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A Blockchain-Enabled IoT Logistics System for Efficient Tracking and Management of High-Price Shipments: A Resilient, Scalable and Sustainable Approach to Smart CitiesThe concept of a smart city is aimed at enhancing the quality of life for urban residents, and logistic services are a crucial component of this effort. Despite this, the logistics industry has encountered issues due to the exponential growth of logistics volumes, as well as the complexity of processes and lack of transparency. Consequently, it is necessary to develop an efficient management system that offers traceability and condition monitoring capabilities to ensure the safe and high-quality delivery of goods. Moreover, it is crucial to guarantee the accuracy and dependability of distribution data. In this context, this paper proposes a blockchain-enabled IoT logistics system for the efficient tracking and management of high-price shipments. A smart contract based on blockchain technology has been designed for automatic approval and payment, with the aim of distributing shipping information exclusively among legitimate logistics parties. To ensure authentication, a zero-knowledge proof is used to conceal the blockchain address. Moreover, an intelligent parcel (iParcel) containing piezoresistive sensors is developed to pack delivered goods during the shipping process for violation detection such as severe falls or theft. The iParcels are automatically tracked and traced, and if a violation occurs, the contract is cancelled, and payment is refunded. The transaction fee per party is reasonable, particularly for high-price products that guarantee successful shipment.
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The influence of AmeriCorps members on ecosystem management, Journal of Cleaner ProductionMillions of acres of public lands across the United States (U.S.) face imminent threats from invasive species and wildfires, jeopardizing ecosystem health, wildlife habitats, recreational resources, and community safety. The responsible agencies, including the U.S. Forest Service, National Park Service, Bureau of Land Management, and state park agencies, are limited in their capacity to effectively manage these lands. Consequently, AmeriCorps members play a crucial role in curbing invasive species proliferation and mitigating wildfire fuel loads, safeguarding communities, and habitats. Here, the results of a national evaluation to assess the impact of AmeriCorps members on invasive species and wildfire fuel loads is presented. This study is a comprehensive analysis of AmeriCorps members' effectiveness in achieving ecosystem management objectives, revealing positive responses in native species cover and notable improvements in treated plots. AmeriCorps units that yielded significant results operated under specific temperature conditions and adhered to distinct pre- and post-intervention data collection intervals. Despite occasional challenges, the overall efficacy of AmeriCorps' efforts in eradicating targeted invasive species and promoting positive native species responses was confirmed. While linear regression models indicated successful forest fuel mitigation in AmeriCorps programs, the Difference-in-Difference models revealed less conspicuous outcomes, suggesting limited modifications in parameters such as the height of the lowest living branch, canopy cover, and litter depth. By analyzing data from multiple AmeriCorps programs in diverse geographic regions, this study contributes valuable insights into the effectiveness of AmeriCorps programs in ecological conservation. It represents one of the first multi-state and comprehensive examinations of AmeriCorps members' effectiveness in invasive species management and wildfire fuel mitigation in the USA, underscoring its significance in the field of ecosystem management.
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The Application of AutoML Techniques in Diabetes Diagnosis: Current Approaches, Performance, and Future DirectionsArtificial Intelligence (AI) has experienced rapid advancements in recent years, facilitating the creation of innovative, sustainable tools and technologies across various sectors. Among these applications, the use of AI in healthcare, particularly in the diagnosis and management of chronic diseases like diabetes, has shown significant promise. Automated Machine Learning (AutoML), with its minimally invasive and resource-efficient approach, promotes sustainability in healthcare by streamlining the process of predictive model creation. This research paper delves into advancements in AutoML for predictive modeling in diabetes diagnosis. It illuminates their effectiveness in identifying risk factors, optimizing treatment strategies, and ultimately improving patient outcomes while reducing environmental footprint and conserving resources. The primary objective of this scholarly inquiry is to meticulously identify the multitude of factors contributing to the development of diabetes and refine the prediction model to incorporate these insights. This process fosters a comprehensive understanding of the disease in a manner that supports the principles of sustainable healthcare. By analyzing the provided dataset, AutoML was able to select the most fitting model, emphasizing the paramount importance of variables such as Glucose, BMI, DiabetesPedigreeFunction, and BloodPressure in determining an individual’s diabetic status. The sustainability of this process lies in its potential to expedite treatment, reduce unnecessary testing and procedures, and ultimately foster healthier lives. Recognizing the importance of accuracy in this critical domain, we propose that supplementary factors and data be rigorously evaluated and incorporated into the assessment. This approach aims to devise a model with enhanced accuracy, further contributing to the efficiency and sustainability of healthcare practices.
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A Blockchain Based Framework for Efficient Water Management and Leakage Detection in Urban AreasSustainable urban water management is essential to handle water scarcity, leakage, and inefficient distribution. This paper covers water management in urban areas, including an intro- duction, an overview of water management practices, the characteristics and functioning of water distribution systems, monitoring and control systems for efficient distribution, smart systems for optimization, strategies for water conservation and waste management, per capita water demand analysis, and desalination plant overviews. The article proposes a blockchain-based water manage- ment architecture with IoT sensors for accurate reporting. The framework uses blockchain technology to authenticate and share real-time data between sensors and the water distribution dashboard. It also has a modular API for water leakage detection and flow control to decrease water waste and enhance distribution. The suggested approach might enhance water management; however, its execution is complex. Maintaining the framework’s efficacy is advised. The research provides insights into water management and proposes a technology solution employing blockchain and IoT sensors for trustworthy data reporting and effective water distribution to promote sustainable urban water management.
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Government and the global digital transformation: the other side of the mirrorAmid the global digital transformation, the seemingly simple question of the government delivering on peace, justice and strong institutions turns into a conundrum. Several factors weigh in on what the government can do, envisages to do and dares to do today. State power, raison d’étre, the nature of the global order, including global governance, are just a few of the issues that need to be factored in the analysis. Clearly, questions of ideology, including the degree of government intervention in the economy, the very definition of market economy and even of the scope of (economic) freedom are equally important in the conversation about the government today. People, and so personalities, do matter too, in that they influence the way things are done, expressed and communicated, regardless of the institutional, i.e. frequently rigid, structural confines.
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A Blockchain-Enabled IoT Logistics System for Efficient Tracking and Management of High-Price Shipments: A Resilient, Scalable and Sustainable Approach to Smart CitiesThe concept of a smart city is aimed at enhancing the quality of life for urban residents, and logistic services are a crucial component of this effort. Despite this, the logistics industry has encountered issues due to the exponential growth of logistics volumes, as well as the complexity of processes and lack of transparency. Consequently, it is necessary to develop an efficient management system that offers traceability and condition monitoring capabilities to ensure the safe and high-quality delivery of goods. Moreover, it is crucial to guarantee the accuracy and dependability of distribution data. In this context, this paper proposes a blockchain-enabled IoT logistics system for the efficient tracking and management of high-price shipments. A smart contract based on blockchain technology has been designed for automatic approval and payment, with the aim of distributing shipping information exclusively among legitimate logistics parties. To ensure authentication, a zero-knowledge proof is used to conceal the blockchain address. Moreover, an intelligent parcel (iParcel) containing piezoresistive sensors is developed to pack delivered goods during the shipping process for violation detection such as severe falls or theft. The iParcels are automatically tracked and traced, and if a violation occurs, the contract is cancelled, and payment is refunded. The transaction fee per party is reasonable, particularly for high-price products that guarantee successful shipment.
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The Application of AutoML Techniques in Diabetes Diagnosis: Current Approaches, Performance, and Future DirectionsArtificial Intelligence (AI) has experienced rapid advancements in recent years, facilitating the creation of innovative, sustainable tools and technologies across various sectors. Among these applications, the use of AI in healthcare, particularly in the diagnosis and management of chronic diseases like diabetes, has shown significant promise. Automated Machine Learning (AutoML), with its minimally invasive and resource-efficient approach, promotes sustainability in healthcare by streamlining the process of predictive model creation. This research paper delves into advancements in AutoML for predictive modeling in diabetes diagnosis. It illuminates their effectiveness in identifying risk factors, optimizing treatment strategies, and ultimately improving patient outcomes while reducing environmental footprint and conserving resources. The primary objective of this scholarly inquiry is to meticulously identify the multitude of factors contributing to the development of diabetes and refine the prediction model to incorporate these insights. This process fosters a comprehensive understanding of the disease in a manner that supports the principles of sustainable healthcare. By analyzing the provided dataset, AutoML was able to select the most fitting model, emphasizing the paramount importance of variables such as Glucose, BMI, DiabetesPedigreeFunction, and BloodPressure in determining an individual’s diabetic status. The sustainability of this process lies in its potential to expedite treatment, reduce unnecessary testing and procedures, and ultimately foster healthier lives. Recognizing the importance of accuracy in this critical domain, we propose that supplementary factors and data be rigorously evaluated and incorporated into the assessment. This approach aims to devise a model with enhanced accuracy, further contributing to the efficiency and sustainability of healthcare practices.
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Study of image sensors for enhanced face recognition at a distance in the Smart City contextSmart monitoring and surveillance systems have become one of the fundamental areas in the context of security applications in Smart Cities. In particular, video surveillance for Human Activity Recognition (HAR) applied to the recognition of potential offenders and to the detection and prevention of violent acts is a challenging task that is still undergoing. This paper presents a method based on deep learning for face recognition at a distance for security applications. Due to the absence of available datasets on face recognition at a distance, a methodology to generate a reliable dataset that relates the distance of the individuals from the camera, the focal length of the image sensors and the size in pixels of the target face is introduced. To generate the extended dataset, the Georgia Tech Face and Quality Dataset for Distance Faces databases were chosen. Our method is then tested and applied to a set of commercial image sensors for surveillance cameras using this dataset. The system achieves an average accuracy above 99% for several sensors and allows to calculate the maximum distance for a sensor to get the required accuracy in the recognition, which could be crucial in security applications in smart cities.
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Implementing enhanced web-based application security using shadow daemon WAFThe importance of companies taking proactive steps to defend their web applications is growing as the number of web-based threats rises. Utilizing web application firewalls (WAFs) is one such measure. In this study, we investigate the possible advantages of integrating Shadow Daemon into the design of regional start-up businesses to enhance their cybersecurity defence. We start by giving a general review of Shadow Daemon and its features, emphasizing its capacity to recognize and stop typical web application threats like SQL injection, XSS, and LFI. The significance of web application security and the possible repercussions of a successful attack are then covered. Then, we look at the current state of web application security in local start-up businesses, including frequent security flaws and the difficulties they encounter in putting in place workable security measures. Following that, describing the crucial processes required in the implementation process, we suggest a framework for incorporating Shadow Daemon into the architectural design of regional start-up businesses. We also talk about how employing Shadow Daemon might enhance security, lower the chance of data breaches, and boost customer confidence. Finally, we emphasize the value of proactive web application security and the part Shadow Daemon may play in assisting regional start-up businesses to defend their web apps against attacks as we draw to a close. We suggest implementing Shadow Daemon can improve cybersecurity defences and reduce potential security risks for nearby start-up businesses in an efficient and cost-efficient manner.
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Managing Safety and Security in the Smart City: Covid-19, Emergencies and Smart SurveillanceThe chapter examines the role and potential inherent in surveillance systems in smart cities today. The Covid-19 pandemic and the resultant restrictions to mobility, on the one hand, and the need for strengthened enforcement measures highlighted the already existing weaknesses and contingencies besetting surveillance in smart cities. The chapter makes a case that the adoption of smart city surveillance and infrastructure management systems may contribute to the improvement of safety and security in the smart city as well as to an overall enhancement of the smart city’s resilience. The discussion in this chapter focuses on the complex processes of data acquisition, data sharing, and data utilization to explain in which ways they all add to smart surveillance systems that—while aware of individual freedoms and privacy issues—contribute to the process of making a smart city resilient. To showcase the applicability of these findings, a wireless mesh network (WMN) surveillance system is presented.
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Artificial Intelligence and Its Context: An IntroductionConsidering the scope of novelty that AI brings into politics, economics, and, notably, daily life, it is imperative that it is discussed from as many perspectives as possible. It is imperative that it is discussed in detail, and that the debate on AI is approachable to a variety of stakeholders. This chapter outlines the main themes and issues that have been discussed in chapters that constitute this volume, including those as diverse as the very technical characteristics of AI, AI and politics, ethical questions pertaining to the use of AI in the battlefield, strategic communication (StratCom), military planning, and management and the decision-making process in the domains of business and public administration. The intersection of AI and international trade and national AI strategies from a range of countries around the globe are queried too.
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Artificial Intelligence (AI): Explaining, Querying, DemystifyingArtificial intelligence (AI) is a buzzword today, reminding us of the concept of “globalization” and the relating debate two decades ago. As with globalization then, for the greater part of society, AI remains a concept poorly understood, vague, and approached with fear of the unknown. While AI is hailed as the panacea to all the ills of the prevailing socio-economic model and a source of unimaginable opportunities, it is also seen as a source of substantial risks and threats to safety, security, and the operation of the markets. The objective of this chapter is to explain, query and demystify AI and by so doing to highlight the areas and domains that are crucial for AI to develop and serve society at large. To this end, the “dry”, i.e., quite technical, facets of AI are discussed, and a case for an AI ecosystem is made. Technology-related limitations of AI, as well as possibilities, are outlined briefly. An overview of AI’s implications for the (global) economy and selected policies follows. The ethical concerns are discussed in the concluding section.
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Think human, act digital: activating data-driven orientation in innovative start-upsThe study queries the drivers of innovation management in contemporary data-driven organizations/companies. It is argued that data-driven organizations that integrate a strategic orientation grounded in data, human abilities and proactive management are more effective in triggering innovation. Research reported in this paper employs constructivist grounded theory, Gioia methodology, and the abductive approach. The data collected through semi-structured interviews administered to 20 Italian start-up founders are then examined. The paper identifies the key enablers of innovation development in data-driven companies and reveals that data-driven companies may generate different innovation patterns depending on the kind of capabilities activated.
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Effective Management of the Smart City: An Outline of a ConversationAdvances in information and communication technology (ICT) are central for the transformation of cities into smart cities. Yet, it is the ability to apply ICT efficiently that defines whether a smart city will be resilient and sustainable. The art of management stands at the heart of the process and this book explores it. In this chapter an outline of the key research topics that emerge at the intersection of smart cities research and management science is presented. These include among others management and decision-making for urban design and the built environment development, the relationship between the smart city services and business growth dynamics, new forms of asset acquisition, and entrepreneurship development. Finally, the research topics include questions of the social accountability of local governments vis-à-vis smart city development; inclusion and participation in the decision-making process in the smart city; and for instance, access to and management of cultural goods and heritage.
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Big data and Decision-making: How Big Data Is Relevant Across Fields and DomainsBig data is a buzzword of our times, and yet the awareness of what big data is, how it permeates our daily lives, and how it is applied either in the policy-making process or in the business sector remains relatively low. From a different perspective, while specialists, that is, practitioners and researchers, dealing with the technical facets of big data successfully uncover new features, new domains, and new opportunities related to big data, there is a need of evaluating and examining these findings through the lens of social sciences and management. This chapter offers an insight into key issues and developments that shape the broad and multi-directional big data debate. To this end, the content of the book is elaborated and the key findings are highlighted. In this way, this chapter serves as a very useful guide into the question of how big data is applied across issues and domains and how it is valid and relevant to all of us today.
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Artificial Intelligence and Its Contexts. Advanced Sciences and Technologies for Security ApplicationsThis book offers a comprehensive approach to the question of how artificial intelligence (AI) impacts politics, economy, and the society today. In this view, it is quintessential for understanding the complex nature of AI and its role in today’s world. The book has been divided into three parts. Part one is devoted to the question of how AI will be used for security and defense purposes, including combat in war zones. Part two looks at the value added of AI and machine learning for decision-making in the fields of politics and business. Part three consists of case studies—covering the EU, the USA, Saudi Arabia, Portugal, and Poland—that discuss how AI is being used in the realms of politics, security and defense. The discussion in the book opens with the question of the nature of AI, as well as of ethics and the use of AI in combat. Subsequently, the argument covers issues as diverse as the militarization of AI, the use of AI in strategic studies and military strategy design. These topics are followed by an insight into AI and strategic communication (StratCom), including disinformation, as well as into AI and finance. The case-studies included in part 3 of the book offer a captivating overview of how AI is being employed to stimulate growth and development, to promote data- and evidence-driven policy-making, to enable efficient and inclusive digital transformation and other related issues. Written by academics and practitioners in an academically sound, yet approachable manner, this volume queries issues and topics that form the thrust of processes that transform world politics, economics and society. As such, this volume will serve as the primer for students, researchers, lectures and other professionals who seek to understand and engage with the variety of issues AI implicates.
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Managing Smart Cities: Sustainability and Resilience Through Effective ManagementThis book adopts the managerial perspective to the study of smart cities. As such, this book is a necessary addition to the existing body of literature on smart cities. The chapters included in this book prove the case that transformation of cities to smart cities is a function of effective and efficient management practices implemented at diverse levels of smart cities. While advances in information and communication technology (ICT) are crucial, it is the ability to apply ICT consciously and efficiently that drives the transformation of cities to smart cities in a manner conducive to cities’ sustainability and resilience. The book covers three sets of interconnected topics: Management and decision-making for urban design and infrastructure development Management and decision-making in context of smart cities development Ways of promoting and ensuring participation, representation and co-creation in smart cities These three groups of topics offer a great opportunity to acquire a clear, direct, and practice-driven knowledge and understanding of how effective management allows ICT-enhanced tools and applications to change smart cities, possibly making them smarter.
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Big Data and Decision-Making: Applications and Uses in the Public and Private SectorBig Data and Decision-Making: Applications and Uses in the Public and Private Sector breaks down the concept of big data to reveal how it has become integrated into the fabric of both public and private domains, as well as how its value can ultimately be exploited.
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Government regulation and organizational effectiveness in the health-care supply chainIn the context of the debate on ensuring health-care efficiency, this study aims to identify and prioritize factors and subfactors that influence organizational effectiveness (OE) in the health-care supply chain. For the purpose of this qualitative study, triangulation was applied to identify, explore and systematically analyze the OE-related practices used by diverse stakeholders along the health-care supply chain. Sixty-two OE practices were thus identified. Subsequently, these were grouped in six different nodes before the analytical hierarchical process (AHP) was used to identify the weightings of specific practices (and related factors) and their impact on OE. The findings suggest that external factors associated with government regulation, including government directives and branding, are the most critical factors that influence OE-related practices, while cost-related factors are the least important.
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The case of rWallet: A blockchain-based tool to navigate some challenges related to irregular migrationMigration (irregular and forced) represents one of the major challenges the international community faces today. Inasmuch as the phenomenon of irregular and forced migration is the marker of the state of socio-economic systems around the world, the response to and the ways of navigating the resulting multi-scalar challenges mirror not only the efficiency of the global regulatory frameworks, but also our civility. Recognizing the potential inherent in sophisticated information and communication technology (ICT), specifically the blockchain technology and smart contracts, this paper focuses on the special case of “welcome centers” that irregular migrants enter in the hope of acquiring international legal protection and thus refugee status. Since the process may be time-consuming and the living conditions undignified, this paper proposes a tool, named here “responsible wallet”, aka rWallet, that bears the promise of navigating some of these challenges. rWallet derives from the recognition that in modern societies ICT should serve the purpose of improving the quality of life of all people.
