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Medical IoT devices that use miniature sensors to collect patient’s bio-signals and connected medical applications are playing a crucial role in providing pervasive and personalized healthcare. This technological improvement has also created opportunities for the better management of personal health information. The Personal Health Information Management System (PHIMS) supports activities such as acquisition, storage, organization, integration, and privacy-sensitive retrieval of consumer’s health information. For usability and wide acceptance, the PHIMS should follow the design principles that guarantee privacy-aware health information sharing, individual information control, integration of information obtained from multiple medical IoT devices, health information security, and flexibility. Recently, blockchain technology has emerged as a lucrative option for the management of personal health information. In this paper, we propose eHealthChain—a blockchain-based PHIMS for managing health data originating from medical IoT devices and connected applications. The eHealthChain architecture consists of four layers, which are a blockchain layer for hosting a blockchain database, an IoT device layer for obtaining personal health data, an application layer for facilitating health data sharing, and an adapter layer, which interfaces the blockchain layer with an application layer. Compared to existing systems, eHealthChain provides complete control to the user in terms of personal health data acquisition, sharing, and self-management. We also present a detailed implementation of a Proof of Concept (PoC) prototype of eHealthChain system built using Hyperledger Fabric platform.

Machine vision based monitoring system refers to the monitoring system that captures the target attributes (pixel distribution, brightness, colour, etc.) using visual devices, and transmit and process the digital image to carry out a variety of detection and control operations for equipment action. The advantage of machine vision is that with a proper setup it can reaches high precision non-destructive monitoring in the machining process, and improves the flexibility and automation of production.

Tremendous progress has been made in the aspect of material design, especially in nanomaterials fabrication over the past decades. And as such, the rapid development of nanomaterials through nanotechnology has opened up new avenues for their use in photoelectrochemical applications. Their unique characteristics at nano dimensions compared to their bulk counterparts make them very much attractive. Largely, optical properties and functionalities in emerging technologies, flexibility and semiconducting properties to mention a few make the use of nanomaterials indispensable for photoelectrochemical applications. Owing to this effect, the present chapter deals with the photoelectrochemical (PEC) application of nanomaterials in specific areas such as photoelectrochemical biosensing, hydrogen production via solar water splitting and photovoltaic devices. The chapter also addresses the progress in the utilization of the nanomaterials and illustrates the designs for their integration in biosensors, highly efficient and stable photoelectrode in PEC water splitting for hydrogen production and outstanding smart solar energy harvesting devices for third-generation photovoltaics.

The strategic importance of unmanned aerial vehicles (UAV) has attracted the attention of many researchers. Nowadays, there are numerous serious projects and researches that have gained momentum in recent years, particularly in order to improve the technical performance of the system and to increase the search and practice skills of the model. UAVs, which are the latest technological inventions, are very advantageous in terms of practicality, flexibility, cost advantage and multitude of usage areas.

In March 2020 the world as we know it was shaken to the core by the Covid-19 global pandemic. This crisis has had a significant impact on the EU27 economy and triggered unprecedented policy responses across Europe and the globe. Collaborative Robotics, or Cobots, have made a significant difference to many manufacturing plants by facilitating social distancing between workers, introducing flexibility on the factory floor and allowing the plants to manufacture high quality items for use in the medical field. California-based DCL Logistics, a third-party logistics company, decided to employ Cobots to manage a 30% increase in orders in the immediate aftermath of the outbreak and the Cobots have led to a 300% increase in productivity and a 60% jump in labor cost savings [1]. This paper will look at these flexible robots and discuss the benefits they brought to manufacturing in a time of global crisis.

Currently, crystalline silicon (c-Si) photovoltaics (PV) modules are dominating the PV market, and have the best performance and stability. These PV modules are sensitive to the increasing temperature, and it has been reported that the efficiency of these PV modules goes down by ~ 0.4% for every single degree rise of cell temperature above 25 °C. In that sense, this temperature issue is a major concern, especially in the hot areas of the world. A significant fraction of the solar radiation remains unused in a conventional c-Si PV, which gets converted into heat energy, thus affecting the efficiency. Therefore, it becomes crucial to regulate the module temperature to or near the optimum temperature (around) 25 °C to generate constant power. Several cooling techniques are utilized and are under study; however, the phase change materials (PCMs)-based system to control the PV temperature seems to have higher flexibility. This is due to the long list of available materials to select for the required temperature, better surface covering, and efficiency. Again, among the different PCMs, organic PCMs have a better environmental upshot and are non-corrosive in nature. Paraffin wax and fatty acids are the most used organic PCMs for PV thermal management systems. Therefore, in this chapter key findings of these two organic PCM-based passive PV cooling results are reviewed in the materials prospectives, covering the latest finding and strategies taken and also highlighting future possibilities.

The future of production is smart and autonomous and so should the technologies that allow it. The application of machine learning (ML) in the manufacturing sector has been designated as one of the key players in the digital transformation. In general, the tendency of most ML application fields goes into more complex algorithms and deeper architectures. However, it is not the only available approach and often times not the most suitable for all use cases in the manufacturing industry where environments and products are highly standard. ML has the potential to solve complex problems by finding and learning relationships that humans cannot identify. Our hypothesis is that these relationships are invariant and exist inside specific tasks but also between a wide catalogue of similar, although not yet known, problems. An algorithm that finds and solves these invariances at a general and abstract level creates a general solution that can be directly deployed into new use cases leading to an increase in efficiency by reducing the required effort, time, and knowledge and allowing to leverage ML. Through three preliminary use cases, we demonstrate the potential of applying ML at a general and abstract level to increase the flexibility and reduce the model, task, and dataset dependency of ML solutions. This approach facilitates the conversion of automated environments into autonomous environments.

The technological evolution that enterprises are facing is increasingly fast and dynamic, demanding more and more flexibility and agility in the application of new technologies in an increasingly globalized and competitive business environment. This work has a broad view of the influence of industry 4.0 in the global supply chain companies, as it is essential for long-term business sustainability and one of the main drivers of profitability and growth. The model proposed in this research effort considers the application in the processes of a global company in the supply chain, which includes from the beginning of the demand request, also the industrial activities, to the analysis of the post-sale; finally, an unprecedented indicator was developed to measure the level of application of industry 4.0 concepts in organizations according to the state of the art in the evaluated processes. Then, the model was applied to the real case of a global high-tech supply chain company. The first findings demonstrate the ease of understanding and applicability of the model for companies to analyze the digital transformation in their processes and identify gaps to convert them into real opportunities to leverage their business.

Since current battery cell production lines are not flexible regarding format and material, it is necessary to develop new production systems. It is also required to develop this production line as an agile system in order to be able to flexibly counteract unit-specific capacity fluctuations. In addition, only low scrap rates are allowed when integrating new material systems which requires a holistic in-process or in-line control and the associated quality assurance. Agile production systems open up new possibilities for developing the battery cell product. Therefore, this article will present a novel product-production co-design that can be specifically adapted to customer requirements.

The growing demand for individualized products, shorter product life cycles and rapid technological change are challenging the manufacturing industry. In order to cope with these challenges and satisfy individual customer demands, alternative production systems like the Matrix-Structured Manufacturing System or Fluid Manufacturing System have to be considered. However, to enable the alternative production system to have the necessary flexibility and to ensure an efficient material supply, adaptable and flexible logistics concepts are necessary. In order to develop innovative logistics concepts, the exploration of present and future requirements and challenges for logistics concepts have to be discovered. Therefore, this paper analyzes requirements and challenges for production logistics by conducting qualitative research with a sample of various German companies from five industry sectors (automotive supply, automotive production, mechanical engineering as well as special mechanical engineering and component manufacturing). It concludes with specific approaches to developing new logistics concepts.

Industry faces the market demand towards individualized and functionalized products, which challenges traditional linear linked manufacturing. To overcome limitations of current production concepts the authors presented in previous works the manufacturing concept called Incremental Manufacturing (IM). IM is a hybrid-manufacturing concept where standardized base parts are produced first, then assembled and finalized by additive and subtractive manufacturing steps. This tool free concept can help to reduce machine and equipment investment costs and enables a scalable production concerning batch size, material, process and product spectrum of multi-material parts. Furthermore, this concept affects a design method with minor restrictions with regard to production and a process planning with flexible manufacturing sequences. These new possibilities result in a high degree of freedom in both disciplines, which must be used purposefully and should be restricted in a manageable way. For this purpose, part design and process planning must be interlinked in an early manufacturing planning stage. This paper presents an approach for evaluating different production routes of an IM-designed part based on Inter-process interactions and manufacturing key figures.

Mass customization has become more attractive but requires a transformation towards more flexible solutions in contrast to dedicated manufacturing systems. Flexibility includes complex tasks such as the introduction of new products or new manufacturing processes as well as to efficiently handle daily balancing. The main challenge when it comes to flexibility in manufacturing is to be able to handle the technical aspects and still be competitive. In this article we consider the cost for flexibility to include two main things; (1) setup time, e.g., time for planning, design, programming and configuration, installation, ramp-up, scrapping of old equipment, preparation of facility, hardware installation, and (2) need of competence, inhouse knowledge, external competence, or external expert competence.

In the paradigm of smart factories, flexible and scalable manufacturing resources are essential. The human worker offers great flexibility; however, the operators are often a sparse resource in high-wage countries. Consequently, they are often responsible for several tasks at once and must prioritise the most critical ones. Consequently, the productivity on less critical tasks will suffer in the absence of the operator. In this paper, we present a case study on the effect on productivity when deploying a collaborative robot assistant in a plug and produce fashion to substitute a human worker at manual workstations on a production line. Realistic cycle and changeover times are derived from physical experiments and used in discrete event simulation to analyse two scenarios. The results show that if an operator must abandon his/her workstation, deploying a robot assistant as a substitute reduces the loss of productivity.

Human capabilities to interact, interfere, support, supervise and take over different tasks in a production environment are often not considered in the context of automated and self-organizing factories (smart factories). Adaptive Task Sharing (ATS) is a method to combine the strengths of automation and human skills to provide flexible and resilient factories. ATS offers huge potential for improving and enhancing ergonomics and human factors in production. The assembly worker plays a vital role in such hybrid manufacturing as a partner and coordinator of production services. In recent years, techniques have been developed to efficiently compute production execution graphs optimized for various technical criteria, like minimum makespan or energy consumption. This paper shows how to use these techniques for a flexible production planning that incorporates human workers and investigates different scenarios of task allocation between humans and machines and their impact on production workflows. Such socio-technical interactions between humans and machines need to be at the core of resilient production systems to deal with unforeseen circumstances, provide the flexibility required in high-variability, low-volume production scenarios, and increase productivity and workplace quality of human workers.

Due to increased customization of products, manufacturing companies are subjected to assembly challenges arising from unique product configurations and higher number of variants to be able to produce in low volumes. This translates to an increased production flexibility requirement, while maintaining a high production efficiency. A higher variation among products would potentially disrupt the assembly line and create discrepancies in sequence, tools, and flow of operations. This paper explores how discrete event simulation (DES) can be used as a tool for assembly cell planning by applying key performance indicators (KPIs). A literature review is conducted to identify relevant KPIs that can be applied to compare different assembly scenarios. The KPIs are grouped under the three pillars of sustainability and analyzed as to how DES can be applied to assess them, thereby helping in evaluation and planning of an automation-assisted assembly cell. Finally, an interplay between Automation, KPI and DES, is presented.

While clinical use of whole slide imaging is governed by medical device regulations, non-clinical uses have fewer constraints, and there is far more flexibility in this domain. As a result the newest technology in whole slide imaging may become available to research laboratories and investigators well before it is available in the clinic. For many years in the United States, there were no digital pathology scanners available that had been approved or cleared by the FDA. During this time, scanners were primarily used for education and research. This chapter will focus on the some of the research applications of whole slide imaging including advanced imaging methods, algorithmic research, and clinical/preclinical research.

Human Robot Collaboration (HRC) is considered as a major enabler for achieving flexibility and reconfigurability in modern production systems. The motivation for HRC applications arises from the potential of combining human operators’ cognition and dexterity with the robot’s precision, repeatability and strength that can increase system’s adaptability and performance at the same time. To exploit this synergy effect on its full extent, production engineers must be equipped with the means for optimally allocating the tasks to the available resources as well as setting up appropriate workplaces to facilitate HRC. This chapter discusses existing approaches and methods for task planning in HRC environments analysing the requirements for implementing such decision-making strategies. The chapter also highlights future trends for progressing beyond the state of the art on this scientific field, exploiting the latest advances in Artificial Intelligence and Digital Twin techniques.

This paper proposed an Interactive Online Roadshow Model that comprises three components which are Roadshow Core, Delivery Agent and Digital Interface. The model is developed in action to the impact of physical engagement difficulties during COVID-19, the global pandemic that forced individuals to keep social distance between each other. Thus, it aims to lower the business impact from movement control order that significantly reduces the level of engagement towards the public physically while enhancing stability, flexibility and usability by using digital devices. The proposed model complies essential criteria of physical roadshow in the Roadshow Core component as the fundamental elements of a roadshow. By leveraging the VARK Learning Model, the proposed framework urges a higher degree of engagement for better understanding and deeper impression to overcome the inefficient of non-face-to-face interaction in an Online Roadshow with dynamic advertising and interactive content delivered by Delivery Agent. The Digital Interface in the proposed model allows participants without physical contact, eliminating them from constraints of specific geographical location and time, alternatively lays the foundation for any Online Roadshow implementations. This launches the next generation marketing era that leverages the rising capability of the Internet and big data waves.

In this chapter, we discuss the methodology of the value of information which is one of the central topics of this book (together with the value of flexibility, Chap. 8). The theoretical and mathematical formalism is presented, and several examples are discussed. The logic behind the value of information, for increasing the value of a project through improved chances of success, is explained. The values of perfect, imperfect and fuzzy data are discussed.

This chapter explains the logic of applying real options to improve the value of projects, which is one of the central topics of this book. Different categories of real options and methods for implementing real valuations are presented. Justification and workflow of the ‘engineering approach’ of real options are discussed, and several examples are presented.

Even the reliable distributed filtering problem has been investigated in [139] using type-1 T-S fuzzy model, there exist some space to improve the results, which lies in both how to describe the uncertainties and reduce modeling errors and how to enhance the flexibility of fuzzy distributed filter. Recalling the advanced properties in describing uncertainties, IT-2 T-S fuzzy model provides an more effective approach to designing a fuzzy distributed filter for nonlinear uncertain systems. However, there exist few results for sensor networks with random failures using the IT2 fuzzy approach to reducing design conservativeness and improving robustness. Therefore, the main purpose of this chapter is to propose an IT2 fuzzy reliable distributed filter for sensor networks with random failures.

Printed flexible sensors have the potential to be seamlessly applied to soft and irregularly shaped surfaces such as human skin or textile fabrics. Owing to its varied range of applications in the field of flexible and wearable electronics, soft robotics, human–machine interaction, and biomedical devices, it is required of these sensors to be flexible and stretchable conforming to the arbitrary surfaces of their soft or stiff counterparts. The challenges in maintaining the fundamental features of these sensors, such as flexibility, sensitivity, repeatability, linearity, and durability, are tackled by the progress in the fabrication techniques and customization of the material properties. As a result, materials and structures for innovative flexible sensors, as well as their integration into systems, continue to be in the spotlight of research. This chapter outlines the current state of flexible sensor technologies and the impact of material developments on this field. Special attention is given to stress, strain, temperature, chemical, electropotential, and magnetic sensors, as well as their respective applications.

Sitting postural monitoring is essential to identify risky situations and avoid long-term muscle diseases. In this work, a low-cost sitting monitoring prototype is presented. It consists of 16 FSR pressure sensors located both in seat and backrest of a cushion. To evaluate the system several tests have been carried out, comparing the results with a commercial mat. The results show that the device allows to track efficiently postures reducing the cost in hardware and gaining flexibility for subsequent data storage and management.

Industries make use of multi-cloud strategy which allows them to select different services from different providers because of reasons like flexibility, availability of computing resources and data storage, risk management, etc. A cloud broker also called as cloud agent is required in this case for cost-effective work distribution between cloud service providers. The broker also provides services such as deduplication, encryption, and data life cycle management. The abovementioned operations performed by a cloud broker necessitate the importance of a broker with desirable properties and smart functionalities for capturing, management, and sharing of knowledge. Therefore, an architectural procedure for textual-based information extraction that uses data mining technique to classify the documents is presented. It also discusses about multi-cloud-based data broadcasting services and computing that can improve the outcome of the system.

The numerical model of material damping adaptable to the finite element structural dynamic analysis is proposed in the paper. In comparison to the classic external damping model used in this research internal damping model is considered nonlocal in time according to the hereditary mechanics concept. Efficiency of the nonlocal model, its advantages over the classic Kelvin-Voight hypothesis and its flexibility is considered on the example of the composite beam dynamic analysis. The example of nonlocal in time damping model key parameters using the 3Dnumerical simulation results is shown. The 3D numerical simulation was implemented in Simulia Abaqus considering the orthotropic properties of the material.

Throughout history, wood as a building material has been used extensively thousands years ago. It is considered the second known building material after stone; thus, it is recognized as an ancient building resource material. However, wood as a natural rigid material of plant origin has been substituted and associated with man-made common rigid materials in the building construction such as steel and concrete that are characterized with higher carbon emissions and lower sustainability. But with the rise of environmental stewardship in response to the high carbon emissions in cities, sustainable material usage in buildings has reached spotlights. Wood or timber is recently revitalized in contemporary construction as a sustainable built material for being highly renewable and nontoxic and having low embodied energy feature, even though it has been recently used in skyscrapers. In addition, stakeholders are encouraged to integrate timber in all various building types and to reinvest the sustainable material in construction for its recognized insulation, durability, flexibility, affordability, and aesthetic nature, not to ignore the benefits of wood to the building occupants in enhancing their emotional and physical connection with their occupied spaces. This work is intended to compose a significant achievement in educating architects to look at subjects normally ignored and to consider the use of organic material such as wood (timber) in meaningful ways. It focuses on enriching the knowledge base of sustainable wood and lumber in new and existing buildings by highlighting the advantages and disadvantages of timber material usage; it also aims at encouraging the sustainable experience of timber and broadens its usability and functionality in the industry. In this book chapter, local and global existing building cases are reviewed and assessed for the integration of timber as a construction material in the structure, skeleton, interior, and facade of these buildings. The advances and drawbacks of timber usage in construction are highlighted through the assessment of cases. Finally, a comparative analysis that emphasizes the sustainability of timber as a building material in different case studies is provided. Further, life cycle analysis of timber usage in buildings is to be studied.

Online learning is a cost-effective way of promoting social inclusion and enhancing the work-life economy. International employees and higher education students can find that they have limited opportunities to access comprehensive supplemental training opportunities, related to employment, in Finland. Tailored services based on UX design can be beneficial for their supplemental studies. This paper explores key elements identified on the basis of UX design, and how these elements can be utilized to develop effective online learning designs. An innovative case design was implemented in a Finnish firm (with 270 service users) between 2015 and 2019. The key design elements identified relate to types of accessibility, namely pedagogical, lingual, technological and economical. The study explains noticeable improvements in the overall process due to UX design utilization, such as flexibility, cost-effectiveness, user-empathetic solutions and an interactive learning experience. The practical implications of this service include enhancing required competencies and strengthening sustainable work culture.

The 3-Dimensional (3D) printing in footwear has become a significate development direction. Researchers have found that providing a suitable insole can combat pain and help to avoid fall-related injuries. Therefore, many research projects focusing on insole designs and functionality were conducted. In contrast, a midsole design for footwear has not been adequately developed in functional footwear. A lot of footwear research projects looked at insole or midsole modification but did not include a changeable drop-in sole inside the midsole. Different softness and construction of 3D-printed midsole were developed to simulate the foot comfort and biomechanics measurement. The changeable 3D-printed midsoles can increase comfort and flexibility for customization in functional footwear. The current study demonstrates functionality and flexibility simultaneously for footwear design in terms of comfort control. Footwear consisting of different 3D-printed midsole structures that improve comfort can decrease injury frequency. Consequently, the midsole providing a comfortable, soft, and movable dual-density footbed is critical to wearers.

As a result of the research, a combined neural network method, models and modeling tools for the operational control of complex technical systems have been developed and investigated. The analysis of existing methods, models and software for operational management of complex technical systems; the requirements for the developed methods, models and software for managing these systems have been substantiated. A combined neural network method for modeling complex technical systems based on a hierarchical neural network model is proposed, combining the capabilities of analytical and neural network approaches and allowing to significantly increase the efficiency of operational management (quality and efficiency of management decisions), as well as the flexibility of modeling and the possibility of operational management of calculation of structural and parametric optimization of the model based on information about the system obtained in real time. A hierarchical neural network model for the operational control of complex technical systems has been developed, consisting at the lower level of a set of interconnected neural network models corresponding to the elements of the modeled system, and a supervisor at the upper level designed to identify structural errors at the lower level of the model. A method of decision support for managing complex technical systems in real and pseudo-real time, based on the proposed hierarchical neural network model, is proposed. Software tools for combined neural network modeling and decision support have been developed for the operational control of complex technical systems.

Towards greener production, manufacturing companies face several challenges, for example peak load shaving or flexible production planning as parts of demand-side management (DSM). DSM uses processes that can be shut down, shifted, or controlled. Advances in digitalization in the energy sector and manufacturing systems create transparency which in turn offers new opportunities to commercialize energy flexibility potentials as optimally and automatically as possible. The variety of flexibilities in manufacturing systems and various dependencies of different kinds of complex manufacturing processes complicate the modelling and aggregation of flexibility. To overcome this challenge, we developed a method for the aggregation of energy flexibilities that is based on a generic energy flexibility data model. The method proposes a two-step approach to aggregate flexibilities cost efficiently and considers manufacturing specific limitations. For cost-efficient aggregation, we use in the first step the merit-order model known from the energy industry and in the second step the bin-packing problem originating from combinatorial optimization, adapted according to the generic data model. The two-step approach allows energy flexibilities to be aggregated across industries, facilities, and systems, thus ensuring broad applicability.

3D printing (3DP) is one of the emerging technology in 21th century and popular in academics and industries. Wide range of materials printed by 3DP and it should be noted that the flexibility of 3DP materials comes from the range of 3DP systems and the seven categories specified in the ISO/ASTM standard. Also, it is not surpassed by all new printers or processes for novel materials. 3DP can never be used as a stand-alone process, being an integral part of a multi-process system or an optimised multi-system process to suit the production of innovative materials and new product requirements. 3DP technology has gained popularity in all fields such as automobile, healthcare, aerospace, recreation, textiles, apparel and the fashion market, etc. Researchers, textile technologists, designers of apparel, suppliers and distributors have since the last decade; they have been working on implementing 3DP technology in their respective fields. 3DP has been recognised as an innovative and efficient process but still, a lot of work is going on these days to use sustainable materials in 3DP from an environmental viewpoint. This chapter provides a brief review on biomaterials that can be used in this technology for sustainable development and strengthen the knowledge bank to the young researchers working in this field.

The state-of-the-art field of bio-inspired soft robotics promises emergent and inventive methods to utilize robots made from soft, flexible materials in a wide range of applications. This research is on-trend, describes an advancement in the design of a wearable robotic glove to assist people suffering from hand impairments regain their ability to control their environment. This study built a streamlined, inexpensive electro-pneumatic system that can work with small and simple actuators to increase for finger movement flexibility. The use of pneumatic pressure to stretch and bend the actuators through inflation or deformation of elastic chambers, making this glove work effectively. The designed pneumatic device consists of STM32F101C8T6 microcontroller combined with a series of pumps and a matrix of solenoid valves. Along with the electro-pneumatic system, we also applied it to develop a wearable robotic glove (made from Ecoflex™ rubbers) which is suitable for the pneumatic system by the series of experiments that mechanically characterize the actuators. The results gathered in this study validate the feasibility of the first prototype of our soft robotic glove as an effective device to assist hand function in individuals. However, performance and ease of use of the system should be improved further in future development phases.

In modern society, the ever-growing prevalence of stroke or hand disability, putting an extreme burden on the limited financial resources and capacities of health care providers in some of the Low and Middle-Income Countries (LMIC). Due to the large scale of flexibility and adaptability, soft robots turn into a smart solution for hand rehabilitation, especially soft pneumatic actuators. In this paper, we proposed a general design of soft robotic glove for hand rehabilitation with functional grasp pathologies by applying soft pneumatic actuator technology. Soft actuator included integrated channel connect each chamber to lead the air to go through the entire actuator and produce bending motion conforms with human finger shape and motion. Base on this aspect, before fabrication, a Finite Element Model (FEM) was built to analyze the bending curvature of these actuators. The soft actuator is cast in 3D printing molds using low-cost elastomer Ecoflex™ 0050. We carried out empirical tests to validate the findings of experimental actuators and compared them to FEM data displaying strong agreement. Finally, the design of a wearable for patients to practice rehabilitative activities such as grasping patterns. The outcome of this paper shows that the design of rehabilitation glove using soft actuator technology gave a successful solution for the health care system with low-cost material.

Today’s warehouse processes require more flexibility than conventional methods in behalf of greater responsiveness to customer orders. The improvement of a smart warehouse strategy through the use of big data technologies and AI, including smart data collection and data manipulation tools, to improve warehouse efficiency and productivity is a popular discussion topic. AI-based data manipulation techniques are used to categorize the types of optimal input for the operation of retrieving goods from specified storage locations based on customer orders, order picking to provide more flexible planning and predictive analysis in the warehouse and distribution center. This study focuses specifically on a web-based interactive AI order picking optimization tool for data manipulation. The AI-based data manipulation tool has a mainspring effect on warehouse performance to get a robust dynamic order picking operation that allows for changes of picking lists during a picking cycle in the warehouse.

In today’s world, products are getting more complex due to raising complexity of the real-life problems. Therefore, while creating a product or service, organizations should work together to combine different technologies. Also, working with best supplier for an organization is significant to make products or services more flexible, competitive, and profitable. In this study, suppliers are evaluated based on quality, cost, maintenance, and flexibility criteria with twelve sub-criteria under them. A hybrid method with VIKOR and Intuitionistic Fuzzy in MCDM problems is represented and the methodology is used for the choosing supplier for a product that is used in IT projects. Decision making matrix is observed with intuitionistic fuzzy due to uncertain data, and the problem is solved by VIKOR to find best alternative for group utility. In case study is performed to exposed efficiency of the hybrid model.

The globalizing world economy is constantly challenging producers with difficult competitive conditions. Manufacturer companies determine various methods to overcome these difficulties. Industry 4.0 and IoT applications, which have been popular in recent years, are the main ones of these methods. With these methods, solutions covering almost every area of our lives started to be produced. This approach, which we also call digitalization, has spread rapidly all over the world. In order for manufacturers to have more advantageous status, they need to reduce production costs in order to achieve more production flexibility and more efficient processes. Digitalization provides great advantages for efficiency increase. This method, which covers the product life cycle in all aspects and provides instant answers, seems to be developed for many years and will remain in the focus of the manufacturers.

Flexibility and high throughput rate in storage and retrieval systems are essential criteria in today’s competitive marketing. Recent developments in information technology enable the intelligent design of systems. This study aims to propose a tier-captive aisle-to-aisle shuttle-based storage and retrieval system (SBS/RS) where shuttles can make autonomous decisions to prevent deadlocks and collisions as well as the efficient process of transactions. Deadlock prevention algorithms are one of the primary concerns in today’s autonomous vehicle environment. In the considered tier-captive aisle-to-aisle SBS/RS, multiple shuttles can travel between aisles in a dedicated tier. The advantage of this design is that there may be the fewer total number of shuttles running in the system compared to a traditional tier-captive SBS/RS. Due to the complexity of the proposed system and autonomous shuttle-based decision-making target, we utilized the advantage of an agent-based modeling approach by simulating the system. Agent definitions, roles, and behaviors are specified to ensure that no collision and blockage take place in the system. Thanks to the intelligent abilities of agents so that the system can run effectively by using real-time information.

The Internet of Things (IoT) has been a prominent application for Intelligent Systems in recent years. While the increasing demand for explanations led to many advances in Explainable Artificial Intelligence (XAI), most solutions focus on systems where a single agent takes all decisions to be explained. However within the IoT context, Cyber-Physical Systems (CPS) are decentralized, with multiple agents coordinating their decisions to control the overall CPS. By contrast users expect coherent system-wide explanations, as if they were generated by a single agent. We propose a decentralized Explanation System generating such explanations while preserving the advantages of decentralized control: separation of concerns, heterogeneity, flexibility, ...Our architecture relies on: i) decentralized XAI Component specialists for providing partial explanations based on local knowledge; ii) a central generic Spotlight composing local explanations into a global explanation. We illustrate and qualitatively evaluate our approach via a proof-of-concept implementation for a smart home system.

In control theory, the ITAE criterion (integral of time-multiplied absolute value of error) is very well suited for setting the parameters of controllers, as it uses a step response and integrates the difference between the desired and actual value weighted over time. This criterion is to be minimized when setting the controller parameters. In the state of the art, parameters as example for PID controllers are found by hand and with the help of computing or Matlab toolboxes in order to minimize the ITAE or other criterions. The method presented here uses a machine learning algorithm for the automated search for the optimal controller parameters, in order to minimize the ITAE criterion. It can even be used both, in the simulation and directly on the real system. Since PTn systems have to be regulated in many cases, these are used here as example. With the application of this method, it is possible to find the parameters either using a Simulation, or directly on the real system. In the specific system, the temperature control of a thermal actuator with a small temperature chamber was applied. In particular with thermal actuators, it is often difficult or even impossible to place the sensor directly next to the heat source. This leads to PTn plant systems. The method works for this specific example and, due to its flexibility, can be extended to a huge number of applications in control theory.

Smart city concept gained a lot of attention from the research community due to its effect on everyday life. The implementation of smart city applications requires a reliable smart infrastructure. New applications set high requirements on the communication network infrastructure such as low latency, high bandwidth, availability, and security. This paper aims to provide an overview of key developments in optical networking for 5G transport and their application in connecting a huge number of devices in smart infrastructures. Requirements on optical networks to support 5G transport, development activities, and research directions are pointed out in this paper.

Data visualization, especially if we are talking about a large volume, can, and should, be presented as a graphical and visual representation supported by a computer in an interactive way, and in this way it allows supporting the decision maker and increasing his cognition. The appropriate tools, methods and techniques can increase the understanding of data, with greater importance if they were large in volume and multidimensional. These visual and interactive representations, associated with analysis methods, enable decision makers to combine flexibility, creativity and human knowledge with the resources of computer storage and processing to obtain a more effective view of complex problems. Decision makers, too, should be allowed to interact directly with data analysis, adapting it to their tastes and needs. In this article, the mlv-viewer prototype will be presented, which, in short, consists of a universal decision support system, allowing multilevel data visualization, associating a set of data with a symbol.

Demand Response (DR) concept, introduced by the Smart Grid paradigm, is presented as one of the main solutions to mitigate the effects of the intermittency of Distributed Generation sources in the network. With this, the consumer’s role in the energy market is empowered and their flexibility is crucial. The technology advances allow bidirectional communication providing signals to the active consumers, given by the entity manager, participating in DR events to alleviate problems in the grid. The authors proposed a methodology, resorting to load shifting, to lessen voltage limit violations in some points of the grid. After the detention, the DR event is triggered and the small resources are scheduled, requesting a reduction to the active consumers. The authors believe that uncertainty in the response must be considered so, the willingness and availability from the active community are contemplated on the problem. The results show the necessity of pondering consumer behavior.

One of the main purposes of financial technology solutions is to reduce the infrastructure costs of financial institutions. Adopting the use of FinTech technologies implies, in addition to a reduction in costs derived from physical entities, an improvement in user satisfaction, greater flexibility in terms of data access on different devices and more transparency in financial management. As a result, this paper proposes a blockchain-based scalable platform for investment recommendations. The platform proposed in this research serves as a decision support tool. A use case is included showing the process from the initial product search to the final investment recommendation that concludes in the optimal portfolio.

In the current research on software reliability models, the total number of faults and its relevance to imperfect debugging are not sufficiently emphasized or considered. To solve this problem, we firstly classify and analyze the total number of faults in the current reliability models, and then establish a generalized imperfect debugging framework model that considers fault detection and repair. On this basis, we further consider the phenomenon of introducing new faults in debugging and establish concrete imperfect debugging models from the perspective of incremental changes in the total number of faults. By using 12 publicly published failure data sets (FDS) widely used to evaluate the reliability models, we demonstrate the overall performance of the proposed models. The results show that the proposed models have an excellent account in fitting and prediction, which proves their effectiveness and flexibility.

3D-printing technologies give better alternatives to improve on the functional abilities and traditional manufacturing technique of custom orthotics aids. The flexibility of design, mass customization, use of biodegradable materials and ability to build complex structures make it more popular over traditional methods. The traditional plaster of Paris method is bulky, breathless and fixed. The traditional method of fabrication may cause skin diseases, uneasiness and misalignment of joints. 3d printed orthotic aids overcome these problems. In this work recent advance in 3d-printed orthotics aids are presented. The design of fully customized 3d-printed orthotic aids is presented in a proper sequence. The geometrical data from patients affected parts are taken using photogrammetry software or 3-D scanner and data is imported in CAD modeler followed by modeling and topological optimization for stress analysis. The literature available on different 3d printing technologies used for printing orthotics aids is minutely reviewed. The selection of the right material for an orthotic aid is very important for its success. The physical properties of materials like strength, toughness, density, wear resistance and heat resistance are considered while selecting the materials for printing. The different types of polymers used for fabrication of orthotics are discussed in this paper in detail. Apart from this challenge for the fabrication of orthotics aids using 3d printing also reviewed.

In addition to the standard methods introduced in the previous chapters, there are some interesting alternative discretization techniques that might be useful for special classes of continuum mechanics problems in order to enhance the accuracy, efficiency, and/or flexibility of the numerical predictions. A selection of such methods is discussed in this chapter.

Robotic-based human assistance is increasingly spreading both in clinical practice and in the industrial sector. The use of wearable robots, such as exoskeletons, is considered one of the most promising applications since it allows for merging human flexibility and creativity with machine endurance. Such devices shall be comfortable and lightweight to be as transparent as possible to the user’s perception. At the same time, they shall also be stiff enough to drive body parts while bearing the exchanged forces efficiently. The gold standard methodology to achieve such results is the topology optimization, which, even if used mainly in structural optimization, is still relatively new to wearable robots. In this paper, the authors propose a topology-optimization-based design strategy suitable to be applied, if adapted case by case, to a generic design of a wearable system. The presented test case, used as a benchmark to assess the optimization-based design process, focuses on a hand exoskeleton made of aluminum alloy, resorting to an additive manufacturing process.

The introduction of Cyber-Physical Systems (CPS) in the industry through the digitalization of equipment, also known as Digital Twins, allows for a more customized production. Due to high market fluctuation, the implementation of a CPS should guarantee a high flexibility in both hardware and software levels to achieve a high responsiveness of the system. The software reconfiguration, specifically, introduces a question: “With heterogeneous equipment with different capabilities - namely processing and memory capabilities - where a certain software module should execute?”; that question fits on the task/resource allocation area applied to CPS software reconfiguration. Although in task allocation issue several approaches address such a problem, only a few of them focus on CPS resources optimization. Given that, an approach based on the Dutch Auction algorithm is proposed, implemented at the CPS level enables the software reconfiguration of the CPS according to the existing equipment resources. This approach, besides the optimization of the CPS resources and the energy consumption, transforms the CPS in more reliable and fault-tolerant systems. As shown by the results, despite the demonstration of its suitability in task/resource allocation problems in decentralized architectures, the proposed approach also as a major advantage of quickly finding a near-optimal solution.