mode estimation is proposed to improve estimation accuracy under dynamic conditions. The key contribution in TK is the use of multiple orthogonal sliding windows rather than a single pair of sliding windows. Simulation results under various scenarios, such as different types of faults and load changes are used to evaluate the performance of the proposed method. The proposed method will accurately extract system modes. This method will also reduce the required memory and the calculation time of estimation in nonstationary signals. In complement to successful applications in studying the dynamic behavior of the power system, identifying and analyzing low-frequency electromechanical oscillations, and removing signal noise, this method can accurately estimate the modes of a power system. To validate the accuracy of the proposed method, the Wavelet and the Prony methods have been used for comparison. The proposed method is implemented using simulated ringdown data of standard two-area power system and real measurement data of the WSCC system breakup on 10 August 1996. [ABSTRACT FROM AUTHOR]

Nowadays, Mobile Ad hoc Network (MANET) gains more attraction due to its seamless and dynamic use. The existing methods in MANET are plagued by some weaknesses, like higher computation time, undermining their real-time responsiveness. Whereas, lower accuracy in intrusion detection compromises the reliability of these systems, potentially leading to missed threats posing a challenge for effective security measures in dynamic MANET environments. To mitigate these weaknesses, an Auto Metric Graph Neural Network optimized with Woodpecker Mating Algorithm is proposed in this paper for detecting Network Layer Attack in MANET (AGNN-WMA-DNLA-MANET). Initially, the data are gathered from CIC-IDS 2019 dataset. The gathered data undergoes pre-processing to eliminate redundancy and missing values replacement using the Local Least Squares method. Afterward, the preprocessing output is fed to AGNN for classification, which classifies the attack as active, passive, or normal. Then, Woodpecker Mating Algorithm (WMA) is proposed to optimize the AGNN weight parameters to ensure the accurate classification. The proposed technique is executed in NS2 tool. The metrics, like accuracy, false positive rate (FPR) and attack detection rate (ADR), computation time, and RoC is analyzed. The proposed AGNN-WMA-DNLA-MANET approach provides 14.68%, 7.142%, and 4.65% higher accuracy for active attack, 15.19%, 2.77%, and 9.85% higher accuracy for passive attack and 38.18%, 12.02%, and 7.59% better accuracy for normal compared with existing methods, such as Dependable intrusion detection scheme under deep convolutional neural network (DCNN-MANET), deep learning-based intrusion detection scheme for MANET (AE-DNN-MANET), and Benchmarking of machine learning for anomaly based intrusion detection scheme (ML-MANET), respectively. [ABSTRACT FROM AUTHOR]

The paper considers the design of sparse arrays in the interference active environment to minimise the system complexity and achieve reduced hardware cost. For this, we propose a sparse array design methodology to attain maximum signal-to-interference plus noise ratio (MaxSINR) in the presence of interfering signals. We formulate the optimisation problem as a real-valued quadratically constrained quadratic program (QCQP) with the non-convex $\textstyle\ell_p$ ℓ p norm to promote sparsity, which is iteratively controlled in the proposed approach. We employ the semidefinite relaxation (SDR) technique and the principle of the majorization-minimisation (MM) algorithm to solve the non-convex QCQP problem. The efficacy of the proposed algorithm is demonstrated through the simulation results. The sparse array obtained through the proposed method performs well when compared with the reweighted $\textstyle\ell_1$ ℓ 1 algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we propose a sparse least squares (SLS) optimization model for solving multilinear equations, in which the sparsity constraint on the solutions can effectively reduce storage and computation costs. By employing variational properties of the sparsity set, along with differentiation properties of the objective function in the SLS model, the first-order optimality conditions are analysed in terms of the stationary points. Based on the equivalent characterization of the stationary points, we propose the Newton Hard-Threshold Pursuit (NHTP) algorithm and establish its locally quadratic convergence under some regularity conditions. Numerical experiments conducted on simulated datasets including cases of Completely Positive(CP)-tensors and symmetric strong M-tensors illustrate the efficiency of our proposed NHTP method. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new relaxed method for solving the split feasibility problem in Hilbert spaces. In our method, the projection to the halfspace is replaced by the one to the intersection of two halfspaces. We give convergence of the sequence generated by our method under some suitable assumptions. Finally, we give a numerical example for illustrating the efficiency and implementation of our algorithms in comparison with existing algorithms in the literature. [ABSTRACT FROM AUTHOR]

This paper proposes a user-biometric-based image encryption using chaotic dynamical systems, SHA-256 hash function, and zigzag transformation. Three chaotic systems, namely, the Henon map, logistic map, and Lorenz system, are used to apply the chaotic properties to the encryption system. The hash value of the user biometric image is used to generate the initial value of the Henon map. The initial values for the logistic map and Lorenz system are generated from the hash value of the plain image. SHA-256 hash algorithm generates the hash values of the biometric and plain images. The plain image is scrambled using the two-dimensional chaotic values generated by the Henon map first. Next, the first scrambled image's pixels are XORed with the one-dimensional chaotic values generated from the logistic map and produce the second scrambled image. Finally, take the second scrambled image's pixels, and XOR them with the three-dimensional chaotic values generated from the Lorenz system and create the final encrypted image. The robustness and flexibility of the encryption system are analyzed using various security and performance analyses. The experiment results are compared with the existing algorithms to prove the efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper studies the problem of event-triggered impulsive control for discrete-time systems. A novel periodic event-triggering scheme with two tunable parameters is presented to determine the moments of updating impulsive control signals which are called event times. Sufficient conditions are established to guarantee asymptotic stability of the resulting impulsive systems. It is worth mentioning that the event times are different from the impulse times, that is, the control signals are updated at each event time but the actuator performs the impulsive control tasks at a later time due to time delays. The effectiveness of our theoretical result with the proposed scheme is illustrated by three examples. [ABSTRACT FROM AUTHOR]

Structured light sensing systems, as one of the most common optical-based nondestructive evaluation techniques, have been widely applied for inline pipeline inspection. The sensor can be inserted inside the pipe to generate 3D visualisation and evaluate the cracks in the materials. The precise calibration of the camera-projector measurement system is of great significance to ensure the measurement accuracy of the 3D sensing system. Conventional calibration methods for structured light sensors involve complicated and time-consuming procedures and are easily affected by ambient light. The paper presents a novel algorithm to automatically calibrate the projection module and estimate the stereo parameters between the camera and the projector. The calibration algorithm exploits the cylindrical nature of the inspected pipe to create a set of geometric constraints and automatically calibrate the sensor without the need for reference calibration points. Experimental and simulation results showed that the algorithm could successfully estimate the projector's intrinsic and extrinsic parameters by simply acquiring the data inside a cylindrical pipe with a known diameter. The proposed algorithm highly reduces the data collection time for the calibration (only 53 s), improves the accuracy, and simplifies the calibration process. [ABSTRACT FROM AUTHOR]

In the distribution system, voltage sags and swell have adverse effects on sensitive and critical loads. Dynamic Voltage Restorer (DVR) play a vital role to mitigate voltage sags and swell through its organized control when connected in series with the system. Various conventional control schemes have been reported for the control of DVR. The performance of DVR with these schemes has been successful in the mitigation of voltage sags/swell, but drawbacks, such as undershoot, and overshoot during sag dynamics are observed in the regulated load terminal voltage. These drawbacks may cause malfunctioning of the equipment connected and hence their elimination is indispensable. With this inspiration, a novel Adaline filter-based adaptive control scheme using learning-rate parameter-based Least-Mean-Square algorithm has been proposed. This scheme has the capability to overcome the drawbacks with the conventional control schemes thus improving the performance of DVR. In this paper, the performance of DVR using proposed algorithm is compared with that using synchronous reference frame (SRF)-based conventional control scheme. The performance is studied and validated through simulation results under different voltage sag and swell conditions in MATLAB software using Sim Power System toolboxes. [ABSTRACT FROM AUTHOR]

The introduction of ChatGPT 3.5 in November 2022 ignited a sensation in the academic community, leaving many astounded by its capabilities. This new release more closely emulates human responses than its predecessors. Among its remarkable capabilities, it can answer questions, catalog items in MARC21, recommend reading lists, and make suggestions on a wide array of topics. To assess ChatGPT’s efficacy in aiding library users, the authors of this paper conducted an experiment comparing ChatGPT’s performance with that of librarians in answering reference questions. Thirty questions were randomly selected from the transaction log of the reference inquiries between June 1, 2023 to July 31, 2023 at the Rider University Libraries. These queries constituted 34% of the total user questions during this two-month period. The authors compared the answers by ChatGPT and those by reference librarians for their accuracy, relevance, and friendliness. The findings indicate that reference librarians markedly outperformed their robotic counterpart. An evident issue arises from ChatGPT’s deficiency in understanding local policies and practices. This consequently hinders its ability to provide satisfactory answers in those areas. OpenAI posits that ChatGPT’s proficiency can be enhanced through targeted fine-tuning using locally specific information. At the moment, ChatGPT remains a great tool for librarians. [ABSTRACT FROM AUTHOR]

This paper explores the different insider employee-led cyber frauds (IECF) based on the recent large-scale fraud events of prominent Indian banking institutions. Examining the different types of fraud and appropriate control measures will protect the banking industry from fraudsters. In this study, we identify and classify Cyber Fraud (CF), map the severity of the fraud on a scale of priority, test the mitigation effectiveness, and propose optimal mitigation measures. The identification and classification of CF losses were based on a literature review and focus group discussions with risk and vigilance officers and cyber cell experts. The CF was analyzed using secondary data. We predicted and prioritized CF based on machine learning-derived Random Forest (RF). An efficient fraud mitigation model was developed based on an offender-victim-centric approach. Mitigation is advised both before and after fraud occurs. Through the findings of this research, banks and fraud investigators can prevent CF by detecting it quickly and controlling it on time. This study proposes a structured, sustainable CF mitigation plan that protects banks, employees, regulators, customers, and the economy, thus saving time, resources, and money. Further, these mitigation measures will improve the reputation of the Indian banking industry and ensure its survival. [ABSTRACT FROM AUTHOR]

In this paper, Lamb mode identification method based on small-sample dictionary algorithm is proposed and applied for the separation of specific Lamb modes, the reconstruction of Lamb waves upon propagating a certain distance and damage identification. This approach includes the creation of small-sample dictionary and querying procession in a dictionary. Firstly, Lamb wave signals upon propagating at a series of distances are simulated, and signal features, {mode, distance, time of flight (Tof), wavelet energy}, are extracted to create a dictionary; secondly, Tof of the received signal is extracted, and then Lamb modes are identified by searching the dictionary; finally, energy parameters are estimated to reconstruct wavepackets. The feasibility of this algorithm is validated in AAA laminate, and the results are presented. In a 2D-simulation model of a pitch-catch configuration, A0 and S0 modes can be identified and reconstructed effectively when the direct waves and the reflected waves are synchronously received, with the propagation distance of 0.3 m and 0.5 m, respectively. In addition, a Lamb-wave-based delamination location is conducted in three-dimensional AAA laminate. The experimental results show that the delamination can be located relatively by combining the identified damage-scattered S0 waves and the probability-based diagnostic imaging. [ABSTRACT FROM AUTHOR]

The domain of material science has made great strides in recent years, especially in the fields of metallurgy and ceramic materials and the production of highly trustworthy, cost-effective and economically useful components for use in many industries. For the production of contours and intricate forms in conductive materials, Electrical Discharge Machining (EDM) is by far the most versatile and cost-effective alternative to standard machining processes. In-depth discussion and analysis of the following topics may be found throughout this review paper study. Surface topography and machining properties are investigated in this literature review to determine the impact of mechanical, chemical, electrochemical, and thermal material removal techniques. Different characteristics of dielectric mediums are also covered. Non-conductive and conductive ceramic composite performance characteristics, surfacetexture, mechanical and electrical qualities and geometrical tolerances are investigated as a function of electrode material. Through the perspective of composite materials, a variety of electrical discharge devices' performance metrics and properties are compared and contrasted. The performance metrics and characteristics of a wide variety of electrical discharge machines are compared and contrasted in light of the composite materials in order to identify their distinguishing characteristics. EDM research is being conducted on a variety of sophisticated conductive materials, to explore their unpredictable effects on EDM and their specialized applications. Methods for optimizing the study of composite material and their effects on EDM's numerous aspects include Design of Experiments, Analysis of Variance (ANOVA), Response Surface Methodology, Taguchi with Grey Relational Analysis, and so on. X-ray Diffraction (XRD) and Energy-Dispersive X-ray analysis (EDAX) are used to study the topography of a variety of composite materials and the latest optimization strategies are also investigated using a variety of algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes an unconventional architecture and algorithm for implementing reservoir computing on FPGA. An architecture-oriented algorithm with improved throughput and architecture designed to reduce memory and hardware resource requirements are presented. The proposed architecture exhibits good performance in terms of benchmarks for reservoir computing. A prediction accelerator for reservoir computing that operates on 55.45 mW at 450 K fps with <3000 LEs is realized by implementing the architecture on FPGA. The proposed approach presents a novel FPGA implementation of reservoir computing focussing on both algorithms and architecture that may serve as a basis for applications of AI at network edge. [ABSTRACT FROM AUTHOR]

Criminal justice systems have traditionally relied heavily on human decision-making, but new technologies are increasingly supplementing the human role in this sector. This paper considers what general limits need to be placed on the use of algorithms in sentencing decisions. It argues that, even once we can build algorithms that equal human decision-making capacities, strict constraints need to be placed on how they are designed and developed. The act of condemnation is a valuable element of criminal sentencing, and using algorithms in sentencing – even in an advisory role – threatens to undermine this value. The paper argues that a principle of "meaningful public control" should be met in all sentencing decisions if they are to retain their condemnatory status. This principle requires that agents who have standing to act on behalf of the wider political community retain moral responsibility for all sentencing decisions. While this principle does not rule out the use of algorithms, it does require limits on how they are constructed. [ABSTRACT FROM AUTHOR]

The multi-objective optimisation of high-speed rail profile with small radius curve is studied in the paper. A multi-objective mathematical model for rail profile optimisation of high-speed railway is established. The CN60 rail profile is parameterised into a series of generalised functions of design variables. In order to guarantee the smoothness of the rail profile and meet the maximum grinding depth of rail in China, the constraints are employed. The wheel-rail vertical clearance and equivalent conicity of wheelset are taken as objective functions, and a rail wear prediction programme is compiled. Contact line method is employed to complete the detection algorithm of wheel-rail contact points. Finally, NSGA-II Algorithm is adopted to solve the Pareto-optimal front of the optimisation model. A set of solutions are retrieved from the Pareto optimal front solution as the optimised profile. The optimised rail profile and the original rail profile are matched with the LMA wheel profile (a certain worn type of wheel profiles for EMU in China) respectively. It is testified that the rail profile could effectively reduce the rail wear and improve curving performance. The new method proposed in this paper can provide some reference for the optimisation design of high-speed rail profile with small radius curve. [ABSTRACT FROM AUTHOR]

Geometric numerical integration has recently been exploited to design symplectic accelerated optimization algorithms by simulating the Bregman Lagrangian and Hamiltonian systems from the variational framework introduced by Wibisono et al. In this paper, we discuss practical considerations which can significantly boost the computational performance of these optimization algorithms and considerably simplify the tuning process. In particular, we investigate how momentum restarting schemes ameliorate computational efficiency and robustness by reducing the undesirable effect of oscillations and ease the tuning process by making time-adaptivity superfluous. We also discuss how temporal looping helps avoiding instability issues caused by numerical precision, without harming the computational efficiency of the algorithms. Finally, we compare the efficiency and robustness of different geometric integration techniques and study the effects of the different parameters in the algorithms to inform and simplify tuning in practice. From this paper emerge symplectic accelerated optimization algorithms whose computational efficiency, stability and robustness have been improved, and which are now much simpler to use and tune for practical applications. [ABSTRACT FROM AUTHOR]

This paper presents a novel application of the metaheuristic Slime Mould Algorithm (SMA) to the optimal tuning of interval type-2 fuzzy controllers. Inserting the information feedback model F1 in SMA leads to a new version of the metaheuristic algorithm, further referred to as SMAF1. The paper discusses implementation details specific to interval type-2 fuzzy controllers for the position control of processes modelled by nonlinear servo systems with an integral component and dead zone plus saturation nonlinearity. The linear PI controllers are tuned on the basis of the Extended Symmetrical Optimum method using only one tuning parameter and next fuzzified to result in interval type-2 fuzzy controllers. The optimisation requires the minimisation of a discrete-time objective function expressed as the sum of time multiplied by squared control errors, and the vector variable is the parameter vector of the Mamdani PI fuzzy controller. Experimental results conclusively illustrate the superiority of SMAF1 and SMA in comparison with other metaheuristic algorithms. [ABSTRACT FROM AUTHOR]

This work presents a new three-term projection algorithm for solving nonlinear monotone equations. The paper is aimed at constructing an efficient and competitive algorithm for finding approximate solutions of nonlinear monotone equations. This is based on a new choice of the conjugate gradient direction which satisfies the sufficient descent condition. The convergence of the algorithm is shown under Lipschitz continuity and monotonicity of the involved operator. Numerical experiments presented in the paper show that the algorithm needs a less number of iterations in comparison with existing algorithms. Furthermore, the proposed algorithm is applied to solve signal recovery problems. [ABSTRACT FROM AUTHOR]

Traditional detection algorithms of pipeline non-destructive testing extract information from a large number of defect samples to ensure the detection performance, but even if an adequate of defect samples are collected, it is difficult to enumerate the possible defect morphology in nature. In this paper, we proposed a new semi-supervised anomaly detection algorithm to solve the existing problems. We consider using the most representative feature vectors generated by feature extractor as memory items to represent background information. In addition, this paper also imports a few defect samples to form a semi-supervised structure in the training stage and introduces a metric learning module to make the memory items have the ability to fully represent the background and enhance robustness. To prove the effectiveness of our algorithm, this paper has verified its performance in micro-size pipeline defects. In the experiment, the high-definition industrial camera was used to scan and record the image sequence from the inner surface of the pipeline sample. The latest anomaly detection algorithms have been used as a platform for objective performance evaluation. The result shows our algorithm is more effective in pipeline defect detection and has strong robustness for anti-interference. [ABSTRACT FROM AUTHOR]

The two basic equations in the mentioned article are wrong. [ABSTRACT FROM AUTHOR]

This article examines the datafication of ageing by drawing on a practice approach toward care. We describe the datafication of ageing as a matter of care, achieved through the local tinkering of actors – technology designers, care staff, older adults, and highlighting the practices necessary to develop, maintain and implement data infrastructures. This paper draws on research conducted in a qualitative interview study in a LTC facility that uses AI-supported sensors to detect, predict and alarm care staff about falls of older residents. 18 interviews with developers, staff, residents and interest groups were conducted, as well as 24 h of participant observation in the care facility. The results reveal how AI-development for older target groups is characterized by absent data on these populations. Designers turn to practices that decontextualize data from the realities of older adults, relying on domain experts or synthetic data. This decontextualization of data requires recontextualization, with staff and older residents ensuring that the system functions smoothly, adapting their behavior, protecting the system from making false decisions and making existing care arrangements 'fit' the databases used to monitor activities in these arrangements. The ambivalent position of older adults in this data assemblage is further highlighted, as their caring practices are made invisible by different actors through ageist stereotypes, positioning them as being too frail to understand and engage with the system. While their bodily behavior is core for the databases, their perspective on and engagements with the operating system are marginalized, rendering some aspects of ageing hyper-visible, and others invisible. [ABSTRACT FROM AUTHOR]

This paper introduces a robust procedure for controlling the false discovery rate utilizing empirical likelihood. Traditional approaches assume a normal or parametric distribution as the null distribution. However, it may be challenging to constrain the null distribution within specific parametric models. We focus on the cases where the null distribution may not precisely follow a normal distribution. Multiple testing procedures based on exact normality can lead to misleading outcomes. To address this issue, we adopt the empirical likelihood to estimate the null distribution. Additionally, we introduce the concept of a pilot distribution to establish constraints on the null distribution, which aids in estimating the empirical null distribution. We present a fast algorithm and provide theoretical justification for its efficiency. Furthermore, simulation studies demonstrate that our method outperforms existing approaches in controlling the false discovery rate. We also include examples involving gene expression data and compare the performance of different methods. [ABSTRACT FROM AUTHOR]

Three-colour four-way traffic light completed 100 years in 2020. Even though the traffic light in the form of Semaphore arms has been in use in London since 1868, electric traffic lights came into existence in 1912 and the standard three-colour four-way light in 1920. Research is continuously being carried out to develop better algorithms to improve safety, reduce travel delays, and optimize road capacity. Hence a review of the evolution of traffic lights is warranted. This paper presents an all-round review using a six-prong approach. Timeline of the evolution of the literature in the last 100 years, the evolution of hardware, algorithms, traffic control schemes, standards and the pedestrian lights and count down timer are the six areas in which the review is carried out. A timeline of the different keywords related to the various algorithms in use is presented. This article delves into the thinking and meticulous approach of early researchers and practitioners of the field while dwelling on the past. They laid the rock-solid foundation of today's research. Also, future research areas like connected vehicles and automated vehicles are pointed out, and a summary of the findings is presented at the end. [ABSTRACT FROM AUTHOR]

The global surge in adolescent suicide necessitates the development of innovative and efficacious preventive measures. Traditionally, various approaches have been used, but with limited success. However, with the rapid advancements in artificial intelligence (AI), new possibilities have emerged. This paper reviews the potentials and challenges of integrating AI into suicide prevention strategies, focusing on adolescents. Method: This narrative review assesses the impact of AI on suicide prevention strategies, the strategies and cases of AI applications in adolescent suicide prevention, as well as the challenges faced. Through searches on the PubMed, web of science, PsycINFO, and EMBASE databases, 19 relevant articles were included in the review. Results: AI has significantly improved risk assessment and predictive modeling for identifying suicidal behavior. It has enabled the analysis of textual data through natural language processing and fostered novel intervention strategies. Although AI applications, such as chatbots and monitoring systems, show promise, they must navigate challenges like data privacy and ethical considerations. The research underscores the potential of AI to enhance future suicide prevention efforts through personalized interventions and integration with emerging technologies. Conclusion: AI possesses transformative potential for adolescent suicide prevention by offering targeted and adaptive solutions, while they also raise crucial ethical and practical considerations. Looking forward, AI can play a critical role in mitigating adolescent suicide rates, marking a new frontier in mental health care. [ABSTRACT FROM AUTHOR]

In this paper, we proposed a new primal-dual second-order corrector interior-point algorithm for semidefinite optimization. The algorithm is based on Darvay–Takács neighbourhood of the central path. In the new algorithm, the search directions are determined by the Darvay–Takács's direction and a second-order corrector direction in each iteration. The iteration complexity bound is $ O(\sqrt {n}L) $ O (n L) for the Nesterov–Todd scaling direction, which coincides with the best-known complexity results for semidefinite optimization. Finally, numerical experiments show that the proposed algorithm is promising. [ABSTRACT FROM AUTHOR]

The traditional BFGS algorithm has been proved very efficient. It is convergent for convex nonlinear optimization problems. However, for non-convex nonlinear optimization problems, it is known that the BFGS algorithm may not be convergent. This paper proposes a robust BFGS algorithm in the sense that the algorithm superlinearly converges to a local minimum under some mild assumptions for both convex and non-convex nonlinear optimization problems. Numerical test on the CUTEst test set is reported to demonstrate the merit of the proposed robust BFGS algorithm. This result shows that the robust BFGS algorithm is very efficient and effective. [ABSTRACT FROM AUTHOR]

In this paper, a random derivative-free modified tensor method with curvilinear linesearch technique is considered for solving nonlinear programming problems. The proposed algorithm is designed to build polynomial interpolation models for the objective function and build the tensor model using the information of the interpolation function. At the same time, we give a new curvilinear tensor step which guarantees the monotonic decrease on the tensor model. The modified tensor step also asymptotically approaches the modified Newton direction as the step length shrinks to zero, and the objective function of problem will be descendent. Under general assumptions, we give the global and local superlinear convergence of the algorithm. Numerical results are recorded, and the compare results with a tensor algorithm without curvilinear linesearch technique and Newton algorithm show that our algorithm is more effectiveness. [ABSTRACT FROM AUTHOR]

Although the use of algorithms has become increasingly prominent in the digital market, such algorithms are often opaque and prone to risks of making biased decisions. Algorithms could also be used to harm competition, such as by facilitating cartels. Such developments make it necessary to examine the readiness of existing competition law to tackle cases involving algorithms. This paper focuses on analysing Indonesian competition law to address the following issues: (1) how current Indonesian competition law deals with algorithms-related cases; (2) which indicators could detect anti-competitive algorithms; and (3) which competition policy approach could be considered in Indonesia to tackle the problem resulted from the use of algorithms. [ABSTRACT FROM AUTHOR]

Three-stub waveguide is a common impedance matching device, which is widely used in microwave impedance matching system. In this paper, an impedance matching algorithm for three-stub waveguide with large stubs is proposed based on the equivalent circuit analysis method, which can be used to realize continuous real-time impedance matching. First, the equivalent circuit of a single stub is established by using series and parallel reactance, and the equivalent circuit of three-stub waveguide is further established by cascading three single-stub models. Then, the two-port scattering parameters of three-stub waveguide, the load impedance and the impedance of three-stub waveguide are derived. In the matching process, by using the currently detected reflection coefficient and two-port scattering parameters, the terminal load impedance is calculated, and the optimal insertion depths of the stubs are finally determined by using the impedance of three-stub waveguide with different stub depths. This algorithm can reduce the voltage standing wave ratio, and can be easily redesigned for other waveguides. [ABSTRACT FROM AUTHOR]

Cost is the foremost factor in decision-making for profit-driven organizations. However, hybrid flow shop scheduling (HFSS) research rarely prioritizes cost as its optimization objective. Existing studies primarily focus on electricity costs linked to machine utilization. This paper introduces a comprehensive cost-based HFSS model, encompassing electricity, labor, maintenance, and penalty costs. Next, the Tiki-Taka Algorithm (TTA) is improved by increasing the exploration capability to optimize the problem. The cost-based HFSS model and TTA algorithm have been tested using benchmark and case study problems. The results indicated that the TTA consistently outperforms other algorithms. It delivers the best mean fitness and better solution distribution. In industrial contexts, the TTA able to reduces costs by 2.8% to 12.0% compared to other approaches. This holistic cost-based HFSS model empowers production planners to make more informed decisions. Furthermore, the improved TTA shows promise for broader applicability in various combinatorial optimization domains. [ABSTRACT FROM AUTHOR]

The overlap between edge computing and unmanned aerial systems enables Unmanned Aerial Vehicles (UAV) to quickly offload image processing tasks onto edge devices, avoiding the transmission of images over long distances. To improve the speed and efficiency of UAV image stitching in an edge computing environment, this paper proposes an adaptive UAV image parallel stitching algorithm in an edge computing environment. The algorithm incorporates both route-based parallel processing and inverted binary tree-based parallel processing, dividing the image stitching task into multiple processes and allocating them to different cores based on the CPU core count, number of flight routes, and number of images, thereby enhancing computational efficiency in edge scenarios. The experimental results indicate that, when the number of flight routes is greater than or equal to the number of CPUs, the adaptive algorithm will employ the more efficient route parallelism. Conversely, when the number of flight routes is less than the number of CPUs, the efficiency of inverted binary tree parallelism is higher. In the same experimental environment and dataset, the adaptive image stitching algorithm demonstrates an efficiency improvement of approximately 2–10 times compared to other algorithms, with no significant degradation in image quality. This demonstrates that in edge environments, the utilization of multi-threaded adaptive route and inverted binary tree-based parallel approaches can effectively harness the computing resources of edge devices, significantly improving the stitching speed of UAV images and providing technical support for rapid real-time monitoring by UAV. [ABSTRACT FROM AUTHOR]

This paper introduces the monotone extended second-order cone (MESOC), which is related to the monotone cone and the second-order cone. Some properties of the MESOC are presented and its dual cone is computed. Projecting onto the MESOC is reduced to the pool-adjacent-violators algorithm (PAVA) of isotonic regression. An application of MESOC to portfolio optimization is provided. Some broad descriptions of possible MESOC-regression models are also outlined. [ABSTRACT FROM AUTHOR]

In this paper, we mainly consider a class of nonconvex non smooth optimization problems, whose objective function is the sum of a smooth function with a Lipschitz continuous gradient and a convex non smooth function. We first propose a proximal stochastic recursive momentum algorithm(ProxSTORM) with mini-batch for solving the optimization problems and consider its convergence behaviour. Then, based on the Polyak–Łojasiewicz inequality, we establish the global linear convergence rate of ProxSTORM. Finally, some numerical experiments have been conducted to illustrate the efficiency of our method. [ABSTRACT FROM AUTHOR]

Semi-supervised instance segmentation algorithms are mainly divided into algorithms based on pseudo-label generation and algorithms based on transfer learning. The algorithms based on pseudo-label generation need to design a specific pseudo-label generation process, but the process is not scalable for different types of source tasks. The algorithms based on transfer learning that started late have relatively high scalability, but the algorithm research ideas are relatively simple. To expand the research on semi-supervised instance segmentation based on transfer learning, this paper proposes a feature transfer-based semi-supervised instance segmentation algorithm Feature Transfer Mask R-CNN (FT-Mask). The FT-Mask algorithm is more scalable than algorithms based on pseudo-label generation and can be used to transfer knowledge from different types of source tasks. Compared with other semi-supervised instance segmentation algorithms based on transfer learning, FT-Mask uses the feature transfer method to achieve semi-supervised instance segmentation for the first time. The experimental results show that the FT-Mask model improves the semi-supervised instance segmentation accuracy of the Mask R-CNN benchmark model through the semi-supervised learning process, and can achieve effective transfer learning. [ABSTRACT FROM AUTHOR]

The RSSI-based location fingerprinting method is currently a hot and challenging area of research in indoor positioning algorithms, which uses the degree of signal attenuation during spatial propagation to build a database, match data and ultimately determine the target location. This paper introduces and compares common indoor positioning techniques and algorithms, and elaborates on positioning algorithm improvement methods including signal filtering methods, received signal clustering algorithms and location fingerprint matching optimisation algorithms. Through a comparative analysis of the characteristics of the improved algorithms, a reference direction is provided for the selection of suitable improved location fingerprint fusion algorithms to improve positioning accuracy and efficiency in complex environments. [ABSTRACT FROM AUTHOR]

In this paper, we develop a Jacobi-type algorithm for the approximate diagonalization of tensors of order $ d\geq 3 $ d ≥ 3 via tensor trace maximization. For a general tensor, this is an alternating least squares algorithm and the rotation matrices are chosen in each mode one-by-one to maximize the tensor trace. On the other hand, for symmetric tensors, we discuss a structure-preserving variant of this algorithm where in each iteration the same rotation is applied in all modes. We show that both versions of the algorithm converge to the stationary points of the corresponding objective functions. [ABSTRACT FROM AUTHOR]

This paper introduces a hybrid multilevel inverter (MLI) with reduced switch count, which can generate higher output voltage level with minimum number of DC input sources. The operation of this proposed MLI is carried out with unequal DC sources to achieve the desired output voltage level. The reduced MLI output voltage is set to minimal total harmonic distortion (THD) with the help of modified JAYA (MJAYA) algorithm. A JAYA algorithm with improved steps by adopting an accelerating parameter has been proposed in this research work to obtain a faster convergence of the objective function. The MJAYA algorithm has provided the suitable switching angles for the proposed three-phase 15-level MLI and reduced the output voltage THD to 2.23%, which satisfies the standard set by IEEE-519. To prove the efficiency of this proposed modified algorithm, the comparative analysis is carried out through MATLAB program and Simulink tool using common JAYA and modified particle swarm optimisation algorithms. The performance and productivity of the proposed MLI have been investigated through simulation and experimental setups. [ABSTRACT FROM AUTHOR]

High-performance and low-cost emission characteristic prediction is very crucial for diesel engine design optimization and emission aftertreatment control and diagnosis. In this paper, a novel hybrid model that combines Convolutional Neural Network (CNN) and Gated Recurrent Unit Network (GRU) was proposed to predict the emission characteristic from diesel engines, encompassing CO, THC, CO2, NOx, exhaust temperature and exhaust pressure. Nine operating parameters from WHTC and WHSC cycles, including speed, torque, intake pressure, intake flow, intake temperature, oil pressure, fuel rate, oil temperature, water temperature, were considered as inputs. Firstly, the importance of each variable is evaluated by Random Forests algorithm to determine the optimal inputs for each emission characteristic parameter and reduce redundancy. Then the effect of different hyperparameters on the model performance was discussed in detail and PSO algorithm was used to obtain the optimal hyperparameters. Finally, the CNN-GRU hybrid model was assessed for its generalization and compared with ANN, LSTM and GRU models. The result demonstrates that the CNN-GRU hybrid model with PSO optimization has excellent prediction performance in either the training dataset or the validation dataset. The average value of R² is 0.993 in the training dataset and 0.985 on the validation dataset. In the test dataset, the average R² is 0.961, showing a minor decrease of 3.19% and 2.47% compared to the training and validation dataset, respectively. This indicates that the CNN-GRU hybrid model has strong generalization ability. Compared with other algorithms in the test dataset, the CNN-GRU hybrid model exhibits better comprehensive performance, with the average R² value exceeding that of ANN, LSTM and only GRU by 5.96%, 2.69% and 3.23%, respectively. [ABSTRACT FROM AUTHOR]

In recent days, cryptographic algorithm hardware is the need of IoT devices. However, limited resources demand an efficient approach towards designing the said cryptographic algorithm hardware. This paper introduces the PUF (Physical Unclonable Function) based approach to design the key generator used in cryptographic algorithm hardware to minimize the area and power consumption. A customizable key generation unit has been introduced in the form of a Standard Synchronization Unit (SSU) to match the desired key size requirements. The results were generated with PUF based designs from literature and compared with the proposed PDL (Programmable Delay Logic) PUF. All parameters considered, a proposed PDL PUF key generator is an efficient option that can be integrated with an Advanced Encryption System (AES) as the key generator. The modified AES design result was compared with the literature's results on the Xilinx Virtex XC7VX690T platform. The modified AES is an efficient solution with 12.10% less area consumption and a 44.51% increase in throughput. [ABSTRACT FROM AUTHOR]

In this paper, a modified two-grid algorithm based on block-centred finite difference method is developed for the fourth-order nonlinear extended Fisher–Kolmogorov equation. To further improve the computational efficiency, an effective second-order accurate backward difference formula is considered. The modified two-grid method based on Newton iteration is constructed to linearize the nonlinear system. The method solves a miniature nonlinear system on a coarse grid accompanying a larger time step to get the numerical solution, then computes a linear system constructed by the previous result with the Taylor expansion on a fine grid accompanying a smaller time step to get the correct numerical solution. Theoretical analysis shows that the modified two-grid algorithm can achieve second-order convergence accuracy both in time and space domain. Several numerical experiments are provided to verify the theoretical result and the high efficiency of this approach. The practical problem illustrates the actual applicable value of the algorithm. [ABSTRACT FROM AUTHOR]