A smart city represents an improvement of today’s cities, both functionally and structurally, that strategically utilizes several smart factors, capitalizing on Information and Communications Technology (ICT) to increase the city’s sustainable growth and strengthen the city’s functions, while ensuring the citizens’ enhanced quality of life and health. Cities can be viewed as a microcosm of interconnected “objects” with which citizens interact daily, which represents an extremely interesting example of a cyber physical system (CPS), where the continuous monitoring of a city’s status occurs through sensors and processors applied within the real-world infrastructure. Each object in a city can be both the collector and distributor of information regarding mobility, energy consumption, air pollution as well as potentially offering cultural and tourist information. As a consequence, the cyber and real worlds are strongly linked and interdependent in a smart city. New services can be deployed when needed, and evaluation mechanisms can be set up to assess the health and success of a smart city. In particular, the objectives of creating ICT-enabled smart city environments target (but are not limited to) improved city services; optimized decision-making; the creation of smart urban infrastructures; the orchestration of cyber and physical resources; addressing challenging urban issues, such as environmental pollution, transportation management, energy usage and public health; the optimization of the use and benefits of next generation (5G and beyond) communication; the capitalization of social networks and their analysis; support for tactile internet applications; and the inspiration of urban citizens to improve their quality of life. However, the large scale deployment of cyber-physical-social systems faces a series of challenges and issues , which requires more smart sensing and computing methods as well as advanced networking and communications technologies to provide more pervasive cyber-physical-social services. In this paper, we discuss the challenges, the state-of-the-art, and the solutions to a set of currently unresolved key questions related to CPSs and smart cities. Cyber physical systems (CPS) are complex, heterogeneous, distributed systems where the cooperation among cyber components  and physical processes  is deeply intertwined. A CPS is defined as a system where the computation, networking, and physical processes are integrated to monitor and control physical environments . The difSundmaeker et al.  stated tAvailable IoT devices may be equipped with a microcontroller unit (MCU) and/or a microprocessor unit (MPU) , exploitAdministrative: The environments belong to several owners, private or public, that are free to make their own choices according to various factors: financial, bureaucratic constraints, etc.Technologies advancement: CPS realized at different times adopts different technologies.Incompatibility with previously deployed technologies.A traditional fire system delivers the perceived signal to a central processing system that correlates the signals with other sources and decides whether to activate the alarms, while also informing firefighters and surveillance. A CPS typically operates side by side with a multitude of other CPSs , making it difficult, or even unfeasible, to have a unique framework to manage the resulting whole system for several reasons:CPS federation is the new direction followed by many researchers, which allows integrated autonomous CPSs through federation to manage, coordinate, and organize sensors, actuators, and host resources and to provide support for the development and maintenance of high-level services .A first example of a CPS is the Smart Building (SB), historically defined in 1981 with the term Intelligent Building, coined by the United Technology Building Systems Corporation, then, implemented in the City Place Building in Hartford, Connecticut . “Smart”In 2009, the European Commission’s Information Society provided a long and complex definition of a Smart Building ; a simplAnother category of CPS that is relevant and interesting is that of public infrastructures. Streets, public buildings, and undergrounds are commonly considered part of a smart city . EffortsIf the answer is yes, we can model such interconnection in a tightly coupled way by adopting a Software-Defined Building approach.If the answer is no, we are obliged to consider a loosely coupled interaction where not all the capabilities of the systems are shareable, according to a federated cooperative approach.A smart city experiences a combination of several CPSs that have to be interconnected with the others by exchanging data, raw or pre-processed, enabling workflows involving the city to support everyday citizens’ lives. With regard to the realization of such interconnections, the following question arises: Do the CPSs belonging to the same administrative domain? The answer guides us toward one of the following two solutions:to control and manage devices of the lower layer,to orchestrate, aggregate, filter, and preprocess data coming from the infrastructure layer, andto offer functionalities exploitable by the applications that provide abstraction of the IoT devices available in the infrastructure layer.A Software-Defined Building see  can be dA summary of this vision is depicted in management layer, which provides platform-advanced features and, at the same time, enhances the infrastructure core mechanisms.In this way, the infrastructure becomes very complex and has to be properly managed, therefore this layer has to provide core mechanisms for the smart city, enabling as many as possible users/citizens to access all the available resources. Evolving 5G and future technologies for network and computer infrastructure management have to be considered as well. On top of the infrastructure layer is the Application layer enables the smart city scoped services and applications to operate on several urban contexts, such as mobility, waste, public safety, energy, e-health, water, and BIM, to name a few. Finally, the stakeholder layer includes all the players involved in the smart city everyday life .This may include AAA, monitoring, profiling, SLA and QoS mechanisms, security, privacy, policies, orchestration strategies and placement, credit-reward systems, incentive mechanisms, and so on. The The #SmartME project  is a croOne of the main novelties of the #SmartME project is to set up a new, crowd-sourced and shared form of a smart city where anybody, from shops and businesses to private buildings and from public administrations to citizens, can share their sensing and actuating hardware facilities to build up infrastructure. The resulting system has to be properly managed; thus, a specific framework allows the contributors to share their resources with application developers and users to ensure a simple and powerful access to all the available resources.In this paper, the Stack4Things (S4T)  was adopMore specifically, the state of the art on complex CPSs is presented in We refer to a complex CPS as a system composed of several smaller CPSs that belong to different administrative domains (such as different private owners or a mix of private and public owners), according to a federated cooperative approach. In this scenario, a crucial topic is how the shared CPSs facilities are exploited ,13. DuriThe cooperation system has to avoid SLA’s overwhelming limitations as defined and agreed by the involved entities. For this reason, coordination and cooperation patterns for service selection were also evaluated, with consideration of the approaches adopted in the literature, both in the cases of brokered and decentralized ones.allows more data available for applications running on CPS,enables the sharing of computation resources between CPSs, andcreates an infrastructure enabling the exploitation of Cloud, Fog, Edge, and Cloud Continuum approaches without increasing the cost for the CPS owner.In particular, the complex CPS represents the next step in the smart city research. This aggregation of CPSs, as shown in All the advantages discussed until now regarding the cooperation among CPSs makes possible the realization of platforms and applications, improving citizens’ lives decisively. The applications exploitable in similar scenarios are uncountable. They span from advanced traffic monitoring, management and driving utilities, to the realization of enhanced Intrusion Surveillance System based on neighborhood surveillance systems and to cooperative emergency management that supports rescue activities.Another interesting aspect related to CPS and, in particular to CPSs cooperation, is the distribution of computation among the available computing elements. Let us make an analogy among a human and a CPS: we can assume that the eyes and the hands of a CPS are represented by the IoTs, while the body and the brain are equivalent to the Cloud. In this way, the CPS becomes a perfect infrastructure where it is possible to apply the Cloud Continuum  principles ; in partFog and Edge computing ,20 are pThese techniques, supported by new emergent computing paradigms , as theyIn this research path, cooperation schemes and techniques for the selection of resource providers are important issues to be addressed. A literature review to understand the differences between brokered and decentralized Federated Cloud Service Providers is available in .According to this vision, it is possible to consider a smart city as an ecosystem of services and relative infrastructures implementing the characteristics already discussed. In this way, the scenario depicted has to be observed through a holistic view, i.e., an all-encompassing approach combining heterogeneous services and technologies to provide a wider  solution to (Smart) city problems.In this regard, it is necessary to realize a scalable architecture aiming at multiplexing, sharing, and reusing services and technologies on the urban scale. The goal is the creation of a homogeneous ecosystem that enables the applications to be scaled out to a metropolitan (or even wider) scope; this requires an ICT infrastructure that is open, shared, able to access storage resources, process data, provide networking, and finally sense and act on the real world.Stack4Things  is a manSmart cities are continuously embracing the IoT technological evolution, combined with relevant advances in the areas of 5G networks and Mobile Edge Computing (MEC). Under this perspective, smart cities of the future can be considered as a microcosms of interconnected  “objects” where advanced and human-centric services can be provided to citizens in the form of cyber physical systems.This evolution moves in parallel with the increase in the heterogeneity of IoT technologies ,26 in teAs the number of IoT devices and solutions expands, any proprietary approach does not scale and, thus, slows the growth of the IoT ecosystem. Such heterogeneity and multiplicity make inherent the need for the design of architectural approaches that are able to support a high level of convergence and integration among existing and evolving IoT technologies. To do so, as stated at the World Economic Forum report for “Realizing the Internet of Things” , the ariToward the design of next generation CPS systems for Smart Cities, novel schemes msut be developed that are able to tackle convergence, openness, and interoperability. Next-generation CPS will enable and will be enabled by current and future advances in several emerging technologies, such as 5G, cloud/edge-native computing, tactile internet, and artificial intelligence (AI), presenting a tremendous potential for the development of solutions applicable to Smart Cities.In the following, we highlight a set of identified challenges toward the development of innovative solutions for the smart cities of the future, as well as a set of enabling technologies that can boost their development. An overview of the set of identified challenges and the technologies that can be exploited to address them is provided in I/OCloud concept exploiting the Stack4Things framework and presented in [The mirroring of IoT objects can be a catalyst for supporting interoperability with IoT devices that support different communication protocols or semantic representations, as well as for significantly enhancing privacy and security aspects by protecting the IoT device through its digital twin (DT) in the edge part of the infrastructure . This peented in . This caThrough the interpretation of different types of semantics, IoT devices and CPS systems can be managed in a neutral way in terms of semantic representation, while the collected information can be automatically interpretable and integrable by the edge and cloud part of a smart city application, avoiding lock-in in a specific modeling approach. With regard to the semantic interoperability, various information models targeted to the development of CPS have been made available, such as the W3C Web of Things , OMA IPSMoving one step further, modularity, openness, and interoperability should be supported by design toward the development of distributed and self-adaptive IoT applications. The adoption of cloud-native principles and the adherence to microservices-based architecture along with lightweight containerization mechanisms based on containers, allows the flexible, scalable, and dynamic composition of IoT applications with different application components. Each IoT application component can be independently deployable and orchestratable in the edge or cloud part of the infrastructure.Generic IoT functions or enablers can be easily introduced and constitute a part of the overall application—a characteristic that cannot be feasible in a monolithic architectural approach. As discussed in To take advantage of the aforementioned technologies and fully support interoperability aspects, the development of end-to-end semi-automated and dynamic orchestration mechanisms for IoT applications is required. Emerging cloud and edge computing orchestration solutions by jointly exploiting 5G and AI technologies can act as a catalyst for the development of novel IoT orchestration mechanisms. 5G and IoT worlds evolution are highly interrelated, as 5G is considered an enabler for the support of massive IoT (MIoT) interconnecting numerous devices that require ubiquitous connectivity, whether mobile, nomadic, or stationary, as well as more advanced solutions that may be categorized as Critical IoT with advanced security, automation, and reliability requirements.To cover the needs of numerous IoT applications with diverse requirements, the IoT slice concept has been introduced following similar network slicing specifications in the 5G domain. An IoT slice regards a partition of the available network and the programmable infrastructure that can be reserved and isolated for serving IoT application needs.Context awareness is important for mapping application needs to IoT slice specifications and assuring high QoS and Quality of Experience (QoE) levels. Over a dedicated context-aware IoT slice for an IoT application, semi-automated orchestration mechanisms can be applied, deploying and managing the operation of the application components and IoT functions in both edge and cloud computing.Serverless techniques, in particular FaaS approaches, can be easily exploited to create the above-mentioned IoT slices. As described in Next generation CPS interconnection and management solutions that facilitate the smart city of the future operation have to be human-centric, serve human needs, and lead to applications that increase social well-being. Tactile and haptic communications  based on physical or remote interactions have to be supported through easy-to-use human-machine interfaces, while low latency and high availability, reliability, and security requirements also have to be guaranteed ,38.The convergence of tactile internet technologies with IoT slicing and IoT object mirroring technologies appears as promising for tackling these needs. The term “tactile internet” was defined by the IEEE P1918.1  as: “A nInvolving human-in-the-loop (HITL) by considering human perception and enhancing the collaboration and interaction between humans and machines in real, virtual, and remote environments based on explainable AI mechanisms, is crucial. Augmented and virtual reality (AR/VR) technologies and intelligent IoT devices can undertake a significant role in this process. The potential delivery of physical tactile experiences, remotely or locally, enables the delivery of skills in digital form. This novel domain, named the Internet of Skills (IoS) , will reOf significant importance is the consideration of more realistic user behaviors in accessing the available communications and computing resources, thus, giving rise to risk-aware and cognitive data offloading approaches in MEC environments and, thereby, realizing an HITL methodology.Data management is key to a viable IoT strategy. The nature of IoT data is frequently heterogeneous and unstructured when collected at the cloud. Data lakes have become popular for storing, crawling, cataloging, and indexing data from IoT devices . One stBased on the collected data, IoT devices also need to be able to learn from each other, which means that there is a need for leveraging on all the local AI models created on IoT/edge devices. Federated learning techniques make this possible since the IoT end devices use their local data to train the machine learning models required by central services. IoT devices send the model updates rather than raw data to the centralized services for aggregation . FederatMore generally, this is valid for all the distributed learning programming techniques that may take advantages from the IoT facilities exploitation, as made in  with theIn this section, we present the main aspects that are examined considering the interplay between cloud and edge computing resources toward the development and management of IoT applications in a smart city. In most IoT applications, diverse computer, network, and storage requirements have to be satisfied to provide the required levels of Quality of Service (QoS) and Quality of Experience (QoE) for the end users.Such requirements have to be satisfied while taking into account a set of constraints that may be related to the existence of limited resources at the edge part of the infrastructure, the dynamic network topologies based on the activation and deactivation of IoT nodes and the fluctuations in the posed workload by the end users.The trade-off between the optimal usage of the available resources and the associated impact on the IoT applications performance has to be dynamically assessed and must lead to resource management actions at the edge and the core part of the infrastructure. To achieve this, part of the IoT functions that compose an IoT application graph may be executed at the edge part of the infrastructure, while another part may be executed at the cloud part. Decision making regarding the amount of resources to be allocated per part of the infrastructure  is usually made by centralized orchestration mechanisms that have a full and accurate view of the current status of both the available resources and the application performance.Various orchestration approaches have been developed in recent years under work realized within different working groups in the areas of Multi access Edge Computing (MEC), Network Function Virtualization (NFV) and—under a wider umbrella—5G technologies. One of the three main categories of 5G usage scenarios regards the Massive Machine Type Communications (mMTC) where very dense IoT deployments can take place, particularly in smart city environments. In the MEC approach, focus is given to the application part that is placed at the edge of the infrastructure.Use cases include applications offering video analytics, location-based services, augmented reality, optimized local content distribution, and data caching at the edge. MEC applications may be provided by application providers or telecom operators. In the latter case, a MEC application is part of a service offered by the telecom operator. In the NFV approach, focus is given to satisfying the network requirements posed by the application through the deployment of network services composed of a set of virtual network functions. Such network services are provided on behalf of telecom operators. Use cases include virtual firewalls, IP multimedia subsystem controllers, virtual Customer Premises Equipment (CPE), virtual authentication, authorization and accounting functions, and virtual content delivery networks (CDN).However, moving toward the next generation smart IoT applications, there is a need for convergence of MEC and NFV technologies and the support of the end-to-end management of IoT applications considering both application-oriented and network-oriented performance metrics. Radical new applications can be developed through the joint adoption of these technologies. Several 5G research activities are taking place toward this direction taking advantage of the evolution of 5G orchestration mechanisms and dynamic network slice management approaches .5G was designed to leverage network software technologies, such as NFV and MEC, to expose customized network instances and resources at the edge of the infrastructure to vertical stakeholders . One appIn this case, an IoT-oriented network slice is managed by a telecom operator and made available to application providers. Over the IoT-oriented network slice, the application providers are able to deploy and manage the IoT applications, taking advantage of the evolving 5G capabilities.In addition to approaches for the convergence of MEC and NFV technologies, various application orchestrators targeted to edge computing environments were also made available, stemming from both open-source communities and commercial releases .These orchestrators include modules for managing application components deployed at the cloud and edge part of the infrastructure, as well as managing the IoT devices interconnected at the edge components. Specific resource managers are made available for managing the deployment of—part of or entire—IoT applications at the edge and the provision of continuous feedback regarding the status of the edge application components .In this section, we present an end-to-end approach for tackling the lifecycle management of the design, development, and orchestration of IoT-based applications over cloud and edge computing resources, targeted to smart city environments. We tackle issues related to the design of IoT applications based on the adoption of cloud-native principles and the development and adoption of generic IoT functions that can serve generic application requirements, and we cover needs that are present in various application domains, the orchestration of IoT applications over dynamic programmable infrastructure, and the appropriate modeling of IoT devices to tackle interoperability issues.The proposed approach is in accordance with the four-layer model of a smart city that is presented in The proposed approach aims to tackle the set of challenges that are identified in Improved intelligence and automation is supported by IoT applications management mechanisms that are provided by emerging orchestration platforms for cloud and edge computing resources. Efficient IoT data storage is partially tackled in the Infrastructure management part with the provision of storage capabilities at different parts of the infrastructure, as well as the IoT applications development part with the provision of various generic functions that support distributed data management and analysis techniques. Finally, the design of human-centric IoT solutions is also tackled in the Infrastructure management part where the IoT devices can provide advanced interfaces for human-machine interactions.An IoT application is represented in the form of an application graph, following cloud-native principles and enabling the enforcement of a cloud-native IoT management approach. The application graph consists of a set of components. Each component is considered as a microservice that exposes a set of APIs. It is usually packaged in the form of a container or a Virtual Machine (VM), while its lifecycle is managed by an agent.It is assumed that each application component is independently manageable, while it may have a lightweight and (where required) short lifetime, which is key for supporting scalability and reliability characteristics. Smooth integration between edge and cloud computing mechanisms can be realized to optimize end-to-end IoT applications delivery, since application components may be deployed and managed at the edge or cloud part of the infrastructure, considering a continuous interplay and interaction among them.Part of an application graph can be a generic IoT function that serves specific application needs. The portability of such functions in the edge and the cloud part of the infrastructure is considered a catalyst for enabling mass, scalable, and efficient development and deployment of cloud-native IoT solutions. Generic IoT functions can support IoT-oriented functionalities , as well as functionalities at the edge of the infrastructure .Such functionalities are considered as assistive functionalities for the provision of IoT applications and may be provided following a service mesh approach. A service mesh enables managed, observable, and secure communication across a number of microservices (which formulate an IoT application graph), making it easier for IoT application developers and providers to focus on creating and managing applications for their users, while being able to adopt and reuse a wide range of generic IoT functionalities.IoT application management may take place based on an end-to-end orchestration platform able to manage the deployment and management of IoT workloads over the available edge and cloud computing infrastructure, considering the set of requirements per IoT application and the interplay between cloud and edge resources. A set of existing open-source orchestration frameworks can be considered . We will refer to the main modules and functionalities that have to be supported. These modules concern the IoT applications manager, the cloud/edge resource managers, and the IoT device managers.The IoT applications manager has the main responsibility for tackling the operational lifecycle of cloud-native IoT-containerized applications. This includes modules for managing application components deployed at the cloud and edge part of the infrastructure considering the interplay between cloud and edge resources in terms of the resource usage efficiency and performance aspects, as well as managing the IoT devices interconnected at the edge components. The IoT applications manager has a view on the allocated resources for serving each application graph.This produces a deployment plan that is realized over the materialized network slice in case of a deployment over 5G infrastructure or over the available compute clusters in the case of a pure cloud/edge computing infrastructure. The deployment plan guides the instantiation of the containerized application components at the cloud and edge computing part of the infrastructure. The IoT applications manager interacts with the resource manager at the cloud and edge part of the infrastructure.The resource managers are responsible for managing the deployment part at the edge/cloud part of the infrastructure, the provision of continuous feedback regarding the status of the edge/cloud application components , and the related consumption of resources. Each resource manager is able to handle events related to computer offloading and mobility aspects, taking advantage of the activated IoT functions. They are able to manage various edge/cloud clusters, considering the deployment needs of each application and the available VIMs and/or computing clusters.IoT device managers are also provided for synchronizing the device updates from the cloud to the edge node and vice versa and scheduling actions on the IoT device, considering the supported APIs and the exposed functionalities per intelligent IoT device. Through a continuous interaction among the main application manager, the various resource managers and the IoT device managers, end-to-end orchestration of IoT applications over programmable infrastructure is taking place.Infrastructure management regards both the management of the network and computational resources that are required for the provision of the various IoT application parts, as well as the management of the deployed IoT devices at the edge part of the infrastructure. We provide details per category regarding resource management.Network, computer, and storage resource management has to be provided based on the needs of each IoT application to guarantee the optimal usage of the available resources, while providing the required QoS levels. In the case of 5G environments, by considering the set of IoT applications requirements, a context-aware IoT slice has to be created and managed. Each IoT slice is going to be instantiated, given the request for the deployment of an IoT application.The main types of the considered slices regard IoT services are classified as massive Machine Type Communication (mMTC) services and URLLC services. mMTC relates to a very large number of devices transmitting a relatively low volume of non-delay-sensitive data (leading to the creation of a Massive IoT Slice), and URLLC relates to services with stringent requirements for capabilities, such as throughput, latency, and availability. The IoT network slice is realized and managed on behalf of a telecom operator through the deployed orchestration  components and network management systems.Following evolving 3GPP specifications, the mechanisms that manage the IoT application netwwork slice are responsible for realizing the setup of virtual (isolated/shared) 5G networks built upon the combination of standard Network Functions, such as the User-Plane Function (UPF), the Session Management Function (SMF), and the Network Exposure Function (NEF), etc.Cloud and edge computing resource management concerns the reservation of the required computational and storage resources at the cloud and edge part of the infrastructure. Multiple edge locations and edge clusters are considered, with the continuous interplay of resource management mechanisms between cloud and edge resources as well as among edge resources.Interfaces toward Virtual Infrastructure Managers (VIMs) and computer resource clusters allow the reservation of resources and to create tenant spaces for hosting IoT application components and VNFs at edge computing facilities. Computer offloading mechanisms can be supported for deploying workloads closer to users and IoT devices, and properly scheduling the execution of resource-intensive tasks.Management of IoT devices can be realized in two ways; through management of their virtual counterpart  or through management mechanisms applied by IoT agents in the IoT devices. In the first case, the developed software components are considered as an extension of the IoT application graph and can be managed by the developed orchestration mechanisms, while, in the latter case, interfaces for the management of IoT devices are made available.In both cases, proper abstractions for the supporting management of the IoT devices represented based on different semantic models have to be provided. Focus has to be given to techniques that enable semantic interoperability even if different information models are used. To achieve this, alignment with well-defined information models  has to take place, considering the IoT node capabilities, communication protocols, and type of the sensed context.Adoption of the provided solutions by humans, through the support of interactiosn with real and virtual devices, exploiting advancements in AR/VR technologies, is considered as crucial. Given that tactile interaction refers to a level of responsiveness that works at a human scale, the combination of efficient deployment and provision of IoT applications with effective, usually synchronous, and human-friendly interaction among humans and IoT devices has to be supported.Mechanisms for delivering a seamless user experience also have to be considered. Analysis of the way that people interact with the IoT devices can be realized, leading to mechanisms that learn from their behavior and adapt the context provisions accordingly.Stack4Things (S4T) is a platform that extends the OpenStack framework with IoT capabilities. The design of S4T is split into two subsystems: the first is hosted in a datacenter where IoTronic is deployed; the second subsystem is represented by a number of geo-distributed IoT devices that host the S4T device-side agents, named Lightning-Rod (LR).The communications between the Cloud-side, IoTronic, and its device-side counterpart, LR, are built exploiting a mechanism based on WebSockets with a reverse tunneling approach that is able to bypass firewall and NAT systems see .Authorization/ Authentication: S4T can manage users’ authentication exploiting the OpenStack identity service called Keystone; it is also able to provide authorization to access and manage remote IoT devices.Remote access and management:Exploiting the service forwarding facilities through the Cloud offered by S4T, a user can access  their IoT devices without having to consider the device localization or networking configurations. This is possible thanks to a reverse-tunneling mechanism, based on Websockets.Remote customization/contextualization:Using S4T, the application logic to be executed on the devices can be defined by an user and then distributed in form of functions and deployed on IoT devices, according to authorization and privacy policies, even at runtime. Python and Node.js are available as runtime environments in S4T.As S4T is compatible with OpenStack, the interaction with other (OpenStack) services  is easily provided, and advanced user-facing features, such as containerized applications at the network edge and virtual networking are granted. In a nutshell, S4T provides the support of (among others):S4T was developed keeping in mind the holistic approach introduced in the previous section. S4T strongly exploits the concepts of interoperability and network and device management through the adoption of virtualization. S4T also simplifies the development and management of applications by adopting a serverless paradigm for the network edge using the OpenStack FaaS subsystem Qinling, as described as follows.Stack4Things is used to create virtual networks  among distributed IoT devices. Therefore, they can reach each other as if they were on the same physical network , thus, providing a mechanism to enable VNFs on the tenant space, as discussed in In our approach, we consider as binding-hosts (where the Neutron L2 agents are running in addition to software switches) nodes hosting the S4T WS tunnel agents while the instances are the remote IoT nodes. Consequently, Neutron ports are created and managed on these nodes  along with their networking facilities . In our design, the ports are created on the cloud-side ; yet, they will be attached to our approach instances, which are the remote IoT nodes located at the edge of the network, where Virtual InterFaces (VIFs) are instantiated.The S4T Cloud side networking system is illustrated in According to This later component cooperates with the Zun-scheduler to identify the IoT device where the runtime/function should be deployed then, the Zun-API server sends a request to create, on this device, the containers needed . To make users able to reach the capsule and in particular, the runtime container, IoTronic exposes it, on the cloud side, using a public IP address and a port, and then a WS tunnel is created between the Cloud and the IoT device. Hence, a request that reaches the cloud on that IP address/port will be forwarded to the WS tunnel and reach the device. On the device-side, the request is received through the S4T wstunnel plugin and forwarded to the reverse proxy that routes it to the correct runtime.Our goal is to create a homogeneous environment where IoT objects interact with each other and with other components from the existing web world, offering their functionalities  through RESTful APIs. In such a way, a device with an embedded temperature sensor can provide real-time sensed data as a web service. Smart objects can offer their web services to other devices, web services, and applications to provide appealing applications. Through S4T it is possible to expose services running on IoT devices to the web, by integrating IoTronic with the Designate—the DNS-as-a-Service system of OpenStack, as shown in This subsystem manages the records regarding the URLs associated with the services running on the IoT devices while IoTronic deals with their reachability  by creating Websockets tunnels and configuring NGINX reverse proxies for traffic redirection/forwarding. To enable secure communication (using HTTPS) between the services and clients, S4T integrates, within the system, an automated approach  based on the ACME protocol for X.509 certificate issuance and validation. This approach uses the Certbot agent See  with thesmart city of the Future, i.e., an integrated environment with several subsystems to be integrated, controlled, and managed. We intends to demonstrate how the theoretical concepts described so far can be put into place in specific use cases exploiting the main features of the Stack4Things framework.This section aims to synthetically describe the main experiences of CPSs to pave the way toward the A CPS agnostic framework able to manage IoT devices.A common point to archive data related to the CPS.An optional visual system to present CPS related data.The use cases represent the milestones of the journey we have made to exploit the mechanisms and procedures introduced in Computing Continuum principles to optimize the use of CPS devices, and limit the network latency for data migration from the edge to the cloud.In the second step, we want to foster the utilization of the In the third step, the cooperation among CPSs is exploited to show how the interaction among CPSs is beneficial to the whole system. Usually, the different CPSs of a smart city are independent subsystems that do not interact each other, while data exchange and access to sensors and actuators of other CPSs can strongly reduce execution times and simplify the management procedures .Toolsmart [The framework presented in oolsmart , in whicInfrastructure, Management, and Presentation layers. The template was configured in the form of a skeleton composed of three parts: the Indeed, the Controller entity has to set up the networking facilities enabling the edge to be monitored and controlled by the CPS’s administrator, thus, allowing differentiation of the device behavior on the basis of the plugin that is injected and executed. Due to the plugin’s logic running on the IoTs, the perceived data are sent to the Application layer (the Open data repository) where they are cataloged and pulled by the application logic defined through a graphical development platform, such as NodeRED.smart city skeleton is a fundamental step to enable the cities in their transformation into a “Smart City of the Future”. Indeed, thanks to the intrinsic characteristics , the template developed allows a reduction in the amount of work to create and manage the CPS and to deal with the management of the physical aspects related to the city life . Toolsmart [The realization of this oolsmart  project.As a use case to study the distribution of the computation loads involving a cyber physical system, in , a scenaThis example focuses on the creation of an industrial IoT-based sensing system to monitor the temperature of a set of machines. The simplicity of deploying functions on the devices through the exploitation of FaaS facilities is an extremely suitable programming model for such a scenario.nodeSelector) by exploiting the Qinling component of Openstack.Through the NodeRED dashboard, the administrator writes the functions that reflect the business logic of the application and easily injects them on the IoT devices a. Our FaIn the running condition, the temperature is monitored by each device. Each component can autonomously react to external events, such as an overheating event. In this case, a request could be sent to the nearby devices to observe the temperature they detect and conclude if malfunctioning is occurring or if the temperature is actually over the threshold see b. For exTo investigate the cooperation occurring among CPSs, we analyzed the possible interactions among vehicles moving in a city and the traffic light subsystem. The smart city is made of several subsystems, each controlling a specific aspect of life . A strong cooperation among such subsystems would enable the vehicles to agree on a path that is able to reduce the air pollution and limit traffic congestion. Each vehicle can be seen as an autonomous CPS that should be put in relation with the surrounding environment managed by the smart city. The cooperation occurs if the traffic management and the traffic light subsystems interact, exchanging info and allowing the actuation of specific actions.The proposed architecture exploits the computing continuum approach that was previously described, taking advantage of the interactions among several CPSs. In this way, the computation load can be distributed on the smart city Infrastructure Nodes  and computes the optimal speed profile to be adopted. This information is sent to a Simulink-based “power-train model” to evaluate and compare the traversing time, fuel consumption, and greenhouse gas emissions against the New European Driving Cycle standard [In this scenario, cloud computing facilities act as a “standard .As shown in In this paper, we presented some of the main challenges, the state-of-the-art, and solutions to a set of currently unresolved key questions related to CPSs and smart cities. We recognize the great ferment in both the research and development related to cyber physical systems. We highlighted the main lines of research and outlined the possible evolutions and the challenges to be faced.These challenges include the consideration of interoperability aspects at various levels (protocol and semantic interoperability), the development of distributed IoT applications taking advantage of cloud-native principles, the design and implementation of intelligent orchestration mechanisms injecting automation characteristics in the various parts of the infrastructure, the development of human-centric solutions, and the efficient and secure management of the collected data.To address these challenges, a holistic approach for the development of IoT based applications for smart cities was detailed considering the application development, orchestration, and infrastructure management parts. The approach is generic and can be applied to manage IoT applications over dynamic and programmable infrastructures, considering the network, computer, and storage. Through a set of intelligent orchestration mechanisms, automation can be injected in the various parts of the infrastructure, while the interplay of resources allocation in the cloud and edge parts of the network can be efficiently managed.The provided approach was instantiated in a novel CPS framework, the Stack4things platform. Specific considerations were provided regarding the implementation aspects and illustrating the principles and mechanisms offered by the Stack4Things platform. In addition, some application cases were presented that highlighted the usefulness of federating resources to offer services with ever greater added value.


The necessity of including observational studies in meta-analyses has been discussed in the literature, but a synergistic analysis method for combining randomized and observational studies has not been reported. Observational studies differ in validity depending on the degree of the confounders’ influence. Combining interpretations may be challenging, especially if the statistical directions are similar but the magnitude of the pooled results are different between randomized and observational studies (the ”gray zone”).To overcome these hindrances, in this study, we aim to introduce a logical method for clinical interpretation of randomized and observational studies.We designed a stepwise-hierarchical pooled analysis method to analyze both distribution trends and individual pooled results by dividing the included studies into at least three stages .According to the model, the validity of a hypothesis is mostly based on the pooled results of randomized studies (the highest stage). Ascending patterns in which effect size and statistical significance increase gradually with stage strengthen the validity of the hypothesis; in this case, the effect size of the observational studies is lower than that of the true effect . Descending patterns in which decreasing effect size and statistical significance gradually weaken the validity of the hypothesis suggest that the effect size and statistical significance of the observational studies is larger than the true effect .We recommend using the stepwise-hierarchical pooled analysis approach for meta-analyses involving randomized and observational studies. In the literature, the number of meta-analyses that include observational studies has steadily increased in recent decades . NeverthThe abovementioned criticism raises the fundamental question of whether meta-analyses including observational studies should exist in the clinical field. It is clear that randomized studies form the basis of clinical research and have the greatest influence on therapeutic advances and clinical decisions. However, not all decisions in actual clinical practice can be supported only by robust evidence obtained from randomized studies . In partAs an example, in the treatment of liver cancer , there iIn this study, we identify points that require improvement during the process of planning and conducting meta-analyses, and we suggest a method to synergistically interpret results from both nonrandomized and randomized studies.Meta-analyses are performed to aid clinical decision-making in intractable oncologic situations in which a single standard modality has not been established. These meta-analyses must inevitably include observational and randomized studies. The limitations that we recognized must be overcome are as follows.Cochrane Handbook for Systematic Reviews of Interventions states that only observational studies with at least moderate or low risk of bias should be selected in systematic reviews [When comparing intervention and control groups, the randomization of participants has the advantage of evenly distributing both known and unpredictable confounders . Because reviews . The Gra reviews .On the other hand, as methods of disease assessment advance, more factors are being identified that influence a patient’s prognosis. Recent well-designed observational studies were designed to control a variable number of confounders in the study. A study in which clinical confounders were controlled using methods such as propensity matching and multiple regression analysis ,15 in a In addition, when analyzing observational studies, consideration should be given to how the treatment decisions have been established. Consider two studies that verified the effectiveness of adjuvant radiotherapy after biliary tract cancer surgery  . All relIn summary, observational studies are more likely to be affected by confounders than randomized studies. However, observational studies need to be weighted differently according to their design as well as the degree of control for confounders.If there are enough well-designed randomized studies on a subject to be analyzed, there is little need for a meta-analysis including nonrandomized studies. However, the more detailed the clinical topic to be studied and the more incurable the disease, the more difficult it is to make a therapeutic decision using only data from randomized studies. In their randomized sampling analysis of Cochrane reviews, Shrier et al  reportedIn a meta-analysis that includes both randomized and observational studies, if the pooled results of randomized studies and observational studies have similar effect sizes in the same direction without a notable difference in statistical significance, there will be little disagreement in the interpretation of these results. In contrast, if the directions of the two results clearly contradict each other, the majority of scholars will agree to adopt the results of the randomized studies and reject the results of the observational studies, under the assumption that the randomized studies lack significant design flaws. However, there is a “gray zone” where the results of studies with different designs  have the same direction, but the magnitude of the effect size differs ; no cleaPhysicians should make clinical decisions by using studies with different designs in gray zone situations. Many clinicians review literature found through subjective searches and tend to rely more on research published by authoritative institutions. However, this unsystematic method should be avoided, and a recommended clinical logic flow of interpretation may be as follows:The pooled results from RCTs determine the direction of the clinical hypothesis and the representative effect size. In the gray zone, complementation from data synthesized from observational studies may be necessary. If the pooled results from observational studies are more significant than those from RCTs, the clinical hypothesis could be weakened and confounding bias could be present among the observational studies. In other words, the clinical hypothesis seems more meaningful in clinical studies with a possibility of bias and a low evidence grade, but it has less significant results than previously expected in high-grade studies such as randomized studies. If the pooled effect of observational studies is less significant, the clinical hypothesis can be strengthened, and there is less possibility of bias. This suggests that clinical hypotheses are less meaningful in clinical studies with possible bias and a low grade of evidence, but more meaningful results are produced in high-grade studies such as randomized studies. Finally, the quality of observational studies can be assessed, and trends of pooled effects according to study design  can be investigated . This prThis clinical logical flow will be set as a model and is introduced in detail below.Stepwise-hierarchical pooled analysis is a method of interpreting the pooled results of studies categorized according to their design and validity. In general, the studies included in a meta-analysis are analyzed by dividing them into at least three groups, and then the individual results of each group and the changing trends among groups are analyzed. In the first level, all studies are analyzed, and in the second level, balanced studies in which major confounders are controlled for are analyzed. Balanced studies are generally defined as those in which major clinical factors are evenly distributed, either based on the study design or statistical method, with additional consideration of the treatment strategy of affiliated institutions whenever possible (discussed in the previous section). Randomized studies can also be included at this stage in the analysis as balanced studies, especially when the number of nonrandomized and balanced studies is small. The final step is to analyze randomized studies. Randomized studies can be analyzed at one level lower if the design is suboptimal  or the number of included patients is too small.Briefly, the interpretation of stepwise-hierarchical pooled analysis is as follows: The pooled results and statistical significance of the randomized study mainly determine the validity of the hypothesis. When proceeding from an analysis that includes all studies with a low evidence grade to an analysis of more selected studies, this trend further supports the validity of the hypothesis if it is a pattern in which the magnitude and statistical significance of the result increase. However, a decreasing pattern may weaken the validity of the hypothesis, suggesting that there may be biases in the design and results of studies with low evidence ratings.The descriptive interpretation of the four representative patterns  is as foThe effect size and statistical significance increase gradually: The results of the randomized study analysis are statistically significant, and the effect size gradually increases, strengthening the support for the hypothesis. Therefore, the probability is high that the hypothesis is true and strongly positive. The effect size in the observational studies will be lower than the true effect, and if confounders are controlled for, the effect size can be increased. The results of the pooled analyses of observational studies with confounders may not be statistically significant.The effect size gradually increases and the results are statistically significant at all stages: The results of the randomized study analysis are statistically significant, and the pattern of increasing effect size gradually strengthens the reliability of the hypothesis. Therefore, the probability is high that the hypothesis is true and strongly positive. The effect size of observational studies is lower than that of the true effect. Confounders may have a negative effect on the results of observational studies, but because they show statistically significant results, this effect is assumed to be smaller than that in pattern 1.The effect size and statistical significance decrease gradually: The target hypothesis is rejected because the results of the randomized study analysis are not statistically significant. The effect size and statistical significance of the observational studies are not trustworthy. Observational studies are likely to be affected by confounders and researchers’ bias.The effect size gradually decreases and the results are statistically significant at all stages: The target hypothesis is judged to be true because the results of the randomized study analysis are statistically significant. However, the pattern of the effect size gradually decreases, which lowers the reliability of the hypothesis. The effect size of observational studies is larger than the true effect. Observational studies are likely to be affected by confounders and researchers’ bias. Once again, out of the above patterns, the hypothesis is true if the effect sizes are similar in the pooled analyses of both randomized and observational studies, and both analyses are statistically significant. In contrast, if the results of the randomized and observational studies contradict each other, the pooled results of the randomized studies should be weighted more heavily and further investigation of this contradiction should be performed. The stepwise-hierarchical method may not be highly necessary for these situations.Our team recently published two meta-analyses that used the stepwise-hierarchical method ,22. The P value was marginally nonsignificant at P=.053. The effect size in the pooled analysis of balanced studies  was 1.417 (95% CI 1.171-1.715), and the P value was highly significant at P<.001. The effect size in the pooled analysis of only randomized studies was 1.440 , which was also highly significant. The trend of these results correlates with the first of the four typical patterns described above. In other words, the hypothesis of this meta-analysis  is strongly supported. The trend in which the effect size increases from considering all studies to considering only balanced or randomized studies strengthened the validity of the hypothesis. The results of observational studies may have underestimated the effect size relative to the true effect due to the influence of confounders . Furthermore, the low heterogeneity in the analyses of balanced and randomized studies suggests that the pooled results of those studies are reliable and well designed, and they are less affected by possible confounders.The first study focused on adjuvant radiochemotherapy versus chemotherapy after surgery for gastric cancer. In general, the role of additional radiotherapy has not been accepted widely after D2 gastrectomy, including extensive lymphatic dissection . This isP value was significant (P<.001). In the analysis of balanced studies, the pooled effect size was 2.560 (95% CI 1.791-3.659), and the P value was also highly significant (P<.001). In the final analysis of randomized studies, the P value was significant (P=.04); however, the pooled effect size was 1.406 (95% CI 1.015-1.949), which was smaller in magnitude than that in the previous analyses. The trend of these results correlates with the fourth of the four typical patterns  and with higher metastatic burden (studies with <5 metastases compared to those with <3 metastases) in further subgroup analyses. Therefore, the authors concluded that although local treatment for oligometastases is beneficial, patients must be carefully selected with consideration of the type of disease or metastatic burden, and the design of future observational studies needs to be improved.The second study was on the benefit of local treatment for oligometastases. Oligometastases refer to a disease state with ≤3 or ≤5 metastatic lesions (definitions differ between studies) . In the patterns . In otheThe number of meta-analyses in the literature that include observational studies has been steadily increasing . In actuCochrane Handbook for Systematic Reviews of Interventions [Other previous publications have discussed the justification for including observational studies in meta-analyses or how to select studies with valid qualities. The ventions , which pLimitations of observational studies are categorized and explained in the GRADE handbook . They inRecently, the integration of different studies into designs in the field of network meta-analysis has been discussed. In a network meta-analysis, direct and indirect evidence should be analyzed and integrated. A methodology integrating randomized and observational studies has also been studied in the process of synthesizing evidence with different levels of validity ,34. EfthThe methods suggested in the field of network meta-analysis and the method of the present study are similar in principle. That is, the results are integrated into a differential consideration of the validity of the evidence. On the other hand, the model of this study is distinct from those suggested in network meta-analysis, in that it is a clinically logical model that analyzes the trend of the synthesized results after differential analysis by considering study quality. In addition, the model proposed in this study is less difficult to apply because it does not require additional statistical analysis or software use. It also has the advantage that clinical interpretation is easy and intuitive, even for physicians without mathematical expertise, because it is based on clinical logical flow. These distinctive features and practical merits provide a summary of the significance of the stepwise hierarchical model, which is a novel method suggested for integration of nonrandomized and randomized studies in frequentist  meta-analyses.P value of .05 will find the process of analyzing trends in changes in statistical significance unfamiliar [The limitations of this study are as follows. The four typical patterns described in this study cannot explain all possible patterns and their variations. For a detailed interpretation of clinical decisions, indicators of heterogeneity and publication bias should be interpreted as well. Researchers who are accustomed to making bidirectional decisions based on a specific familiar . TherefoWe recommend using the stepwise-hierarchical pooled analysis approach as a model for interpreting meta-analyses involving randomized and observational studies in a synergistic manner.


In this work we use a network-based approach to investigate the complex system of interactions among the 17 Sustainable Development Goals (SDGs), that constitute the structure of the United Nations 2030 Agenda for a sustainable future. We construct a three-layer multiplex, in which SDGs represent nodes, and their connections in each layer are determined by similarity definitions based on conceptualization, communication, and achievement, respectively. In each layer of the multiplex, we investigate the presence of nodes with high centrality, corresponding to strategic SDGs. We then compare the networks to establish whether and to which extent similar patterns emerge. Interestingly, we observe a significant relation between the SDG similarity patterns determined by their achievement and their communication and perception, revealed by social network data. The proposed framework represents an instrument to unveil new and nontrivial aspects of sustainability, laying the foundation of a decision support system to define and implement SDG achievement strategies. In recent years, several emergencies on a global scale prompted policy makers and citizens to reconsider the established development model. Problems such as COVID-19 pandemic, global warming, the financial crisis, increasing inequalities in the access to medical assistance, food and education, highlighted the need for countries to undertake joint, fast and effective actions Assembly . Since tachievement similarity if they show similar fulfilment levels for a large number of countries. A further similarity criterion between goals is related to their definition, based on the official statements of the respective targets: in particular, we can define a conceptual similarity between two goals, based on the semantic analogy between their formulations. Discrepancies between conceptual similarity and achievement similarity are particularly interesting, since they reveal the existence of several factors which affect the progress towards sustainability in nontrivial ways. In general, motivations behind the differences between the two kinds of similarity can be related to strengths and weaknesses of countries, political willingness, consciousness and sensitivity of public opinion towards a specific goal. The way in which a goal is communicated and perceived is actually crucial in determining the success of related initiatives, and social media represent an essential means for large-scale diffusion of information on goals, both by institutions and by the citizens. The latter are no longer passive recipients, but actively co-create communication on SDGs , which makes it reasonable to compare them to each other; second, use of Twitter is widespread, with all continents except Oceania represented in the list of twenty countries with the largest number of users  techniques. Similarities in the achievement network, instead, will be established by constructing 17 We present the main properties of the three constructed networks, related to conceptualization, communication, and achievement of SDGs, and compare link weights and node centrality rankings in the three cases. Moreover, we use methods of multiplex analysis to characterize the role of each node across the layers.Semantic similarities among text items subsection). The similarity values, represented in the heatmap of Fig. We investigate the conceptual analogies among SDGs by means of a complex network that reproduces semantic similarities between their targets. This network, complete by construction, consists of 17 nodes connected to each other by links whose weights are determined by a conceptual similarity measure; details on the computation of such quantity are reported in Materials and Methods . The 17 nodes are connected according to the semantic similarity between tweets that mention the corresponding SDGs. The similarity values of the resulting network, reported in the heatmap of Fig. The achievement network, whose nodes represent the 17 SDGs, is constructed by comparing the performances reached by different countries in each pair of goals. The procedure to determine such network, described in greater detail in Materials and Methods, requires as a preliminary step the construction of 17 performance networks, one for each goal, in which nodes coincide with UN member states, while weighted links quantify similarity across countries in the indicators related to the specific goal. The performance networks are then compared to each other through the Weighted Jaccard (WJAC) similarity, whose values provide the weights of links in the achievement network. The heatmap in Fig. The p-values do not allow us to reject the null hypothesis of no correlation. Interestingly, only the weighted degree centrality values in the communication and achievement networks are correlated in a statistically significant way, with We compare the similarity patterns emerging from the three networks, with a special focus on the centrality hierarchies of their nodes. First, as reported in Table The results of the proposed analysis unveil the different relevance of SDG synergies in terms of conceptualization, communication and achievement. Large values of weighted degree centrality in the conceptual layer highlight similarities that are implicitly incorporated in the official target formulations, thus reflecting the initial expectations of policy-makers on inter-goal connections. Centrality in the communication layer, instead, provides relevant information for SDG dissemination. Actually, such centrality points out goals that, being frequently associated with others by social network users, can serve as drivers to promote other multiple objectives. Finally, centrality in the achievement layer unveils those SDGs whose implementation is particularly compatible with the other ones, and can thus be strategic in designing coordinate actions to pursue more tasks together. On the other hand, weakly connected nodes in the achievement network are in no way less relevant than the most central ones, but require dedicated effort. Considerations on SDG centralities within each layer are integrated with information on the multiplex participation coefficient.Goal 9  has a pivotal role in the conceptual network, which indicates that the themes contained in the related targets have a frequent semantic matching to the other goals’ topics, that implicitly refer to innovation as a driver of development. The relevance of Goal 9 is utterly confirmed by the results for the other layers, since it ranks among the nodes with highest centrality in both of them. In particular, its synergy with different aspects of development is confirmed by the result in the achievement network ranking, in which Goal 9 is very close to the top. The other SDGs that reach the top half of all the centrality rankings are Goal 8 (Decent work and economic growth), particularly relevant in the communication network, and Goal 12 (Life on land). All the aforementioned nodes rank among those with the highest multiplex participation coefficient, which confirms that each of them plays equally important roles in the conceptual, communicative and achievement networks.Goal 3  and Goal 1 (No poverty) are much more relevant in the achievement network than in the other ones, in particular the conceptual network. This result, corroborated by the relatively lower values of multiplex participation, shows that, though these SDGs are not strongly semantically correlated to other goals, they have important synergies with development achievements in different sectors. This result is not surprising, considering the essential role of health and absence of poverty as proxies of general development.The centrality of Goal 13 (Climate action) and Goal 2 (Zero hunger) in the conceptual network has no strong counterpart in the other layers. Goal 7 (Affordable and clean energy) represents another outstanding case, since it is frequently associated to other goals by social network users, but much less connected in terms of conceptualization and achievement. A possible explanation for the oversized communication centrality of Goal 7 can be related to the bias due to the more widespread use of social networks in developed countries, where users are more sensitive to the matter of clean energy and tend to associate it to other sustainable development topics.Finally, it is worth mentioning two SDGs that exhibit weak connections in general, namely Goal 5  and Goal 16 , which are much more focused on civil rights and politics compared to the other goals. These goals also rank among those with the lowest multiplex participation coefficient, since they have comparatively negligible connections in one  or two layers . Being a sectorial goal, however, does not imply poor ability to raise engagement, as testified by the fact that Goal 5 ranks second in terms of collected tweets in the reference period , after another goal with a social focus such as 4 .a posteriori phenomena, though very different from each other: the spontaneous expression of users on a social network, and the results achieved by countries. On the other hand, no statistically relevant correlation is found with node centralities in the conceptual network, that is constructed a priori, namely using official statements that were issued before the quest for SDGs started.The comparison of SDG centrality rankings, in terms of Pearson, Spearman and Kendall correlations, unanimously shows that the one relating communication and achievement networks is the only statistically significant correlation. This result is particularly striking, since it shows that two networks constructed with completely different procedures are more similar to each other than the two networks based on NLP. Remarkably, statistically significant similarity of centrality rankings exists between the networks built from two We remark that the results on network centrality are not meant to highlight more or less hidden hierarchies of importance among SDGs, which would also go against the official statements. Instead, our study points out the transversality, on different grounds, of specific goals, which constitutes a relevant asset in view of accomplishing the SDG roadmap. Actually, a central node in conceptualization, communication, or achievement, can have a driving effect on the other ones. Since centrality rankings depend on the specific criteria of network construction in each layer, it would be desirable to exploit central goals in different domains, with their own peculiarities and pivotal connections, to promote the realization of the 2030 Agenda.We report here the sources of data employed in the construction of the three SDG similarity networks, and describe preliminary processing steps.The 17 Sustainable Development Goals are articulated in a series of 169 targets  token lists T(j) lists n_similarity function. The average of such a set of T(i)T(j)/2 similarity measures so obtained is used to weight the link connecting The weight of the link between goals The connection between each pair of goals in the achievement network is determined by comparing the related performance networks. In this subsection, we illustrate the procedure to construct performance networks and define the Weighted Jaccard distance used for network comparison.removal of indicators with unavailable values;removal of indicators with practically vanishing standard deviation  similarity Ioffe  to compai among the three layers, we compute the multiplex participation coefficient (Battiston et al. The set of three networks considered in our research can be potentially identified as a multiplex of three layers, all with weighted links. However, a direct application of multiplex tools in our framework is dangerous, since it implies the comparison of link weights obtained through non-homogeneous procedures. In order to exploit the multiplex structure, we associate the network on each layer with its binarized counterpart, constructed using the median of the distribution of link weights within the layer as a threshold. Specifically, the binarized version of a given weighted network is obtained by associating an adjacency matrix element 1 to links whose weight is larger than the median, and 0 to other links. Then, to characterize the connectivity distribution of each node


The use of cranial fixation devices in neurosurgery is very common, which is considered to be an important auxiliary method for many craniotomies. However, previous studies have reported complications of using cranial fixation devices, including brain tissue, nerve and blood vessel damage, scalp laceration, subcutaneous hematoma, etc. Some of the complications are serious and even potentially fatal, and the causes of which may be related to the incorrect use of cranial fixation devices. Although there are no serious complications in our review, the cause of that needs to be further summarized and analyzed, as so to minimize the serious consequences caused by the cranial fixation device slippage and ensure the safety of the patients’ surgical procedure.In our recent work, we have continuously found three cases of unstable cranial fixation devices, which make us to analyze the possible factors and summarize experience combined with the review of other senior neurosurgeons (more than 3 years of working experience) from different departments of neurosurgery.Based on our recent incidents of unstable cranial fixation and the experience of investigating and analyzing senior doctors from different neurosurgery centers, we summarized experience to minimize the risk of unstable cranial fixation. We tried a variety of options, including a safe anatomical location for cranial fixation, teamwork, and communication with anesthesiologists and itinerant nurses, to ensure the stability of the patient’s cranial fixation devices. The data obtained in this survey has great limitations, including the doctor’s personal prejudice and dependence on anecdotal memories. Therefore, the data should be interpreted with caution. However, there are still some modes that can help to better understand the use of safe cranial fixation. Based on the above research and analysis, we have made recommendations that may help neurosurgeons to avoid preventable complications The use of cranial fixation devices in neurosurgery is very common and considered to be an important auxiliary method for many craniotomies. Gardner designed the first cranial fixation devices for sitting surgery in 1935, and later Mayfield cranial fixation devices were developed to locate the skull and prevent movement in the proceed of neurosurgery as a safe equipment .However, previous studies have reported complications of using cranial fixation devices in adults and children –5. Some Despite few cases published, several neurosurgeons discussed complications of cranial fixation devices during their careers in informal conversations, suggesting that their risks must be underestimated . In our Case 1, male, 53 years old, was diagnosed with cerebellar vermin meningioma. The patient was adopted left abdominal supine position and fixed with Mayfield head frame, where double pins were located in the left temporal part and a single pin was located in the right temporal part. The pressure of pins achieved 70 pounds. The surgical approach adopted the posterior median approach, and 2 holes were drilled during the operation. The whole operation time was 4 h. The patient showed head movement when we sutured scalps. When the Mayfield head frame was removed after the operation, the pressure of pins was changed into 50 pounds.Case 2, female, 42 years old, was diagnosed with acoustic neuroma located in the left cerebellopontine angle (CPA). The patient was adopted right abdominal decubitus and fixed with Mayfield head frame, where double pins were located in the left temporal part and a single pin was located in the right temporal part. The pressure is 60 pounds. The surgical approach adopted the right retro-sigmoid approach, and 2 holes were drilled during the operation. The whole operation time was 4 h. The patient showed head movement when we sutured scalps. When the Mayfield head frame was removed after the operation, the pressure of pins was changed into 40 pounds.Case 3, female, 61 years old, was diagnosed with meningioma located in the left frontraoparietal. The patient was adopted supine position and fixed with Mayfield head frame, where double pins were located in the right temporal part and a single pin was located in the left temporal part. The pressure is 60 pounds. The surgical approach adopted the left frontraoparietal approach, and 2 holes were drilled during the operation, and the whole operation time was 4 h. When the head frame was removed after the operation, the head frame scale was found to be 50 pounds.These incidents aroused our great attention. If we did not discover and summarize the experience in time, it may appear again in the subsequent work and cause serious consequences. To this end, we also communicated with senior physicians and investigated cerebral fixation device instability events that occurred among different neurosurgeons. All illustration figures of the three cases are shown in Fig. We investigated the neurosurgeons from Beijing Tiantan Hospital, Jinzhong First People’s Hospital, Linfen People’s Hospital, and The Seventh Medical Center Of Chinese People’s Liberation Army General Hospital, respectively. The content of this survey contains operation position, emergency operation, depth of anesthesia, pressure of pins, the skin of the head, time of operation, and appearance of unstabilization.Among the seven doctors surveyed, five doctors were found to have experienced cranial fixation device instability events, of which one doctor experienced one incidence, three doctors experienced two incidences, and one doctor experienced three incidences. In all incidences, there is no complete slip of head frame and serious complications, and the main results are partially displacement of cranial fixation devices and reduction of pressure. In addition, with the growth of these senior doctors, the incidence of these events has declined significantly, and most of the incidents occurred during the period of serving as a young doctor.In the events of cranial fixation device instability, the ratio of men and women is the same. Among these incidents, three patients occurred in the supine position, accounting for 30%, and seven patients occurred in the lateral/prone position, accounting for 70% Fig. . Among tComplications of cranial fixation devices have been reported in the literature, including skull fractures, needle infections, venous air embolism, and subdural and epidural hematoma , 7. DespFrom these results, we found that patients in the lateral prone position were more prone to induce cranial fixation device instability, indicating that there were large instability factors in the prone/lateral prone abdominal position, which may be due to the weight of bodies was more distributed on the cranial fixation devices than the supine position. It required that we should pay more special attention to the stability of the cranial fixation device installation when patients were performed in the prone/lateral prone abdominal position. Among the ten patients, 40% of them are emergency surgery and 60% are routine surgery. The proportion of routine surgery seems to be higher than emergency surgery; however, the overall number of routine surgery is much higher than that of emergency surgery. From this point, we can obtain that the probability of cranial fixation device instability in emergency surgery is much higher than the proportion of routine surgery. These results showed that the preparation for emergency surgery might not be as full as in routine surgery, and errors were more likely to occur.In addition, 40% patients had involuntary movements before removing the cranial fixation devices. And in one patient who recently had unstable cranial fixation devices, we paid special attention to the installation position and the amount of pressure applied after installing the headgear and checked with the superior doctors and traveling nurse. At the end of the operation, we found that the patient’s head appeared involuntary activities. We communicated with the anesthesiologist in me to enhance the control of anesthetic drugs. In the end, we found that the pressure of the cranial fixation devices was changed from 70 pounds to 50 pounds. Two of the three patients who recently found the cranial fixation device unstable were accompanied by involuntary shaking of the head during the operation. These results suggested that the depth of patient’s anesthesia also plays an important role in the stability of cranial fixation devices. The shaking of the patient’s head during the operation may affect the stability of the cranial fixation devices.Among all patients, the pressure of cranial fixation devices is strictly controlled for women 60 pounds and men for 70 pounds. Among them, there were four cases of unstable cranial fixation devices in patients with thicker tissues, accounting for 40%, and six cases of normal and thin tissue patients accounted for 60%. These results indicated that in patients with a thicker scalp, the pressure should be appropriately increased to prevent the head pins to shift or even slip. The length of the operation time is also closely related to the stability of the tissues. With the extension of the operation time, the higher proportion of cranial fixation device slipping or displacement happens.In the cases investigated, there were no serious complications. Four cases showed cranial fixation device displacement, and the distance was less than 1 cm. Another six patients showed pressure reduction to 40–50 pounds, and no obvious head displacement occurred. Although there were no serious complications, the cause of its occurrence needs to be further summarized and analyzed, so as to minimize the serious consequences and ensure the safety of patient’s surgical procedure.Among the doctors surveyed, about 71% doctors experienced unstable incidences of cranial fixation devices, and 80% occurred in the first 3 years of their work career, rarely in the subsequent work period. This showed that the stability of the cranial fixation devices in the beginning of the work was closely related to the doctor’s experience (Fig. Based on our recent unstable cranial fixation incidents and the experience of investigating and analyzing senior doctors from different neurosurgery centers, we conducted experience analysis and summary to minimize the risk of unstable cranial fixation pins. We tried a variety of options, including a safe anatomical location for cranial fixation, teamwork, and communication with anesthesiologists and itinerant nurses to ensure the stability of the patient’s cranial fixation devices. Although the statistical value of these 10 cases is limited, just based on empirical analysis and retrospective studies, more clinical studies can be conducted to assess related risks and make recommendations for the safe installation of the cranial fixation devices.The data obtained in this survey has great limitations, including the doctor’s personal prejudice and dependence on anecdotal memories. Therefore, the data should be interpreted with caution. Despite these limitations, there are still some modes that can help to better understand the use of safe cranial fixation. Based on the above research and analysis, we have made recommendations that may help avoid preventable complications.Strictly follow the process to install cranial fixation devices, and at least two doctors are present to verify the stability of cranial fixation devices. The junior doctors need to install the head frame with the assistance of the senior doctors, and gradually accumulate experience;Fully assess the patient’s head circumference size, weight, and skin thickness. In patients with a heavier head and thicker scalp, we should increase the pressure appropriately to ensure the stability of the cranial fixation devices;The position of the cranial fixation devices needs to be carefully analyzed to ensure that the center of the head is located at the upper part of the three-point center of gravity of the head frame, so that it is possible to ensure that the cranial fixation devices support the head effectively;In some complex surgical positions, more attention should be paid to the stability of the cranial fixation devices. Due to various changes in its body angle, there are many variable influencing factors;The prolonged operation time may increase the factors of cranial fixation device instability. In patients with long surgical events, the stability of the cranial fixation devices can be checked intraoperatively;During the operation, try to avoid unnecessary pressure on the patient’s head to cause the instability of the head nail;Fully communicate with the anesthesiologist to determine the end time of the operation, so that the anesthesiologist can better evaluate the use of the anesthetic drugs;The re-check with the itinerant nurse is an indispensable part. In our recent incidents, it is that the itinerant nurse discovered and rigorously verified consequent operations that the discussion and empirical analysis of this article.The following suggestions are based on the survey responses and our own practical experience Figs.  and 5:S