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This study aims to improve the complexity of chaotic systems and the security accuracy of information encrypted transmission. Applying five-dimensional memristive Hopfield neural network (5D-HNN) to secure communication will greatly improve the confidentiality of signal transmission and greatly enhance the anticracking ability of the system.

Chaos masking: Chaos masking is the process of superimposing a message signal directly into a chaotic signal and masking the signal using the randomness of the chaotic output. Synchronous coupling: The coupled synchronization method first replicates the drive system to get the response system, and then adds the appropriate coupling term between the drive The synchronization error and the coupling term of the system will eventually converge to zero with time. The synchronization error and coupling term of the system will eventually converge to zero over time.

A 5D memristive neural network is obtained based on the original four-dimensional memristive neural network through the feedback control method. The system has five equations and contains infinite balance points. Compared with other systems, the 5D-HNN has rich dynamic behaviors, and the most unique feature is that it has multistable characteristics. First, its dissipation property, equilibrium point stability, bifurcation graph and Lyapunov exponent spectrum are analyzed to verify its chaotic state, and the system characteristics are more complex. Different dynamic characteristics can be obtained by adjusting the parameter k.

A new 5D memristive HNN is proposed and used in the secure communication

To simplify the circuit design and control complexity of the magnetic coupling resonant wireless charging system, the radio energy transmission constant current and constant voltage charging is realized.

The purpose of this study is to simplify the circuit design and control complexity of the magnetic coupling resonance wireless charging system, in order to achieve constant current and constant voltage charging for wireless energy transmission. First, the principle of LCC/S-S compensation structure is analyzed, and the equivalent mathematical model is established; then, the system characteristics under constant current and constant voltage mode are analyzed, and the design method of system parameters is given; finally, a simulation and experimental system is built to verify the correctness and feasibility of the theoretical analysis.

The results show that the proposed hybrid topology can achieve a constant current output of 2 A and a constant voltage output of 30 V under variable load conditions, and effectively suppress the current distortion problem under light load conditions. The waveform distortion rate of the inverter current is reduced from 33.97% to 10.45%.

By changing the high-order impedance characteristics of the compensation structure, the distortion of the current waveform under light load is suppressed, and the overall stability and efficiency of the system are improved.

The purpose of this paper is to create optimally located Facilitation Centers on this tourist circuit, evaluated through network analysis, thus creating an effective linkage between tourism and economic activities of the craftsmen who are the custodians of the cultural heritage of Puri.

The craft villages lying in and around this tourist circuit are surveyed to establish socio-economic condition of artisans, significance of the craft and spatial distribution of craft villages and the willingness of artisans to travel closer to the transport spine. Network analysis is used to assess the suitability of Facilitation Center location using travel time and distance as parameters. Finally, the sustainability of the Facilitation Centers is evaluated using a cost-benefit analysis (CBA).

The Facilitation Centers can be spatially developed at the strategic locations to expand tourist market. This will help in leveraging the economic benefits of tourism to a marginalized rural artisan community by creating a sustainable model.

The focus on festival can help to protect local cultural traditions, develop tourism and promote the economic, social and cultural developments of the destination. Dispersal strategies adopted aim to increase visitors' satisfaction with the product and thus entice them to stay longer in the destination.

Through the reconstruction of the events of some foreign entrepreneurs who worked in the territory of the Italian city of Bari in the first half of the 19th century, this paper aims to analyze the role of entrepreneurial migration in the economic development of Apulia land in this period.

This study adopts a theoretical framework that combines the concept of mixed embeddedness in a multifocal perspective, with the model of the diffusion of innovation focusing on the role of the so-called agency of actors, and of the network, in the dissemination of innovation. The theoretical framework is applied to multiple case studies to compare the evidence that emerged from the simultaneous analysis of several situations.

By analyzing how innovations have spread within the network of entrepreneurs of that time, it is possible to identify some relevant aspects related to the mechanisms of dissemination of innovations in the context of entrepreneurial migration. Specifically, the opportunity structure is intended in an even broader sense than indicated in the classic approach to mixed embeddedness: it is considered as the result of the joint interaction of the political, institutional and economic context of several places, and the behavioral dynamics of several groups.

Due to the specific method chosen, the outcomes of the research might apply to a narrow context. Therefore, the results need to be tested and confirmed in further empirical studies, and by applying multiple research methods.

Findings are useful and significant in the analysis of the link that exists between the diffusion of innovations and migrant entrepreneurship, and then the conclusions can be applied and extended to the current phenomenon of migration-related innovations, with specific reference to developing countries.

Findings can be applied and extended to the current phenomenon of migration-related innovations and highly skilled migration, with specific reference to developing countries.

This paper contributes to shed new light on the contextual and multifocal factors that influence the development of innovations in the networks of migrant entrepreneurship, in a specific historical period and a specific context. Combining social, human and financial capital with the wider opportunity structure, this study also provides a comprehensive understanding of the modalities through which migrant and high-skilled entrepreneurs could innovate.

Process mining has emerged in the last decade as one of the most promising tools to discover and understand the actual execution of processes. This paper addresses the application of process mining techniques to analyze the performance of automatic guided vehicles (AGVs) in one of the Body in White circuits of the factory that Stellantis has in Vigo, Spain.

Standard process mining discovery and conformance algorithms are applied to analyze the different AGV execution paths, their lead times, main sources and identify any unexpected potential situations, such as unexpected paths or loops.

Results show that this method provides very useful insights which are not evident for logistics technicians. Even with such automated devices, where the room for decreased efficiency can be apparently small, process mining shows there are cases where unexpected situations occur, leading to an increase in circuit times and different variants for the same route, which pave the road for an actual improvement in performance and efficiency.

This paper provides evidence of the usefulness of applying process mining in manufacturing processes. Practical applications of process mining have traditionally been focused on processes related to services and management, such as order to cash and purchase to pay in enterprise resource planning software. Despite its potential for use in industrial manufacturing, such contributions are scarce in the current state of the art and, as far as we are aware of, do not fully justify its application.

This paper aims to concentrate on recent innovations in flexible wearable sensor technology tailored for monitoring vital signals within the contexts of wearable sensors and systems developed specifically for monitoring health and fitness metrics.

In recent decades, wearable sensors for monitoring vital signals in sports and health have advanced greatly. Vital signals include electrocardiogram, electroencephalogram, electromyography, inertial data, body motions, cardiac rate and bodily fluids like blood and sweating, making them a good choice for sensing devices.

This report reviewed reputable journal articles on wearable sensors for vital signal monitoring, focusing on multimode and integrated multi-dimensional capabilities like structure, accuracy and nature of the devices, which may offer a more versatile and comprehensive solution.

The paper provides essential information on the present obstacles and challenges in this domain and provide a glimpse into the future directions of wearable sensors for the detection of these crucial signals. Importantly, it is evident that the integration of modern fabricating techniques, stretchable electronic devices, the Internet of Things and the application of artificial intelligence algorithms has significantly improved the capacity to efficiently monitor and leverage these signals for human health monitoring, including disease prediction.

To prevent the coil from burning or getting damaged, it is necessary to estimate the duration of its operation as long as its temperature does not exceed the permissible limit. This paper aims to investigate the effect of switching on the thermal behavior of impregnated and nonimpregnated windings. Also, the safe operating time for each winding is determined.

The power loss of the winding is expressed as a function of the winding specifications. Using homogenization techniques, the equivalent thermal properties for the homogenized winding are calculated and used in a proposed thermal equivalent circuit for winding modeling and analysis. The validity and accuracy of the proposed model are determined by comparing its analysis results and simulation and measurement results.

The results show that copper windings have better thermal behavior and lower temperature compared to aluminum windings. On the other hand, by impregnating or increasing the packing factor of the winding, the thermal behavior is improved. Also, by choosing the right duty cycle for the winding current source, it is possible to prevent the burning or damage of the winding and increase its lifespan. Comparing the measurement results with the analysis results shows that the proposed equivalent circuit has an error of less than 4% in the calculation of the winding center temperature.

In this paper, the effect of temperature on the electrical resistance of the coil is ignored. Also, rectangular wires were not investigated. Research in these topics are considered as future work.

By calculating the thermal time constant of the winding, its safe operation time can be calculated so that its temperature does not exceed the tolerable value (150 °C). The proposed method analyzes both impregnated and nonimpregnated windings with various schemes. It investigates the effects of switching on their thermal behavior. Additionally, it determines the safe operating time for each type of winding.

The purpose of this study is to increase awareness of current supply chain (SC) security-related issues by providing an extensive analysis of existing SC security solutions and their limitations. The security of SCs has received increasing attention from researchers, due to the emerging risks associated with their distributed nature. The increase in risk in SCs comes from threats that are inherently similar regardless of the type of SC, thus, requiring similar defence mechanisms. Being able to identify the types of threats will help developers to build effective defences.

In this work, we provide an analysis of the threats, possible attacks and traceability solutions for SCs, and highlight outstanding problems. Through a comprehensive literature review (2015–2021), we analysed various SC security solutions, focussing on tracking solutions. In particular, we focus on three types of SCs: digital, food and pharmaceutical that are considered prime targets for cyberattacks. We introduce a systematic categorization of threats and discuss emerging solutions for prevention and mitigation.

Our study shows that the current traceability solutions for SC systems do not offer a broadened security analysis and fail to provide extensive protection against cyberattacks. Furthermore, global SCs face common challenges, as there are still unresolved issues, especially those related to the increasing SC complexity and interconnectivity, where cyberattacks are spread across suppliers.

This is the first time that a systematic categorization of general threats for SC is made based on an existing threat model for hardware SC.

As data centres grow in size and complexity, traditional air-cooling methods are becoming less effective and more expensive. Immersion cooling, where servers are submerged in a dielectric fluid, has emerged as a promising alternative. Ensuring reliable operations in data centre applications requires the development of an effective control framework for immersion cooling systems, which necessitates the prediction of server temperature. While deep learning-based temperature prediction models have shown effectiveness, further enhancement is needed to improve their prediction accuracy. This study aims to develop a temperature prediction model using Long Short-Term Memory (LSTM) Networks based on recursive encoder-decoder architecture.

This paper explores the use of deep learning algorithms to predict the temperature of a heater in a two-phase immersion-cooled system using NOVEC 7100. The performance of recursive-long short-term memory-encoder-decoder (R-LSTM-ED), recursive-convolutional neural network-LSTM (R-CNN-LSTM) and R-LSTM approaches are compared using mean absolute error, root mean square error, mean absolute percentage error and coefficient of determination (R²) as performance metrics. The impact of window size, sampling period and noise within training data on the performance of the model is investigated.

The R-LSTM-ED consistently outperforms the R-LSTM model by 6%, 15.8% and 12.5%, and R-CNN-LSTM model by 4%, 11% and 12.3% in all forecast ranges of 10, 30 and 60 s, respectively, averaged across all the workloads considered in the study. The optimum sampling period based on the study is found to be 2 s and the window size to be 60 s. The performance of the model deteriorates significantly as the noise level reaches 10%.

The proposed models are currently trained on data collected from an experimental setup simulating data centre loads. Future research should seek to extend the applicability of the models by incorporating time series data from immersion-cooled servers.

The proposed multivariate-recursive-prediction models are trained and tested by using real Data Centre workload traces applied to the immersion-cooled system developed in the laboratory.

This paper aims to review in-memory computing (IMC) for machine learning (ML) applications from history, architectures and options aspects. In this review, the authors investigate different architectural aspects and collect and provide our comparative evaluations.

Collecting over 40 IMC papers related to hardware design and optimization techniques of recent years, then classify them into three optimization option categories: optimization through graphic processing unit (GPU), optimization through reduced precision and optimization through hardware accelerator. Then, the authors brief those techniques in aspects such as what kind of data set it applied, how it is designed and what is the contribution of this design.

ML algorithms are potent tools accommodated on IMC architecture. Although general-purpose hardware (central processing units and GPUs) can supply explicit solutions, their energy efficiencies have limitations because of their excessive flexibility support. On the other hand, hardware accelerators (field programmable gate arrays and application-specific integrated circuits) win on the energy efficiency aspect, but individual accelerator often adapts exclusively to ax single ML approach (family). From a long hardware evolution perspective, hardware/software collaboration heterogeneity design from hybrid platforms is an option for the researcher.

IMC’s optimization enables high-speed processing, increases performance and analyzes massive volumes of data in real-time. This work reviews IMC and its evolution. Then, the authors categorize three optimization paths for the IMC architecture to improve performance metrics.