Digital machine intelligence has evolved from its inception in the form of computation of numbers to AI, which is centered around performing cognitive tasks that humans can perform, such as predictive reasoning or complex calculations. The state of the art includes tasks that are easily described by a list of formal, mathematical rules or a sequence of event-driven actions such as modeling, simulation, business workflows, interaction with devices, etc., and also tasks that are easy to do “intuitively”, but are hard to describe formally or as a sequence of event-driven actions such as recognizing spoken words or faces. While these tasks are impressive, they fall short in applying common sense reasoning to new situations, filling in information gaps, or understanding and applying unspoken rules or norms. Human intelligence uses both associative memory and event-driven transaction history to make sense of what they are observing fast enough to do something about it while they are still observing it. In addition to this cognitive ability, all bio-logical systems exhibit autopoiesis and self-regulation. In this paper, we demonstrate how machine intelligence can be enhanced to include both associative memory and event-driven transaction history to create a new class of knowledge-based assistants to augment human intelligence. The digital assistants use global knowledge derived from the Large Language Models to bridge the knowledge gap between various participants interacting with each other. We use the general theory of information and schema-based knowledge representation to create the memory and history of various transactions involved in the interactions.

Typhoons bring great damages to transmission line systems located in coastal areas. Strong wind and extreme precipitation are the main sources of damaging effects. Transmission lines suffered from wind-driven rain exhibit more susceptibility to damage due to the coupled effect of wind and rain water. This paper presents an integrated numerical simulation framework based on mesoscale WRF model, multiphase CFD model and FEM model to analyze the motions of a transmission line subjected to coupled wind and rain loads during typhoon events. A full-scale transmission line in Zhoushan island is employed to demonstrate the effectiveness of the proposed framework by simulating typhoon evolution in terms of wind fields and rainfall, solving the coupled wind and rain fields around the conductor and predicting the dynamic responses of the transmission line during Super Typhoon Lekima (2019). The results show that the horizontal displacements of the transmission line under the joint actions of wind and rain increases approximately 17%~18% compared to those of wind loads only. It is important to consider the coupled effects of wind-driven rain on conductors in the design of transmission lines under typhoon conditions.

In reinforced concrete (RC) structures, splicing is required owing to the length limitations of rebars, insufficient lengths, and transportation issues. In particular, splices connect the rebar of RC structures such as walls, columns, beams, slabs, and joints. Lap splices are the most commonly used method worldwide because they do not require specific equipment or skilled workers. However, lap splices incur high construction costs because of the long splice lengths required for large-diameter rebars in megastructures, as well as issues pertaining to material supply, labor costs, constructability, and project duration. Additionally, approximately 15% more rebar is required because of the overlap. Energy saving for a sustainable built environment is possible if the disadvantage of lap splices, which generate high CO2 emissions due to the excessive use of rebar, are resolved. Hence, mechanical rebar couplers (MRCs) have been developed. However, despite their advantages, they have not been widely applied in construction sites owing to concerns regarding safety, quality, and constructability. Although various MRCs have been developed, most studies focus only on their structural performance. Therefore, a data-driven approach for selecting MRCs based on the reinforcing bar shape and structural characteristics is proposed in this study. Using a data-driven MRC selection algorithm, using the T-threaded coupler for one rebar over two floors resulted in 56% more efficient labor productivity, 15% shorter assembly time, 17% lower costs, and 26% lower CO2 emission. Using a developed algorithm, the appropriate MRC can easily and rapidly be selected for frequent design changes.

This paper presents a theoretical framework aimed at improving IoT network cybersecurity by AI-driven anomaly detection in the context of rapid urbanization and the development of smart cities. The framework draws upon the Complex Adaptive Systems Theory, Theory of Planned Behavior, the Technology Acceptance Model, and the Socio-Technical Systems Theory, and intri-cately examines how AI might be integrated into the intricate ecosystem of smart cities to detect and mitigate cyber threats. It emphasizes AI-driven anomaly detection methods as pivotal in-struments that impact cybersecurity enhancement in smart city infrastructures. The proposed framework also highlights the crucial role that human-related factors, including user behavior and adoption, play in determining how effective AI applications are in enhancing cybersecurity in smart cities. It also recognizes the important moderating roles played by outside factors like legislative frameworks, environmental contexts, and technological breakthroughs in the relationship between AI-enabled anomaly detection and enhanced cybersecurity. The goal of this research is to shed light on the interactions between technology and socio-technical dynamics by offering a thorough theoretical understanding of AI's potential in smart city cybersecurity. It highlights the significance of taking into account a variety of contextual aspects when employing AI to strengthen the digital security of urban areas, and provides a strategic direction for future research and practical implementation.

This paper presents two control design strategies for voltage regulation in a single inductor dual output (SIDO) DC-DC Buck converter system. Based on a nominal multiple input multiple output (MIMO) plant model and performance requirements, both an LQR and a decoupled PI control laws are designed to control the power converter system, under parametric uncertainties such as voltage source variation, CPL power variation, and load resistance variation. Therefore, the SIDO converter can cascade operating, which may cause the undesired effects of voltage oscillation and reduce the system's stability margin. The control performance was assessed under both uncertainties resistive loading and power variation of a constant power load (CPL). A SIDO DC-DC Buck converter board has been developed for experimental tests. The experimental results show that the LQR strategy, compared to the PI strategy, presented a robust performance in the presence of parametric uncertainties in the resistive loads being, however, sensitive to uncertainties due to the presence of CPL loads.

The medium-voltage DC (MVDC) technology has gained increasing interests in recent years. Power electronics devices dominate those grids. Accurate simulation of such a grid, with detailed models of switching semiconductors, can quickly became very time-consuming, according to the number of connected devices to be simulated. A simulation approach based on interactions on a continuous time model can be very interesting, especially for developing a system level control model of such a modern MVDC distribution grid. Aim of this paper is to present all the steps required for obtaining a continuous time modelling of a +/- 10 kV MVDC grid case study, including Solid State Transformer (SST) and Modular Multilevel Converter (MMC) based Active Front End (AFE). Additional aim of this paper is to supply an educational content about the use of the continuous time simulation approach, thanks to a detailed description of the various devices modelled into the presented MVDC grid. The results of a certain number of simulation scenarios are eventually presented.

Enhancing energy efficiency within the building sector is imperative to curbing energy losses, given that this sector alone contributes to over 34% of global energy consumption. Employing a building management system, along with its regular updates, presents a strategic avenue to decrease energy usage, enhance building energy efficiency, and more. Tailored control strategies, aligned with the unique characteristics and usage patterns of each building, are essential for achieving energy savings. This article presents an evaluation of HVAC system efficiency in office buildings, utilizing a data-driven approach coupled with simulations conducted in IDA ICE software. The research explores the control strategy of an office building equipped with a constant air volume HVAC system, featuring a regularly controlled air handling unit. The objective is to boost energy efficiency while striking a balance between occupant comfort and energy consumption. Findings indicate that by analyzing measured data and adjusting configurable parameters, building energy consumption can be significantly reduced. Close monitoring of indoor parameters by building operators and corresponding adjustments to the HVAC system can yield energy savings of up to 16%. Leveraging these insights, the paper suggests integrating data-driven and dynamic simulation methods into building management system models to optimize HVAC systems, enhance energy efficiency, and advance ambitious carbon neutrality objectives.

Addressing the urgent sustainability challenges in the wine industry, this study explores the efficacy of sustainability-oriented innovations (SOIs) and smart farming technologies (SFTs) across wine value chains in Cyprus and Italy. Employing KPIs for a rigorous assessment, the research delves into the environmental, economic, and social impacts of these technologies. In Cyprus (SIP7), the integration of digital labelling and smart farming solutions led to a substantial reduction in pesticide usage by up to 75% and enhanced the perceived quality of wine by an average of 8%. Italy's SIP10 witnessed a 33.4% decrease in greenhouse gas emissions, with an additional benefit of a 5.3% improvement in intrinsic product quality. Notably, SIP10 also introduced a carbon credit system, potentially generating an average annual revenue of €4,140 per farm. These findings highlight the transformative potential of SOIs and SFTs in promoting sustainable practices within the wine industry, demonstrating significant advancements in reducing environmental impact, improving product quality, and enhancing economic viability. The study underscores the critical role of innovative technologies in achieving sustainability goals and provides a compelling case for wider adoption within the agricultural sector.

Responding to changing value creation processes in the sheet metal working sec-tor, where the complexity and interchangeability of products challenge traditional dif-ferentiation strategies, this empirical analysis explores integrating service-oriented and data-driven business models as new paths to ensure competitiveness, especially for small and medium-sized enterprises (SMEs). This study aims to capture the current state and challenges associated with the implementation of these business models in this sector. The research was conducted through semi-structured interviews with SMEs in the industry. The findings indicate that service-oriented and data-driven business models are not yet widely adopted and that manufacturing companies re-quire support in their implementation. Fields of action were identified for the industry. These are "Creating awareness and understanding", "Recognizing added value", "In-creasing company maturity" and "Understanding the change process". Cooperation between science and industry is essential in tackling these fields of action to ensure the successful integration of such business models in manufacturing companies. This pa-per identifies challenges in the fields of action that companies must address through a structured approach promoting awareness, recognizing value, improving organiza-tional maturity, and understanding the change process to successfully implement ser-vice-oriented and data-driven business models.

The wave energy converter (WEC) technology constantly develops new designs and models. To encourage its development within the energy sector, research is required to improve current designs and create durable and effective devices. However, it is also crucial to investigate the suitability of a WEC to a site-specific sea condition as this can impact its optimal power absorption and constructability. This study evaluates and compares the efficiency of a heaving buoy with varied geometries in one of the most energetic places along the Mexican coast. A statistical analysis of the wave climate on the coast of Ensenada during the last 42 years is performed to define the conditions to which the device is subjected. The location of the WEC in shallow waters is chosen using a computational model which solves the modified mild slope equation in its elliptic shape. The heaving buoy floater is studied using three distinct geometries: a semi-sphere, a cylinder, and a proposed rounded semi-rectangle. The hydrodynamic response of the three geometries is then analysed in the frequency and time domain using ANSYS AQWA. The hydrodynamic study involves the assessment of the floating body dynamics, exerted forces, the power absorbed as well as the suitability of the proposed power take-off (PTO) system. Findings reveal that the proposed geometry absorbs the most energy, with an annual power of 135.11 MW, and that the transmission PTO design is appropriate for this type of technology.

The swift progress in low-dimensional nanocarbons, specifically in the realm of the authentic one-dimensional carbon chain featuring sp¹ hybridization, has unveiled exciting prospects for integrating them into cutting-edge technologies across diverse industries. To fully harness their potential, precise control over the growth process is necessary to obtain desired nanostructure and functionality. However, optimizing properties through traditional approaches has been challenging due to complex interactions. To address this, we implement a focused data-driven strategy for nanocarbon inverse design by leveraging a state-of-the-art data-driven nanocarbon genome approach (NCGA). By uncovering relationships between growth parameters and resultant traits, this serves as an expedient catalyst in engineering nanostructures with tailored attributes. We introduce an extensive array of technological approaches aimed at precisely controlling the growth process. These methods encompass stimulating and precisely adjusting synergistic effects, coordinating atomic vibrations at a collective level through the implementation of multilayer nano-interfaces, harnessing the potential of active screen plasma surface engineering, utilizing nano-patterning and allotropic phase transformations, incorporating heteroatom doping, and effectively directing self-assembly. These aim to unlock nanomaterials' latent potential and reveal novel neural pathways within the data-driven NCGA, enhancing its predictive capabilities. Specifically, triggering self-organization during growth can potentially unlock previously unexplored neural pathways. The data-driven NCGA offers a paradigm shift, accelerating discovery and design of nanocarbons with optimized, application-specific properties.

The purpose of this paper is to examine the role and impact of public governance on European supranational public policies that seek to foster scientific and economic growth within innovation ecosystems (IEs) through the PITCCH initiative. The PITCCH is a European innovation development initiative funded by Horizon 2020 under the 'Innovation in SMEs' measure.The research methodology for this study is based on a 10-month real-time ethnographic field study of the PITCCH project, which allowed for the observation of the project governance and the capture of the perspectives of innovation-driven small and medium enterprises (i-SMEs) in their engagement with IE stakeholders.The findings of this research demonstrate the role of public governance in providing result-oriented practical support to the IE through the lens of i-SMEs, and they contribute to the existing literature on regional economic growth supported by supranational public policies. These findings have both academic and practical applications. They enrich the current literature on this topic and provide insights for policymakers and governing representatives seeking to implement effective public policies.

The study investigated the usage of open data in public organizations, focusing on the motives, preferences, and governance ideals that determine their quality. The study shed light on how open data platforms are created and improved, as well as their influence on government operations. The primary goal was to understand the motivations behind the usage of open data, evaluate gov-ernance ideals, and examine institutions' efforts to standardize data formats. Hierarchical regres-sion analysis divides variables into core reasons and different preferences, demonstrating a need for "diversification" in order to use open data effectively. Efficiency, accountability, community impact, knowledge, and high-quality service delivery are all emphasized in governance value analysis. The institution's dedication to standardizing open data formats is evidenced by legislative measures and the integration of many technologies. The findings illustrate the institution's dual strategy of em-ploying traditional techniques and new technologies for effective data management. The report suggests increasing data openness and standardization, investing in ongoing training, upgrading alerting and awareness systems, and adopting specific regulations for open data use. Monitoring and feedback techniques are critical for adjusting and refining open data services to satisfy de-velopment needs. This study adds to the scientific knowledge of open data use by highlighting important motives, preferences, and governance ideals. The offered guidelines provide practical insights for public organizations looking to improve their open data platforms by promoting openness, efficiency, and community participation.

Purpose of review The forest industry has deployed ICTs to improve productivity of forest supply chains. In this sense, digitalization of data flows significantly impacts wood supply chains, from forest to mills, which must react flexibly to market fluctuations of forest products. The goal of this study was to conduct a literature review on data flow implementations in management systems of wood supply chains and to evaluate their applicability in supply chains to analyze the opportunities for improving them in practice. Recent findings We utilized the ScienceDirect database, Scopus and Web of Science in order to document data flows in systems and actual applications. Due to ongoing outsourcing, the wood procurement chains, and the wood supply chains were identified in the forest industry. Several different technologies can be implemented also in wood supply chains to digitalize the data flow depending on the specific needs of organizations. In this regard, digitalization of Big Data causes significant changes in elements of supply models that should be integrated and standardized in the optimization and simulation systems. Summary A modelling guide is suggested for accurate dynamic solution of problems of forest logistics. Then, the opportunities in digitalization for connecting different data flows, technologies, and stakeholders are discussed thoroughly. We conclude that data-driven dynamic optimization, and locally implemented digitalization contributes to wood supply modeling in the establishment of standards for cloud-based platforms at time-related and sequential measures which will ensure successful forest logistics through the planning and the monitoring of wood supply chains.

The capacitor-mismatch among diverse defects caused by the manufacturing process variations significantly affects the linearity of capacitor-array used to implement the capacitive digital-to-analog converter (CDAC) in the successive-approximation-register (SAR) analog-to-digital converter (ADC). Accordingly, the linearity of the SAR ADC is limited by that of capacitor-array, resulting in serious yield loss. This paper proposes an efficient foreground self-calibration technique to enhance the linearity of the SAR ADCs by mitigating the capacitor-mismatch based on the split ADC architecture along with variable capacitors. In this work, two ADC channels (i.e., ADC1 and ADC2) for the split ADC architecture include their capacitive DACs (CDACs) whose binary-weighted capacitor-arrays consist of variable capacitors. A charge-sharing SAR ADC is used for each ADC channel. In the normal operation mode, their digital outputs are averaged to be the final ADC output, as in a conventional split ADC. In the calibration mode, every single binary-weighted capacitor for the two ADCs is sequentially calibrated by making parallel or/and antiparallel connection among two or thee capacitors from the two channels. For instance, because the capacitors of the CDACs ideally exhibit the binary-weighted relation as Cn=2×Cn−1, variable capacitor Cn of ADC1 can be updated to be closest to the sum of Cn−1 of ADC1 and Cn−1 of ADC2 for the calibration. For the process, the two capacitor-arrays of the two ADCs can be reconfigured to be connected to each other, so that Cn of ADC1 can be connected with two of Cn−1 of ADC1 and ADC2 in antiparallel. The two voltages at the top and the bottom plates of the CDAC are compared by a comparator of ADC1, and the comparison results are used to update Cn. This process is iterated, until Cn is in agreement with the sum of two of Cn−1. Finally, all the capacitors can be calibrated in this way to have the binary-weighted relation. The simulation results based on the proposed work with a split SAR ADC model verified that the proposed technique can be practically used, by showing that the total-harmonic-distortion and the signal-to-noise-and-distortion ratio were enhanced by 21.8-dB and 6.4-dB, respectively.

A three-phase multilevel inverter (MLI) synthesized with 31-levels in the output voltage is used to provide ac supply to a three-phase, squirrel-cage induction motor. The gating angles required for thirty power switches in MLI are optimized by both Genetic Algorithm (GA) and Gray Wolf Op-timization (GWO). In the optimization technique, the optimal angles are determined by solving the trigonometric equations from the Fourier analysis for targeting minimum total harmonic distortion (THD) at the MLI output. Simulation model and experimental prototype are developed for performance analysis and validation. The results obtained demonstrate that the MLI is effec-tively able to produce 31-levels of three-phase ac output voltages with THD not exceeding 5% when loaded with a resistive load and a three-phase induction motor. The voltages and currents are measured and recorded for different loads and operating conditions, including the amount of energy consumed by the loads, as well as frequency analysis, which shows the absence of most of the triple harmonics that have a negative impact on the efficiency of the inverter.

The separation of Li+ and [Bmim]+(1-Butyl-3-methylimidazolium) by electrically driven membrane technology was studied. The effects of the types and modification processes of cation exchange membranes, current density, total cation concentration, temperature, and anion types on separation efficiency were investigated. The results indicated that it is feasible to achieve efficient separation of Li+ and [Bmim]+ using the selectivity of cation exchange membranes under electric field. Membrane CMX and the modified membrane A and B which obtained from the CMX showed obvious selective of Li+ from [Bmim]+. When the current density is 5 mA/cm2 and the feed concentrations of Li+ and [Bmim]+ are 0.3 mol/L and 0.2 mol/L, the selective transport coefficient of the modified membrane B can reach 6.2 in the first 120 minutes of the process. Reducing current density and increasing total cation concentration can improve separation efficiency. Lowering the temperature can improve the separation effect, but improvement is not obviously. Changing the anion of the feed from chloride ion to acetate has no effect on the separation.

The variety of risks due to climate change with a potential impact on Cultural Heritage CH requires a multidisciplinary approach and a preliminary overview of the existing methods for risk analysis and assessment in different fields. Thus, the starting point of the research conducted by the Sapienza PNRR working group consists in a systematic literature review of risk assessment approaches, methods and indicators. As a first outcome, the research has designed a multidisciplinary comprehensive framework consisting of the definition of risk categorization, the framework represented by a measurable index and the main target, according to the classification of Cultural Heritage based on the harmonization of European vocabularies definitions and protocols. The risk assessment framework sets the methodological core of a future multi criteria matrix and digital platform for decision focused on the historic built environment and it is functional to the definition of specific operational guidelines connected to each risk for elaborating descriptive/predictive Architectural Heritage’s transformation scenarios to support the development of mitigation and adaptation strategies.

This article reviews recent innovations in piezoelectric actuators, then indicates the future research targets. In the materials, the piezoelectric performances of relaxor-lead titanate (PZN-PT, PMN-PT) single crystals have been improved significantly by an AC electric poling technique. Also new loss characterization techniques facilitate developing high-power density piezoelectrics. The RMS vibration velocity reaches 0.6 m/s in the k₃₁ mode in lead zirconate titanate (PZT) nowadays, which corresponds to power density 30 W/cm³. Pb-free materials are urged due to the recent political regulation on toxic materials. In device designs, the original multilayer actuators (MLA) with Ag/Pd electrode have been switched to the ones with Cu or pure Ag, in order to suppress the heat generation, then to enhance the device power density. Piezoelectric micro-electro-mechanical-systems (MEMS’s) started to utilize the <1 0 0> oriented epitaxial films of rhombohedral PZT compositions, which exhibits enhanced piezoelectric properties. Also for higher-power applications, aero-sol deposition (AD) methods have been introduced to 10 -thick films. Regarding drive/control techniques in piezo-actuators, three developments are on-going, that is, “negative capacitance” power supplies for driving off-resonance (pseudo-DC) piezo-actuators, capacitive AC power supplies for efficiently driving resonance piezo-actuators, and DC/DC converters for harvesting electric energy from high-impedance piezoelectric devices under noise vibration. The applications of piezoelectric actuators in the 21^st century seem to be oriented toward “politico-engineering” with four keywords, “cooperation, protection, reduction, and continuation”. The sustainability technologies include the following piezoelectric items: (a) Pb-free piezoelectrics, (b) ultrasonic technology for decomposing hazardous materials, (c) reduction of contamination gas, (d) piezoelectric energy harvesting, and (e) device improvement with high energy-efficiency. On the other hand, the following crisis technologies have been developed with piezoelectrics: (a) detection and neutralization technologies of infectious/epidemic diseases such as anthrax, (b) autonomous unmanned underwater, aerial, and land vehicles with piezoelectric actuators, (c) pin-point target weapons, (d) high voltage supplies with piezoelectric transformers, and (e) compact energy sources for remote actuating/sensing systems.

The integration of Artificial Intelligence (AI) in dairy farm management through biometric facial recognition for cows is a significant stride in livestock management. This review critically evaluates the evolution, applications, and challenges of AI-driven biometric facial recognition in dairy farming. It emphasizes the role of this technology in enhancing individual monitoring of dairy cows, providing accurate health, behavior, and productivity tracking. Originally derived from human facial recognition systems, this approach utilizes distinctive bovine facial features for essential, non-invasive, real-time monitoring in large-scale operations. The progression of AI from elementary pattern recognition to advanced Convolutional Neural Networks (CNNs) and deep learning models marks a shift toward data-driven farming. This study addresses key challenges such as environmental variability, data collection hurdles, ethical concerns, and technological limitations. It also contrasts various AI models, spotlighting their unique strengths and practical utility in dairy farming scenarios. Despite these challenges, facial recognition technology holds promise for improving farm efficiency, animal welfare, and sustainable practices, highlighting the need for continuous research and development. The review concludes by advocating for future research focused on environmental adaptability, ethical AI application, cross-breed compatibility, and integration with other farming technologies. Ultimately, it underscores AI's transformative potential in modernizing dairy farming towards a more data-oriented, responsible agricultural future.

This paper introduces an innovative nanotechnology-based approach that provides a pathway to enhance the energy release efficiency of nanoenergetic materials (nEMs) by harnessing self-synchronized collective atomic vibrations and phonon wave resonance within the transition domains between nanocomponents, without altering the material composition. The key innovation involves incorporating finely-tuned 2D-ordered linear-chain carbon-based multilayer nano-enhanced interfaces as programmable nanodevices into the transition domains using advanced multistage processing and assembly techniques. These programmable nanodevices enable precise control over self-synchronized collective atomic vibrations and phonon wave propagation, leading to synergistic effects. To activate and optimize these effects, a combination of various methods is employed, including energy-driven initiation of allotropic phase transformations, surface acoustic wave-assisted micro/nano-manipulation, heteroatom doping, directed self-assembly using high-frequency electromagnetic fields, and data-driven inverse design approaches. By leveraging a data-driven inverse design strategy and uncovering hidden structure-property relationships, we maximize energy release efficiency using the carbon nano-materials genome approach derived from multifactorial neural network-based predictive models. This approach not only unlocks new functionalities in nEMs but also improves environmental performance and safety levels. By pioneering transformative pathways for nEMs through harnessing phonon wave resonance in low-dimensional nanocarbon transition interfaces, this research brings significant advancements in the field.

This manuscript employs a surface stress-driven nonlocal model to explore the combined effects of long-range interaction and surface energy on higher vibration modes of functionally graded nanobeams. The nanobeam theory, based on Bernoulli-Euler kinematics, incorporates surface effects such as surface elasticity, surface residual stresses, surface density, and rotary inertia. Hamilton's principle is applied to derive the governing equation with size-dependent considerations. The main outcomes of a parametric investigation, considering four different kinematic boundary conditions (Cantilever, Simply-Supported, Clamped-Pinned, and Doubly-Clamped) while varying the nonlocal parameter and material gradient index, are presented and discussed. Additionally, the normalized natural frequencies for the second, third, fourth and fifth modes of vibrations are provided and analyzed for each case under study. The results underscore the model's effectiveness in capturing surface energy effects on the overall dynamic behavior of functionally graded Bernoulli-Euler nanobeams, offering a cost-effective approach for designing and optimizing nano-scaled structures.

Background: Learning arrangements in health care profession education are increasingly taking place in digital environments. VR in nursing education, as one of the digital elements, is the subject of controversial debate. On the one hand it supports the authenticity of case studies by adding realistic perspectives and information. On the other hand, the costs of developing and maintaining software and hardware hinder long-term implementation. Methods: In this paper, we describe our findings and insights from two different research projects focused on the incorporation of digital tools, particularly virtual reality (VR), into nursing education. Based on the German context, our aim is to promote the adoption of innovative digital methods in nursing education and to offer invaluable experiences from the field. Results: Starting with a brief recapitulation of the projects, we then elucidate the pedagogical strategies for embedding VR-driven scenarios in nursing education. Based on our experiences during the projects we identify various positive aspects, such as change of perspective and simulating acute situations. Key findings: Although potential drawbacks remain, we advocate long-term implementation and specific use of VR at the interface between theory and practice. Nevertheless it is crucial to establish regular evaluation observing the value of digitalisation, especially VR; for nursing education.

Power sources that can consistently deliver high levels of energy over extended periods are crucial for various space applications. Among the many methods of energy generation, direct heat-to-electricity conversion without intermediate steps or moving components shows particular promise but also presents significant challenges. The aim of this research is to address these challenges by enhancing the energy efficiency of thermionic conversion, which transforms thermal energy into electrical energy, through the development and utilization of a novel class of emission nanomaterials known as multilayer carbyne-enriched 3D-shaped nano-interfaces. By manipulating the topological, physicochemical, and functional characteristics of these interfaces, we seek to optimize their thermionic conversion capabilities. Building on the recent discovery of collective atomic vibrations called phonon waves within transition domains of multilayer nanostructures, we have devised a new approach to enhance the efficiency of thermionic energy converters. This involves unlocking the predictive functionality of 2D-ordered linear-chain carbon-based multilayer emitters through the excitation and fine-tuning of collective atomic vibrations and nanoarchitecture. To achieve this, we propose employing a combination of techniques that leverage interface effects, enable phonon wave propagation, facilitate energy exchange, and capitalize on synergistic effects offered by the multilayer nano-enhanced interfaces. The resulting devices for thermionic energy conversion have the potential to significantly surpass the efficiency of existing plasma thermionic converters by several orders of magnitude. Moreover, these electricity-generating sources based on thermionic conversion represent fundamental options for the advancement of Lunar and Martian research missions, opening up possibilities for sustainable energy production in extraterrestrial environments.

Recent advances in nanomaterials have been heavily influenced by low-dimensional nanocarbon allotropes. In particular, carbyne has attracted attention for its potential as a true one-dimensional carbon chain with sp1 hybridization. To maximize the capabilities of this material, we employ a focused data-driven inverse design approach based on the carbon nanomaterials genome concept. This involves using deep learning neural network models to identify key descriptors tied to desired properties, enabling property prediction and reverse engineering of nanocarbons. Our iterative approach entails: (i) gathering growth/property data on nanostructures; (ii) identifying informative numerical/categorical predictors; (iii) developing deep learning models mapping descriptors to properties; (iv) refining models with new insights; (v) determining required descriptors/conditions for target properties via inverse mapping; (vi) validating models by synthesizing predicted nanostructures; and (vii) enhancing models with validation data. This allows uncovering hidden growth-property connections, precisely tuning nanocarbons for desired attributes. We introduce new methodologies including exciting synergistic effects, synchronizing atomic vibrations, active screen plasma, energy-driven transformations, surface acoustic micro/nano-manipulation, doping and directed self-assembly to expose relationships and integrate insights into the inverse design flow. This research promises to accelerate discovery of next-generation low-dimensional nanocarbons with exceptional properties and applications.

Industrial Control Systems (ICS) are integral to managing and optimizing processes in various industries, including manufacturing, power generation, and more. However, the scarcity of widely adopted ICS datasets hampers research efforts in areas like optimization and security. This scarcity arises due to the substantial cost and technical expertise required to create physical ICS environments. In response to these challenges, this paper presents a groundbreaking approach to generating synthetic ICS data through a data-driven ICS network simulation. We circumvent the need for expensive hardware by recreating the entire ICS environment in software. Moreover, rather than manually replicating the control logic of ICS components, we leverage existing data to autonomously generate control logic. The core of our method involves the stochastic setting of setpoints, which introduces randomness into the generated data. Setpoints serve as target values for controlling the operation of the ICS process. This approach enables us to augment existing ICS datasets and cater to the data requirements of machine learning-based ICS intrusion detection systems and other data-driven applications. Our simulated ICS environment employs virtualized containers to mimic the behavior of real-world PLCs and SCADA systems, while control logic is deduced from publicly available ICS datasets. Setpoints are generated probabilistically to ensure data diversity. Experimental results validate the fidelity of our synthetic data, emphasizing its ability to closely replicate temporal and statistical characteristics of real-world ICS networks. In conclusion, this innovative data-driven ICS network simulation offers a cost-effective and scalable solution for generating synthetic ICS data. It empowers researchers in the field of ICS optimization and security with diverse, realistic datasets, furthering advancements in this critical domain. Future work may involve refining the simulation model and exploring additional applications for synthetic ICS data.

This paper offers a comprehensive examination of the process involved in developing and automating supervised end-to-end machine learning workflows for forecasting and classification purposes. It offers a complete overview of the components (i.e. feature engineering, model selection, etc), principles (i.e. bias-variance decomposition, model complexity, overfitting, model sensitivity to feature assumptions and scaling, output interpretability, etc), models (i.e. neural networks, regression models, etc), methods (i.e. Cross-Validation, data augmentation, etc), metrics (i.e. Mean Squared Error, F1-score, etc) and tools that rule most supervised learning applications with numerical and categorical data, as well as their integration, automation, and deployment. The end goal and contribution of this paper is the education and guidance of the non-AI expert academic community over complete and rigorous machine learning pipelines and data science practices, from problem scoping to design and state-of-the-art automation tools, including basic principles and reasoning in the choice of methods. The paper delves into the critical stages of supervised machine learning workflow development, many of which are often omitted by researchers due to brevity, and covers foundational concepts essential for understanding and optimizing a functional machine learning workflow, thereby offering a holistic view of task-specific application development for applied researchers who are not AI experts.

In this paper, a buck DC-DC converter with the proposed twin frequency control scheme (TFCS) and accurate load current sensing (ALCS) have been designed and implemented with 0.18µm CMOS technology for a supply voltage ranging from 2.0-3.0V which is compatible with state-of-the-art batteries (NiCd/NiMH: 1.1-2V, Li-Ion: 2.5-4.2V). The proposed converter yields a peak efficiency of about 92.7% with a load current of 30mA. Furthermore, it only occupies a silicon area of 1.3mm2. The proposed buck converter is dedicated for smartphone applications whereby it spends most of its time in idle, low load conditions.

An advanced driver-assistance system (ADAS) is critical to driver-vehicle-interaction systems. Driving behaviour modelling and control significantly improves the global performance of ADAS. A haptic assistive system assists the driver by providing a specific torque on the steering wheel according to the driving-vehicle-road profile to improve the steering control. However, the main problem is designing a compensator dealing with the high-level uncertainties in different driving scenarios with haptic driver assistance, where different personalities and diverse perceptions of drivers are considered. If not properly accounted for, these differences can lead to poor driving performance. This paper focuses on designing a data-driven model-free compensator considering various driving behaviours with a haptic feedback system. A back propagation neural network (BPNN) models driving behaviour based on real driving data (speed, acceleration, vehicle orientation, and current steering angle). Then using the genetic algorithm (GA) and the integral time absolute error (ITEA) criterion to optimize multiple PID compensation parameters for various driving behaviour (such as speeding/braking, lane-keeping and turning), which are then be combined by the fuzzy logic to provide different driving commands. An experiment was conducted with 5 participants in a driving simulator. During the second experiment, seven participants drove in the simulator to evaluate the robustness of the proposed combined GA-PID offline and the Fuzzy-PID controller applied online. The experiment results evaluated the ITAE of lateral displacement and yaw angle, during various driving behaviour. The results validated the proposed method by significantly enhancing the driving performance.

Collaboration among individuals with diverse skills and personalities is crucial in producing high-quality software. The success of any software project depends on the team’s cohesive functionality and mutual complementation. This study introduces a data-centric methodology for forming Software Engineering (SE) teams centred around personality traits. Our study analyzed data from an SE course where 157 students in 31 teams worked through four project phases and were evaluated based on deliverables and instructor feedback. Using the Five Factor Model (FFM) and a variety of statistical tests, we determined that teams with higher levels of extraversion and conscientiousness and lower neuroticism consistently performed better. We examined team member interactions and developed a predictive model using extreme gradient boosting. The model achieved a 74% accuracy rate in predicting inter-member satisfaction rankings. Through graphical explainability, it underscored incompatibilities among members, notably those with differing levels of extraversion. Based on our findings, we introduce a team formation algorithm using Simulated Annealing (SA), built upon the insights from our predictive model and additional heuristics.

The benefits of event-driven architecture (EDA) derive from how systems and components are loosely coupled, which can facilitate independent development and deployment of systems, improved scaling and fault tolerance, and integration with external systems, especially in comparison to monolithic architectures. With the advent of new technologies such as containers, and microservices, a new generation of distributed event streaming platforms are commonly used in event-driven architecture for efficient event-driven communication. However, the asynchronous and distributed nature of EDA poses several problems that include handling failures, the dependence of an end-to-end transaction on individual component stability, etc. In this paper, we describe a new approach to designing self-regulating distributed applications with autopoietic and cognitive workflow management. This approach is based on the new science of information processing structures derived from the General Theory of Information. Just as a genome enables self-organizing and self-regulating biological structures, a digital genome enables a specific software application with several components, the ability to use distributed resources and self-regulate the evolution of the system based on functional and non-functional requirements, and best-practice policies that maintain the stability, safety, and survival under non-deterministic fluctuations in the demand for resources. In addition, cognitive workflow management assures end-to-end transaction delivery.

The article is dedicated to addressing the current challenge of augmenting the load- bearing ca-pability of pile foundations. This predicament is most effectively addressed by employing piles with unconventional geometries, along with atypical methodologies for installing these foun-dation piles. The primary objective of research is to examine the impact of various fill materials (including both soil and rigid substances) on the energy consumption during pile driving and the resistance to static loads by piles with multiple shaft expansions. The findings derived from model-based investigations demonstrate that the load-bearing capacity of piles with shaft ex-pansions, installed with bulk material filling, surpasses that of conventional piles (prismatic and pyramidal) by a factor ranging from 1.5 to 4.6. Furthermore, the research outcomes also indicate greater energy consumption (1.14-1.66 times) and enhanced load-bearing capacity (1.15-1.68 times) for piles with shaft expansions driven with backfill, in comparison to piles installed without backfill. It is noteworthy that the type of backfill material significantly influences the energy consumption during pile driving and their stability under axial static loads. The correla-tion relationships can be applied to approximate projection of the energy-related and structural parameters of piles with shaft expansions embedded with the addition of bulk materials.

The Genji firefly is a key symbol of the conservation and management of rural environments. The species lives underwater as larvae, in the soil when they pupate, and emerges on the ground as adults. Because of the large differences in habitat conditions between larvae and adults, the habitat requirements of the Genji firefly must be clarified over a wide range, from the instream to the surrounding riparian environment. In this study, we built a habitat suitability model for the Genji firefly by relating local landscape features to the number of individuals surveyed in a small river in an agricultural watershed. For habitat suitability modelling, tree-based machine learning methods, namely Random Forests (RF) and classification and regression trees (CART), are used to model species distributions and extract quantitative information on their ecological characteristics. Because the model performance on the test dataset was poor, we removed noisy absences using model outputs from the RF computation using the entire dataset. As a result, we observed improved model performance, in which there seems to be an optimal level for removing noisy absences. Variable importance and partial dependence plots were then used to visualize the relationship between the local landscape features and habitat suitability of the adult firefly. Detailed information on landscape features extracted using RF and CART is useful for a deeper understanding of the ecology of the Genji fireflies.

Hexakis(2-alkoxy-1,5-phenyleneimine) macrocycles were synthesized using a simple one−pot procedure through by precipitation−driven cyclization. The acetal−protected AB−type monomers, 2-alkoxy-5-aminobenzaldehyde diethyl acetals, underwent polycondensation in water or acid−containing tetrahydrofuran. The precipitation−driven cyclization based on imine dynamic covalent chemistry and π−stacked columnar aggregation played a decisive role in the one−pot synthesis. The progress of the reaction was analyzed by MALDI−TOF mass spectrometry. The macrocycles with alkoxy chains were soluble in specific organic solvents such as chloroform, allowing their structures to be analyzed by NMR. The shape−anisotropic, nearly planar, and persistent macrocycles aggregated into columnar assemblies in polymerization solvents, driven by aromatic π−stacking. The octyloxylated macrocycle OcO−Cm6 exhibited an enantiotropic columnar liquid crystal−like mesophase between 165°C and 197°C. In the SEM image of (S)-(−)-3,7-dimethyloctyloxylated macrocycle (−)BCO-Cm6, columnar substances with a diameter of 100−200 nm were observed. The polymerization solution for 2-(2-methoxyethoxy)ethoxy)ethoxylated macrocycle (TEGO−Cm6) gelled and showed thixotropic properties by forming a hydrogen bond network.

Eutrophication is a major environmental issue with many negative consequences, such as hypoxia and harmful cyanotoxins production. Monitoring coastal eutrophication is a crucial, especially for island countries like the Republic of Cyprus, which are economically dependent on the touristic sector. Additionally, the open-sea aquaculture industry in Cyprus has been exhibiting an increase in the last decades and environmental monitoring to identify possible signs of eutrophication is mandatory according to the legislation. Therefore, in this modelling study, two different types of Artificial Neural Networks (ANNs) are developed based on in situ-data collected from stations located in the coastal waters of Cyprus. Theses ANNs aim to model the eutrophication phenomenon based on two different data-driven modelling procedures. Firstly, the self-organizing map (SOM) ANN examines several water quality parameters (specifically water temperature, salinity, nitrogen species, ortho-phosphates, dissolved oxygen and electrical conductivity) interactions with the Chlorophyll-a parameter. The SOM model enables us to visualize the monitored parameters relationships and to comprehend complex biological mechanisms related to Chlorophyll-a production. A second feed-forward ANN model is also developed for predicting the Chlorophyll-a levels. Based on this ANN model, several scenarios associated to the eutrophication-related water quality parameters can be extracted. The combination of these two ANNs models is considered a holistic modelling approximation for the identification of eutrophication scenarios, since it enables not only the prediction of the Chlorophyll-a parameter levels, but also the “capturing” of hidden biological mechanisms associated with algal production.

The migration from MATLAB to Python (M-to-PY) has gained significant traction in recent computational research. While MATLAB has long served as a linchpin in myriad scientific endeavors, there's an emerging trend to rejuvenate these projects using Python's extensive AI tools and libraries. This study presents a semi-automated process for M-to-PY conversion, using a detailed case study of an image skeletonization project comprising fifteen MATLAB files and a 1404-image dataset. Skeletonization is foundational for ongoing 3D motion detection research using AI transformers, predominantly developed in Python. The utilization of ChatGPT-4, acting as an AI co-programmer, is pivotal in this conversion. By leveraging the public OpenAI API, we developed an M-to-PY converter prototype, evaluated its efficacy using test cases from the Bard bot, and subsequently employed the converted code in an AI application. The dual contributions encompass a well-tested M-to-PY converter and a Skeleton App capable of sketching and skeletonizing any given image, enriching the AI toolset. This study accentuates how AI resources, like ChatGPT-4, can simplify code transitions, opening doors for innovative AI implementations using primarily MATLAB-coded scientific research.

This paper argues that popular misrepresentation of the nature of AI has important consequences concerning how it should be regulated. Viewing AI as something that exists in itself, rather than as a set of cognitive technologies whose characteristics – physical, cognitive, and systemic – are quite different from ours (and at times from each other) leads to inefficient approaches to regulation. It limits our ability to anticipate the consequences of its foreseeable developments and social diffusion. It undermines attempts to protect ourselves from the social and political dangers it presents. After a short introduction, section 2 retraces rapidly the history of the idea that intelligence is essentially a quality, one that AI shares with human intelligence and that this resemblance trumps the differences that exist between artificial and natural cognitive systems. Section 3 reviews two approaches to the dangers of AI that reflect the illusion that AI exists in itself. Section 4 turns to the proper understanding of regulations and what is their main goal and purpose. Section 5 analyses three central characteristics of artificial cognitive systems which section six compares with corresponding characteristics of natural cognitive systems. Finally, section 7 draws some conclusions regarding how we should regulate artificial intelligence.

Digital filtering is a fundamental technique in digital signal processing, which operates on a digital sequence without any information on how the sequence was generated. This paper proposes a methodology for designing the equivalent of digital filtering for neuromorphic samples, which are a low power alternative to conventional digital samples. In the literature, filtering using neuromorphic samples is done by filtering the reconstructed analog signal, which is required to belong to a predefined input space. We show that this requirement is not necessary, and introduce a new method for computing the neuromorphic samples of the filter output directly from the input samples, backed by theoretical guarantees. We show numerically we can achieve a similar accuracy compared to the conventional method. However, given that we bypass the analog signal reconstruction step, our results show significantly reduced computation time for the proposed method and good performance even when signal recovery is not possible.

This study presents the manufacturing process driven development of an interlocking metasurface mechanism (ILM) for Fused Filament Fabrication (FFF) with a focus on open-source accessibility. The presented ILM is designed to enable strong contact between to planar surfaces. The mechanism consists of spring elements and locking pins which snap together when forced into contact. The mechanism is designed to optimize mechanical properties, functionality and printability with common FFF printers. The mechanisms is printed from thermoplastic polyurethane (TPU) filament which was selected for its flexibility, crucial for spring element performance and tolerances of the fabrication method. To characterize the designed mechanism a tensile test is carried out to assess the holding force of the ILM. The force-displacement profiles are analyzed and categorized into distinct phases, highlighting the interplay between spring deformation, sliding, and disengagement. The results show variations in holding forces attributed to geometric and material-related factors. The testing results are compared and discussed to a numerical simulation carried out with a frictionless approach with a nonlinear Neo–Hookean material law. The study underscores the importance of meticulous parameter control in 3D printing for consistent and reliable performance of interlocking metasurface mechanisms. The investigation leads to a scalable model of a ILM element pair with a distinct three-phase snapping characteristics ensuring reliable holding capabilities.

The effect of neutron irradiation of Si-Ge/Si film structures obtained by gas-phase epitaxy on the processes of charge carrier generation and the magnitude of the dark short-circuit current and open-circuit voltage caused by it is studied, it is shown that their magnitude practically does not change with time. The results obtained indicate that all types of structural defects, both associated with dopant impurities and radiation exposure, contribute to the generation of charge carriers in heated silicon structures with the participation of deep energy levels, which indicates the applicability of neutron processing of silicon materials to create thermal energy converters, operating at high temperatures.

This study proposes self-calibration of capacitor mismatch errors for high-resolution pipeline analog-to-digital converters (ADCs). The proposed calibration circuit recursively amplifies the capacitor mismatch error by re-utilizing a multiplying digital-to-analog converter in a pipeline stage without increasing the circuit complexity, and the amplified error voltage is converted into digital code by utilizing the remaining pipeline stages. Error correction is performed by subtracting the digital code from the ADC output during normal operation. A prototype of a 12-bit pipeline ADC is fabricated in a 0.18 µm standard CMOS process. The ADC comprises eight 1.5-bit stages, followed by a 4-bit flash ADC as the final stage; the capacitor mismatch errors in the first two pipeline stages are corrected by utilizing the proposed self-calibration technique. At a sampling rate of 30 MS/s, the measured differential and integral nonlinearities improve from +0.82/-0.75 and +1.12/-1.79 to +0.45/-0.41 and +0.47/-0.91 after calibration, respectively. Further, the measured dynamic performances improve significantly with calibration. For an input frequency of 2.09 MHz, the ADC achieves a spurious-free dynamic range (SFDR) and signal-to-noise and distortion ratios (SNDR) of 69.3dB and 63.9dB before calibration, respectively. After calibration, the SFDR and SNDR increase to 84.1dB and 68.9dB, respectively, and the effective number of bits is approximately 11.1bit. The power consumption of the ADC is 35mW under 1.8V supply, and the resulting figure of merit is 514fJ/conversion step.

Process safety plays a vital role in the modern process industry. To prevent undesired accidents caused by malfunctions or other disturbances in complex industrial processes, considerable attention has been paid to data-driven fault detection techniques. To explore the underlying manifold structure, manifold learning methods including Laplacian eigenmaps, locally linear embedding, and Hessian eigenmaps have been utilized in data-driven fault detection. However, only the partial local structure information is extracted from the aforementioned methods. In this paper, these typical manifold learning methods are synthesized to find the underlying manifold structure from different angles. A more comprehensive local structure is discovered under a unified framework by constructing an objection optimization function for dimension reduction of the process data. The proposed method takes advantage of different manifold learning methods. Based on the proposed dimension reduction method, a new data-driven fault detection method is developed. Hotelling’s T2 and Q statistics are established for the purpose of fault detection. Experiments on an industrial benchmark Tennessee Eastman process and a real blast furnace ironmaking process are carried out to demonstrate the superiority and effectiveness of the proposed method.

The paper investigates into the intricacies of semiconductor manufacturing, a highly complex process entailing a wide array of subprocesses and diverse equipment. Semiconductors are miniaturized integrated circuits comprising numerous components. The semiconductor manufacturing process begins with the thin disc-shaped silicon wafers. On each wafer, up to thousands of identical chips can be prepared depending upon the diameter of the wafer to build up the circuits layer by layer in a wafer fab. The size of the semiconductors requires a high number of units to be produced, thus necessitating a large amount of data to control for improving the semiconductor manufacturing process. Therefore, the collection and analysis of the equipment data, process data, and machine history data throughout the manufacturing process are required to diagnose faults, monitor the process, and manage the manufacturing process effectively. This research is focused on improving the semiconductor manufacturing process through a rigorous analysis of collected manufacturing process data, employing statistical process control (SPC), data mining techniques, and data-driven decision models. The project's primary objective is to increase the manufacturing process stability and productivity by utilizing the latest data-driven technologies in the scientific community. A structured review was undertaken, exploring contemporary data-driven methodologies in semiconductor manufacturing process improvement, specifically pertaining to process capability, product yield rate, and process stability. This review accentuates a comprehensive evaluation of data-driven methodologies applicable to conventional semiconductor manufacturing facilities, aiming to drive substantial process improvements. It features a detailed demonstration facilitating the selection of optimal semiconductor manufacturing processes to enhance overall operational performance. This study of process improvement in the semiconductor manufacturing steps through the application of data-driven methodologies will be effective in delivering advanced, real-time, and proactive control decisions throughout the manufacturing facilities. This endeavor is expected to promptly provide critical insights for enterprise manufacturing decision-makers to reduce manufacturing cycle time, improve the product yield rate, and increase the overall efficiency of the manufacturing process.

The ankle is a complex joint with a high injury incidence. Rehabilitation Robotics applied to the ankle is a very active research field. We present cable-driven reconfigurable robot kinematics and statics. We studied how the tendons pull mid-foot bones around the talocrural and subtalar axes. Likewise, we propose a hybrid serial-parallel mechanism analogous to the ankle. Then, using screw theory, we synthesized a cable-driven robot with the human ankle in the closed-loop kinematics. We incorporate a draw-wire sensor to measure the axes’ pose and compute the product of exponentials. We reconfigure the cables to balance the tension and pressure forces using the axis projection on the base and platform planes. Likewise, we also computed the workspace to show that the reconfigurable design fits several sizes. The data used is from anthropometry and statistics. Finally, we validated the robot’s statics with MuJoCo for various cable length groups corresponding to the axes’ range of motion. We suggested a platform adjusting system and an alignment method. The design is lightweight, and the cable-driven robot has advantages over rigid parallel robots, such as Stewart platforms. We will use compliant actuators.

The main objective of the paper is to provide a thorough analysis of currently used modulation control schemes for single phase bidirectional dual active bridge DC/DC converter. In this article, it will be shown that single phase shift, extended phase shift and Dual phase shift modulation schemes are special cases of triple phase shift (TPS) modulation scheme. The article aims to highlight six TPS switching modes and their complements with the operational constraints. Unlike previous studies that regarded TPS as a complex scheme, this paper simplifies the analysis of each mode and aims to standardize the understanding of TPS modulation in DAB converters. Power equations, range of power transferred and zero voltage switching (ZVS) are derived for all the modes under TPS without assuming fundamental component analysis. Additionally, a generic optimization algorithm is developed to show the advantages of TPS modulation and thus, detailed analysis presented in this paper provides valuable insights for single phase DAB converter designers in identifying a wide range of optimization algorithms to achieve higher efficiency under TPS modulation. This analysis contributes to the advancement of bidirectional DAB converter technology and facilitates its application in various power electronic systems.

The rapid adoption of hydrogen as an eco-friendly energy source has necessitated the development of intelligent power management systems capable of efficiently utilizing hydrogen resources. However, guaranteeing the security and integrity of hydrogen-related data has become a significant challenge. This paper proposes a pioneering approach to ensure secure hydrogen data analysis through the integration of blockchain technology, enhancing trust, transparency, and privacy in handling hydrogen-related information. By combining blockchain with intelligent power management systems, the efficient utilization of hydrogen resources becomes feasible. The utilization of smart contracts and distributed ledger technology facilitates secure data analysis, real-time monitoring, prediction, and optimization of hydrogen-based power systems. The effectiveness and performance of the proposed approach are demonstrated through comprehensive case studies and simulations. Notably, our prediction models, including ABiLSTM, ALSTM, and ARNN, consistently delivered high accuracy with MAE values of approximately 0.154, 0.151, and 0.151, respectively, enhancing the security and efficiency of hydrogen consumption forecasts. The blockchain-based solution offers enhanced security, integrity, and privacy for hydrogen data analysis, thus contributing to the advancement of clean and sustainable energy systems. Additionally, the research identifies existing challenges and outlines potential future directions for further enhancing the proposed system. This study adds to the growing body of research on blockchain applications in the energy sector, with a specific focus on secure hydrogen data analysis and intelligent power management systems.

Deep Neutral Networks (DNNs) were initially proposed towards the midst of the 20th century, motivated by the neural structure and mechanism of the human brain. Thanks to major ad-vancements in computational resources, during the past decade DNN-based systems have demonstrated unprecedented performance in terms of accuracy and speed. However, recent work has shown that such models may not be sufficiently robust during the inference process. Furthermore, due to the data-driven learning nature of DNNs, designing interpretable and gen-eralizable networks is a major challenge, especially in critical applications, such as medical Computer Aided Diagnostics (CAD) and other medical imaging tasks, including classification, regression and reconstruction. Within this context, a line of physics-driven approaches for deep learning has recently emerged, aimed at improving the stability and generalization capacity of DNNs for medical imaging applications. In this paper, we review recent work focused on phys-ics-driven or prior-information learning for a variety of imaging modalities and medical applica-tions. We discuss how the inclusion of domain-related knowledge into the learning process and networks’ design supports their stability and generalization capability. In addition, we highlight current and future challenges within this scope.

In the wake of rapid advancements in artificial intelligence (AI) and sensor technologies, a new horizon of possibilities has emerged across diverse sectors. Livestock farming, a domain often sidelined in conventional AI discussions, stands at the cusp of this transformative wave. This paper delves into the profound potential of AI and sensor innovations in reshaping animal welfare in livestock farming, with a pronounced emphasis on a human-centric paradigm. Central to our discourse is the symbiotic interplay between cutting-edge technology and human expertise. While AI and sensor mechanisms offer real-time, comprehensive, and objective insights into animal welfare, it's the farmer's intrinsic knowledge of their livestock and environment that should steer these technological strides. We champion the notion of technology as an enhancer of farmers' innate capabilities, not a substitute. Our manuscript sheds light on: Objective Animal Welfare Indicators: An exhaustive exploration of health, behavioral, and physiological metrics, underscoring AI's prowess in delivering precise, timely, and objective evaluations. Farmer-Centric Approach: A focus on the pivotal role of farmers in the adept adoption and judicious utilization of AI and sensor technologies, coupled with discussions on crafting intuitive, pragmatic, and cost-effective solutions tailored to farmers' distinct needs. Ethical and Social Implications: A discerning scrutiny of the digital metamorphosis in farming, encompassing facets like animal privacy, data safeguarding, responsible AI deployment, and potential technological access disparities. Future Pathways: Advocacy for principled technology design, unambiguous responsible use guidelines, and fair technology access, all echoing the fundamental principles of human-centric computing and analytics. In essence, our paper furnishes pioneering insights at the crossroads of farming, animal welfare, technology, and ethics. It presents a rejuvenated perspective, bridging the chasm between technological advancements and their human beneficiaries, resonating seamlessly with the ethos of the Human-Centric Intelligent Systems journal. This comprehensive analysis thus marks a significant stride in the burgeoning domain of human-centric intelligent systems, especially within the digital livestock farming landscape, fostering a harmonious coexistence of technology, animals, and humans.

Plasma generation by means of electrical discharge requires specialized power supply systems. The applicability of plasma for various plasma processes depends on its parameters, and these in turn depend on the parameters of the power supply systems. Arc plasma can be unstable, generating a lot of electromagnetic interference and overvoltage and overcurrent. The power system of a plasma reactor must guarantee good plasma control characteristics, be immune to disturbances and ensure good cooperation with the power grid. The article analyzes the cooperation of a three-phase plasma reactor, with gliding arc discharge, with a power supply system of a new type. This system integrates an AC/DC/AC converter with a five-column transformer of special design in a single device. Using the properties of magnetic circuits, it was possible to integrate the functions of ignition and sustaining the burning of the discharge in the reactor in a single transformer. Proper design of the transformer is crucial to achieve good cooperation of the AC/DC/AC converter with both the plasma reactor and the power supply network. The presented power supply design shows a number of positive features predisposing it to powering arc plasma reactors.

Induction heating (IH) technology is widely recognized and utilized in residential applications due to its high efficiency and safe operating characteristics. Resonant inverter circuits are widely used in IH systems because of their high efficiency and ability to perform soft-switching. Among the various resonant inverters used in IH systems, the single-switch quasi-resonant (SSQR) inverter topology is typically preferred for low-cost and low-output-power applications. Despite its cost advantage, the SSQR topology has a relatively narrow soft-switching range, which can be unstable depending on the electrical parameters of the load and the resonant converter circuit. Accurately determining the capacitance value of the resonant capacitor and the inductance value of the induction coil, which are the key circuit elements of the SSQR induction cooker, is crucial for designing a reliable, efficient, and durable cooking system. In other words, there exists a critical relationship between the resonant converter circuit parameters, load characteristics, and safe operating conditions. Additionally, when considering closed-loop control methods used for power control and safety, selecting appropriate resonant circuit elements becomes vital in ensuring both reliable and efficient operation. This paper focuses on a novel and simplified design method for the SSQR inverter utilized in household appliances. The proposed method and its advantages in terms of the safe operating area of the switch are theoretically investigated and verified through simulations and prototype circuits.

This study delves into the dynamic intersection of artificial intelligence (AI) and English Language Teaching (ELT) within the post-pandemic educational landscape. Grounded in Constructivism, Connectivism, and Technological Pedagogical Content Knowledge (TPACK), this research explores how AI integration impacts language instruction efficiency, benefits and challenges perceived by St. Michael's College students, differences in learning outcomes, and its potential to address the digital divide. Employing a qualitative approach, the study employs a rigorous method that includes intensive in-depth reviews of literature, maximum purposeful sampling, informal interviews, observation, and thematic analysis. Through these methods, data was collected from 30 participants, leading to the identification of five key themes. Findings suggest that AI enhances instructional efficiency, offers personalized learning experiences, and bridges gaps in remote settings. However, challenges and the importance of pedagogical balance emerged. The study underscores the need for adaptive pedagogical strategies to fully harness AI's potential. Future directions and limitations are discussed, emphasizing the study's implications for shaping the future of ELT.

Water quality assessments are crucial for human health and environmental safeguards. The utilization of a subset of artificial intelligence such as Machine Learning (ML) presents significant impacts to enhance the prediction and classification of water quality. In this research, a set of diverse ML algorithms was evaluated to handle a comprehensive dataset of water quality measurements over an extended period. The aim was to develop a robust approach for accurately forecasting water quality. This approach employed machine learning classifiers such as Logistic Regression (LR), Support Vector Machine (SVM), Stochastic Gradient Descent (SGD), K-Nearest Neighbors (KNN), Gaussian Process Classification (GPC), Gaussian Naive Bayes (GNB), Random Forest (RF), Decision Tree (DT), XGBoost, and Multilayer Perceptron (MLP). The water quality parameters assessed for pH, hardness, solids, chloramines, sulfate, conductivity, organic carbon, trihalomethanes and turbidity. The XGBoost model exhibited the highest accuracy of 89.47% among the classifiers and Stacked Ensemble Classifiers (SEC) improved the prediction further to 92.98%. The findings suggest that XGBoost and the SEC hold promise as reliable approaches for water quality assessments in contrast of artificial intelligence.

Amidst the dynamic landscape of education, this paper embarks on a transformative journey that explores the integration of Artificial Intelligence (AI) in literature and language education. Through a qualitative exploratory approach, purposeful and maximum variation sampling method, the researcher delves into the experiences of educators and learners, unraveling the multifaceted dimensions of AI-driven pedagogy in SMC. Drawing upon a theoretical framework encompassing Constructivism, Connectivism, and Socio-Cultural Theory, the study examines AI's role in reshaping pedagogical landscapes, fostering critical thinking, and expanding access to education. The results resonate with participants' narratives, unveiling AI's potential in fostering personalized learning pathways, enhancing collaborative engagement, and bridging digital divides. Ethical considerations in AI education are explored, leading to the proposition of responsible guidelines. These findings underline the transformative power of AI in education, aligned with the institution's commitment to quality education and innovation. Overall, the study calls for ongoing exploration, emphasizing the qualitative research method's depth and richness, as well as the theoretical framework's guidance in realizing the study's objectives. As AI continues to shape education's frontiers, this research underscores the significance of adaptability, ethical awareness, and holistic engagement for school administrators, educators, learners, and researchers alike.

This study investigates the nonlinearities in three-phase inverters for SiC-based systems and compares their performance to IGBT-based systems. An analytical model of inverter voltage distortion is developed, which accounts not only for dead time (td), switching delay time, switching frequency (fs), and voltage drops of power devices, but also for output parasitic capacitance (Cout). Experimental tests validate the model, which provides a more accurate estimate of the inverter’s output phase voltage distortion. The power device characteristics are obtained from datasheets, while Cout is determined through experimentation. Three-phase inverters with varying switching frequencies, fundamental frequencies, and dead-time values are used in simulations and experiments to determine the influence of nonlinearity on phase voltage deviation and current distortion. The results show that, due to SiC devices’ faster switching time, the phase voltage deviation and phase current distortion are lower in SiC-based inverters than in IGBT-based ones for high-frequency applications, as the dead time can be reduced.

This paper proposes a novel symmetrical active-switched coupled-inductor high-step-up DC/DC converter. A new active-switched coupled inductor is used to allow much higher voltage gain of the proposed converter. The symmetrical circuit structure of the proposed converter evenly divides large input currents and reduce input current ripple by operating in interleaved mode so power density is increased. The switches also have low voltage stress. The operating principle and design considerations for the converter topology are described. The proposed converter with an input voltage of 40 V, an output voltage of 400 V and an output of 1000 W at a switching frequency of 50 kHz is implemented. The simulations and experimental results are thus depicted to verify the theoretical analysis. The highest efficiency is 95.5% at 200 W measured by using a HIOKI 3390. Consequently, the proposed converter is suitable for renewable energy generation systems that require high power, high voltage gain and high efficiency.

The COVID-19 led to changes modifying the role of digital transformation and unleashing unparalleled opportunities to improve business production processes in all three dimensions of sustainability - economic, social, and environmental through data-driven innovative service implications that significantly impact restaurant business, resulting in an increased competitive advantage. This paper identifies the innovative service implications and summarizes sustainable social systems measures in the context of strategic management in the restaurant industry triggered by COVID-19. Based on in-depth interviews with sixteen key experts in the restaurant industry, the paper clarifies and concretizes the rapidly transforming problem of identifying the main changes in the restaurant business, using three dimensions: consumer demand, corporate strategy optimization, and the use of the company's innovative potential. The findings reveal how companies need to expand their digital strategies and adapt their management approach to all stakeholders bringing sustainable business development in the post-pandemic period into focus. This paper serves as a framework for understanding the digital transformation role in resilient business management within restaurant social systems, with friction occurring along the workflow from kitchen to customer.

Ultrasonic-guided lamb wave approach is an effective non-destructive testing (NDT) method used for detecting localized mechanical damage, corrosion, and welding defects experienced in metallic pipelines. Signal processing of the guided waves is often challenged due to the complexity of operational conditions and environments in the pipelines. Machine learning approaches in recent years, including convolutional neural networks (CNN) and long short-term memory (LSTM), have exhibited their advantages to overcome these challenges for signal process and data classification of complex systems, thus great potential for damage detection of critical oil/gas pipeline structures. In this study, a CNN-LSTM hybrid model is utilized for decoding ultrasonic guided waves for damage detection of metallic pipelines, and twenty-nine features are extracted as input to classify different types of defects from metallic pipes. The prediction capacity of the CNN-LSTM model is assessed by comparing it to the CNN and LSTM. The results demonstrate that the CNN-LSTM hybrid model exhibits much higher accuracy, with 94.8%, as compared to those of CNN and LSTM. Interestingly, the results also reveal that predetermined features, including time-, frequency-, and time-frequency domains, could significantly improve the robustness of the deep learning approaches, even though the deep learning approaches are often believed they include automated feature extraction, without the hand-crafted steps as the shallow learning do. Furthermore, the CNN-LSTM model displays higher performance when the noise level is relatively low (e.g., SNR=9 or higher), as compared to the other two models, but its prediction drops gradually with the increase of the noise.

Electricity demand forecasting plays a significant role in energy markets. Accurate prediction of electricity demand is the key factor to optimize power generation, consumption, saving energy resources, and determining the energy prices. However, integrating energy mix scenarios, including solar and wind power which are highly non-linear and seasonal, into an existing grid increases uncertainty in generation, adds the challenges for precise forecast. To tackle these challenges, state-of-the-art methods and algorithms have been implemented in literature. We have developed Artificial Intelligence (AI) based deep learning models that can effectively handle the information of long time-series data. Based on the pattern of dataset, four different scenarios were developed and two best scenarios were selected for prediction. Dozens of models were developed and tested in deep AI networks. In the first scenario (Scenario1), data for weekdays excluding holidays was taken and in the second scenario (Scenario2) all the data in the basket was taken. Remaining two scenarios, weekends and holidays were tested and neglected because of their high prediction error. To find the optimal configuration, models were trained and tested within a large space of alternatives called hyper-parameters. In this study, an Aritificial Neural Network (ANN) based Feed-forward Neural Network (FNN) showed the minimum prediction error for Scenario1 while a Recurrent Neural Network (RNN) based Gated Recurrent Network (GRU) showed the minimum prediction error for Scenario2. While comparing the accuracy, the lowest MAPE of 2.47% was obtained from FNN for Scenario1. When evaluating the same testing dataset (non-holidays) of Scenario2, the RNN-GRU model achieved the lowest MAPE of 2.71%. Therefore, we can conclude that grouping of weekdays as Senario1 prepared by excluding the holidays provides better forecasting accuracy compared to the single group approach used in Scenario2, where all the dataset is considered together. However, Scenario2 is equally important to predict the demand for weekends and holidays.

Power converter technologies have become increasingly vital in various applications due to their efficient management of electrical energy. With the growing prominence of renewable energy sources like solar and wind, the high penetration of power electronic converters has been justified. However, ensuring power quality has emerged as a significant challenge for grid-connected power converters. The divergence from the ideal sinusoidal waveform in terms of magnitude and frequency impacts both grid-side currents and voltages. Several studies have proposed solutions to address power quality issues at the load side. The advancement of power converters has been fueled by the development of high-performance microprocessors and the emergence of high-speed switching devices, such as SiC-MOSFETs. This paper focuses on the design of voltage source converters, particularly those based on SiC-MOSFET semiconductor devices. The article presents the design of H-Bridge cells, discusses two-level voltage source converters based on cascade H-Bridge cells in parallel configuration with experimental fault analysis, addresses the seven-level voltage source converters topology, and explores the design and experimental results of the matrix converter. The findings underscore the importance of considering the entire converter design for improved performance at high switching frequencies. The article concludes by summarizing the main outcomes and implications of this research.

The process of building microgrids on top of existing passive distribution networks warrants a multi-criteria analysis. Besides the calculation of investment outlays needed for the modernization of distribution networks, such an analysis covers an assessment of the technological and economic effects of building microgrids. The resulting effects depend on the topology and configuration of distribution networks, specific microgrid features, the choice of current type for the entire microgrid or its individual parts, the ways of connecting distributed energy resources (DER), the availability and condition of information and communications technology (ICT) infrastructure, and other factors. Complete input data allow one to design an optimal microgrid configuration, but the main technological and economic effects are determined by the algorithms of operation and the parameters setting of the automatic control system (ACS) and the protection system. Known approaches to designing microgrids focus on addressing basic tasks while minimizing investment required for their implementation. The above is fully justified when constructing new microgrids, but building microgrids on top of existing distribution networks, given the uniqueness of their topology and configuration, does not allow the use of standardized solutions. The development of approaches to the design of microgrids under such constraints, with minimized investment for the modernization of existing distribution networks, is an urgent task. The use of different types of current for microgrid segments determines the choice of particular ACS and protection system, which depends on the availability of information and communications technology infrastructure. This article contributes a review of approaches to designing AC and AC-DC microgrids to maximize their technological and economic effects. We review techniques for analyzing existing distribution networks aimed at choosing the type of current for the entire microgrid or its individual parts, the optimal connection points of microgrids to distribution networks, the mix and capacity of DERs, with such choices informed by the condition of switching devices and information and communications technology infrastructure. The article presents the results of the analysis of approaches to choosing the optimal configuration of microgrids, microgrid ACS and protection system, with an evaluation of the technological and economic effects subject to minimization of investment for the modernization of existing distribution networks.

The work focuses on the possibility of application of tunable magnetic devices in electrical power systems with adaptive features. The idea underlying this type of operation, which depends on interaction with magnetic fluxes in a quassi-linear range of magnetic core characteristics, is a new approach to the design of magnetic elements. The suitable examples of adaptive electrical power systems can be devices for improving the quality of electricity. When used in compensators of reactive power or both reactive and distortion power, tunable magnetic devices offer much wider possibilities for the compensation, compared to “standard“ solutions, involving compensators based on fixed inductors. Nevertheless, the application of the proposed device in electrical systems such as is only an example of its possible implementations in the area of electrical power. In this work the following issues are covered: exemplary solution of an adaptive ‘passive’ compensator, rules for the operation of tunable magnetic device, and results investigation of the laboratory model of an electrical system based on such device.

In econometrics and finance, volatility modelling has long been a specialised field for addressing a variety of issues pertaining to the risk and uncertainties of an asset. This study presents a robust framework, through a step-by-step design, that is relevant for effective Monte Carlo simulation (MCS) with empirical verifications to estimate volatility using the Generalized Autoregressive Score (GAS) model. The framework describes an organised approach to the MCS experiment that includes "background (optional), defining the aim, research questions, method of implementation, and summarised conclusion". The method of implementation is a workflow that consists of writing the code, setting the seed, setting the true parameter a priori, data generation process, and performance assessment through meta-statistics. Among the findings, it is experimentally demonstrated in the study that the GAS model with a lower unconditional shape parameter value can generate a dataset that adequately reflects the behaviour of financial time series data, relevant for volatility estimation. This dynamic framework is intended to help interested users on MCS experiments utilising the GAS model for reliable volatility calculations in finance and other areas.

The automotive industry is continuously seeking innovative solutions to improve the energy efficiency and sustainability of vehicles. Among the various energy-consuming systems in automobiles, air conditioning plays a crucial role. However, conventional vapour compression-based air conditioning systems are known for their high energy consumption and environmental impact. In recent years, adsorption technology has gained significant attention as a promising alternative due to its potential for higher energy efficiency and reduced greenhouse gas emissions. This review paper presents a comprehensive analysis of advancements in adsorption air conditioning technology specifically designed for automotive applications. The paper examines the recent research and development efforts in enhancing the performance and practicality of these systems, encompassing areas such as advanced materials selection, and system integration strategies. Furthermore, this review highlights the benefits and challenges associated with implementing adsorption air conditioning systems in vehicles. The environmental impact and potential for waste heat recovery are also discussed. Additionally, emerging trends, such as hybridization with conventional vapour compression systems, are explored to further optimize the energy efficiency and sustainability of automotive air conditioning. By critically evaluating the existing literature and research advancements, this review provides insights into the state-of-the-art thermally driven technologies for automotive applications.

This work analyzes a new indoor laboratory data set looking at early fire indicators in controlled and realistic experiments representing different incipient fire scenarios. The experiments were performed within the confines of an indoor laboratory setting using multiple distributed sensor nodes on different room positions. Each sensor node collected data of particulate matter (PM), volatile organic compounds (VOC), CO, CO2, H2, UV, air temperature and humidity in terms of a multivariate time series. These data hold immense value for researchers within the machine learning and data science communities who are keen on exploring innovative advanced statistical and machine learning techniques. They serve as a valuable resource for the development of early fire detection systems. The analysis of the collected data was carried out in dependence of the manhattan distance between the fire source and the sensor node. We found that especially larger particles (&gt; 0.5 μm) and VOCs show a significant dependency with respect to the intensity as a function of the manhattan distance to the source. Moreover, we observed differences in the propagation behavior of VOCs, PM, and CO, which are particularly relevant in incipient fire scenarios due to the presence of strand popagation effects.

This paper focuses on quantifying the economic and financial viability of NB-IoT and LoRaWAN technologies, two low-power wide-area network (LPWAN) technologies with unique characteristics that make them suitable for IoT applications. The purpose of the study is to propose an artefact for performing life cycle cost analysis and demonstrate its application to these technologies. The methodology uses pragmatic computational tools to facilitate the analysis and considers all relevant economic and financial factors, such as operating costs, equipment costs, and revenue potential. The main finding of the study is that NB-IoT and LoRaWAN technologies have different cost structures and revenue potentials, which may affect their economic and financial viability for different IoT applications. Ultimately, the study concludes that a comprehensive life cycle cost analysis is critical to making informed decisions about technology adoption, and that the proposed methodology can be applied to other IoT technologies to gain insight into their economic and financial viability.

Ocean wave energy is a significantly well-liked renewable energy source. The Internet of Things (IoT) offers a great opportunity for development of the ocean wave energy research. The IoT technology significantly enhanced the performance of the ocean wave energy converter (WEC). The data obtained from IoT devices reduce the risk of using WEC. However, Integrating IoT devices with the WEC is a daunting task because controlling the WEC needs real-time data. This paper investigated the performance of WEC and estimate the lifetime of the modules. IoT devices integrated with various sensors determine different parameters and conditions of the ocean. The IoT platform is used for wireless voltmeters, wireless AC current meters, wireless voltage detection, wireless resistance sensors, wireless temperature sensor, wireless accelerometer, wireless pressure meters, and wireless water level sensors to determine the real-time condition of the ocean wave. These devices are also proven to be reliable and accurate.

Variations in groundwater levels that are noticed in different parts of the world are typically the primary focus of research carried out in hydrology. The fluctuating levels of groundwater can be ascribed to several different factors, some of which include an increase in the demand for water, inappropriate irrigation practices, improper management of soil, and unregulated extraction from aquifers. It is necessary to effectively manage groundwater resources to have a trustworthy method of measuring and forecasting groundwater levels. The dynamics of groundwater are inherently uneven and difficult to understand. As a result, applying methods driven by data could potentially yield significant gains in hydrology. In this study, two data-driven models were utilized to estimate groundwater levels at a total of 4 monitoring wells located in Colorado state, USA. These models included ARIMA and ANN. The comparative analysis of the algorithms made use of a data set that had one month's worth of information from each of the years 1980 to 2019. The models were put through their paces, and the results of those tests were statistically and visually assessed. To evaluate the relative accuracy and precision of the models, the mean absolute error (MAE), mean square error (MSE), and root-mean-square error (RMSE) were used. When compared to ARIMA, the analysis demonstrates that ANN can produce the most accurate forecasts of groundwater levels in Colorado state USA.

This paper presents a comprehensive study and the analysis of a topology of a no isolated DC-DC intituled Double Quadratic Boost converter with high static gain. This converter has the main advantage of high static gain and low voltage stress on its switches. The article will first present the theoretical analysis of the converter operating in an open loop. The objective of the work is the mathematical modeling and the control strategy of the converter, as well as the validation through closed-loop experimental results. Besides, we presented the practice test results to demonstrate the operation of the converter, such as the static gain experimental curve, the practice efficiency of the converter, and the control of the output voltage, as well as the capacitor voltage balance control. The authors designed a prototype for 1 kW, with a switching frequency of fs=50kHz, with FPGA-based command and modulation.

This paper proposes a new battery balancing control strategy based on state of charge (SOC) for addressing the problem of cell-to-cell differences in lithium-ion battery systems. The strategy utilizes a reconfigurable converter system to transfer energy from high-SOC battery modules to low-SOC battery modules and allows balancing the battery modules while they are powering the load. A MATLAB/Simulink simulation model with five batteries was built to validate the effectiveness of the proposed balancing strategy under unloaded and loaded conditions. The simulation results demonstrate that the proposed strategy achieves more efficient and accurate battery module balancing compared to the previous balancing modes, thus increasing the overall lifespan and safety of the battery pack.

In this paper, based on the pseudo-totem pole (PTP) circuit, a family of five-level PTP dual boost converters (PDBC) is proposed. Dual boost converter has some unique advantages such as no bridge arm shoot-through risk and no switch body diode reverse recovery problem, so that it has a good application prospect. Compared with conventional three-level PTP converters, PDBCs has higher power density, higher efficiency, and low voltage and current stress. The topological derivation process, working principle, modulation and strategy of the topology are analyzed firstly. Further, the number of power devices, the switches voltage and current stress of the proposed topology is analyzed. Finally, a representative five-level PDBC experimental prototype is designed with AC input 220V/50Hz, DC output 400V/1kW, and peak efficiency of 98.27%. The experimental results show that the five-level PDBC proposed in this paper has higher efficiency and verify the correctness of the topology.

Interference on Power Line Communication (PLC), which is a wired communication technology that provides communication and data transmission over the existing electrical network, is examined in this paper. Many applications employ PLC technologies, which have the benefit of leveraging existing power connections for both power and data transfer, reducing cost and complexity. These interactions may be observed in contemporary smart grids and automobile power networks, where lengthy cables, switching power supplies, and communication links all work together but exacerbate electromagnetic interference problems. This research examines the effects of Spread Spectrum methods used to reduce EMI from power converters on PLC systems. Spread spectrum modulation and its three variants Sine, Random and Triangle are frequently employed to meet the requirements of electromagnetic compatibility, however there are some repercussions, that might be detrimental to the converter or the rest of the electrical network. These outcomes occur for various modulation algorithm settings and at various frequencies. Measurements are made utilising the Frame Error Rate value provided by the PLC link system to ascertain the interference produced by a SiC-based DC-DC converter in order to investigate these concerns and standardise an assessment approach.

In this paper, a fuzzy logic-based energy management system and multi-port quasi-z-source converter are proposed that utilizes hybrid renewable energy sources. The system ensures that each energy source module can be used individually by employing fuzzy logic to define power modes. This approach also helps prevent switching losses resulting from extra switching of the source modules. Also, the proposed energy management does not have a mathematical model, so applicability is simple and it is suitable for different multiple input topologies. The Mamdani fuzzy inference system can be designed to capture the nonlinear behavior of the system because of the linguistic rules. Moreover, by adopting the quasi-z-source network to the end of the multi-port converter, the switching losses of the multi-port modules have been significantly reduced. The simulation results are obtained using MATLAB Simulink, and the experimental results are obtained by connecting the circuit to MATLAB Simulink via an Arduino Due.

Background: MOST-plus is a multicenter, randomized, open-label, adaptive Phase II trial evaluating the clinical benefit of targeted treatments matched to molecular alteration in advanced/metastatic solid tumors. Sorafenib was tested on patients with tumors harboring sorafenib-targeted genes Methods: The MOST-plus trial used a randomized discontinuation design. After 12 weeks of sorafenib (400 mg, po BID), patients with progressive disease dis-continued study, patients with objective response were proposed to continue sorafenib, whereas patients with stable disease (SD) were randomly assigned (1:1) to maintenance or interruption of treatment. Primary endpoint was RE-CIST version 1.1 progression-free rate at 16 weeks after randomization (PFR-16w). Secondary endpoints included progression-free survival (PFS), overall survival (OS), and toxicity. Statistical analyses used a sequential Bayes-ian approach with interim efficacy analyses. The enrolment could be stopped in the case of a 95% probability for the estimated PFR-16w to be higher in the maintenance than in the interruption arm (NCT02029001). Results: 151 patients were included, of whom 35 had SD at 12 weeks of Soraf-enib. For the 35 patients with SD on sorafenib, the PFR-16w was 65% [95% credibility interval 43.4–83.7] in the continuation arm and 25% [7.8–48.1] in the interruption arm. Median PFS and OS were improved in maintenance versus interruption arm (mPFS: 5.6 [95%CI 1.97–6.77] months versus 2.0 [95%CI 1.61–3.91] months (p =0.0231) and mOS : 14.3 [95%CI 8.9–23.8] versus 8.0 months [95%CI 3.5–15.2] (p =0.0857)). Conclusion: Sorafenib showed activity in progressive patients with solid tu-mors harboring somatic genomic alterations in sorafenib-targeted genes. Con-tinuing sorafenib when SD is achieved improves PFR compared to interruption

This paper proposes an optimal trading algorithm based on a novel application of the conventional Control Engineering (CE). We consider a fundamental CE concept, namely, the feedback control and apply it to the Algorithmic Trading (AT). The concrete feedback control strategy is designed in a form of the celebrated Proportional-Integral-Derivative (PID) model. The highly fluctuating nature of the modern financial markets has led to the adoption of a model-free realisation of the generic PID framework. The control theoretical methodology we propose is combined with the advanced statistics for the historical market data. We obtain a specific log-normal probability distribution function (pdf) associated with the specific quantities associated with the available stock data. The empirical log-normal pdf mentioned above makes it possible the necessary PID gains optimization. For this aim, we apply the data-driven optimization approaches and consider the corresponding Monte Carlo solution procedure. The optimized PID trading algorithm we propose is also studied in the Fourier analysis framework. This equivalent frequency domain representation involves a new concept in the financial engineering, namely, the "stock market energy" concept. For the evaluation, we implement the proposed PID optimal trading algorithm and develop a Python based prototype software. We finally apply the corresponding prototype software to a dataset from the Binance BTC / USDT stock market. The experimental result illustrates the implementability of the proposed optimal PID trading scheme and also shows the effectiveness of the proposed CE methods in the modern AT.

Physics-informed machine learning (PIML) refers to the emerging area of extracting physically relevant solutions to complex multiscale modeling problems and has enjoyed significant interest from the research community. This paper discusses the recent critical advancements in the PIML domain. Novel methods and applications of domain decomposition in physics-informed neural networks (PINN) in particular are highlighted. Additionally, we explore recent Works toward utilizing neural operator learning to intuit relationships in physics systems traditionally modeled by sets of complex governing equations and solved with expensive differentiation techniques. Finally, expansive applications of traditional physics-informed machine learning and potential limitations are discussed. In addition to summarizing recent work, we propose a novel taxonomic structure to catalog physics-informed machine learning based on how the physics-information is derived and injected into the machine learning process. The taxonomy assumes the explicit objectives of facilitating interdisciplinary collaboration in methodology, thereby promoting a wider characterization of what types of physics-problems are served by the physics-informed learning machines, and assisting in identifying apt targets for future work. To summarize, the major twofold goal of this work is to summarize recent advancements and introduce a taxonomic catalog for applications of physics-informed machine learning.

Plant Factory Transplanter (PFT) is a key component of the plant factory system. Its operation status directly affects the quality and survival rate of planted seedlings, which in turn affects the overall yield and economic efficiency. To monitor the operation status and transplanting quality of a transplanting machine in a timely manner, the primary task is to use a computerized and easy-to-use method to monitor the transplanting units. Inspired by the latest developments in augmented reality and robotics, a DT model-based and data-driven online monitoring method for plant factory transplanting equipment is proposed. First, a data-driven and virtual model approach is combined to construct a multi-domain digital twin of the transplanting equipment. Then, taking the vibration frequency domain signal above the transplanting manipulator and the image features of the transplanting seedling tray as input variables, the evaluation method and configuration method of the PFT digital twin system are proposed. Finally, the effect of the transplanter is evaluated, and the cycle can be repeated to optimize the transplanter to achieve optimal operation parameters. The results show that the digital twin model can effectively use the sensor data to identify the mechanical vibration characteristics and avoid affecting transplanting quality due to mechanical resonance. At a transplanting rate of 3000 plants/h, the transplanting efficiency can be maintained at a high level and the vibration signal of X, Y, Z-axis above the transplanting manipulator is relatively calm. In this case, Combined the optimal threshold method with the traditional Wiener algorithm, the identification rate of healthy potted seedlings can reach 94.3%. Through comprehensively using the optimal threshold method and 3D block matching filtering algorithm for image threshold segmentation and denoising, the recognition rate of healthy seedlings has reached over 96.10%. In addition, the developed digital twin can predict the operational efficiency and optimal timing of the detected transplanter, even if the environmental and sensor data are not included in the training. The proposed digital twin model can be used for damage detection and operational effectiveness assessment of other plant factory equipment structures.

Wave buoy measurements were carried out near the north-eastern Black Sea coast at the natural reserve Utrish in 2020-2021. In total, about 11 months of data records were collected during two stages of the experiment at 600 and 1500 meters offshore and depths of 18 and 42 meters. The measured waves propagate almost exclusively from the seaward directions. Generally, the waves do not follow the local wind directions, thus, implying a mixed sea state. Nevertheless, dimensionless wave heights and periods appears to be quite close to the previously established empirical laws for the wind-driven seas. The results of the wave turbulence theory are applied for estimates of spectral energy fluxes and their correspondence to the energy flux from the turbulent wind pulsations. These estimates are consistent with today's understanding of wind-wave interaction. It is shown that the main fraction of the wind energy flux goes to the direct Kolmogorov-Zakharov cascade to high wave frequencies and then to dissipation in small scales. Less than 1% of the wind energy flux is directed to low frequency band (the so-called inverse Kolmogorov-Zakharov cascade), thus, providing wave energy growth.

Modeling is essential to understand better the generative mechanisms responsible for experimental observations gathered from complex systems. In this work, we are using such an approach to analyze the electrocardiogram (ECG). We present a systematic framework to decompose ECG signals into sums of overlapping lognormal components. We used reinforcement learning to train a deep neural network to estimate the modeling parameters from ECG recorded in babies of 1 to 24 months of age. We demonstrate this model-driven approach by showing how the extracted parameters vary with age. After correction for multiple tests, 10 of 24 modeling parameters showed statistical significance below the 0.01 threshold, with absolute Kendall rank correlation coefficients in the [0.27, 0.51] range. We presented a model-driven approach to the analysis of ECG. The impact of this framework on fundamental science and clinical applications is likely to be increased by further refining the modeling of the physiological mechanisms generating the ECG. By improving the physiological interpretability, this approach can provide a window into latent variables important for understanding the heart-beating process and its control by the autonomous nervous system.

This paper presents a proposal for a non-isolated bidirectional converter (NIBC) controlled by a deep neural network (DNN) to enable vehicle-to-vehicle (V2V) and vehicle-to-grid (V2G) charging, which contributes to the development of more efficient and sustainable transportation and energy systems. The DNN controller manages power flow in both directions, making it possible to charge electric vehicles (EVs) and discharge power from EVs to the grid with improved efficiency and performance compared to traditional control methods. The non-isolated topology used in this proposal offers several benefits, including reduced cost, smaller size, and higher efficiency. To train the DNN controller, a large dataset of simulations was used, and the results were validated with a hardware setup. The real-time performance of the DNN controller was compared to a proportional-integral (PI) based controller through simulated results. The findings of the study show that the DNN controller outperforms traditional PI controllers.

The purpose of this research is to present a thorough evaluation of the energy management system, which consists of hybrid energy storage systems, and its control algorithms, which may be used in electric vehicles. This paper outlines the characteristics of electric vehicles, research methods, analysis of the hybrid energy storage system architecture, converter topology, and energy management techniques. The strength and co-occurrence of keywords over the past ten years are shown in this study using a systematic research framework for performing a literature review and using keyword analysis techniques. The study reveals a pattern of recently and frequently used terms in works of literature. Consequently, their suitability, benefits, and drawbacks are assessed. In this study, the hybrid energy storage system and converter circuit architecture are evaluated and rated. Non-isolate DC-DC converter converter connected to SC is a suitable configuration for the hybrid converter because it is simple to build, reliable, and has minimal loss/weight/cost, which improves vehicle performance. In terms of the application of control strategies, it is shown that deterministic and fuzzy rule-based control techniques are successfully assessed using real-scale vehicle experiments and selected for manufacturing. On the other hand, real-time optimization-based energy management techniques have been effectively shown in lab-scale tests and may be used in a future real-scale vehicle.

The composite post insulator composed of a large diameter mandrel and flange pressed by one-time integral pultrusion has high strength and good toughness, widely used in the existing flexible converter valve. For constructing the finite element model of the crimped post insulator, most of the flange surface and the mandrel surface are directly connected, making the simulation model's stiffness deviate from the actual model and the model distortion. Based on this, this study proposes a new finite element simplified model of crimped composite post insulator based on Multi-Point Constraints (MPC), which accurately simulates the crimping area of man-drel and flange in engineering practice. It verifies the model's reliability through static tests and characteristic vibration tests. The results show that the difference between the finite element simulation and test results is within 5 %. The finite element modeling method based on MPC local constraints is very reliable in applying crimped composite post insulators. Based on the proposed model, the influence of flange height and thickness on the mechanical properties of composite post-insulators is further explored. The results show that the influence of flange height on mechanical properties can be ignored when the height of the insulator stiff-ener ( non-press common area ) is constant. Within the allowable thickness range of the flange crimping process, the increase in flange thickness will significantly reduce the maximum bend-ing stress of the insulator and increase its safety margin. It provides a good design idea for the design of crimping composite insulators.

For the emerging autonomous swarm technology, from the perspective of systems science and systems engineering, there must be some novel elements and methods to aggregate multiple systems into a group, which distinguish with the general components with specific functions, and we expect to provide the presentation of their existence in the swarm development processes. The inspiration of our methodology origins from the integration of swarm ontology, multi-paradigm modeling, multi-agent system, cyber-physical system, etc. Therefore, we choose the model-driven technology as a framework to acquire an approach of model representation across the multiple levels of abstraction and composition. The autonomous strategic mechanism is defined and formed in parallel with ConOps analysis and systems design, so as to effectively solve the cognitive problem of emergence caused by the nonlinear causation among individual and whole behaviors. This approach highlights to use the MBSE processes and their artifacts to embed the meso mechanism to integrate the operational and the functional, and which means to connect the macro and micro aspects in formalism and so to become a whole with its expected goals, and then to verify and validate within a L-V-C simulation environment.

With the widespread application of Voltage Source Converter (VSC) in the power system with high penetration of renewable resources, the problem of wideband oscillations caused by the interaction between the power converters and the power grid has drawn large attention. This paper presents a new methodology for simplifying the mathematical derivations of the impedance model of VSC. Using the new simplified model, the stability and the spectrum characteristic of the performance of the VSC can be clearly analyzed. The proposed second-order d-q impedance model of the grid connected VSC at presence of PLL, not only mathematically proves that the wideband oscillatory modes always occurs in conjugate pairs, but also deliver a clear vision of the physical essence of the wideband oscillations. The classic modal analysis is executed in the paper to demonstrate the stability and frequency spectral characteristics of the wideband oscillations in the VSC integrated system. The impact of control parameters of the VSC on the wideband oscillatory stability is thouroughly analyzed using the results of the modal analysis. The methodology can be considered a powerful tool for the control design of VSC in the modern power system.

AI is extraordinarily useful technology but it is becoming increasingly powerful with rapidly growing capabilities to disrupt and harm human society. We call on international and national organizations and individuals to join forces in banning the development of superintelligent AI and introducing regulations to prevent and mitigate AI-caused harm.

Cable-Driven Parallel Robots (CDPRs) are a special kind of parallel manipulator which use cables to control the position and orientation of the mobile platform or end-effector. The usage of cables instead of rigid links offer some advantages over their conventional rigid counterparts. As cables can only pull, but not push, the number of cables (n) required to command the end-effector is always n+1. This configuration is known as fully-constrained and it is the most extended configuration for CDPRs. Although CDPRs have many advantages, such as their ability to cover large working areas, one of their main problems is that their working area (workspace) is limited in comparison to its frame area (planar case), or frame volume (spatial case), due to the minimum and maximum allowed tensions. Depending of these tensions values the workspace can be notoriously decreased. In order to tackle this problem, lots of works focus on solving kinematics or dynamics problems for cable sagging, i.e., they take into account sagging when modelling the robot kinematic and include these poses inside the usable robot workspace. Taking into account phenomenons like this increases the mathematical complexity of the problem, and much more complex techniques are required. On the other hand, the lack of workspace problem can be tackled by adding active or passive elements to the robot design. In this sense, this paper proposes two mechanical modifications: to add passive carriages to the robot frame and to use a single cable loop to command the end-effector position and orientation. This work presents the kinematic, static and dynamic models of the novel design and shows the gain of workspace for a planar case while taking into account different parameters of the robot.

Machine Learning (ML) was used to assess and predict urban air temperature (Tair) considering the complexity of the terrain features in Yerevan (Armenia). The estimation was performed based on the PLSR model with a high number (30) of input variables. The relevant parameters include a newly purposed modification of spectral index IBI-SAVI, which turned out to be strongly impacting on Tair prediction together with land surface temperature (LST). Cross-validation analysis on temperature predictions across a station-centered 1000m circular area revealed quite a high correlation (R2Val = 0.77, RMSEVal = 1.58) between predicted and measured Tair from the test set. It was concluded the remote sensing is an effective tool to estimate Tair distribution where a dense network of weather stations is not available. However, further developments will include incorporation of additional weather parameters from the weather stations such as precipitation and wind speed, and the use of non-parametric ML techniques.

In modern inverter applications, multilevel converters are used thanks to their capability of reducing the passive fi lter volumes, improving the overall THD, sharing the power sources across the several converter cells, and providing a reconfi guration ability in case of failure. However, increasing the number of cells makes more complex the control of the converter. This article proposes a decentralized control technique based on the use of elementary modular controllers, associated with each converter cell and communicating with their close neighbors to obtain the balancing of the powers they supplied. Each modular controller can be dynamically removed or added to allow reconfi guration of the converter for functional safety purpose. This method is applied to a 240V/3A 5-cells Cascaded Full-Bridge Multilevel Converter. The response of the system to load transients and cell voltage disturbances demonstrates the robustness of the proposed decentralized control method. Thanks to the use of modular controllers, the number of levels of the converter can be extended to the order N without adding complexity to its control.

The concepts of fractional calculus and the related techniques have been gaining momentum in circuit system fields, and an increasing body of evidence suggests that fractional-order characteristics are widely distributed in electronic components. In this paper, we consider the fractional characteristics of non-solid aluminum electrolytic capacitors and focus on the nonlinear dynamic characteristics of peak-current-mode controlled (PCMC) dc-dc boost converters with such components. In the work, we establish a piecewise smooth non-commensurate fractional-order model to describe the converter, in which the fractional-order equivalent impedance model of capacitors is adopted and the detailed control loop is considered. In order to analyze the converter, we use the fractional Adams-Bashforth-Moulton typed method (F-ABM) and stroboscopic map technique to obtain the time-domain solutions and the bifurcation diagram. The results of bifurcation diagrams indicate that, with the change of different parameters, the converter enters chaotic state through period doubling bifurcation. In order to provide empirical evidence for the nonlinear dynamic analysis, we consider both circuit-level simulation and experiments in the work, the results of which further confirm the results of theoretical analysis.

In the context of the emergence and development of varieties of English around the globe, this paper focuses on Kuwait, where the use of English has greatly increased since the mid-20th century. Drawing on the principles of nativisation and indigenisation, I investigate substrate- and superstrate-derived discourse-pragmatic markers (DPMs) in Kuwaiti English: the Arabic loanwords alhamdulillah and ya'ni and their English equivalents (e.g. I mean and thankfully). Data are drawn from the Kuwaiti English National Corpus, a spoken corpus of almost 800,000 words containing interviews, samples of situated language use, and mediated data (podcasts, TV, radio and YouTube). Keyness and concordance analyses were performed to identify the frequency and functions of the DPMs in Kuwaiti English compared to reference corpora of British and American English. The analysis also takes into account contextual and sociolinguistic information such as the speakers’ social identity. Results show that the Arabic-derived DPMs are abundant in both formal and informal settings, indicating that they have become an integral part of this variety of English. Their English equivalents perform similar roles, but are vastly less common (with the exception of I mean for ya'ni). Both the Arabic- and English-based markers show somewhat narrowed discourse-pragmatic functions and are more likely to be placed in clause-initial position than in ‘native’ contexts. Their use appears to be linked to the social identity of the speakers.

We consider a class of density-driven flow problems. We are particularly interested in the problem of the salinization of coastal aquifers. We consider the Henry saltwater intrusion problem with uncertain porosity, permeability, and recharge parameters as a test case. The reason for the presence of uncertainties is the lack of knowledge, inaccurate measurements, and inability to measure parameters at each spatial or time location. This problem is nonlinear and time-dependent. The solution is the salt mass fraction, which is uncertain and changes in time. Uncertainties in porosity, permeability, recharge, and mass fraction are modeled using random fields. This work investigates the applicability of the well-known multilevel Monte Carlo (MLMC) method for such problems. The MLMC method can reduce the total computational and storage costs. Moreover, the MLMC method runs multiple scenarios on different spatial and time meshes and then estimates the mean value of the mass fraction. The parallelization is performed in both the physical space and stochastic space. To solve every deterministic scenario, we run the parallel multigrid solver ug4 in a black-box fashion. We use the solution obtained from the quasi-Monte Carlo method as a reference solution.

With the rise of powerful computational technologies, healthcare systems are going through a paradigm shift to the era of healthcare 5.0, also known as" smart healthcare". This new-age healthcare system not only enhances the life of patients but also aims to reduce healthcare costs significantly. People's health data collected through various means, including various Internet of Things (IoT) devices, is too sensitive to be regulated by particular organisations owing to increasing privacy and security threats. The sheer size of data makes it difficult for centralised bodies to regulate, due to which third-party involvement is introduced, which again poses threats to the privacy of the data holder. Motivated by the abovementioned gaps, we propose a decentralised approach using Ethereum blockchain technology and Oasis protocol. The patient-driven solution approach effectively solves the privacy problem by providing complete control of data to the owner. Along with this, byproducts such as fine-grained access control and on-chain data processing capabilities are also obtained.

CO2 has characteristic properties and reactions at converter smelting temperature, and the chemical reaction between CO2 and elements such as C and Si in the molten pool has bubble proliferation and cooling effects, which can effectively improve the kinetic and thermodynamic conditions of converter smelting. Here, an experimental study and industrial test on the application of CO2 in converter smelting were carried out. The smelting effects of Mode-1 and Mode-2 with total CO₂ injection amounts of 229 Nm³ and 196 Nm³, respectively, were compared, and the changes in molten steel and slag compositions, dust removal, and gas are analyzed. The test results show that converter top and bottom blowing CO₂ technology (COMI-B)technology had significant metallurgical advantages over the N-Mode; the dephosphorization rate increased by 4.2%, slag (FeO) content was reduced by 2.04%, end point nitrogen content of molten steel was reduced by 20%, gas recovery increased by 8.29 Nm³/t, and soot production reduced by 14.7%. The results of the study provide reference for the application of COMI-B technology in converters in the iron and steel industry and develop a new path for resource utilization of CO₂.

This paper presents a comprehensive review that highlights the characteristics of non-isolated step-up converters based on high boost voltage lifting techniques. The paper categorises the high boost techniques: multistage/multilevel, switched capacitor, voltage multiplier, voltage lift, switched inductor, and magnetic coupling. The paper also discusses in detail the advantages and disadvantages for each category such as cost, complexity, power density, reliability and efficiency. The number of passive and active components, voltage gain, voltage stress, switching frequency, efficiency and power rating are also compared. Although the paper considers coupling inductors in the context of the non-isolated converter, the focus of the entire article is on the non-isolated high voltage step-up techniques. The key contribution in this paper is the review of high boosting techniques rather than the DC /DC converters. This allows divergence of new ideas and new power converters that will help provide highly efficient and flexible power converters for several applications where the sending end voltage is very low as photovoltaic systems. In addition, many applications and control techniques of DC/DC converters are summarised in this paper.

The general circuit topology and principles of low-noise high-voltage power supply are investigated to meet the requirements of the high-voltage bias application in air kerma meters. Two topologies, flyback converter and Royer converter are simulated using SPICE simulation program. The simulation results indicate that the circuit structure of the Royer converter is more complex, but it obtains lower output high voltage noise. While we develop an adjustable high-voltage power supply according to the circuit structure of the Royer converter, and tested it to ensure the design requirements for continuously adjustable output high-voltage linearity. The test results show that the linear adjustment rate is not more than ±0.0025%, the load regulation rate is less than ±0.1%, and the output ripple noise voltage percentage is less than 0.01%. These tested performance make it more suitable for accurate nuclear measurements.

Microgrids offer flexibility in power generation in a way of using multiple renewable energy sources. In the past few years, microgrids become a very active research area in terms of design and control strategies. Most of the microgrids use DC/DC converters to connect renewable energy sources to the load. In this paper, the simulation model of a DC microgrid with three different energy sources (Lithium-ion battery (LIB), photovoltaic (PV) array, and fuel cell) and external variant power load is built with MATLAB/Simulink and the simulative results show that the stability of DC microgrid can be guaranteed by the proposed maximum power point controller MPPT. The three energy sources are connected to the load through DC/DC converters, one for each. This type of topology ensures protection for each energy source as well as optimum stability at the load.

In this paper, the authors consider the influence of the type of power supply and the internal parameters of the DC-DC converter on the stability of the system at constant power load. The stability of the system under consideration is important in the design and development of the power supply system for an autonomous consumer. Such a typical system can be a DC microgrid. In the work, when analyzing the stability of the system, the power source is not considered an ideal voltage source, but the active-inductive nature of the voltage source is taken into account, which is equivalent to using a synchronous machine with permanent magnets. The Rauss-Hurwitz algebraic criterion was used as a criterion for analyzing the stability of the system. This will make it possible to build and analyze the areas and boundaries of the system's stability. As a result, the areas and boundaries of the stability of the system were presented. The general trend towards the behavior of the zone of instability of the system was given. At the same time, dependences describing the boundaries of instability are presented, which are combinations of parameters of the DC-DC converter, beyond which the system becomes unstable. The presented definitions of the instability limits can be used as a corrective device of the control system to ensure the stability of the system in the upper limits of the power consumption.

The development trends of the modern world of power electronics dictate the requirements for the use of AC voltage converters as soft starters for induction motors. A direct connection of the motors to the mains voltage negatively affects both the motor itself and the mains system as a whole due to high starting currents values, which entail, as a rule, more frequent accidents and shorter the drive system service life. The paper presents a study of the control system of a multi-zone AC voltage regulator. The use of capacitive voltage dividers will also compensate for the consumed reactive power. The article analyzes the features of modern soft starters, describes the circuit design, presents a mathematical calculation by the method of algebraization of differential equations, a performed simulation modeling in Matlab/Simulink, and also an assembled experimental stand for further research. Particular attention is paid to the definition of the multizonality concept of the proposed converter and the analysis of the control method. The developed algorithm of the double-loop automatic control system will minimize the influence of induction motors on the mains voltage, and thus improve electromagnetic compatibility.

Application of pulsed optical sources on the base of stable mode-locked lasers, which are known for their very low time jitter, provides the opportunity for creation a high precision photonic analogue to digital converters of signals in the microwave range. However, the repetition rate of modern commercially available mode-locked lasers is limited to a few gigahertz. The increase of repetition rate is possible using the schemes that implement a passive chirp of ultra-short pulses prior to electro-optic amplitude modulator, which is driven by the signal under test, and demultiplexing of modulated signal after a modulator. In the given article we analyzed a continuous time-stretch chirp using single mode fiber as dispersive element. The limitations of input signal bandwidth and source pulses energy are considered.

A Shape Memory Alloy (SMA) enabled Overtopping Wave Energy Converter (OWEC) that maximizes its overtopping discharge, and thus energy output under different wave conditions is presented. Among all the parameters affecting the overtopping discharge rate, the crest freeboard height is the most influential one to control the overtopping discharge rate and the stored overtopping volume behind it. Currently, all the OWEC crest freeboard heights are fixed by design to maximize the discharge rate on one particular sea state. In the present study, we show that the SMA can adjust the crest freeboard height through a control system based on the sea state and achieve an optimal overtopping discharge rate. A scaled OWEC model is built in the lab with its crest freeboard height controlled by springs made of SMA. The length (and thus tension) of the springs is controlled by temperature changes by changing the passing current through the springs. By adjusting the length of the springs based on the incoming wave condition, we adjust the freeboard to an optimal height known to generate a maximum overtopping discharge rate for energy conversion. This smart material-enabled design can maximize the overtopping discharge and thus the output power of the OWEC under various wave conditions. Furthermore, the simplicity of using SMA springs as the actuator leads to the minimum number of moving mechanical parts, which can remarkably decrease maintenance costs. As the proof of concept, two types of tests are conducted in the laboratory using the same OWEC model under several random wave trains generated from spectra with different significant wave heights - one type with a fixed crest freeboard height and the other type featuring the adjustable crest freeboard height controlled by the springs. The substantial increase of harvested output power in the OWEC with the adjustable crest freeboard height may pave the way for more efficient wave energy conversion systems.

The integrated DC-DC converter is appropriate for use in many domains, namely, display, cellular, and portable applications. This paper presents an integrated monolithic voltage-mode DC-DC boost converter with a low-power control circuit. The driver circuit requires an integrated converter to power up a digital logic circuit and converts the unregulated DC input to the controlled DC output at the desired voltage level. It is the integration of both power switches and control circuitry within the same CMOS technology to buck down and boost voltages using a switch mode regulator. In order to increase power efficiency in the DC-DC boost converter that provides low-power operation with a small chip size, a low-voltage operation is applied to the unique circuit characteristic. The operational transconductance amplifier(OTA), comparator, and oscillator in the control circuit are designed with the supply voltage of 3.3V and the operating frequency of 5.5 MHz. A compensator is used to create a pole that has sufficient phase margin for high stability. The DC- DC boost converter is measured in both experiment and simulation. Testing of the proposed circuit on the 0.35μm CMOS process shows that the output transient time of the amplifier can be controlled within of 7μsec and the output voltage is accurately controlled with a ripple ratio of 3%.