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Abstract Energy management controllers (EMCs) are pivotal for optimizing energy consumption and ensuring operational efficiency across diverse systems. This review paper delves into the various control strategies utilized by energy management controllers and explores their coordination mechanisms. Additionally, it examines the architectures of energy management controllers and their real-world implementations. The paper surveys a spectrum of EMCs, including conventional-based, rule-based, optimization-based, hybrid methods, and commercial software-based approaches, highlighting their respective advantages and drawbacks. It investigates how these controllers are coordinated within complex energy systems to achieve optimal performance and adaptability. Furthermore, the review outlines different architectures of energy management controllers, ranging from centralized to decentralized designs, discussing their suitability for various applications and their impact on system performance. Real-world applications of energy management controllers in sectors such as smart grids, buildings, industrial processes, and transportation systems are examined. Case studies and examples demonstrate the efficacy of different control strategies and architectures in addressing specific energy management challenges and achieving desired outcomes. Overall, this review provides valuable insights into the current landscape of energy management controller design and implementation, offering direction for future research and development in the pursuit of energy optimization and sustainability.

Energy management controllers (EMCs) are pivotal for optimizing energy consumption and ensuring operational efficiency across diverse systems. This review paper delves into the various control strategies utilized by energy management controllers and explores their coordination mechanisms. Additionally, it examines the architectures of energy management controllers and their real-world implementations. The paper surveys a spectrum of EMCs, including conventional-based, rule-based, optimization-based, hybrid methods, and commercial software-based approaches, highlighting their respective advantages and drawbacks. It investigates how these controllers are coordinated within complex energy systems to achieve optimal performance and adaptability. Furthermore, the review outlines different architectures of energy management controllers, ranging from centralized to decentralized designs, discussing their suitability for various applications and their impact on system performance. Real-world applications of energy management controllers in sectors such as smart grids, buildings, industrial processes, and transportation systems are examined. Case studies and examples demonstrate the efficacy of different control strategies and architectures in addressing specific energy management challenges and achieving desired outcomes. Overall, this review provides valuable insights into the current landscape of energy management controller design and implementation, offering direction for future research and development in the pursuit of energy optimization and sustainability. [ABSTRACT FROM AUTHOR]

Precision agriculture (PA) is meant to automate the complete agricultural processes with the sole target of enhanced crop yield with reduced cost of operation. However, deployment of PA in internet-of-things (IoT) based architecture demands solutions towards addressing various challenges where most are related to proper and precise predictive management of agricultural data. In this perspective, it is noted that learning-based approaches have made some contributory success towards addressing different variants of issues in PA; however, such methods suffer from certain loopholes, primarily related to the non-inclusion of practical constraints of IoT infrastructure in PA and lack of emphasis towards bridging the trade-off between higher accuracy and computational burden that is eventually associated with this. This paper contributes towards highlighting the strengths and weaknesses of recent learning approaches and contributes towards novel findings. [ABSTRACT FROM AUTHOR]

Accessibility is a considerable and growing issue in the design of many public buildings and vital infrastructure. This research study uses video diaries to explore how service design methods were used to observe user experiences of inclusivity within a crowded island platform station at Canary Wharf station during a November evening rush hour. Auto-ethnography is discussed as part of a broader methodological debate about how to explore and consider inclusive design issues from a user's perspective, and in the context of empathetic design. The study concludes that this method of enquiry is appropriate to investigate the movement of people within crowded underground stations and, identifies actionable insights into how spatial factors and congestion impact the mobility requirements of station users. [ABSTRACT FROM AUTHOR]

Developing sustainable and affordable anode materials that are capable of delivering high performance in both lithium‐ion batteries (LIBs) and sodium‐ion batteries (SIBs) remains a significant challenge. Bimetallic selenide@carbon hybrids are considered as one of the most promising anode materials in LIBs and SIBs due to their high electronic conductivity, high specific capacity, and fast reaction kinetics. Herein, a series of bimetallic selenide@carbon hybrid nanotubes are successfully prepared as anodes of LIBs or SIBs based on the dual regulation of component and micro‐nanostructure. The selenization strategy plays a key important role in determining the composition, microstructure, and electrochemical energy storage properties of anode materials. As a consequence, the ZnSe/CoSe2@NPC NTs(I)‐600 exhibit a reversible capacity of 1328.3 mAh g−1 at 0.1 A g−1 and superior rate capability (269.1 mAh g−1 at 10 A g−1) towards Li+ storage. Meanwhile, ZnSe/CoSe2@NPC NTs(II)‐700 achieve 354.1 mAh g−1 at 0.1 A g−1 and ultralong cycling stability (97.6% of capacity retention after 40 000 cycles at 10 A g−1) used as anode materials in SIBs. This study provides a feasible strategy to fabricate selenide‐based composites as anode materials for high‐performance LIBs and SIBs via architecture engineering and composition tailoring. [ABSTRACT FROM AUTHOR]

The demand for reconfigurations of production systems is increasing, driven by shorter innovation and product life cycles and economic volatility. Another trend in the domain of industrial automation is the emergence of cyber-physical production systems, which offer promising potentials, for example, self-organization capabilities. A suitable cyber-physical production system architecture that incorporates knowledge modelling and management concerns, plus a reconfiguration management methodology, is crucial for realizing self-organized reconfiguration management. In this paper, first reference architectures, architectural patterns, and basic principles, as well as knowledge modelling and management approaches, are discussed in general. Afterwards, these are examined concerning the reconfiguration management use case focusing on UML/XML-based and ontology-based approaches. A novel approach comprising a multi-agent system and the MAPE-K concept for reconfiguration management is presented. In addition, the approach contains a service-oriented architecture for a deterministic plant control within a layered architecture. The knowledge modelling is realized through a UML information model, which can be integrated into the system utilizing XML files. Furthermore, the provided tool support is described. It enables a user to describe system components in an effort-reduced manner and conform to the schema defined by the information model and its restrictions via a GUI. [ABSTRACT FROM AUTHOR]

Artificial neural network (ANN) is the backbone of machine learning, specifically deep learning. The interpolating and learning ability of an ANN makes it an ideal tool for modelling, control and various other complex tasks. Fractional calculus (FC) involving derivatives and integrals of arbitrary non-integer order has recently been popular for its capability to model memory-type systems. There have been many attempts to explore the possibilities of combining these two fields, the most popular combination being the use of fractional derivative in the learning algorithm. This paper reviews the use of fractional calculus in various artificial neural network architectures, such as radial basis functions, recurrent neural networks, backpropagation NNs, and convolutional neural networks. These ANNs are popularly known as fractional-order artificial neural networks (FANNs). A detailed review of the various concepts related to FANNs, including activation functions, training algorithms based on fractional derivative, stability, synchronization, hardware implementations of FANNs, and real-world applications of FANNs, is presented. The study also highlights the advantage of combining fractional derivatives with ANN, the impact of fractional derivative order on performance indices like mean square error, the time required for training and testing FANN, stability, and synchronization in FANN. The survey reports interesting observations: combining FC to an ANN endows it with the memory feature; Caputo definition of fractional derivative is the most commonly used in FANNs; fractional derivative-based activation functions in ANN provide additional adjustable hyperparameters to the networks; the FANN has more degree of freedom for adjusting parameters compared to an ordinary ANN; use of multiple types of activation functions can be employed in FANN, and many more. [ABSTRACT FROM AUTHOR]

The rapid evolution of 5G and beyond technologies has sparked an unprecedented surge in the need for networking infrastructure that can deliver high speed, minimal latency, and remarkable flexibility. The programmable data plane, which enables the dynamic reconfiguration of network functions and protocols, is becoming increasingly important in meeting these requirements. This paper provides an overview of the current state of the art in programmable data planes implemented in 5G and beyond architectures. It proposes a classification of the reviewed studies based on system architecture and specific use cases. Furthermore, the article surveys the primary applications of programmable devices in emerging telecommunication networks, such as tunneling and forwarding, network slicing, cybersecurity, and in-band telemetry. Finally, this publication summarizes the open research challenges and future directions. In addition to offering a comprehensive review of programmable data plane applications in telecommunication networks, this article aims to guide further research in this promising field for network operators and researchers alike. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has become a cornerstone of modern technology, revolutionizing industries from healthcare to finance. Convolutional neural networks (CNNs) are a subset of AI that have emerged as a powerful tool for various tasks including image recognition, speech recognition, natural language processing (NLP), and even in the field of genomics, where they have been utilized to classify DNA sequences. This paper provides a comprehensive overview of CNNs and their applications in image recognition tasks. It first introduces the fundamentals of CNNs, including the layers of CNNs, convolution operation (Conv_Op), Feat_Maps, activation functions (Activ_Func), and training methods. It then discusses several popular CNN architectures such as LeNet, AlexNet, VGG, ResNet, and InceptionNet, and compares their performance. It also examines when to use CNNs, their advantages and limitations, and provides recommendations for developers and data scientists, including preprocessing the data, choosing appropriate hyperparameters (Hyper_Param), and evaluating model performance. It further explores the existing platforms and libraries for CNNs such as TensorFlow, Keras, PyTorch, Caffe, and MXNet, and compares their features and functionalities. Moreover, it estimates the cost of using CNNs and discusses potential cost-saving strategies. Finally, it reviews recent developments in CNNs, including attention mechanisms, capsule networks, transfer learning, adversarial training, quantization and compression, and enhancing the reliability and efficiency of CNNs through formal methods. The paper is concluded by summarizing the key takeaways and discussing the future directions of CNN research and development. [ABSTRACT FROM AUTHOR]

The integration of Virtual Reality (VR) environments with Internet of Things (IoT) infrastructure can enable more intuitive and immersive interactions. However, realizing the potential of this convergence requires overcoming technical constraints and implementation challenges. This paper reviews the motivations, architectures, applications, and open issues associated with combining VR and IoT. This study also aims to provide a comprehensive analysis of architectures, use cases and technical challenges involved in integrating VR and IoT to guide further research and real-world deployment. Use cases in manufacturing, energy, retail, entertainment and other sectors highlight the benefits like remote monitoring, training and collaboration unlocked by interfacing VR's realistic 3D visualizations with real-time IoT sensor data. This paper also discusses VR simulation of IoT systems for testing, limitations in interoperability, and security considerations. The outlook for VR-IoT solutions is promising, with 5G and edge computing advancements expected to address current bottlenecks to adoption. Human-centric design approaches focused on high-value use cases can enable impactful deployments across domains. [ABSTRACT FROM AUTHOR]

Due to the daily use of mobile technologies, we live in constant connection with the world through the Internet. Technological innovations in smart devices have allowed us to carry out everyday activities such as communicating, working, studying or using them as a means of entertainment, which has led to smartphones displacing computers as the most important device connected to the Internet today, causing users to demand smarter applications or functionalities that allow them to meet their needs. Artificial intelligence has been a major innovation in information technology that is transforming the way users use smart devices. Using applications that make use of artificial intelligence has revolutionised our lives, from making predictions of possible words based on typing in a text box, to being able to unlock devices through pattern recognition. However, these technologies face problems such as overheating and battery drain due to high resource consumption, low computational capacity, memory limitations, etc. This paper reviews the most important artificial intelligence algorithms for mobile devices, emphasising the challenges and problems that can arise when implementing these technologies in low-resource devices. [ABSTRACT FROM AUTHOR]

Abstract In this article we review the current serverless architectures, abstract and categorize their founding principles, and provide an in-depth security analysis. In particular, we: show the security shortcomings of the analyzed serverless architectural paradigms; point to possible countermeasures; and, highlight several research directions for practitioners, Industry, and Academia.

There is an exponential increase in the number of smart devices, generating helpful information and posing a serious challenge while processing this huge data. The processing is either done at fog level or cloud level depending on the size and nature of the task. Offloading data to fog or cloud adds latency, which is less in fog and more in the cloud. The methods of processing data and tasks at fog level or cloud are mostly machine learning based. In this paper, we will discuss all three levels in terms of architecture, starting from the internet of things to fog and fog to cloud. Specifically, we will describe machine learning-based offloading from the internet of things to fog and fog to cloud. Finally, we will come up with current research directions, issues, and challenges in the IoT–fog–cloud environment.

One of the major paradigm shifts that will be predictably observed in the energy mix is related to distribution networks. Until now, this type of electrical grid was characterized by an AC transmission. However, a new concept is emerging, as the electrical distribution networks characterized by DC transmission are beginning to be considered as a promising solution due to technological advances. In fact, we are now witnessing a proliferation of DC equipment associated with renewable energy sources, storage systems and loads. Thus, such equipment is beginning to be considered in different contexts. In this way, taking into consideration the requirement for the fast integration of this equipment into the existing electrical network, DC networks have started to become important. On the other hand, the importance of the development of these DC networks is not only due to the fact that the amount of DC equipment is becoming huge. When compared with the classical AC transmission systems, the DC networks are considered more efficient and reliable, not having any issues regarding the reactive power and frequency control and synchronization. Although much research work has been conducted, several technical aspects have not yet been defined as standard. This uncertainty is still an obstacle to a faster transition to this type of network. There are also other aspects that still need to be a focus of study and research in order to allow this technology to become a day-to-day solution. Finally, there are also many applications in which this kind of DC microgrid can be used, but they have still not been addressed. Thus, all these aspects are considered important challenges that need to be tackled. In this context, this paper presents an overview of the existing and possible solutions for this type of microgrid, as well as the challenges that need to be faced now. [ABSTRACT FROM AUTHOR]

Smart cities are faced with the challenges of systematically initiating the strategies needed to implement predetermined goals according to their plan. Therefore, this study aims to identify smart cities components and build an initial model that can be used for smart cities initiatives in Indonesia. The systematic review methods were used to overview research trends, architecture, components, and challenges to build an initial model for Indonesia. The systematic review results show popular topics, such as the standardization of a smart city and the strategies used to determine relevant models in each city's uniqueness and context. The results also identified thirteen smart city components and their challenges. Furthermore, this study's novelty is proposed the smart city's initial models with the smart government as a key component and to be a centre of other smart city components. The triangulation method determined the research validation using systematic review finding, expert judgment, and a questionnaire for collecting random data from 66 respondents from various cities in Indonesia. The collected survey data and initial models were validated by expert judgment, which showed that the proposed smart city's initial model component's agreement is 90%. In future, these results may be used to develop an assessment in smart cities implementation.

Visualization of the knowledge extraction process is a front line to reveal the detail process and data structure, which is an advanced technique for the presentation of data modeling. However, the mechanisms for healthcare are challenging and dynamic processes to gain a clear insight or understanding of patient care. In this paper, we proposed a new approach of architecture and optimization of data mining modeling for visualization of knowledge extraction by analyzing clinical data sets to define the determinant attributes through modeling techniques. Therefore, architecture for the visualization of the knowledge extraction process is a systematic approach to support users to the best of their knowledge of the issues over the challenge of visualizing techniques. The proposed approach is capable and dynamic to handle and analyze large-scale data in its dimension and context. Such a variable is defined using various techniques to characterize them towards the detection of determinant variables as its influential circumstance. We focused on modeling based visualization as model representation, factor's interaction and integration. The detection process experimented in a different approach and justification as discussed in section five. The finding showed a deep understandability for an advanced and dynamic data mining modeling techniques to integrate applications with domain contexts for the optimal and understandable decision process. The strength of this approach is the depth for visualization towards the knowledge extraction process and its understandability for users as per their background and circumstances. It is also essential to inference for architecture based modeling and visualization for large scale data. Researchers, physicians, experts, and other users are the potentials to refer to these novel ideas and findings.

Scientists are often inspired by nature, where naturally occurring morphologies, such as those that resemble animals and plants, can be created in the lab. In this review, we have provided an overview on complex superstructures of animals, plants and some similar shapes from the natural world. We begin this review with a discussion about the formation of various animal‐like shapes from small organic molecules and polymers, and then move onto plants and other selected shapes. Literature surveys reveal that most of the polymers studied tend to form micellar structures, with some exceptions. Nevertheless, small organic molecules tend to form not only micellar structures but also other animal shapes such as worms and caterpillars. These superstructures tend to have high surface areas and variable surface morphology, making them very useful material for applications in various field such as catalysis, solar cells, and biomedicine, amongst others. [ABSTRACT FROM AUTHOR]

Deep Learning neural networks have shown applicability in segmentation of brain tumor images. This research have been carried for comprehensive review of several deep learning neural networks. The datasets included in this study are standard datasets Multimodal Brain Tumor Segmentation (BraTS). This paper has summarized the performance of various deep learning neural network algorithms on BraTS datasets. Algorithms have been compared and summarized against the baseline models with specific attributes like dice score, PPV and sensitivity. It has been found that out of the different models applied on the BraTS 2015 dataset GAN in the year 2020 algorithm is showing better results on this data set. GAN architecture termed RescueNet gave the best segmentation results in terms of 0.94 dice score and 0.88 Sensitivity. This has been also observed that models used cascaded deep learning models had independent deep learning models at each stage which had no correlation among the stages which can cause class imbalance. Further it have found that the Attention models tried to solve problem of class imbalance in the brain tumor segmentation task. This work also found that existing CNN's is having overfitting issues. For this ResNet models can add a rapid connect bounce relationship parallel to the layers of CNN to accomplish better outcomes for the brain tumor segmentation task. [ABSTRACT FROM AUTHOR]

This article discusses the design principles and strategies and the structural outcome of various supramolecular architectures constructed by utilizing well‐defined coordination complexes as the metalloligands. We have included selected examples of metalloligands, offering either pyridyl or arylcarboxylic acid groups as the appended functional groups, for illustrating the construction of their supramolecular architectures. Both geometrical position and the number of the appended functional groups emerging from a metalloligand were found to critically regulate the structural aspects and dimensionality of the resultant material. The article concludes by delineating the structure‐directing lessions as well as the potential applications of the metalloligand‐based supramolecular architectures for the generation of next‐level materials. [ABSTRACT FROM AUTHOR]

Electrohydrodynamic (EHD) direct-writing has recently gained attention as a highly promising additive manufacturing strategy for fabricating intricate micro/nanoscale architectures. This technique is particularly well-suited for mimicking the extracellular matrix (ECM) present in biological tissue, which serves a vital function in facilitating cell colonization, migration, and growth. The integration of EHD direct-writing with other techniques has been employed to enhance the biological performance of scaffolds, and significant advancements have been made in the development of tailored scaffold architectures and constituents to meet the specific requirements of various biomedical applications. Here, a comprehensive overview of EHD direct-writing is provided, including its underlying principles, demonstrated materials systems, and biomedical applications. A brief chronology of EHD direct-writing is provided, along with an examination of the observed phenomena that occur during the printing process. The impact of biomaterial selection and architectural topographic cues on biological performance is also highlighted. Finally, the major limitations associated with EHD direct-writing are discussed., (© 2024 Wiley‐VCH GmbH.)

The Internet of Things (IoT) based real-time health monitoring system has contributed towards a brilliant human welfare both in urban and rural areas. Many of such solutions are not well applicable in developing countries like Bangladesh due to lack of uninterrupted communication system. In this paper, we present an IoT-based real-time health monitoring system that can measure, monitor and report people’s health condition online and offline from anywhere. Our proposed IoT based solution is capable to transmit the sensitive health information to medical centres and caregivers in real time. The proposed system has been designed with Arduino UNO, Nodemcu, and Global System for Mobile Communication (GSM) modules to measure body temperature, pulse rate, Oxygen saturation, room temperature, and air quality in a smart home setting. The system can also provide the patient’s historical health records. Our implementation was tested on some test cases which works excellent with accuracy. The proposed system has high potentiality for the rural and urban areas in developing countries.

Data Warehouse (DW) is a common term used in the Data Mining process for storing copious amounts of data ready for analysis. Organizations are starting to prioritize Data Warehouses, which are essential for mining their historical datasets. In the healthcare industry, the significance of data warehouses escalates due to the critical nature of managing and processing medical data, which plays a crucial role in facilitating optimal healthcare delivery and in the generation of valuable insights. While clinical data is complex, its timely integration and analysis are crucial to providing excellent patient care. The research looks into providing an efficient data warehousing solution with multiple privacy and security measures integrated by design. Securing the data at all stages: during data input, exploration, pre-processing, selection, analysis, and presentation, is very challenging. This paper presents a holistic perspective focusing on possible data warehouse architectures, data types, data schemes, storage, security and privacy in a healthcare environment. We discuss existing DW architectures dedicated to clinical data and suggest the best DW approaches for clinical data warehousing.