Automatic milking systems (AMSs) are among the earliest Precision Livestock Farming developments that have transformed dairy farming worldwide. This review aims to gather, evaluate, and summarize papers that focus on the use of modeling approaches in the context of AMS. We provided a review of 60 articles with a specific focus on cows’ health, production, and behavior/management. The most used modeling approach was Machine Learning (ML, present in 63% of the studies), followed by statistical analysis (14%), fuzzy algorithms (9%), deterministic mod-els (7%), and detection algorithms (7%). Most of the reviewed studies (82%) focused on the detection of cows' health, specifically mastitis, while only 11% were concerned with milk production. Accurate forecasting of dairy cow milk yield and knowledge on the deviation between expected and observed milk yields of individual cows would be beneficial in dairy cow management. Likewise, the study of cows’ behavior and the herd management in AMSs is under-explored (7%). Despite the increasing use of ML techniques in this field there is still a lack of a robust methodology for their application. In particular, we identified a significant gap in the systematic balancing of positive and negative classes for health prediction models.

Compared with conventional methods of fault diagnosis for power transformers, which have defects such as imperfect encoding and too absolute encoding boundaries, this paper systematically discusses various intelligent approaches applied in fault diagnosis and decision making for large oil-immersed power transformers based on dissolved gas analysis (DGA), including expert system (EPS), artificial neural network (ANN), fuzzy theory, rough sets theory (RST), grey system theory (GST), swarm intelligence (SI) algorithms, data mining technology, machine learning (ML), and other intelligent diagnosis tools, and summarizes existing problems and solutions. From this survey, it is found that a single intelligent approach for fault diagnosis can only reflect operation status of the transformer in one particular aspect, causing various degrees of shortcomings that cannot be resolved effectively. Combined with the current research status in this field, the problems that must be addressed in DGA-based transformer fault diagnosis are identified, and the prospects for future development trends and research directions are outlined. This contribution presents a detailed and systematic survey on various intelligent approaches to faults diagnosing and decisions making of the power transformer, in which their merits and demerits are thoroughly investigated, as well as their improvement schemes and future development trends are proposed. Moreover, this paper concludes that a variety of intelligent algorithms should be combined for mutual complementation to form a hybrid fault diagnosis network, such that avoiding these algorithms falling into a local optimum. Moreover, it is necessary to improve the detection instruments so as to acquire reasonable characteristic gas data samples. The research summary, empirical generalization and analysis of predicament in this paper provide some thoughts and suggestions for the research of complex power grid in the new environment, as well as references and guidance for researchers to choose optimal approach to achieve DGA-based fault diagnosis and decision of the large oil-immersed power transformers in preventive electrical tests.

This paper explores innovative approaches to determining eigenvalues and eigenvectors through the combination of neural networks and traditional numerical methods. Traditional methods like the Newton-Raphson and Durand-Kerner algorithms are proven effective but often require good initial approximations and can be sensitive to the choice of starting points. In this study, we propose a hybrid approach that uses neural networks to generate better initial approximations, which are then iteratively refined using traditional methods. By combining the pattern recognition power of neural networks with the adaptability of traditional numerical methods, we demonstrate faster convergence and higher accuracy in determining eigenvalues and eigenvectors. Experimental results on various polynomials of different degrees and characteristics show that our hybrid approach outperforms individual methods, providing a more robust and efficient solution.

The rapid advancement of technology has revolutionized language learning, introducing innovative methods that depart from traditional instructional approaches. This study employs a mixed-methods research design to examine the impact of chatbot-based language learning support, adaptive learning algorithms, and virtual reality language immersion on the language learning proficiency and self-regulated learning skills of English as a Foreign Language (EFL) learners. The research design includes quantitative analysis of language proficiency scores and qualitative exploration of learner experiences with the technological interventions. The theoretical implications of this study are rooted in constructivist and socio-cultural learning theories, which underpin the design and implementation of the technological interventions. The triangulation of qualitative and quantitative data revealed that the participants' positive perceptions of the effectiveness of chatbot-based language learning support were supported by the quantitative results, with the variable "chat-bot based support" demonstrating a substantial mean difference compared to other groups. This convergence of findings reinforces the positive influence of chatbot-based support on language learning outcomes and highlights the importance of integrating theoretical perspectives with empirical evidence to gain a comprehensive understanding of the impact of technological interventions on language learning outcomes for EFL learners. The study's findings provide insights into the potential of these technological interventions to optimize language learning outcomes for EFL learners and promote autonomous learning behaviors.

Cyber diplomacy is critical in dealing with evolving cybersecurity dangers and possibilities in the digital era. This article investigates the impact of Artificial Intelligence (AI), the Internet of Things (IoT), Blockchains, and Quantum Computing on cyber diplomacy. AI holds the potential for proactive threat identification and response, while IoT enables international information sharing. Blockchains enable secure data sharing and document verification, but they also pose new threats, such as AI-driven cyber-attacks, IoT privacy breaches, blockchain vulnerabilities, and the potential for quantum computing to break encryption. This article conducts case study reviews, in combination with secondary data analysis, and emphasises the value of international cooperation in developing global norms and frameworks to control responsible technology adoption. Cyber diplomacy can promote cybersecurity, protect national interests, and foster mutual trust among nations in the digital sphere by capitalising on possibilities and reducing threats.

For many years, energy monitoring at the most disaggregate level has been mainly sought through the idea of Non-Intrusive Load Monitoring (NILM). Nevertheless, a practical application of this concept in the residential sector should address the underlying concerns raised by the technical specifications of case studies. From one side, such an operation must handle common matters related to the essence of any NILM system. Although this aspect has been thoroughly investigated by basic research, it is limited to the properties of public datasets. On the other side, it must deal with specific concerns corresponding to uncommon situations. These circumstances impose further restrictions on existent NILM schemes, however, they have been overlooked due to the lack of pertinent databases to scrutinize. Accordingly, this paper presents applied research on a potential solution to NILM for Quebec residences. It carries out a relevant investigation into the multi-faceted nature of this problem in order to reveal barriers to feasible implementations in the context of Quebec. This work commences with a concise discussion about the NILM idea to highlight its essential requirements for a fruitful practice. Afterward, it provides a comparative statistical analysis to represent the specificity and potential challenges of the case study in accordance with NILM necessities. For this purpose, the examination exploits data from real-world measurement systems in the same and European regions. Subsequently, this study focuses on a load identification exercise by proposing a combinatory approach that utilizes the promise of sub-meter smart technologies to integrate the intrusive aspect of load monitoring with the non-intrusive one. The former is aimed at extracting overall heating demand from the aggregate one by a supervised procedure based on Deep Learning (DL) models while the latter is designed for disaggregating the residual load through an unsupervised process on the basis of clustering techniques. The results demonstrate that geographically-dependent cases create electricity consumption scenarios under which existing NILM methods can be questioned. From a realistic standpoint, this research elaborates on critical remarks to realize viable NILM systems, particularly, in Quebec houses.

Advanced machine learning algorithms, have the potential to be successfully applied to many areas of system modelling. In the present study the capability of ten machine learning algorithms in predicting the structural damage of an 8-storey reinforced concrete frame building subjected to single and successive ground motions is examined. From this point of view, the initial damage state of the structural system, as well as 16 well known ground motion intensity measures are adopted as the features of the machine-learning algorithms that aim to predict the structural damage after each seismic event. The structural analyses are performed considering both real and artificial mainshock–aftershock sequences, while the structural damage is expressed in terms of two overall damage indices. The comparative study results in the most efficient damage index, as well as the most promising machine learning algorithm in predicting the structural response of a reinforced concrete building under single or multiple seismic events. Finally, the configured methodology deployed in a user-friendly web-application.

Genomic selection (GS) can accelerate variety improvement when training set (TS) size, and its relationship with the breeding set (BS) are optimized for prediction accuracies (PA) of genomic prediction (GP) models. Sixteen GP algorithms were run on phenotypic best linear unbiased predictors (BLUPs) and estimators (BLUEs) of resistance to both fall armyworm (FAW) and maize weevil (MW) in a tropical maize panel. For MW resistance, 37% of the panel was the TS, and BS was the remainder whilst for FAW, random-based training sets (RBTS) and pedigree-based training sets (PBTS) were designed. PAs achieved with BLUPs varied from 0.66 to 0.82 for MW resistance traits, and, for FAW resistance, 0.694 to 0.714 for RBTS of 37%, and 0.843 to 0.844 for RBTS of 85%, and, these were at least two-fold those from BLUEs. For PBTS, FAW resistance PAs were generally higher than those for RBTS, except for one dataset. GP models generally showed similar PAs across individual traits whilst the TS designation was determinant since a positive correlation (R=0.92***) between TS size and PAs was observed for RBTS and, for the PBTS, it was negative (R=0.44**). This study pioneers the use of GS for maize resistance to insect pests in sub-Saharan Africa.

“You should know the words by the company they keep!” has been one of the most famous slogans attributed to John Rubert Firth, 1957. This has ignited a whole school in linguistic research known as the British empiricist contextualism. Sixty years later, many un- or semi-supervised machine learning algorithms have been successfully designed and implemented aiming at extracting word meaning from within the context of a text corpus. These algorithms treat words, more or less, as vectors of real numbers representing frequencies of word occurrences within context and word meaning as positions of words in a high-dimensional vector space model. Word associations, in turn, are treated as calculated distances among them. With the rise of Deep Learning (DL) and other artificial neural networks based architectures, learning the positioning of words and extracting word associations as measured by their distances has further improved. In this paper, however, we revisited the main stream of algorithmic approaches and set the stage for a partly cross-disciplinary evaluation framework to judge about the nature of the extracted word associations by state-of-the-art machine learning algorithms. Our preliminary results are based on word associations extracted from the application of DL framework on a Google News text corpus, as well as on comparisons with human created word association lists such as word collocation dictionaries and psycholinguistic experiments. The results and conclusions provide some insights into the inherited limitations in interpreting the type of word associations and underpinning relations between words with inevitable consequences in other areas, such as extraction of knowledge graphs or image understanding.

Today, Colebrook’s equation is mostly accepted as an informal standard for modeling of turbulent flow in hydraulically smooth and rough pipes including transient zone in between. The empirical Colebrook’s equation relates the unknown flow friction factor (λ) with the known Reynolds number (R) and the known relative roughness of inner pipe surface (ε/D). It is implicit in unknown friction factor (λ). Implicit Colebrook’s equation cannot be rearranged to derive friction factor (λ) directly and therefore it can be solved only iteratively [λ=f(λ, R, ε/D)] or using its explicit approximations [λ≈f(R, ε/D)]. Of course, approximations carry in certain error compared with the iterative solution where the highest level of accuracy can be reached after enough number of iterations. The explicit approximations give a relatively good prediction of the friction factor (λ) and can reproduce accurately Colebrook’s equation and its Moody’s plot. Usually, more complex models of approximations are more accurate and vice versa. In this paper, numerical values of parameters in various existing approximations are changed (optimized) using genetic algorithms to reduce maximal relative error. After this improvement computational burden stays unchanged while accuracy of approximations increases in some of the cases very significantly.

Inferring algorithmic structure in data is essential for discovering causal generative models. In this research, we present a quantum computing framework using the circuit model, for estimating algorithmic information metrics. The canonical computation model of the Turing machine is restricted in time and space resources, to make the target metrics computable under realistic assumptions. The universal prior distribution for the automata is obtained as a quantum superposition, which is further conditioned to estimate the metrics. Specific cases are explored where the quantum implementation offers polynomial advantage, in contrast to an indispensable exhaustive enumeration in the corresponding classical case. The unstructured output data and the computational irreducibility of Turing machines make this algorithm impossible to approximate using heuristics. Thus, exploring the space of program-output relations is one of the most promising problems for demonstrating quantum supremacy using Grover search that cannot be dequantized. Experimental use cases for quantum acceleration are developed for self-replicating programs and algorithmic complexity of short strings. With quantum computing hardware rapidly attaining technological maturity, we discuss how this framework will have significant advantage for various genomics applications in meta-biology, phylogenetic tree analysis, protein-protein interaction mapping and synthetic biology. This is the first time experimental algorithmic information theory is implemented using quantum computation. Our implementation on the Qiskit quantum programming platform is copy-left and can be found on https://github.com/Advanced-Research-Centre/QPULBA

Microgrid (MG) control is essential for efficient, reliable, and sustainable energy management in distributed energy systems. Genetic Algorithm-based energy management systems (GA-EMS) can optimally control a MG by finding global optima and handling complex, non-linear, and non-convex problems, but they can be computationally intensive, making real-time control challenging. Model Predictive Control (MPC)-based EMS predicts future behaviour over a defined horizon, ensuring optimal performance and constraint satisfaction but typically relies on linear models. This paper proposes a novel Genetic Predictive Control (GPC) method, combining GA and MPC to enhance resource allocation, balance multiple objectives, and adapt dynamically to changing conditions. Integrating GA with MPC significantly improves the handling of non-linearities and non-convexity in the system model, leading to more accurate and effective control. Comparative analysis shows that GPC significantly reduces excess power production—surplus power generated beyond load demand—improving resource allocation efficiency. For instance, in the Mutation–Random Selection scenario, GPC reduced excess power to 76.0W compared to 87.0W with GA. Additionally, GPC balances cost, emissions, and power efficiency. In the Crossover-Elitism scenario, GPC achieved a daily cost of $113.94 compared to GA's $127.80, and carbon emissions of 52.83 kg CO2e compared to GA's 69.71 kg CO2e, demonstrating GPC's cost efficiency and environmental benefits. While MPC optimizes a weighted sum of objectives, designing appropriate weights can be challenging and may result in non-convex problems. GA handles multiple, conflicting objectives through multi-objective optimization, providing Pareto-optimal solutions. GPC's dynamic adaptability maintains optimal performance by forecasting future load demands and adjusting control actions. GPC consistently achieves lower emissions than GA across various scenarios and, although it sometimes incurs higher costs, such as $113.94 compared to $131.90 in the Crossover–Random Selection scenario, its holistic approach balances these costs with other metrics, making it cost-effective in the long run. By reducing excess power production and emissions, GPC contributes to economic savings and promotes sustainable practices. These findings underscore GPC's potential as a versatile, efficient, and environmentally beneficial optimization tool for power generation systems.

Cooling fans are one of the critical components of air-forced (AF) dry-type transformers for regulating internal temperatures. Therefore, effective malfunction detection is crucial to maintain the transformer temperature within an acceptable range and prevent overheating. Regular maintenance occurs periodically and issues with cooling fans may arise between maintenance periods, leading to prolonged operation under malfunctioning conditions and potential failures. In addition, utilities typically have online information about whether a fan works or not without providing information about cooling fan malfunctioning circumstances. To address these challenges, this study proposes learning-based online monitoring techniques to detect malfunctions in AF transformer cooling fans. Random forests (RFs) and convolutional neural networks (CNNs) are developed to classify the audio signals from cooling fans into normal and malfunctioning classes. Unlike RFs, which require separate feature extraction, CNNs are trained based on spectrogram images derived from audio signals. Thus, various time-frequency techniques are utilized for feature extraction in RFs. Besides, multiple data augmentation techniques are employed to enhance the dataset size and diversity. Algorithmic performance is optimized through hyperparameter tuning and classifier threshold adjustment. Simulations reveal that CNNs outperform RFs, whereas the latter provides superior interpretability of acoustic features compared to the former.

In photolithography process, nanometer level precise, wavefront aberration models enable the machine to be able to meet the overlay (OVL) drift and critical dimension (CD) specifications. Software control algorithms take as input these models and correct any expected wavefront imperfections before reaching the wafer. In such way a near optimal image is exposed on the wafer surface. Optimizing the parameters of these models though, involves several time costly sensor measurements which reduce the throughput performance, in terms of exposed wafers per hour, of the machine. In that case, photolithography machines come across the trade-off between throughput and quality. Therefore one of the most common Optimal Experimental Design (OED) problems in photolithography machines (and not only) is how to choose the minimum amount of sensor measurements that will provide the maximum amount of information. Additionally, each sensor measurement corresponds to a point on the wafer surface and therefore we must measure uniformly around the wafer surface as well. In order to solve this problem, we propose a Sensor Marks Selection Algorithm which exploits Genetic Algorithms. The proposed solution first selects a pool of points that qualify as candidates to be selected in order to meet the uniformity constraint. Then, the point that provides the maximum amount of information, quantified by the Fisher based criteria of G, D and A-Optimality, is selected and added to the measurement scheme. This process though is considered "greedy", and for this reason Genetic Algorithms (GA) are exploited to further improve the solution. By repeating in parallel the "greedy" part several times we get an initial population that will be the input to our GA. This meta-heuristic approach outperforms the "greedy" approach significantly. The proposed solution is applied in a real life semiconductors industry use case and achieves interesting industry and academical results as well.

Advancements in Parametric Design, Generative Design, and automation in Building Information Modelling (BIM) have opened new opportunities for architects and engineers working on complex buildings, such as offices. These developments allow designers to enhance their designs, increase project efficiency, improve performance, and reduce project time and costs. To address conflicting objectives that arise during the design process, genetic algorithms with multi-objective optimization (MOO) have been employed in architectural design. The GENIUS project aims to optimize building shape, apertures, and shading systems in the concept and design stages, with a focus on energy and daylight performance. The project integrates BIM and visual programming tools with Artificial Intelligence techniques like Genetic Algorithms and RBFOpt model-based optimization. Optimization algorithms are used to identify the best solutions that meet all design objectives, helping architects optimize their designs and achieve desired outcomes. The workflow was tested on a case study of a large office building, with MOO focused on maximizing daylight performance using Spatial Daylight Autonomy metric and minimizing energy consumption using Energy Use Intensity metric. The project provides architects with a method to improve their designs using Algorithm Aided Design tools, which help identify the best solutions for complex design problems.

Solar radiation prediction is an important process in ensuring optimal exploitation of solar energy power. Numerous models have been applied to this problem, such as numerical weather prediction models and artificial intelligence models. However, well-designed hybridization approaches that combine numerical models with artificial intelligence models to yield a more powerful model can provide a significant improvement in prediction accuracy. In this paper, we propose novel hybrid machine learning approaches that exploit auxiliary numerical data. The proposed hybrid methods invoke different machine learning paradigms, including feature selection, classification, and regression. Additionally, numerical weather prediction (NWP) models are used in the proposed hybrid models. Feature selection is used for feature space dimension reduction to reduce the large number of recorded parameters that affect estimation and prediction processes. The rough set theory is applied for attribute reduction and the dependency degree is used as a fitness function. We investigate the effect of the attribute reduction process with thirty different classification and prediction models in addition to the proposed hybrid model. Then, different machine learning models are constructed based on classification and regression techniques to predict solar radiation. Moreover, other hybrid prediction models are formulated to use the output of the numerical model of Weather Research and Forecasting (WRF) as learning elements in order to improve the prediction accuracy. The proposed methodologies are evaluated using a data set that is collected from different regions in Saudi Arabia.

Abstract: The future of transportation is being reshaped by autonomous vehicles (AVs), which promise to revolutionize road safety, accessibility, and environmental sustainability. Central to their evolution are advancements in artificial intelligence (AI), which enable real-time perception, decision-making, and control. This paper explores the future trajectory of AI in AVs, highlighting innovative AI paradigms such as explainable AI, federated learning, and adaptive regulatory frameworks. It delves into the development of sophisticated perception algorithms for environmental understanding, advanced decision-making systems employing reinforcement learning and deep neural networks, and precise control mechanisms through predictive modeling and optimization. By addressing current challenges, including regulatory hurdles and the complexity of AI algorithms, this paper provides a comprehensive outlook on the potential and implications of AV technology. It envisions a future where AI-driven AVs are seamlessly integrated into society, offering profound insights into their impact on transportation and societal dynamics.

Monogenity is a classical area of algebraic number theory that is very actively researched even today. This paper is a collection of the results obtained during the last few years in this area. Several of these results were presented at the series of online conferences "Monogenity and power integral bases".

We introduce linear probing hashing schemes that construct a hash table of size $n$, with constant load factor $\alpha$, on which the worst-case unsuccessful search time is asymptotically almost surely $O(\log \log n)$. The schemes employ two linear probe sequences to find empty cells for the keys. Matching lower bounds on the maximum cluster size produced by any algorithm that uses two linear probe sequences are obtained as well.

Recently, the Integrated Air Defense System (IADS) has become vital for the defense system as the military defense system is vital for national security. Placing Integrated Air Defense System batteries among locations to protect locations assets is a crucial problem because optimal solutions are needed for interceptor missiles to intercept attacker missiles for maximizing protection of assets across locations or places. In this research, the procedures of using network algorithms along with developing several network algorithms are going to be demonstrated to develop a model for sequential development of seven network securing strategies of placing Surface to Air Missile (SAM) batteries to maximize the protection of assets across locations (based on given asset values) by generating optimal solutions through computation to destroy maximum attacker missiles by using minimum interceptor missiles with given intercept probability. This network securing strategies can be implemented not only for Integrated Air Defense System (IADS) planning but also Counter Air (CA) planning as Integrated Air Defense System (IADS) is conducted with defensive counter air supported by attack operations in offensive counter air.

Search for the new non-invasive methods of investigating the functioning of human internal organs is an urgent task. One of these methods for assessing the functioning of the human res-piratory system is electromagnetic sensing, based on a significant difference in the dielectric permittivity of muscle tissue and air. During breathing, when the lungs are filled with air, the dielectric permittivity of the lungs decreases, which leads to a change in the level of the elec-tromagnetic signal passing through the body. Results of experiments on recording changes in the level of electromagnetic radiation passing through the human body, performed on an experimental device consisting of eight transmitting and receiving antennas located on opposite sides of the chest have been presented in the article. The possibility of visualizing the measured “pulmonograms” in the form of dynamic two-dimensional images showing the process of filling various parts of the lungs with air has been demonstrated.

In data-scarce regions, prudent planning and precise decision-making for sustainable development especially in agriculture remains a challenging task due to the lack of correct information. Remotely sensed satellite images provide a powerful source for assessing land use land cover (LULC) classes and crop identification. Applying Remote Sensing (RS) in conjunction with the Geographical Information System (GIS) and modern tools/ algorithms of artificial intelligence (AI) and deep learning has been proven effective for strategic planning and integrated resources management. The (this)study was conducted in the canal command area of the Lower Chenab Canal system in Punjab, Pakistan. Crop features/ classes were assessed using the Normalized Difference Vegetation Index (NDVI) algorithm. The MODIS (Moderate Resolution Imaging Spectroradiometer) 250 m and Landsat 5 TM (thematic mapper) images were deployed for NDVI time-series analysis with unsupervised classification technique to get LULC classes that helped to discern cropping pattern, crop rotation, and the area of specific crops, which were then used as a key input for agricultural mechanization planning and resources management. The accuracy of the LULC map was 78%, assessed by the error matrix approach. Limitations of high-resolution RS data availability and the accuracy of the results are the concerns observed in this study that could be managed by the availability of good quality local sources and advanced techniques of processing, which will make it more useful and applicable for regional agriculture and environmental management.

Clustering is a data mining task used for the data extraction from the data bases or files. Clustering is used to find unknown groups present in the data sources like files or data bases. This paper focuses on clustering algorithms performance dependency on the parallel clustering platforms and the clustering algorithms along with their clustering criteria. The problems with the present Traditional clustering algorithms were throughput and data source size changes (scalability). So they can’t address the big data. So for handling the huge volumes of data, parallel clustering algorithms along with clustering criteria were used. For processing the big Data Parallel clustering algorithms are of two types based on computing platforms used. They were the horizontal scaling platforms and vertical scaling platforms.

The safe and reliable operation of autonomous vehicles in complex and dynamic environments is heavily dependent upon exteroceptive and proprioceptive sensors. A LiDAR system generates accurate 3D point clouds, which are crucial for the detection, classification, and tracking of multiple targets. LiDAR data, however, presents significant challenges due to its density, noise, and varying sampling rates. In this study, various clustering and MTT techniques for LiDAR point clouds will be identified and classified within the context of autonomous driving to assess the state-of-the-art methods’ key challenges and their performances. We have categorized clustering and MTT methods used in AV applications, identified research gaps and challenges and analyzed existing algorithms and their challenges in detail. Researchers and practitioners in the field of autonomous driving will find this review to be a valuable resource.

There are two reasons to have an efficient algorithm for identifying all maximal Lyndon substrings of a string: firstly, Bannai et al. introduced in 2015 a linear algorithm to compute all runs of a string that relies on knowing all maximal Lyndon substrings of the input string, and secondly, Franek et al. showed in 2017 a linear equivalence of sorting suffixes and sorting maximal Lyndon substrings of a string, inspired by a novel suffix sorting algorithm of Baier. In 2016, Franek et al. presented a brief overview of algorithms for computing the Lyndon array that encodes the knowledge of maximal Lyndon substrings of the input string. Among the presented were two well-known algorithms for computing the Lyndon array: a quadratic in-place algorithm based on iterated Duval's algorithm for Lyndon factorization, and a linear algorithmic scheme based on linear suffix sorting, computing inverse suffix array, and applying to it the Next Smaller Value algorithm. Duval's algorithm works for strings over any ordered alphabet, while for linear suffix sorting, a constant or an integer alphabet is required. The authors at that time were not aware of Baier's algorithm. In 2017, our research group proposed a novel algorithm for the Lyndon array. Though the proposed algorithm is linear in the average case and has O(n log(n)) worst-case complexity, it is interesting as it emulates the fast Fourier algorithm's recursive approach and introduces tau-reduction that might be of independent interest. In 2018, we presented a linear algorithm to compute the Lyndon array of a string inspired by Phase I of Baier's algorithm for suffix sorting. This paper presents theoretical analysis of these two algorithms and provides empirical comparisons of both their C++ implementations with respect to iterated Duval's algorithm.

The different systems in the nuclear power plants (NPP) are critical and complex which requires a continuous rigorous monitoring for both normal and abnormal conditions. However, due to the nonlinearity of the dynamic behavior of these systems, implementation of artificial intelligence within the NPP components is crucial to enhance the monitoring and the predictability of the key operating parameters trend. On the other hand, lessons learned from large nuclear accident proves that a remote real-time coordination between the different stakeholders involved in the safety of the nuclear reactor is needed. This remote feature can be implemented in the existing power plants by embedding a mobile computing networks within the plant components. This network will send early warning and transmit the NPP data on real-time to different authorities such as the regulatory authority and the decision-making committees, to enhance the interpretability of the nuclear event and deliver a collaborative decision which in return mitigate the risk associated with these events and increase the overall safety of the plant. The integration of AI and mobile computing in NPP would be most of interest in countries where nuclear reactors data are challenged to reach during an accident. For instance, during the meltdown of Chernobyl, the Soviet government covered the accident from the news in an attempt to contain the consequences but there was a spread of radioactive contamination to some other Europe countries. Therefore, in the case of such scenario the implementation of mobile computing and AI would result in different outcomes as the European countries would be notified through the mobile networking and the spread of contamination would be forecasted by the AI algorithms. In this paper, we examine the different modern AI and mobile computing technologies, the different potential application, the associated features and challenges, and future work direction.

The core motivation for this study emanates from Voronin S.'s seminal work on the universality of the zeta function. This universality positions the zeta function as a pivotal element in the conceptual framework of global artificial intelligence, suggesting its potential as a foundational component. A critical barrier to harnessing this promising universality has been the absence of efficient algorithms capable of actualizing this concept.

In the face of the burgeoning electricity demands and the imperative for sustainable development amidst rapid industrialization, this study introduces a dynamic and adaptable framework suitable for policy-makers and renewable energy experts working on integrating and optimizing renewable energy solutions. While using a case study representative model for Sub-Saharan Africa (SSA) to demonstrate the challenges and opportunities present in introducing optimization methods to bridge power supply deficits and the scalability of the model to other regions, this study presents an agile multi-criteria decision tool that pivots on four key development phases, advancing upon established methodologies and pioneering refined computational techniques, to select optimal configurations from a set of Policy Decision Making Metrics (PDM-DPS). Central to this investigation lies a rigorous comparative analysis of variants of three advanced algorithmic approaches: Swarm-Based Multi-objective Particle Swarm Optimization (MOPSO), Decomposition-Based Multi-objective Evolutionary Algorithm (MOEA/D), and Evolutionary-Based Strength Pareto Evolutionary Algorithm (SPEA2). These are applied to a grid-connected hybrid system, evaluated through a comprehensive 8760-hour simulation over a 20-year planning horizon. The evaluation is further enhanced by a set of refined Algorithm Performance Evaluation Metrics (AL-PEM) tailored to the specific constraints. The findings not only underscore the robustness and consistency of the SPEA2 variant over 15 runs of 200 generations each, which ranks first on the AL-PEM scale but also validate the strategic merit of combining multiple technologies and empowering policymakers with a versatile toolkit for informed decision-making.

The hybridization of conventional thermal power plants by incorporation of renewable energy systems has witnessed widespread adoption in recent years. This trend aims not only to mitigate carbon emissions but also to enhance the overall efficiency and performance of these power generation facilities. However, calculating the performance of such intricate systems using fundamental thermodynamic equations proves to be both laborious and time-intensive. Nevertheless, possessing accurate and real-time insights into their performance is of utmost significance to ensure optimal plant operation, facilitate decision-making, and streamline power production planning. In this paper, we delve into the application of machine learning techniques, particularly hybrid methodologies, to predict the performance of power plants. Specifically, we employ three approaches: the Adaptive Neuro-Fuzzy Inference System (ANFIS), the ANFIS optimized via Particle Swarm Optimization (ANFIS-PSO), and the ANFIS optimized through Genetic Algorithm (ANFIS-GA). These techniques are alternatively applied to a complex hybrid thermal power plant, namely an Integrated Solar Combined Cycle Power Plant (ISCCPP). This ISCCPP comprises a steam turbine section, a gas turbine section, and a concentrated solar power system utilizing troughs as collectors. The energy transfer fluid, in conjunction with exhaust from the gas turbine section, heats water within a vessel or steam generator, producing steam to propel the steam turbine's rotor. Electricity generation is facilitated by both gas and steam turbines, which are coupled to a generator. The findings of this study underscore the accuracy and appropriateness of ANFIS-based models in evaluating and predicting the performance of intricate hybrid power plants. The ANFIS model exhibited an impressive overall coefficient of correlation of 0.9991. Notably, the integration of evolutionary algorithms (PSO and GA) into the ANFIS framework elevated model performance, yielding correlation coefficients of 0.9994 for ANFIS-PSO and 0.9997 for ANFIS-GA. It is noteworthy that ANFIS-GA demonstrated superior predictive capability. The modeling framework developed in this research offers valuable support to designers, energy managers, and decision-makers. It provides a robust and dependable tool to assess the performance of hybrid thermal power plants, which are poised for a global surge in numbers amid the ongoing energy transition.

A single Information security is of pivotal concern for consistently streaming information over the widespread internetwork. The bottleneck flow of incoming and outgoing data traffic introduces the issue of malicious activities taken place by intruders, hackers and attackers in the form of authenticity desecration, gridlocking data traffic, vandalizing data and crashing the established network. The issue of emerging suspicious activities is managed by the domain of Intrusion Detection Systems (IDS). The IDS consistently monitors the network for identifica-tion of suspicious activities and generates alarm and indication in presence of malicious threats and worms. The performance of IDS is improved by using different signature based machine learning algorithms. In this paper, the performance of IDS model is determined using hybridization of nestrov-accelerated adaptive moment estimation –stochastic gradient descent (HNADAM-SDG) algorithm. The performance of the algorithm is compared with other classi-fication algorithms as logistic regression, ridge classifier and ensemble algorithm by adapting feature selection and optimization techniques

Traffic Inspection (TI) work in this article is positioned as a specific module of road traffic with its primary function oriented towards monitoring and controlling safe traffic and the execution of significant events within a particular geographic area. Exploratory research on the significance of event execution in simple, complicated and complex traffic flow and process situations is related to the activities of monitoring and controlling functional states and performance of categorical variables, which include objects and locations of road infrastructure, communication infrastructure and networks of traffic inspection resources. It is emphasized that the words “work” and “traffic” have the semantic status as synonyms (in one world language), which is explained in the design of the Agent-based model of the complexity of content and contextual structure of TI work at the singular and plural level with 12 points of interest (POI) in the thematic research. An Event Execution Log (EEL) was created for on-site data collection with eight variables, seven of which are independent (event type, activities, objects, locations, host, duration-period and periodicity of the event) and one dependent (significance of the event) variable. The structured data set includes 10,994 input-output vectors in 970 categories collected in the EEL created by 32 human agents (traffic inspectors) over a 30-day period. An algorithmic presentation of the methodological research procedure for preprocessing and final data processing in the ensemble of machine learning models for classification and selection of TI tasks is given. Data cleaning was performed on the available dataset to increase data consistency for further processing. Vector elimination was carried out based on the Location variable, such that the total number of vectors equals the number of unique categories of this variable, which is 636. The main result of this research is the Classification modeling of the significance of events in TI work based on machine learning techniques and the Stacking ensemble. The created machine learning models for event significance classification modeling have high accuracy values. To evaluate the performance metrics of the Stacking ensemble of models, the confusion matrix, Precision, Recall, ND F1-score are used.