prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: What is the definition of mobile device management (MDM)?

Read the following text and answer briefly.

Context 1: Hospitals as critical infrastructures has been historically dependent on various types of devices and equipment that are being revolutionized with digitalized solutions  The digitalization of conventional healthcare equipment is added with the new inclusion of numerous new devices for data collection, analysis, communication, and so on  All in all, the futures digital hospitals in 5G will be exponentially more data-dependent and digital-intensive  For that, this paper looks to theorize how the security scenario in a futures digital hospital would look like, and what relevant business possibilities could emerge for cybersecurity providers in the healthcare context  In this paper, we open up discussions on business possibilities relevant to Internet of Things-mobile device management for critical infrastructures such as future digital hospital  We apply business models as a conceptual lens to analyze how cybersecurity business could evolve for 5G enabled IoT-Mobile device management providers as a cybersecurity vendor 
The healthcare sector has progressed significantly since the introduction of Internet and proliferation of network technologies   Among many issues, the use of data, availability of data, data mass, and access control of data in healthcare remain are critical for keeping healthcare services trustworthy and secure for the end users, both hospital staff and the patients  Disruptions in healthcare services would have severe effects on people’s lives  However, as hospital managers and professionals need to design their data-dependent and digital-intensive networks in a manner, which is highly secure, they also should provide the basis for uninterrupted service for business sustainability  Security is often observed as a tradeoff between risk and business gains   Investing in security is important in order to secure business-critical systems and data for meeting business goals and eventually for creating competitive advantage [3, 4] 
Innovative technologies have the power to disrupt industries and prompt business transformation   The coming of fifth generation (5G) of telecommunications networks is seen to result in this kind of disruption  As we are gradually moving towards 5G, it is worthwhile to theorize how the security scenario in a futures digital hospital would look like, and what relevant business possibilities could emerge from cybersecurity in the healthcare context  From this perspective, in this paper, we open up discussions on business possibilities relevant to Internet of Things mobile device management for critical infrastructures such as future digital hospital  We apply business models as a conceptual lens to analyze how cybersecurity business could evolve for 5G enabled IoT device management providers as a cybersecurity vendor 
A futures digital hospital facility is envisioned to consist tech-aided advanced critical medical devices, intelligent information systems, digital communication tools, hundreds of handheld mobile devices (smartphones, tablets), wireless clinical wearables, in addition to thousands of smart IoT nodes [6, 7]  These devices should be fully integrated to improve staff productivity, hospital functions, patient safety and privacy, and, overall improve patient experience through secure and reliable healthcare services  However, inclusion of these various kinds of digital devices to the hospital context make the overall device network quite complex and heterogeneous   Thus, from a critical infrastructures view point, to manage, configure, update and secure the immense fleet of digital devices besides all the high-tech medical equipment, the futures digital hospital will need to redefine device management policy and services  
Mobile device management (MDM) systems are usually referred to “support centralized control of an entire fleet of mobile devices (smartphones and tablets) and mobile applications by applying and ensuring pre-defined configuration settings” [8, 9]  In the scope of this paper, we broadly use the term IoT-MDM to refer to a device management system that is capable of managing, configuring and updating both handheld mobile devices and IoT devices in combination in a centralized manner  We will briefly open up the concept of MDM and IoT-MDM 
The purpose of this study is to identify business potential for IoT-MDM service providers as cybersecurity vendor in the context of the future digital hospital  In doing so, we apply the concept of business model in order to make sense of a ICT-oriented business environment   Among various available conceptualizations, business model is considered as a boundary-spanning unit of analysis that explain the underlying business logic and the value creation and value capturing logic of an organization [11–15] 
Traditionally, device management has been associated with management and configuration of handheld mobile devices , thus, mobile device management (MDM)  Gartner  perceives MDM software to be a policy tool to configure and manage mobile handheld devices  They also mark that MDM services need to ensure security in reference to connectivity and content that is being transmitted  Along with surge of smart mobile devices, the Internet of Things (IoT) is growing large during the last few years and promises to flood the market with billions of devices in the coming years too   Zhang et al   states scalability, transparency and reliability as important issues that differentiates IoT from the conventional Internet  To that end, there are several IoT platforms available currently in the market for managing, updating and configuring IoT nodes, e g  IBM Bluemix, Cumulocity, ARM mbed OS, etc    However, the transition raises the question about the differences and similarities between MDM and IoT device management as approaches 
Takalo  marks MDM and IoT device management to be quite close on a conceptual level: both need solution for automated management of large device fleets consisting different form factors, device models, and operating system  Additionally, such systems conceptually needs to support various communication channels like: WiFi, cellular network and Ethernet  However, on practical level, MDM is more strictly controlled by operating systems and device vendors  IoT device management, on the other hand, is characterized by multiple operating systems, multiple hardware platform variations, non-complete standards, multiple communication methods and protocols  This agile approach allows the coexistence of various types of devices and nodes in the same environment  Recently, services are appearing where MDM and IoT device management are brought together under the same platform, which in a way reduces the complexity in device management and also improves the overall security of the system 
While we are still in early phase of mass IoT deployment and 5G deployment is approaching, it is important to revisit some of the key security threats that has been identified in recent literature relevant mainly to IoT  Zhang et al  [19, 21] identified privacy preservation as a critical issue for information security in IoT ecosystems  They further state that conventional naming, identification and authentication policies need to be improved and rather needs approaching differently  Farooq et al   look at the four layer generic IoT architecture consisting perception layer, network layer, middle-ware layer & application layer  Additionally, they offer a list of security challenges for each layer  Some of the key challenges are: unauthorized access, tag cloning, eavesdropping, spoofing, RF jamming, sinkhole attack, sleep deprivation attack, denial of service attack, malicious code injection, man-in-the-middle attack, spear-phishing attack, sniffing attack  Backman et al   state, a comprehensive security solution needs to address endpoint security, management and monitoring security, and secure data distribution and storage 
Ortbach et al   enumerate the drivers for adoption of MDM in organization through a quantitative analysis, reflecting three broad drivers: organization, environment, and technology  From organizational perspective, the company size, mobile IT usage, employee innovativeness with IT and BYOD (bring your own device) culture were identified as important drivers  From environmental viewpoint, regulations and other business partner influence are significant drivers for MDM adoption  Finally, from technology point of view, perceived security benefits and perceived cost of the service seems to affect the managerial attitude towards MDM adoption 
MDM systems are today a very common tool to manage users’ devices  With MDM, all mobile device types, tablets and PCs with typical operating systems can be controlled centrally   It is often thought that MDM can manage only mobile phones, but actually the MDM framework includes also users’ identities and profiles  This makes MDM a viable tool for organizations to manage their employees identities, user profiles, all devices, all applications and security controls under same system 
From an emerging technology perspective, SDN (software-defined network)/NFV (network functions virtualization) based 5G Slicing will challenge some of the traditional MDM features   Especially end-to-end security from device to IT cloud is difficult to realize with MDM  Of course, it is possible to force the use of VPN in mobile device with MDM, but many aspects of communications security will be still unsolved  Thus, 5G slicing provides new tools to control and manage the end-to-end communications flow with network functions (VNFs)  In the advent of IoT, this is particularly important since the billions of IoT devices of the 2020s will have only a minimal processing power and memory compared to the smart phones of today  These IoT devices may connect to network only once a month and communicate only with network edge cloud servers  Therefore, managing these new IoT devices cannot be done with conventional MDM systems of today 
Fortunately, many features of MDM can be provided by dedicated 5G slices and their VNFs  If e g  a IoT device does not have the latest anti-virus updates, the network slice may still provide the isolation and security controls so that the IoT device can send the metering data  Moreover, if the IP flow from the IoT device includes other than actual metering data, eg  due to malware in IoT device, this IP flow can be analyzed and filtered by slice specific VNFs before passing it to IoT could 
As a summary, it can be argued that SDN/NFV based 5G Slicing will provide new tools for security management, and, when combined with IoT-MDM system functionalities, together these can deliver a better device management framework for different kinds of user devices of the 2020s 
Hospital organizations are considered as critical infrastructure (CI) to nation states [3, 25]  As a critical infrastructure, hospital organizations are prone to security threats that can affect health policies, public health, healthcare services, surgical procedures, electronic patient records, patient privacy, doctor-patient communication, etc  Lehto and Ahokangas  notes, new technology (e g  next generation mobile networks, smart data storage, IoT) adoption of CIs increases the cybersecurity touchpoints and hence making the CIs more vulnerable  Broadly, from cybersecurity perspective, the hospital organizations in future will be vulnerable from management perspective (e g  organizing healthcare services, managing huge amount of patient data, clinical data, medication data, communication between health professional and patients etc ), healthcare service delivery perspective (e g  in an unwelcoming case of denial of service attacks in hospital context: like wannacry), network perspective (e g  security of the overall hospital network), and last but not the least from an individual privacy perspective (e g  individual patient records, healthcare professional logs, etc ) 
A futures digital hospital will consist various advanced technologies, such as critical medical devices, intelligent information systems and digital communication tools, which are fully integrated to improve staff productivity, hospital operations, patient safety, and the overall patient experience  Among them, many will be wireless mobile devices, wireless wearables and thousands of smart IoT devices for various kinds of measurements  
Innovations help transform healthcare in forms of advanced telecommunications techonology, new drugs and treatments like biological sensor pills or implants, new medical devices, social media interation, etc  [1, 6, 7]  In future, hospitals and surgical tools will be revolutionized with intelligence and connectivity i e  providing more assistive functions by sensors, processors, data collection, software algorithms and interfaces which are, for example, embedded in the tools that will support surgeon’s decision-making in action   Robotic-assisted Surgery (RAS) can be considered as a good example of such transformation  The rapid adoption of Minimally Invasive Surgery (MIS) and today’s information revolution not only improves the surgical outcomes and patient’s life but also changes the patient-physician relationship 
‘Smart’ i e  IoT-based technologies are going to intellectualize medical devices and service systems into Smart Hospitals  Smart hospitals extrapolate from totally digitalized and automated data collection, tracking and delivery between systems, devices, patients, and health professionals and organization   In future, it is also envisioned that virtual hospitals and personalized medication will become part of patient care  Patients will be able to visit virtual hospitals or e-clinincs for clinical purposes without the presence of health professionals, but procedures and communication are managed via remote management and telemedicine solutions  
Other features that futures digital hospitals will include: use of different AR (augmented reality) and VR (virtual reality) applications for healthcare related purposes  While VR applications are quickly evolving in the gaming industry, some applications are deemed suitable in hospital environment too  Patients can be introduced to hospital environment through VR solutions, which can reduce patient stress prior to surgical procedures   Another application of VR in healthcare has been identified as rehabilitation support for temporary physical disabilities and mental trauma  Further, AR solutions can help patients navigate within a large hospital complex 
The term, Business model, has had its root in information systems (IS) and information communication technology (ICT) since the late 1970s originating from business informatics  However, it came into management and strategic management literature as a research interest from the mid-1990s [29, 30]  Although business model had its roots in IS and ICT, the amount of research work on cyber security as a context is still quite negligible harnessing the potential of business model concept 
The concept of business models lies at the intersection of entrepreneurship and strategy, it can be observed as a bridge between abstract strategies and the practical implementation of strategic decisions and actions amidst the uncertainties of the modern business context [11–14, 31]  For instance, Zott and Amit  conceptualize business model as a ‘boundary-spanning’ set of activities aimed at creating and appropriating value  Morris et al   viewed the concept of the business model as a set of decisions related to the venture strategy, architecture, and economics of firm (value creation and capture) that need addressing to create sustainable competitive advantage in the chosen markets and specific contexts 
As a boundary-spanning unit of analysis, business models , connects an organization with its business environment, other organizations, customers, individuals and society as well; with the overall ecosystem at large [34, 35]  Trying to bridge business models and cyber security under current context, there are two core issues  First, as almost all of the entities operating within the digital sphere face multifaceted cyber threats, how can business model approach help organizations to respond to such situations? Second, how can business model approach help to identify opportunities and monetize security in future 5G? In the next two sub-chapters, we present two business model approaches that are suitable for ICT contexts, and, can help find answers to the above mentioned questions 
As the mobile telecommunications industry advanced, so did business model related discussions in the literature about extending organizational boundaries through vertical and horizontal integration in industries   Around this, Wirtz et al   offered four mutually exclusive business models classifying Internet based business models to be precise  According to Wirtz et al  , these business models present in Web 2 0 are related to connection, content, context, and commerce  Building further, Yrjölä et al   interpreted these business models as chronological layers, where “lower” layer business models are pre-requisites for the “higher” layer business models to exist 
Figure 1 The layered 4C ICT business models archetypes [Adapted from 35, 37, 38] 
The first layer is concerned with connection-related business model where a stakeholder provides connection services   Connection- related business models are relevant to connectivity for all sorts of devices and nodes through various communication channels, e g  PCs, smartphones, tablets, IoT devices, etc  The second layer is the business model focusing on monetizing content  At the content layer, all sorts of online content services (e g  mobile video streaming) are classified (i e  relevant, up-to-date and interesting) and are accessible conveniently for the end user  The content might be peer-to-peer/user-oriented contents (i e  exchange of personal content), and web browsing content (audio, video, text etc ) 
The third, context, layer concerns the ability to create and monetize user, content, equipment/user device and system profiles and turn (big) data into meaningful information and knowledge through systemic virtual contextualization  The fourth layer concerns commerce, the ability to monetize any or all of the connection, content, or context specific resources, actors or activities related to the ongoing communications  At this layer, we identify business, consumer and public/government types of communications   Thus, B2B (business-to-business), B2C and B2G communications as well as C2B, C2C and C2G or G2B, G2C and G2G communications may be monetized at this layer 
Casdesus-Masanell and Llanes’s  offers different software business models based on the openness of core software and extension software offering  According to the mixed source software business model approach, services can be: open source (open core- open extension), open core (open core- closed extension), open extensions (closed core- open extension) and proprietary (closed core- closed extension)  Although this model was offered specifically suiting the software industry, we argue that similar approach can be useful in other ICT contexts to scale up value offering by adjusting cooperation and industrial partnering model 
Figure 2 Mixed source business model approach [Adapted form 40] 
Depending on individual organization’s choice of mixed source options, it should eventually translate the value creation and value capture logic of the firm  Casadesus-Masanell & Llanes  argues that purely proprietary models results in higher captured value for organizations but lessens the scope of value creation for users  In contrast, purely open source models can deliver the maximum value to customers but reduce captured value  The key for organizations is to locate the best mix of openness-closeness for an optimum level of value capture and creation  From the business model elements collection, the mixed source approach explain key partners, key activities, key resources and technologies used  From a strategic perspective, this approach to business modeling helps an organization to find a way to scale up or down its business activities 
This study concerns business possibilities of IoT and mobile device management in the context of futures digital hospital  Since future is elusive and the speed of technology advancements has been rapid during recent decades, we consider the most suitable method for conducting this research to be through qualitative approach  Qualitative methods helps with flexibility and sensitivity to the context that is less explored, and it can facilitate understanding of how things work in a particularly complex setting   We adopt a single qualitative case study approach for this research to explain underlying business potential of a scantly explored industrial context within the existing literature  
Interview 1 Pediatric Surgery Administration, teaching, hospital development, technology and innovations Pediatric surgery patient journey, concept development of futures digital hospital, digitalization in healthcare 
Workshop 1 Network specialists, operator service provider, cybersecurity expert Business opportunity creation, new market exploration, security aspects Digitalization in healthcare, security business opportunities in health domain, business modeling 
Workshop 2 Operator service provider, cybersecurity expert Business opportunity creation, new market exploration, security aspects Digitalization in healthcare, security business opportunities in health domain, business modeling 
Table 1 summarizes the data used for this research  The empirical data used for this study was collected through two streams  First, in order to build the conceptual case of future digital hospitals, five interviews were conducted with pediatric surgery specialists, a nurse, and a general physician from two hospital districts in Finland  These semi-structured interviews were organized between April and May 2016  These interviews also focused on understanding the journey path in a pediatrics surgery case to clarify suitability of digitalization in the future  However, in the scope of this paper, we do not discuss the patient journey pediatrics surgery in detail  Second, we explored the cybersecurity and business perspectives in two workshops that were organized with telecommunications and cybersecurity experts from the industry  One of the workshops were organized in November 2016  The other workshop took place in August 2017  While the futures digital hospital context from a security perspective relates more towards hospital management and network functionality, the IoT devices and mobile devices perspective is closer to end user security needs  This paper focuses on the cyber security related business opportunities through IoT-MDM systems/services that broadly covers the above mentioned domains  Findings and analysis from the interviews and workshops are presented next 
In this section, we present our analysis in four steps  First, we briefly discuss the relevance of Internet business models (4C)  to the context of the study, addressing how this approach can help cybersecurity providers identify business opportunities in IoT-MDM  Second, we present a 4-quadrant matrix reflecting four different 5G security-provisioning scenarios, which are also relevant to IoT-MDM  Then, we connect each scenario back to business models by applying a mixed-source business model approach  and identify different possible business models for each kind of cybersecurity provider  Finally, we connect the overall discussion back to the case of future digital hospital 
The 4C business model archetypes helps decoding the boundaries between multiple business models operating either in the same verticals of the ecosystem or in the same horizontals  These archetypes can provide with a basis to classify and analyze business models of suppliers, competitors, and, at the same time, business models of customers  For this case, the 4C business model archetypes are mostly relevant for cybersecurity providers and IoT-MDM system/service providers as a tool to analyze customer business models and identify customer needs, the hospital management style  These customer needs can eventually be turned around as business opportunities  While IoT-MDM systems and services are not only concerned about cybersecurity, there is room for service providers to customize and tailor services based on customer needs  These service providers either can target customers from one layer (connection/content/context/commerce) across multiple industries, or, they can also target a specific industry (e g  healthcare) create customized services for all the layers in that industry 
To make sense of the overall cybersecurity provisioning for future 5G, we created the 4-quadrant matrix (Figure 3) by identifying major security drivers for new business creation in future 5G  These scenarios were created in workshops with telecommunication & cybersecurity experts from the industry  While these service provisioning scenarios are relevant to overall 5G, they are also significantly related to the case of IoT-MDM system/services 
Figure 3 Scenarios for 5G cybersecurity provisioning [Adapted from 35] 
As a result, we find four major drivers of security, which potentially will come with new business opportunities in the 5G era  Device driven security comprises distributed and D2D security techniques  Platform driven security will focus on centralized and D2D security techniques  Whereas, network infrastructure driven security should focus on centralized and infrastructure security methods  Lastly, location driven security should harness distributed and infrastructure security techniques 
In a quest to identify potential business entities operating in each of the quadrants relevant to device management, we recognize a ‘secure device manufacturer/provider’ focuses on device-driven security  Currently, multiple device manufacturers are developing devices where security features are built-in, regardless of which network or websites the users are accessing  This built-in security can be offered to multiple kinds of devices including smartphones, tablets, pcs, wearables, and even to IoT devices with communication and computation capability  These secure devices are built in a way that it will control access to potentially harmful networks, websites, and content; even without any commercially available security applications installed  However, when a customer enterprise (e g  the future digital hospital) is buying a fleet of hundreds of such secure devices, the question raises how to manage and configure all these devices from time-to-time  In such a case, even the secure devices will need IoT-MDM services for proper management and seamless upgrading when needed 
For the platform-driven security, we observe a ‘traditional IoT-MDM system provider’ can be a good example  In many cases, IoT-MDM system providers are selling there device management systems to enterprises directly  And, in other cases, they are selling the service through MNO’s bundled with connectivity and/or infrastructure  While as network infrastructure-driven security, a ‘mobile network operator/carrier’ or a ‘network infrastructure vendor’ can build own IoT-MDM system to offer their clients as well  And, finally, a location-specific micro operator can offer location-driven security  Micro operators offer mobile connectivity combined with specific, local services  The operation of a micro operator is spatially confined to either its premises or to a defined area of operation  As a part of the location-specific services, these micro operators can also offer IoT-MDM services for the users through outsourcing 
Further, we attempt to connect the aforementioned classification and examples of different players offering IoT-MDM services with the mixed source business model approach  Table 2 summarizes our understanding on how each kind of cybersecurity provider can open and mix the core value creation logic for end users  As mentioned previously, the mixed-source business model options are: open source (open core, open extensions), open core (open core, closed extensions), open extensions (closed core, open extensions), and proprietary (closed core, closed extensions)  In relation to these mixed-source options, we analyze the plausible options for each of the four distinct cybersecurity providers in the context of this study 
Secure device manufacturer/provider Secure devices Proprietary (own device, own IoT-MDM platform), Open extensions (own device, outsourced IoT-MDM service) 
From a secure device manufacturer perspective, device business can be considered as the core operation whereas IoT-MDM services would be extended solution  A secure device manufacturer/provider can have either a proprietary model or an open extensions model  In the proprietary model, the secure device manufacturer will offer their own devices alongwith their own IoT-MDM system/service  This is a viable case in a sense that customers who are purchasing the fleet of secure devices might prefer the IoT-MDM service from the same vendor, which is ideally less risk prone  Generally, when a customer organization buys a fleet of secure devices, they would expect the device management functions to be offered by the same service provider since integration of multiple systems/interfaces might increase vulnerability and reduce the customer’s confidence on the system  However, in practice, not all secure device manufacturer will specialize in automated device management solutions, and new R&D expenses to develop the IoT-MDM systems could result to be financially unfeasible  So, the second option for a secure device manufacturer is to have an open extensions model, where they will still offer the secure devices but outsource the IoT-MDM services to other vendors 
A micro operator’s core business relates to location specific network, while they are offering additional location specific services as extensions  Ideally, a micro operator’s business model in this case can be considered to be open source  At one hand micro operators are dependent on appropriate available spectrum resources on carriers and NIVs, and on the other hand, outsourcing the IoT-MDM services to other vendors seems more economically feasible than building own system  In contrast, a traditional IoT-MDM system/ service provider can have either a direct-open extensions business model or an indirect-open extensions business model  Unlike the other three archetypes in this discussion, IoT-MDM system/services are the core business for this kind of actor, as device and network related issues can be considered as extensions  An IoT-MDM system/service provider is characteristically dependent on connectivity providers, i e  MNOs, NIVs  In a direct-open extensions business model, while they are selling the IoT-MDM service directly to the end users, they are using the operator connectivity, but delivering their core value directly  Alternatively, in an indirect-open extensions business model, these IoT-MDM vendors can sell the services through MNOs/NIVs/device vendors to the end users 
Finally, a mobile network operator can either have an open extensions business model, or less likely a proprietary business model for IoT-MDM services  In an open extensions business model, operators will offer own connectivity to end users while outsourcing the IoT-MDM service to vendors  In very few cases though, operators might have their own IoT-MDM system on offering and thus they can sell both connectivity and IoT-MDM service as a proprietary bundle 
Looking back at the case of futures digital hospital, it is deemed to comprise various advanced technology-aided devices, let it be for clinical purposes or communications purpose that support healthcare  With the presence of devices like smartphones, tablets, wearables, connected TVs, VR touchpoints, AR touchpoints, robotic assistance for surgeries, thousands of smart IoT devices, makes the overall device network of the digital hospital complex and need automated centralized management  This centralized management of numerous IoT and mobile devices can be delivered through IoT-MDM system/services  Using the IoT-MDM system/service, the hospital organization can configure, secure, and time-to-time update their device network 
While procuring IoT-MDM services, the above analysis shows that futures digital hospitals can source it either directly from IoT-MDM service providers or through MNOs  Alternatively, if a digital hospital also plays the role of a micro operator, besides offering other location-specific services, the micro operator can also offer device management services by sourcing it from other vendors  In other cases, the future digital hospital can procure fleet of secure devices for the hospital from whom they can also source the device management service as a bundle  From the 4C business models perspective, the hospital organization seems to be a single organizational entity where different activities can be categorized in the 4C layers  Cybersecurity providers can specify and address such issues to highlight and customize their service offering for the future digital hospital 
In this article, we have looked at the futures digital hospital context from a device management perspective, and attempted to portray business model options based on how to create and capture economic value from cybersecurity business  Though in this research, we adopt a high-level stance on cybersecurity from a technical perspective, the overall discussions on business potential are relevant to issues like information security, communication security, storage security, security at vulnerable touchpoints in hospital context (end user interface layer, IoT nodes, system layer, network layer) 
Futures digital hospitals will be vulnerable to cybersecurity threats because of its data-dependency and digital-intensive device network  Thus, the business opportunity for cybersecurity providers in this case can be considered as the need for automated and centralized IoT-MDM service  To that end, this paper presents four distinct players who can provide such service to a critical infrastructure like a digital hospital  The mixed-source business model options further open up multiple alternatives that each type of vendor can consider while tailoring services for the future digital hospital   This paper also connects the 4C ICT business model archetypes to cybersecurity business context which can be used as an analytical tool to identify customer needs and scope for value creation  
Academically, this work contributes by filling up the void in discussing business models for cybersecurity as an industry  In addition, in the existing literature the hospital context has also been less discussed from a cybersecurity business perspective  From an industrial point of view, the business model options discussed in this research are timely and relevant to the market context and need  As mentioned, the mixed source business model options show how cybersecurity providers can extend their offering for different kinds of need for the hospital context based on their core businesses  This study can prove to be helpful for cybersecurity business entities and at the same time hospital managers  The scope of this paper explains business potential of cybersecurity vendors to an emerging industry from a higher level  In this paper, authors do not attempt to analyze the technical aspect of cyber security provisioning in healthcare context, however that is a forthcoming research possibility of this study  Also, since this research is based on a conceptual phenomenon, thus its empirical validation, both qualitatively and quantitatively is still yet to come, which can be considered as a limitation of the study 
All in all, we consider that applying a business perspective to IoT-MDM systems can solve many challenges of a modern mobile IT environment, not only in healthcare but also in other kinds of critical infrastructures   These IoT-MDM systems can be provided by various kind of vendors through a balanced and timely business model 
This study has been supported by the DIMECC Cyber Trust – Digital cyber security program 
 Lehto, I , and Ahokangas, P  (2017)  “Mobile Security Business Models for Critical Infrastructures – An Ecosystemic Approach”, in Proceedings of the 24th Nordic Academy of Management Conference 2017, Bodo, Norway 
 Loebbecke, C , and Picot, A  (2015)  Reflections on societal and business model transformation arising from digitization and big data analytics: A research agenda  The Journal of Strategic Information Systems, 24, 149–157 
 Himidan, S , and Kim, P  (2015)  The evolving identity, capacity, and capability of the future surgeon  In Seminars in pediatric surgery, 24, 145–149, WB Saunders 
 Beimborn, D , and Palitza, M  (2013)  “Enterprise app stores for mobile applications-development of a benefits framework”, in Proceedings of the Nineteenth Americas Conference on Information Systems, Chicago, Illinois 
 Ortbach, K , Brockmann, T , and Stieglitz, S  (2014)  “Drivers for the adoption of mobile device management in organizations”, in proceedings of the Twenty Second European Conference on Information Systems, Tel Aviv 2014 
 Wirtz, B  W , Schilke, O , and Ullrich, S  (2010)  Strategic development of business models: implications of the Web 2 0 for creating value on the internet  Long range planning, 43, 272–290 
 Afuah, A  (2004)  Business Models: A Strategic Management Approach  McGraw-Hill/Irwin 
 Alt, R , and Zimmermann, H  D  (2001)  Preface: introduction to special section–business models  Electronic markets, 11, 3–9 
 Chesbrough, H , and Rosenbloom, R  S  (2002)  The role of the business model in capturing value from innovation: evidence from Xerox Corporation’s technology spin-off companies  Industrial and corporate change, 11, 529–555 
 Shafer, S  M , Smith, H  J , and Linder, J  C  (2005)  The power of business models  Business horizons, 48, 199–207 
 Zott, C , and Amit, R  (2010)  Business model design: an activity system perspective  Long range planning, 43, 216–226 
 Xu, T , Wendt, J  B , and Potkonjak, M  (2014)  Security of IoT systems: Design challenges and opportunities  In Proceedings of the 2014 IEEE/ACM International Conference on Computer-Aided Design, 417–423  IEEE Press 
 Zhang, Z  K , Cho, M  C  Y , Wang, C  W , Hsu, C  W , Chen, C  K , and Shieh, S  (2014)  IoT security: ongoing challenges and research opportunities  In Service-Oriented Computing and Applications (SOCA), 2014 IEEE 7th International Conference, 230–234  IEEE 
 Farooq, M  U , Waseem, M , Khairi, A , and Mazhar, S  (2015)  A critical analysis on the security concerns of internet of things (IoT)  Int  J  Computer Appl  111 
 Backman, J , Väre, J , Främling, K , Madhikermi, M , and Nykänen, O  (2016)  IoT-based interoperability framework for asset and fleet management  In Emerging Technologies and Factory Automation (ETFA), 2016 IEEE 21st International Conference, 1–4  IEEE 
 Isotalo, L  (2017)  “5G Slicing as a Tool to Test User Equipment Against Advanced Persistent Threats”, in International Conference on Network and System Security, 595–603  Springer, Cham 
 Alcaraz, C , and Zeadally, S  (2015)  “Critical infrastructure protection: requirements and challenges for the 21st century”, in International Journal of Critical Infrastructure Protection, 8, 53–66 
 Sutherland, J , and van den Heuvel, W  J  A  M  (2006)  Towards an intelligent hospital environment: adaptive workflow in the OR of the future  In System Sciences, 2006  HICSS’06, in Proceedings of the 39th Annual Hawaii International Conference, 5, 100b–100b  IEEE 
 Haq, M  M  (2008)  U S  Patent No  7,412,396  Washington, DC: U S  Patent and Trademark Office 
 Spector, N  D , Cull, W , Daniels, S  R , Gilhooly, J , Hall, J , Horn, I , and Stanton, B  F  (2014)  Gender and generational influences on the pediatric workforce and practice  Pediatrics, 133, 1112–1121 
 Wirtz, B  W  (2011)  Business model management  Design–Instrumente–Erfolgsfaktoren von Geschäftsmodellen, 2 
 Gomes, J  F , Ahokangas, P , and Owusu, K  A  (2016)  Business modeling facilitated cyber preparedness  Int  J  Business & Cyber Security, 1, 54–67 
 Richardson, J  (2008)  The business model: an integrative framework for strategy execution  Strategic change, 17, 133–144 
 Morris, M , Schindehutte, M , and Allen, J  (2005)  The entrepreneur’s business model: toward a unified perspective  J  Business Res  58, 726–735 
 Zott, C , Amit, R , and Massa, L  (2011)  The business model: recent developments and future research  J  Manage  37, 1019–1042 
 Gomes, J F , Iivari, M , Ahokangas, P , Isotalo, L , Sahlin, B , and Melen, J  (Forthcoming, in press), Cyber security business models in 5G  Comprehensive guide to 5G security, Wiley Publishers, London 
 Ballon, P  (2007)  Business modelling revisited: the configuration of control and value  info, 9, 6–19 
 Yrjölä, S , Ahokangas, P , and Matinmikko, M  (2015)  Evaluation of recent spectrum sharing concepts from business model scalability point of view  In Dynamic Spectrum Access Networks (DySPAN, in 2015 IEEE International Symposiu, 241–250  IEEE 
 Ahokangas, P , Moqaddamerad, S , Matinmikko, M , Abouzeid, A , Atkova, I , Gomes, J  F , and Iivari, M  (2016)  Future micro operators business models in 5G  The Business & Management Review, 7, 143 
 Mitola, J , Guerci, J , Reed, J , Yao, Y  D , Chen, Y , Clancy, T , and Guo, Y  (2014)  Accelerating 5G QoE via public-private spectrum sharing  IEEE Communications Magazine, 52, 77–85 
 Casadesus-Masanell, R , and Llanes, G  (2011)  Mixed source  Manage  Sci  57, 1212–1230 
 Mason, J  (2002)  Qualitative researching  Sage 
 Miles, M  B , and Huberman, A  M  (1994)  Qualitative data analysis: An expanded sourcebook  Sage 
Julius Francis Gomes is pursuing his Ph D  in international business from the University of Oulu  He currently works at the Oulu Business School as a Doctoral Student to research the futuristic business models for digital intensive industries  His research focuses on using business models as a mean to look in to future industries  He is interested to research business ecosystems in different contexts like cyber security, healthcare, future’s network etc  with a business model perspective  He received his M Sc  (2015) in international business from the University of Oulu  Prior to that he acquired MBA (2011) specializing in managing information systems in business applications  Previously, he has also enjoyed about three years in a top tier bank in Bangladesh as a channel innovator 
Marika Iivari is a postdoctoral researcher at the Martti Ahtisaari Institute within Oulu Business School  She defended her doctoral dissertation on business models in ecosystemic contexts  She holds M Sc  in International Business from the Ulster University, Northern Ireland  Her research interests are in the areas of open innovation, business models and strategy in the context of innovation ecosystems and smart cities, digital and ICT business ecosystems  She has been involved in several research projects around 5G and the Internet of Things, most recently in the health care sector  She is also an active member of the Business Model Community, the Open Innovation Community and the Society for Collaborative Networks 
Petri Ahokangas received his M Sc  (1992) and D Sc  (1998) degrees from the University Vaasa, Finland  He is currently Adjunct Professor (International software entrepreneurship) and Senior research fellow at Martti Ahtisaari Institute, Oulu Business School, University of Oulu, Finland  His research interests are in how innovation and technological change affect international business creation, transformation, and strategies in highly technology – intensive or software – intensive business domains  He has over 100 publications in scientific journals, books, conference proceedings, and other reports  He is actively working in several ICT-focused research consortia leading the business-related research streams 
Lauri Isotalo has received his M Sc  from Helsinki University of Technology (currently Aalto University) in 1992  He has also a postgraduate Diploma in Business Administration  At first Lauri worked in Nokia Corp  in Mobile Technology & System Marketing unit specializing in Intelligent Networks  In 1992 he joined Elisa Corp  where he has held several managerial positions in value added services business, system and process security and mobile network development  Since 2005 Lauri has also led Elisa SME teams in various international collaboration projects and acquired a deep knowledge of the cyber security of legacy telecommunication networks, ip core, access networks, user terminals and modern virtualized data center IT platforms/cloud systems  From 2014 Lauri has headed SDN&NFV development in Elisa 
Riikka Niemelä, M Sc , MHSc , is a cross-disciplinary health-tech professional who obtained her degrees in Electrical Engineering (Master of Science) and Medical technology (Master of Health Sciences) from the University of Oulu, Finland  After R&D engineering, she worked as a research assistant to research the national eHealth development of the Finnish healthcare  Thereafter, she has been researching and promoting the adaptation of connected health technologies in surgical processes of a Nordic future hospital, resulting in scientific publications  Currently, she works as a project manager in Tuttlingen, Germany – the world center of Medical Technology – to generate R&D and innovation projects in cooperation with the ICT and LifeScience companies from Oulu, Finland  Her aim is to promote the digitalization and internationalization of the both high-tech regions and to generate innovations serving the increasing demands for healthcare 
Journal of ICT, Vol  5_1, 107–128 
Context 2: \section{Introduction}\label{S1}

The multiple access interferences (MAI) is the root of user
limitation in CDMA systems \cite{R1,R3}  The parallel least mean
square-partial parallel interference cancelation (PLMS-PPIC) method
is a multiuser detector for code division multiple access (CDMA)
receivers which reduces the effect of MAI in bit detection  In this
method and similar to its former versions like LMS-PPIC \cite{R5}
(see also \cite{RR5}), a weighted value of the MAI of other users is
subtracted before making the decision for a specific user in
different stages \cite{cohpaper}  In both of these methods, the
normalized least mean square (NLMS) algorithm is engaged
\cite{Haykin96}  The $m^{\rm th}$ element of the weight vector in
each stage is the true transmitted binary value of the $m^{\rm th}$
user divided by its hard estimate value from the previous stage  The
magnitude of all weight elements in all stages are equal to unity 
Unlike the LMS-PPIC, the PLMS-PPIC method tries to keep this
property in each iteration by using a set of NLMS algorithms with
different step-sizes instead of one NLMS algorithm used in LMS-PPIC 
In each iteration, the parameter estimate of the NLMS algorithm is
chosen whose element magnitudes of cancelation weight estimate have
the best match with unity  In PLMS-PPIC implementation it is assumed
that the receiver knows the phases of all user channels  However in
practice, these phases are not known and should be estimated  In
this paper we improve the PLMS-PPIC procedure \cite{cohpaper} in
such a way that when there is only a partial information of the
channel phases, this modified version simultaneously estimates the
phases and the cancelation weights  The partial information is the
quarter of each channel phase in $(0,2\pi)$ 

The rest of the paper is organized as follows: In section \ref{S4}
the modified version of PLMS-PPIC with capability of channel phase
estimation is introduced  In section \ref{S5} some simulation
examples illustrate the results of the proposed method  Finally the
paper is concluded in section \ref{S6} 

\section{Multistage Parallel Interference Cancelation: Modified PLMS-PPIC Method}\label{S4}

We assume $M$ users synchronously send their symbols
$\alpha_1,\alpha_2,\cdots,\alpha_M$ via a base-band CDMA
transmission system where $\alpha_m\in\{-1,1\}$  The $m^{th}$ user
has its own code $p_m( )$ of length $N$, where $p_m(n)\in \{-1,1\}$,
for all $n$  It means that for each symbol $N$ bits are transmitted
by each user and the processing gain is equal to $N$  At the
receiver we assume that perfect power control scheme is applied 
Without loss of generality, we also assume that the power gains of
all channels are equal to unity and users' channels do not change
during each symbol transmission (it can change from one symbol
transmission to the next one) and the channel phase $\phi_m$ of
$m^{th}$ user is unknown for all $m=1,2,\cdots,M$ (see
\cite{cohpaper} for coherent transmission)  According to the above
assumptions the received signal is
\begin{equation}
\label{e1} r(n)=\sum\limits_{m=1}^{M}\alpha_m
e^{j\phi_m}p_m(n)+v(n),~~~~n=1,2,\cdots,N,
\end{equation}
where $v(n)$ is the additive white Gaussian noise with zero mean and
variance $\sigma^2$  Multistage parallel interference cancelation
method uses $\alpha^{s-1}_1,\alpha^{s-1}_2,\cdots,\alpha^{s-1}_M$,
the bit estimates outputs of the previous stage, $s-1$, to estimate
the related MAI of each user  It then subtracts it from the received
signal $r(n)$ and makes a new decision on each user variable
individually to make a new variable set
$\alpha^{s}_1,\alpha^{s}_2,\cdots,\alpha^{s}_M$ for the current
stage $s$  Usually the variable set of the first stage (stage $0$)
is the output of a conventional detector  The output of the last
stage is considered as the final estimate of transmitted bits  In
the following we explain the structure of a modified version of the
PLMS-PIC method \cite{cohpaper} with simultaneous capability of
estimating the cancelation weights and the channel phases 

Assume $\alpha_m^{(s-1)}\in\{-1,1\}$ is a given estimate of
$\alpha_m$ from stage $s-1$  Define
\begin{equation}
\label{e6} w^s_{m}=\frac{\alpha_m}{\alpha_m^{(s-1)}}e^{j\phi_m} 
\end{equation}
From (\ref{e1}) and (\ref{e6}) we have
\begin{equation}
\label{e7} r(n)=\sum\limits_{m=1}^{M}w^s_m\alpha^{(s-1)}_m
p_m(n)+v(n) 
\end{equation}
Define
\begin{subequations}
\begin{eqnarray}
\label{e8} W^s&=&[w^s_{1},w^s_{2},\cdots,w^s_{M}]^T,\\
\label{e9}
\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!X^{s}(n)\!\!\!&=&\!\!\![\alpha^{(s-1)}_1p_1(n),\alpha^{(s-1)}_2p_2(n),\cdots,\alpha^{(s-1)}_Mp_M(n)]^T 
\end{eqnarray}
\end{subequations}
where $T$ stands for transposition  From equations (\ref{e7}),
(\ref{e8}) and (\ref{e9}), we have
\begin{equation}
\label{e10} r(n)=W^{s^T}X^{s}(n)+v(n) 
\end{equation}
Given the observations $\{r(n),X^{s}(n)\}^{N}_{n=1}$, in modified
PLMS-PPIC, like the PLMS-PPIC \cite{cohpaper}, a set of NLMS
adaptive algorithm are used to compute
\begin{equation}
\label{te1} W^{s}(N)=[w^{s}_1(N),w^{s}_2(N),\cdots,w^{s}_M(N)]^T,
\end{equation}
which is an estimate of $W^s$ after iteration $N$  To do so, from
(\ref{e6}), we have
\begin{equation}
\label{e13} |w^s_{m}|=1 ~~~m=1,2,\cdots,M,
\end{equation}
which is equivalent to
\begin{equation}
\label{e14} \sum\limits_{m=1}^{M}||w^s_{m}|-1|=0 
\end{equation}
We divide $\Psi=\left(0,1-\sqrt{\frac{M-1}{M}}\right]$, a sharp
range for $\mu$ (the step-size of the NLMS algorithm) given in
\cite{sg2005}, into $L$ subintervals and consider $L$ individual
step-sizes $\Theta=\{\mu_1,\mu_2,\cdots,\mu_L\}$, where
$\mu_1=\frac{1-\sqrt{\frac{M-1}{M}}}{L}, \mu_2=2\mu_1,\cdots$, and
$\mu_L=L\mu_1$  In each stage, $L$ individual NLMS algorithms are
executed ($\mu_l$ is the step-size of the $l^{th}$ algorithm)  In
stage $s$ and at iteration $n$, if
$W^{s}_k(n)=[w^s_{1,k},\cdots,w^s_{M,k}]^T$, the parameter estimate
of the $k^{\rm th}$ algorithm, minimizes our criteria, then it is
considered as the parameter estimate at time iteration $n$  In other
words if the next equation holds
\begin{equation}
\label{e17} W^s_k(n)=\arg\min\limits_{W^s_l(n)\in I_{W^s}
}\left\{\sum\limits_{m=1}^{M}||w^s_{m,l}(n)|-1|\right\},
\end{equation}
where $W^{s}_l(n)=W^{s}(n-1)+\mu_l \frac{X^s(n)}{\|X^s(n)\|^2}e(n),
~~~ l=1,2,\cdots,k,\cdots,L-1,L$ and
$I_{W^s}=\{W^s_1(n),\cdots,W^s_L(n)\}$, then we have
$W^s(n)=W^s_k(n)$, and therefore all other algorithms replace their
weight estimate by $W^{s}_k(n)$  At time instant $n=N$, this
procedure gives $W^s(N)$, the final estimate of $W^s$, as the true
parameter of stage $s$ 

Now consider $R=(0,2\pi)$ and divide it into four equal parts
$R_1=(0,\frac{\pi}{2})$, $R_2=(\frac{\pi}{2},\pi)$,
$R_3=(\pi,\frac{3\pi}{2})$ and $R_4=(\frac{3\pi}{2},2\pi)$  The
partial information of channel phases (given by the receiver) is in
a way that it shows each $\phi_m$ ($m=1,2,\cdots,M$) belongs to
which one of the four quarters $R_i,~i=1,2,3,4$  Assume
$W^{s}(N)=[w^{s}_1(N),w^{s}_2(N),\cdots,w^{s}_M(N)]^T$ is the weight
estimate of the modified algorithm PLMS-PPIC at time instant $N$ of
the stage $s$  From equation (\ref{e6}) we have
\begin{equation}
\label{tt3}
\phi_m=\angle({\frac{\alpha^{(s-1)}_m}{\alpha_m}w^s_m}) 
\end{equation}
We estimate $\phi_m$ by $\hat{\phi}^s_m$, where
\begin{equation}
\label{ee3}
\hat{\phi}^s_m=\angle{(\frac{\alpha^{(s-1)}_m}{\alpha_m}w^s_m(N))} 
\end{equation}
Because $\frac{\alpha^{(s-1)}_m}{\alpha_m}=1$ or $-1$, we have
\begin{eqnarray}
\hat{\phi}^s_m=\left\{\begin{array}{ll} \angle{w^s_m(N)} &
\mbox{if}~
\frac{\alpha^{(s-1)}_m}{\alpha_m}=1\\
\pm\pi+\angle{w^s_m(N)} & \mbox{if}~
\frac{\alpha^{(s-1)}_m}{\alpha_m}=-1\end{array}\right 
\end{eqnarray}
Hence $\hat{\phi}^s_m\in P^s=\{\angle{w^s_m(N)},
\angle{w^s_m(N)+\pi, \angle{w^s_m(N)}-\pi}\}$  If $w^s_m(N)$
sufficiently converges to its true value $w^s_m$, the same region
for $\hat{\phi}^s_m$ and $\phi_m$ is expected  In this case only one
of the three members of $P^s$ has the same region as $\phi_m$  For
example if $\phi_m \in (0,\frac{\pi}{2})$, then $\hat{\phi}^s_m \in
(0,\frac{\pi}{2})$ and therefore only $\angle{w^s_m(N)}$ or
$\angle{w^s_m(N)}+\pi$ or $\angle{w^s_m(N)}-\pi$ belongs to
$(0,\frac{\pi}{2})$  If, for example, $\angle{w^s_m(N)}+\pi$ is such
a member between all three members of $P^s$, it is the best
candidate for phase estimation  In other words,
\[\phi_m\approx\hat{\phi}^s_m=\angle{w^s_m(N)}+\pi \]
We admit that when there is a member of $P^s$ in the quarter of
$\phi_m$, then $w^s_m(N)$ converges  What would happen when non of
the members of $P^s$ has the same quarter as $\phi_m$? This
situation will happen when the absolute difference between $\angle
w^s_m(N)$ and $\phi_m$ is greater than $\pi$  It means that
$w^s_m(N)$ has not converged yet  In this case where we can not
count on $w^s_m(N)$, the expected value is the optimum choice for
the channel phase estimation, e g  if $\phi_m \in (0,\frac{\pi}{2})$
then $\frac{\pi}{4}$ is the estimation of the channel phase
$\phi_m$, or if $\phi_m \in (\frac{\pi}{2},\pi)$ then
$\frac{3\pi}{4}$ is the estimation of the channel phase $\phi_m$ 
The results of the above discussion are summarized in the next
equation
\begin{eqnarray}
\nonumber \hat{\phi}^s_m = \left\{\begin{array}{llll} \angle
{w^s_m(N)} & \mbox{if}~
\angle{w^s_m(N)}, \phi_m\in R_i,~~i=1,2,3,4\\
\angle{w^s_m(N)}+\pi & \mbox{if}~ \angle{w^s_m(N)}+\pi, \phi_m\in
R_i,~~i=1,2,3,4\\
\angle{w^n_m(N)}-\pi & \mbox{if}~ \angle{w^s_m(N)}-\pi, \phi_m\in
R_i,~~i=1,2,3,4\\
\frac{(i-1)\pi+i\pi}{4} & \mbox{if}~ \phi_m\in
R_i,~~\angle{w^s_m(N)},\angle
{w^s_m(N)}\pm\pi\notin R_i,~~i=1,2,3,4 \\
\end{array}\right 
\end{eqnarray}
Having an estimation of the channel phases, the rest of the proposed
method is given by estimating $\alpha^{s}_m$ as follows:
\begin{equation}
\label{tt4}
\alpha^{s}_m=\mbox{sign}\left\{\mbox{real}\left\{\sum\limits_{n=1}^{N}
q^s_m(n)e^{-j\hat{\phi}^s_m}p_m(n)\right\}\right\},
\end{equation}
where
\begin{equation} \label{tt5}
q^{s}_{m}(n)=r(n)-\sum\limits_{m^{'}=1,m^{'}\ne
m}^{M}w^{s}_{m^{'}}(N)\alpha^{(s-1)}_{m^{'}} p_{m^{'}}(n) 
\end{equation}
The inputs of the first stage $\{\alpha^{0}_m\}_{m=1}^M$ (needed for
computing $X^1(n)$) are given by
\begin{equation}
\label{qte5}
\alpha^{0}_m=\mbox{sign}\left\{\mbox{real}\left\{\sum\limits_{n=1}^{N}
r(n)e^{-j\hat{\phi}^0_m}p_m(n)\right\}\right\}



ground_truth: Centralized control of mobile devices and applications.

prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: What algorithm is engaged in the PLMS-PPIC method?

Read the following text and answer briefly.

Context 2: 
\end{equation}
Assuming $\phi_m\in R_i$, then
\begin{equation}
\label{qqpp} \hat{\phi}^0_m =\frac{(i-1)\pi+i\pi}{4} 
\end{equation}
Table \ref{tab4} shows the structure of the modified PLMS-PPIC
method  It is to be notified that
\begin{itemize}
\item Equation (\ref{qte5}) shows the conventional bit detection
method when the receiver only knows the quarter of channel phase in
$(0,2\pi)$  \item With $L=1$ (i e  only one NLMS algorithm), the
modified PLMS-PPIC can be thought as a modified version of the
LMS-PPIC method 
\end{itemize}

In the following section some examples are given to illustrate the
effectiveness of the proposed method 

\section{Simulations}\label{S5}

In this section we have considered some simulation examples 
Examples \ref{ex2}-\ref{ex4} compare the conventional, the modified
LMS-PPIC and the modified PLMS-PPIC methods in three cases: balanced
channels, unbalanced channels and time varying channels  In all
examples, the receivers have only the quarter of each channel phase 
Example \ref{ex2} is given to compare the modified LMS-PPIC and the
PLMS-PPIC in the case of balanced channels 

\begin{example}{\it Balanced channels}:
\label{ex2}
\begin{table}
\caption{Channel phase estimate of the first user (example
\ref{ex2})} \label{tabex5} \centerline{{
\begin{tabular}{|c|c|c|c|c|}
\hline
\multirow{6}{*}{\rotatebox{90}{$\phi_m=\frac{3\pi}{8},M=15~~$}} & N(Iteration) & Stage Number& NLMS & PNLMS  \\
&&&&\\
\cline{2-5} & \multirow{2}{*}{64}& s = 2 &  $\hat{\phi}^s_m=\frac{3 24\pi}{8}$ & $\hat{\phi}^s_m=\frac{3 18\pi}{8}$ \\
\cline{3-5} & & s = 3 & $\hat{\phi}^s_m=\frac{3 24\pi}{8}$ & $\hat{\phi}^s_m=\frac{3 18\pi}{8}$ \\
\cline{2-5} & \multirow{2}{*}{256}& s = 2 &  $\hat{\phi}^s_m=\frac{2 85\pi}{8}$ & $\hat{\phi}^s_m=\frac{2 88\pi}{8}$ \\
\cline{3-5} & & s = 3 & $\hat{\phi}^s_m=\frac{2 85\pi}{8}$ & $\hat{\phi}^s_m=\frac{2 88\pi}{8}$ \\
\cline{2-5} \hline
\end{tabular} }}
\end{table}
Consider the system model (\ref{e7}) in which $M$ users
synchronously send their bits to the receiver through their
channels  It is assumed that each user's information consists of
codes of length $N$  It is also assumd that the signal to noise
ratio (SNR) is 0dB  In this example there is no power-unbalanced or
channel loss is assumed  The step-size of the NLMS algorithm in
modified LMS-PPIC method is $\mu=0 1(1-\sqrt{\frac{M-1}{M}})$ and
the set of step-sizes of the parallel NLMS algorithms in modified
PLMS-PPIC method are
$\Theta=\{0 01,0 05,0 1,0 2,\cdots,1\}(1-\sqrt{\frac{M-1}{M}})$,
i e  $\mu_1=0 01(1-\sqrt{\frac{M-1}{M}}),\cdots,
\mu_4=0 2(1-\sqrt{\frac{M-1}{M}}),\cdots,
\mu_{12}=(1-\sqrt{\frac{M-1}{M}})$  Figure~\ref{Figexp1NonCoh}
illustrates the bit error rate (BER) for the case of two stages and
for $N=64$ and $N=256$  Simulations also show that there is no
remarkable difference between results in two stage and three stage
scenarios  Table~\ref{tabex5} compares the average channel phase
estimate of the first user in each stage and over $10$ runs of
modified LMS-PPIC and PLMS-PPIC, when the the number of users is
$M=15$ 
\end{example}

Although LMS-PPIC and PLMS-PPIC, as well as their modified versions,
are structured based on the assumption of no near-far problem
(examples \ref{ex3} and \ref{ex4}), these methods and especially the
second one have remarkable performance in the cases of unbalanced
and/or time varying channels 

\begin{example}{\it Unbalanced channels}:
\label{ex3}
\begin{table}
\caption{Channel phase estimate of the first user (example
\ref{ex3})} \label{tabex6} \centerline{{
\begin{tabular}{|c|c|c|c|c|}
\hline
\multirow{6}{*}{\rotatebox{90}{$\phi_m=\frac{3\pi}{8},M=15~~$}} & N(Iteration) & Stage Number& NLMS & PNLMS  \\
&&&&\\
\cline{2-5} & \multirow{2}{*}{64}& s=2 &  $\hat{\phi}^s_m=\frac{2 45\pi}{8}$ & $\hat{\phi}^s_m=\frac{2 36\pi}{8}$ \\
\cline{3-5} & & s=3 & $\hat{\phi}^s_m=\frac{2 71\pi}{8}$ & $\hat{\phi}^s_m=\frac{2 80\pi}{8}$ \\
\cline{2-5} & \multirow{2}{*}{256}& s=2 &  $\hat{\phi}^s_m=\frac{3 09\pi}{8}$ & $\hat{\phi}^s_m=\frac{2 86\pi}{8}$ \\
\cline{3-5} & & s=3 & $\hat{\phi}^s_m=\frac{2 93\pi}{8}$ & $\hat{\phi}^s_m=\frac{3 01\pi}{8}$ \\
\cline{2-5} \hline
\end{tabular} }}
\end{table}
Consider example \ref{ex2} with power unbalanced and/or channel loss
in transmission system, i e  the true model at stage $s$ is
\begin{equation}
\label{ve7} r(n)=\sum\limits_{m=1}^{M}\beta_m
w^s_m\alpha^{(s-1)}_m c_m(n)+v(n),
\end{equation}
where $0<\beta_m\leq 1$ for all $1\leq m \leq M$  Both the LMS-PPIC
and the PLMS-PPIC methods assume the model (\ref{e7}), and their
estimations are based on observations $\{r(n),X^s(n)\}$, instead of
$\{r(n),\mathbf{G}X^s(n)\}$, where the channel gain matrix is
$\mathbf{G}=\mbox{diag}(\beta_1,\beta_2,\cdots,\beta_m)$  In this
case we repeat example \ref{ex2}  We randomly get each element of
$G$ from $[0,0 3]$  Figure~\ref{Figexp2NonCoh} illustrates the BER
versus the number of users  Table~\ref{tabex6} compares the channel
phase estimate of the first user in each stage and over $10$ runs of
modified LMS-PPIC and modified PLMS-PPIC for $M=15$ 
\end{example}

\begin{example}
\label{ex4} {\it Time varying channels}: Consider example \ref{ex2}
with time varying Rayleigh fading channels  In this case we assume
the maximum Doppler shift of $40$HZ, the three-tap
frequency-selective channel with delay vector of $\{2\times
10^{-6},2 5\times 10^{-6},3\times 10^{-6}\}$sec and gain vector of
$\{-5,-3,-10\}$dB  Figure~\ref{Figexp3NonCoh} shows the average BER
over all users versus $M$ and using two stages 
\end{example}


\section{Conclusion}\label{S6}

In this paper, parallel interference cancelation using adaptive
multistage structure and employing a set of NLMS algorithms with
different step-sizes is proposed, when just the quarter of the
channel phase of each user is known  In fact, the algorithm has been
proposed for coherent transmission with full information on channel
phases in \cite{cohpaper}  This paper is a modification on the
previously proposed algorithm  Simulation results show that the new
method has a remarkable performance for different scenarios
including Rayleigh fading channels even if the channel is
unbalanced 


Context 3: \section*{Dynamical Behaviour of $O$ in Lattice Gases}

The dynamical behaviour of the anisotropic order parameter  $m$ [see Eq ~\eqref{eq:def-m} in the Letter] following a quench to the critical point is well described by
the Gaussian theory for all the three lattice gas models studied, $i e ,$ driven lattice gas with either constant (IDLG) or random (RDLG) infinite drive and equilibrium lattice gas (LG)  In other words, in the short-time regime, $m \sim t^{1/2}$ [see Eq  \eqref{eq:mt}] and the Binder cumulant $g$ of the lowest transverse mode [defined in Eq  \eqref{eq:binder}] is zero in this regime  The alternative order parameter $O,$ however, distinguishes between the driven (IDLG, RDLG) and the equilibrium (LG) lattice gases  

In order to understand  this, we first write the phenomenological scaling form for $O$,  analogous to Eq  \eqref{eq:scalingass} in  the Letter,
\begin{eqnarray}
O (t, L_{\parallel} ; S_\Delta) = L_{\parallel}^{-\beta/[\nu(1+\Delta)]} \tilde f_O (t/L_{\parallel}^{z/(1+\Delta)} ; S_\Delta) \quad
\label{eq:Oscalingass}
\end{eqnarray}
We already remarked that, in the LG, this scaling form is not compatible with the prediction $O \sim t^{1/8}  L_{\parallel}^{-1/2}$ of the Gaussian theory   However, following Ref  \cite{AS2002}, it can be argued that, at short times, the only dependence of $O$ on the system size $L_{\parallel}$ is of the form $O \sim L_\parallel^{-1/2}$ which is very well confirmed by numerical simulations  Accordingly,  the generic behaviour of $O$ can be assumed to be
\begin{eqnarray}
O \sim t^{\alpha} L_\parallel^{-1/2}, \label{eq:O}
\end{eqnarray}
where $\alpha$ is a phenomenological exponent to be determined  This, along with Eq  \eqref{eq:Oscalingass}, implies $\tilde f_O(x) \sim x^{\alpha} $ Comparing the finite-size behaviour in Eq ~\eqref{eq:O} with Eq ~\eqref{eq:Oscalingass} one actually infers,
\begin{eqnarray}
\alpha &=& \frac{1+ \Delta -2 \beta/\nu}{2 \, (4- \eta)}  \label{eq:alpha}
\end{eqnarray}
This equation, together with the hyperscaling relation $\Delta - 2 \beta/\nu= - \eta$ in two spatial dimensions, shows that the prediction $\alpha = 1/8$ of the Gaussian theory [see Eq  \eqref{eq:Ot}] can be obtained only when $\eta=0,$ which is the case for the IDLG (exactly) and the RDLG (approximately) but not for the LG  

On the other hand,  Eq ~\eqref{eq:alpha} predicts $\alpha = 1/10$ upon substituting the values of the critical exponents corresponding to the Ising  universality class (LG)  This is consistent with the numerical simulation results presented in the main text, see Fig  \ref{fig:ising}(b) therein 

\begin{figure}[th]
\vspace*{0 2 cm}
 \centering
 \includegraphics[width=10 cm]{ /compare_binder pdf}

\caption{Comparison between the temporal evolution of the Binder cumulants $g$ corresponding to the $12^{th}$ transverse mode, $i e ,$ with $n_\perp =12,$ in the LG (lowest curve), IDLG and RDLG (two upper curves) on a $32 \times 32$ lattice  \label{fig:b}}
 \label{fig:binder}
\end{figure}


The emergence of this new value $1/10$ of the exponent $\alpha$ must be traced back to the non-Gaussian nature of higher fluctuating modes in the LG  In fact, even though the lowest mode behaves identically in all the three models we considered,  characterized by the same behaviour of $m$, higher modes show a significant difference in the non-driven case   


To illustrate this, we measured the Binder cumulants of higher modes which is defined  analogously to Eq ~(11), using transverse modes other than the first, i e , with $\mu=\tilde \sigma(0,2 \pi n_\bot/L_\bot)$ and $n_\bot>1 $  
 Figure \ref{fig:b} compares the same for all the three lattice gases for the mode with $n_\perp =12$ on a $32 \times 32$ lattice  Clearly, the curve corresponding to the LG (lowest, blue) departs from Gaussian behaviour $g=0$ (in practice, $e g ,$ $|g| \lesssim 0 005,$ corresponding to the shaded gray area) much earlier than it does for the IDLG  or RDLG (two upper curves, red and green respectively) 

Accordingly, the different dynamical behaviour of $O$, which involves a sum over all modes, can be attributed to the non-Gaussian nature of the higher modes in the LG  
Such a departure is not entirely surprising  In fact, for higher modes, mesoscopic descriptions such as the ones in Eqs  \eqref{eq:L-DLG} or \eqref{eq:g_evol} are not expected to hold, while the anisotropy at the microscopic level could be the mechanism leading to the Gaussianity of higher modes in the driven models 





ground_truth: The normalized least mean square (NLMS) algorithm.

prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: What is the dynamical behavior of the anisotropic order parameter following a quench to the critical point?

Read the following text and answer briefly.

Context 4: 

English acceded to the role of Finance Minister in the continuing wake of the financial crisis  In response to New Zealand's rising debt, English made budget deficit-reduction his main priority  His first budget outlined three focuses in New Zealand's financial recovery: "improving the business environment and removing roadblocks to growth; investment in productive infrastructure; and improving the way government works"  One of his first acts was creating the National Infrastructure Unit, charged with formulating a plan for infrastructure projects and investments  He commissioned a government-wide spending review, with an aim to reducing government expenditure—with the exceptions of a two-year stimulus package and long-term increases on infrastructure spending 

In April 2011, the Opposition criticised English for suggesting that New Zealand businesses could use New Zealand's low wages to help it compete with Australia  The National Government campaigned for re-election in 2011 on its economic record  The Government boasted growth for five consecutive quarters up to mid-2010, totalling 1 6% of real GDP 

Strong growth resulted in a surplus of $473 million for the 2015/16 financial year, projected to rise to $8 5 billion by 2020/21  In his 2016 Economic and Fiscal Update address, English stated that reducing debt and tackling the costs of the 2016 Kaikōura earthquake were higher priorities than reducing rates of tax 

Allowances issue
In 2009, the media, including TVNZ and TV3 revealed that English was receiving about NZ$900 a week as part of a living allowance for ministers, to live in his own NZ$1 2 million Wellington home  At the time, English also received $276,200 in his annual salary as Deputy Prime Minister  It was also revealed other ministers with homes in the capital city were also claiming accommodation allowances  On 3 August 2009, Prime Minister John Key started a review of the housing allowances claimed by cabinet ministers  English subsequently paid back $12,000 and only claimed about $24,000 a year in living allowances  The Auditor-General's office said in September 2009 that they were making "preliminary enquiries" into parliamentary housing expenses in response to a letter of complaint from Progressive party leader Jim Anderton  Two days later English stated that he would no longer take up any housing allowance and had paid back all the allowance he had received since the November 2008 election 

Prime Minister (2016–2017)

John Key resigned on 12 December, and endorsed English as his successor in the resulting leadership election  Following the drop-out of both Judith Collins and Jonathan Coleman from the leadership election, English was sworn in as the 39th Prime Minister of New Zealand on 12 December 2016 

English appointed his first cabinet on 18 December  In a reshuffle, he appointed Steven Joyce to succeed him as Finance Minister, while most ministerial portfolios remained the same 

In February 2017, English did not attend Waitangi Day commemorations at the historic treaty grounds, reportedly in response to the Ngāpuhi iwi's decision to stop the Prime Minister from speaking at the marae  Ngāpuhi have protested the Government's negotiation of the Trans Pacific Partnership Agreement (TPPA), which the iwi believe infringes upon Māori sovereignty, and thus does not adhere to the Treaty of Waitangi  English had been invited to attend in an official capacity; his non-attendance was criticised by a Ngāpuhi elder and Opposition leader Andrew Little 

In his first overseas trip as Prime Minister, English travelled to Europe to discuss trade ties, including a prospective New Zealand–European Union free trade agreement  He first travelled to London on 13 January 2017 to meet British Prime Minister Theresa May  Discussing trade relations, English said the two nations were "natural partners" and would "continue to forge ties" after the UK's withdrawal from the EU  He also arranged to meet with London Mayor Sadiq Khan, Belgian Prime Minister Charles Michel and German Chancellor Angela Merkel   In a meeting with Merkel, English received crucial backing from Germany for a trade deal with the EU  On 16 January, English stated that his government would continue to promote TPPA, despite the United States' decision to withdraw from the agreement  He explained that Southeast Asian countries would now be treated as a priority in negotiations—he also asserted that the United States was ceding influence to China by its rejection of the trade pact 

At a press conference at the Beehive on 1 February 2017, English announced that the 2017 general election would be held on 23 September  The Prime Minister later confirmed that his party would approach ACT, United Future and the Māori Party if confidence and supply agreements were required to form a government following the election  In his second cabinet reshuffle on 24 April, English appointed Gerry Brownlee as his new Foreign Affairs Minister; he also promoted Nikki Kaye to the portfolio of Education Minister, and moved Mark Mitchell into the cabinet to become Defence Minister  The reshuffle was perceived as an election preparation 

On 13 February 2017, English welcomed Australian Prime Minister Malcolm Turnbull to Wellington  The two leaders reaffirmed their shared trade agenda, and discussed changes to the Australian citizenship pathway which will affect permanent residents originating from New Zealand 

On 19 June, it was reported that Todd Barclay, who succeeded English as MP for Clutha-Southland, had clandestinely recorded one of his employee's conversations the previous year, and that John Key's leaders' budget was used to pay a confidential settlement after the employee resigned  English admitted that he had been aware of the illegal recording and the settlement, and thus implicated in the scandal 

During the 2017 National campaign launch, English introduced a $379 million social investment package including digital learning academies for high school students, more resources for mathematics, and boosting support for teaching second languages in schools, and maintaining National Standards in the school curriculum  Prime Minister English also sought to defend National's financial management and economic track record and claimed that the opposition Labour Party would raise taxes  Early opinion polling had forecast a poor showing in the election for the Labour Party, but in early August 37-year-old Jacinda Ardern took over as Labour leader and seemingly energised younger voters 

At the 2017 general election, National won the largest share of the party vote (44 4%) and the largest number of seats (56) in the House Representatives  However, National lacked enough seats to govern alone due to two of the party's support partners, the Māori Party and United Future, losing their parliamentary seats  In response, English stated that the party would be entering into talks to form a coalition with New Zealand First  Following talks with the two largest parties, New Zealand First entered a coalition arrangement with the Labour Party  English was succeeded as prime minister by Jacinda Ardern on 26 October 

Opposition (2017–2018)

Leader of the Opposition
English was re-elected as National Party leader on 24 October 2017  At the time of his re-election, English announced his intention to stay on as leader until the next general election  On 13 February 2018, however, he stood down as National Party leader due to personal reasons, and instructed the party to put into motion the processes to elect a new leader  He also retired from Parliament  English's resignation followed weeks of speculation that he would step aside for a new leader  On 27 February, he was succeeded as party leader by Simon Bridges as the result of the leadership election held that day 

Post-premiership 
In 2018, English joined the board of Australian conglomerate, Wesfarmers  English serves in Chairmanships of Mount Cook Alpine Salmon, Impact Lab Ltd and Manawanui Support Ltd  He is also a director of The Instillery, Centre for Independent Studies and The Todd Corporation Limited, and is a member of the Impact Advisory Group of Macquarie Infrastructure and Real Assets 

Political and social views

English is regarded as more socially conservative than his predecessor, John Key  He has stated his opposition to voluntary euthanasia and physician-assisted suicide, same-sex civil unions, and the decriminalisation of prostitution  As Prime Minister he opposed any "liberalisation" of abortion law 

In 2004, English voted against a bill to establish civil unions for both same-sex and opposite-sex couples  In 2005, he voted for the Marriage (Gender Clarification) Amendment Bill, which would have amended the Marriage Act to define marriage as only between a man and a woman  English voted against the Marriage (Definition of Marriage) Amendment Bill, a bill that legalised same-sex marriage in New Zealand  However, in December 2016 he stated, "I'd probably vote differently now on the gay marriage issue  I don't think that gay marriage is a threat to anyone else's marriage" 

In 2009, English voted against the Misuse of Drugs (Medicinal Cannabis) Amendment Bill, a bill aimed at amending the Misuse of Drugs Act so that cannabis could be used for medical purposes 

Personal life 
English met his future wife, Mary Scanlon, at university  She was studying medicine at the time, and became a general practitioner  Both her parents were immigrants, her father being Samoan and her mother Italian, born on the island of Stromboli  They have six children: a daughter and five sons 

English is a practising Roman Catholic, but has stated that he considers his religious beliefs personal and thus separate from politics 

In June 2002, English took part in TV3's Fight For Life, a celebrity boxing fundraiser to raise money for the Yellow Ribbon anti-youth-suicide campaign, influenced by the death of a teenage nephew in 1997  He lost a split decision to former university colleague Ted Clarke 

Honours
In the 2018 Queen's Birthday Honours, English was appointed a Knight Companion of the New Zealand Order of Merit, for services of over 27 years to the State 

See also

List of New Zealand governments
Politics of New Zealand

References

External links

Profile at National Party 
Profile on Parliament nz
Releases and speeches at Beehive govt nz

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1961 births
21st-century New Zealand politicians
Candidates in the 2017 New Zealand general election
Deputy Prime Ministers of New Zealand
Leaders of the Opposition (New Zealand)
Living people
Members of the Cabinet of New Zealand
Members of the New Zealand House of Representatives
New Zealand farmers
New Zealand finance ministers
New Zealand list MPs
New Zealand MPs for South Island electorates
New Zealand National Party MPs
New Zealand National Party leaders
New Zealand Roman Catholics
New Zealand people of Irish descent
People educated at St  Patrick's College, Silverstream
People from Dipton, New Zealand
People from Lumsden, New Zealand
Prime Ministers of New Zealand
University of Otago alumni
Victoria University of Wellington alumni
Knights Companion of the New Zealand Order of Merit
New Zealand politicians awarded knighthoods



ground_truth: It is well described by the Gaussian theory.