Existing Legacy Systems Research Articles

Detection And Accident Prevention Of Animals in Railway Track Using AI And IoT

Railway is the most widespread and welcoming transportation system of most of the large cities in the world. The real-time observation and control methods proposed in this research are based on the Internet of Things and function autonomously without external intervention. There are several ways that IoT might improve the railway system. Railway automation has the potential to revolutionize existing legacy systems and significantly reduce railway-related incidents. The proposed method for identifying and recognizing animals and humans has been tried and refined. Implement real-time control and automated monitoring of different parameters relevant to the railway. Build a fully automatic process that reduces human involvement and retains energy.

AI Challenges and Strategies for Business Process Optimization in Industry 4.0: Systematic Literature Review

The rise of Industry 4.0 has introduced significant transformations in business operations, with Artificial Intelligence (AI) playing a central role in automating processes, enhancing decision-making, and improving efficiency. However, despite its potential, many organizations face critical challenges in implementing AI, particularly in terms of technological infrastructure readiness, workforce capabilities, and ethical considerations. This research aims to identify the challenges and propose effective strategies for AI implementation in business processes, particularly within the context of Industry 4.0. A systematic literature review (SLR) was employed to analyze peer-reviewed studies that discuss AI integration across various industrial sectors. The findings indicate that the primary challenges include the integration of AI with existing legacy systems, data privacy concerns, and employee resistance due to the perceived threat of automation. Additionally, the lack of adequate training and infrastructure investment further hampers AI adoption. In response, the research highlights strategies such as upgrading technological infrastructure, continuous employee training, and the development of clear ethical guidelines to address these challenges. The research contributes by providing a comprehensive framework for businesses to successfully navigate AI implementation, offering a novel perspective on balancing technological advancements with human resource development and ethical concerns, essential for long-term business sustainability in the digital era

Enhancing Security, Transparency, and Efficiency of Blockchain Technology in Pharmaceutical Supply Chain

Blockchain technology has emerged as a promising solution to address persistent challenges like lack of transparency, inefficient tracking, and counterfeiting issues in pharmaceutical supply chains. However, there are still limitations in integrating blockchain with existing legacy systems, lack of common standards, scalability concerns due to its decentralized architecture, and lack of regulatory clarity that need to be addressed before successful implementation in the pharmaceutical industry is feasible. This review explores the potential benefits and current use cases of blockchain in enabling drug traceability, establishing authenticity and integrity of data, facilitating smart contracts for seamless financial transactions, and optimizing overall supply chain operations. The current blockchain-based solutions employed by various stakeholders like drug manufacturers, wholesalers, distributors, retailers, hospitals, and regulators across the different stages of the pharmaceutical value chain are reviewed in this article. Recent research efforts to mitigate the key challenges are discussed, including technical improvements in blockchain architecture for increased security and privacy, the use of permissioned blockchains, innovative consensus protocols to improve scalability, and growing regulatory guidance. The future outlook highlights the tremendous opportunities for blockchain to enable end-to-end visibility, accountability, automation, real-time monitoring and combating counterfeiting in pharmaceutical supply chains through collaborative efforts among stakeholders.

Decision-support system for safety and security assessment and management in smart cities

Counter-terrorism measures and preparedness play a critical role in securing mass gatherings, soft targets, and critical infrastructures within urban environments. This paper introduces a comprehensive Decision Support System developed as part of the S4AllCitites project, designed to seamlessly integrate with existing legacy systems in Smart Cities. The system encompasses urban pedestrian and vehicular evacuation, incorporating predictive models to anticipate the progression of incendiary and mass shooting attacks, alongside a probabilistic model for threat assessment in the case of improvised explosive devices. A notable achievement of this research is the successful implementation and deployment of the system in operational environments through pilot studies. It empowers public and private security operators with real time decision support capabilities during both prevention and intervention stages of potential attacks. The decision support information provided encompasses various aspects, including optimal evacuation strategies, estimated egress times, pedestrian movement profiles, probability assessments, and the potential impact of different terrorist threats in terms of casualties. Additionally, the system offers real-time insights into the status of the traffic network under normal and unusual conditions, enabling efficient emergency management throughout its progression. This includes the ability to identify optimal intervention routes and assess the impact of anomalous traffic resulting from evacuations.

Exploring adaptive mirroring in healthcare IT architectures

ABSTRACT Digital transformation is demanding for incumbent organizations such as healthcare, where legacy-based IT architectures challenge the establishment of effective digital services. We refer to this as the IT silo problem, where multiple non-consolidated IT systems are implemented to support expert practices. In this paper, we analyze this challenge using a mirroring lens. Our research question is, how can we create efficient digital services, utilizing the existing legacy systems in healthcare IT architectures? Our empirical evidence comes from a Norwegian case and contributes to the literature on IT architecture within Healthcare. First, we demonstrate how strict mirroring leading to sub-optimization and silofication is a major cause for the presence of IT silos. Second, we describe a process towards adaptive mirroring, and the resulting adaptive mirroring architecture. Adaptive mirroring is an architectural combinatory device that facilitates the design and use of efficient services, while also improving the flexibility of IT architectures.

Shift2Rail TAURO: Technologies for the Autonomous Rail Operation

Rail automation beyond existing automatic metros and people movers will attract investments in the next years to start with its wide deployment in mainline and in other applications such as marshalling yards, depots or urban rail (tramways). The Shift2Rail project “Technologies for the Autonomous Rail Operation” (TAURO) intends to explore, analyze and propose cutting-edge technologies required to make autonomous rail transport a reality. Such technologies would enable certifiable safe environment perception, remote driving and command, automated monitoring, testing and diagnostics and a seamless migration from existing legacy systems. All in all, automation of rail assets can directly and significantly contribute to at least two of the European Commission's priorities for the period 2019-2024: The European Green Deal (fighting climate change) and the Europe fit for the digital age (digital technologies including artificial intelligence). This paper presents the objectives, structure, methodology and expected results from the project. Its consortium, composed of 16 leading companies of the European rail sector, have divided the activities into four work streams lasting until 2023 to cover all said challenges. Hence, TAURO should be understood as a bridging project between Shift2Rail and its successor, Europe's Rail, whose first projects will be starting by the end of 2022.

From the abacus to enterprise resource planning: is blockchain the next big accounting tool?

PurposeThis study conducts a systematic review using 452 academic and industry articles from an initial set of 60,899 records obtained by 3 databases from 2012 to 2020. The authors compare and contrast blockchains with existing legacy systems. The authors identify existing regulation, accounting standards, guidelines and potential amendments in under-explored areas such as taxation, accounting treatment of crypto-assets/liabilities and detailed auditing procedures. The study aims to highlight the trends, differences and gaps between academic and industry literature. The authors provide a behavioral, social, cultural, organizational, regulatory, ethical, accountability and managerial perspectives of blockchain adoption in accounting. Finally, the study develops two adoption frameworks.Design/methodology/approachThe authors' study follows (Moher et al., 2009) and (Briner and Denyer, 2012) methodology to conduct the systematic review and the steps are mentioned below. The authors construct a final sample of 452 from a preliminary search of three multi-disciplinary databases from 2012 to 2020. First, the authors motivate the review and formulate the research questions. Second, the authors aggregate relevant literature from both industry and academia and implement quality assessments. Third, the authors analyze the literature and construct the final sample of articles. Fourth, the authors conducted textual analysis, keyword frequencies and identify gaps, trends and similarities between academic and industry literature and develop the authors' frameworksFindingsThe authors identify 3 (ABDC, B and A* ranked) journals as publishing top article numbers with the highest article count for 2017 with 96 articles in academia and 2019 for the industry with 21 articles. Second-highest publications for academia occur in 2018 with 77 followed by, whereas in the industry, publications occur in the year 2016 with 16 articles. Two co-authors appear most popular with 103 articles. Word clouds, a mind map and article theme counts are used to identify nine key research clusters: data management, financial applications, sustainability, accounting and auditing, business and industrial, education, governance, privacy/security and disruptive technology.Research limitations/implicationsSystematic reviews can have selection biases mainly due to search and selection criteria distortions when constructing the final sample of articles. The authors address selection bias by refining our search keyword combinations by using different permutations and using keywords from articles already collected. The authors employ three databases and review the reference list of articles collected to add more articles that may have been missed into our sample. In addition, to avoid inconsistent coding of domains/themes and interpretations, the authors carefully review our domain identifications and all our analysis twice independently using two research assistants to obtain the same conclusions.Practical implicationsThe authors' unique contributions include reviewing additional papers, differentiating between industry, academic articles, common trends and gaps in much scattered prior literature. The authors identify existing accounting standards, guidelines, limitations and possible amendments required in future for blockchain adoption in accounting in taxation, accounting treatment of crypto-assets/liabilities and detailed audit procedures. Blockchains are compared with legacy accounting technologies and two frameworks for adoption developed. The authors' results could impact the understanding of existing regulation, accounting standards, future amendments, areas requiring clarity and future collaborative research between academia and industry across multi-disciplines. Practical implications to academics, professional bodies, regulators and industry practitioners exist.Social implicationsThe authors' study identifies significant implications on organizations, environment, culture and society in general. The authors identify that social engagement projects may be easily initiated and implemented with decentralized accounting information systems. Transparency and efficiency would change organization culture, ways accountants and even employees interact with each other and community. Anonymity in blockchains can be used for criminal activities. Coding of negative social dynamics to smart contracts may persist. Transparency of personally identifiable information may place individuals at risk. Regulation and standards would need to identify equity, ethics in blockchains which notwithstanding energy consumption, and could enable environmental protection increasing societal sustainability.Originality/valueTo the authors' knowledge, this is the first study that compares academic and industry literature of 452 articles to identify gaps and similarities from 2012 to 2020 using three multi-disciplinary databases. The authors' study is the first study to in detail existing accounting standards, unclear areas, future amendments for International Financial Reporting Standards (IFRS) standards on taxation, financial reporting and all aspects of auditing procedures. The authors further categorize prior literature into these key areas and develop two frameworks (DAERPS and DAIS) that are linked to our review results and prior literature. The authors identify the impact of blockchain adoption on key stakeholders, regulation, society, culture, organization, accountability and ethics.

Energy-Saving Oriented Manufacturing Workshop Facility Layout: A Solution Approach Using Multi-Objective Particle Swarm Optimization

Low-carbon sustainable development has become the consensus of manufacturing enterprises to fulfill their social responsibilities. Facility layout is an essential part of manufacturing system planning. Current research has demonstrated the advantages of energy saving on the manufacturing system level where operational methods (e.g., energy-efficient production scheduling and path planning) can be utilized and do not require massive investment in the existing legacy system. However, these efforts are mostly based on the existing fixed facility layout. Meanwhile, although facility layout problems have been extensively studied so far, the related work seldom involves the optimization of energy consumption (EC) or other EC-related environmental impact indicators, and does not clearly reveal if EC can be an independent optimization objective in facility layout. Accordingly, whether the energy-saving potential of a manufacturing system can be further tapped through rational facility layout is the gap of the current study. To address this, an investigation into energy-saving oriented manufacturing workshop facility layout is conducted. Correspondingly, an energy-efficient facility layout (EFL) model for the multi-objective optimization problem that minimizes total load transport distance and EC is formulated, and a multi-objective particle swarm optimization-based method is proposed as the solution. Furthermore, experimental studies verify the effectiveness of the presented model and its solution, indicating that EC can be regarded as an independent optimization objective during facility layout, and EFL is a feasible energy-saving approach for a manufacturing system.

Computing Frameworks for VM Migration in Cloud

Cloud Service users world wide are increasing with advancement in IT infrastructre. This leads to the demand of online storage for various operational reasons. The applications across various organizations are exploiting the cloud infrastructure offered by different service providers like Microsoft, Amazon, Twitter, IBM, and Hewlett-Packard, enable developers to distribute software through a central organization's hosting machine. Online storage is the next logical phase in the development of IT infrastructure. Cloud Storage Infrastructure has become a norm in distributed environment to host the multiple application on easily available networks. Rather than bothering about the Capital Expenditutre, the organizations are now using funds to invest in operational cost of running the services through Cloud service provider increasing the efficiency. While attempting to migrate the workload needs to be considered as imbalanced workload may result in system failure. Also the VM consolidation need to be done while the sytem is live without any failure. The security concerns for live migration are enlisted in limitations which is the largest concern for an organization whle deciding future course of action. This article reviews the cloud computing systems in the light of migrating the existing legacy systems to the cloud environment and suggest the operational features that an organization should consider before the migration.

A Prescriptive Model for Migration to Microservices Based on SDLC Artifacts

Microservices architectural style is gaining popularity in industry and is being widely adopted by large corporations like Amazon, Netflix, Spotify, eBay, and many more. Several other organizations are also preferring to migrate their existing enterprise scale applications to microservices architecture. Researchers have proposed various approaches for microservices decomposition to be used in migrating or rebuilding a monolithic application to microservices. Applying any available approach to an existing monolithic application is not a straightforward decision; thus, there is a need for guidelines that assist in the migration process. There are various challenges in a migration process because different migration approaches use different sets of input data to identify microservices. Since the available migration techniques are not structured, logically, selection of an appropriate migration strategy is a difficult decision for any system architect. So, it is a recurrent open research question – which migration technique should be adopted to get microservices for a legacy monolithic application? This paper addresses this research challenge by examining existing approaches for microservices migration and groups them based on software development life cycle (SDLC) artifacts. Our research also proposes a microservices prescriptive model (MPM) from the existing prominent microservice migration techniques. This model provides recommendation (1) for refactoring an existing legacy system to microservices, and (2) for new microservices development projects. Our study also helps in gaining more insight about greenfield and brownfield development approaches in microservices applications. Moreover, researchers and practitioners of the field can benefit from this model to further validate their migration approaches based on the available system artifacts.

Steps towards an Healthcare Information Model based on openEHR

During COVID-19 pandemic crisis, healthcare institutions globally were experiencing a VUCA - Volatile, Uncertain, Complex, and Ambiguous - environment. Effcient clinical and administrative management had never been so emergent. To achieve this goal, different components of the Healthcare Information System (HIS) must cooperate and interoperate flawlessly. Data standardization is a necessary step towards normalization and interoperability between existing Legacy Systems (LSs), and provides for longitudinal, highly reliable and persistent Electronic Health Records (EHRs). The openEHR standard was chosen for its overall dual domain architecture, where the more dynamic clinical information model may evolve independently from the relatively stable Reference Model (RM). Its Information Model (IM) comprises demographic, administrative and clinical systems. Critical clinical terms have been aligned to the FHIR HL7 standard, as to further support interoperability.

A Novel Approach for Integrating IEC 61131-3 Engineering and Execution Into IEC 61499

Automation system engineering becomes more complex, due to the trend toward more flexible, reconfigurable, and modular design approaches, like Industry 4.0. For the modeling and design of the according software, two standards are present: IEC 61131-3 and IEC 61499. In order to satisfy the requirements for modern, large scale, highly distributed applications while also supporting still existing legacy systems, the demand for a combined development framework arises, where the best of breed tool can be chosen for a given automation task. Considering that, the IEC 61499 model is extended to allow the dual development and execution of IEC 61131-3 programs, and enabling easy and correct interaction between the two paradigms. In order to verify the validity of the chosen approach, an IEC 61499 development tool and a runtime environment is modified to support IEC 61131-3. A sample application is implemented, which comprises a pure IEC 61131-3 part with a 1 ms cycle time, a pure IEC 61499 part, and a part with interaction between both subparts, in order to evaluate possible interference between the runtime parts. Experimental results show that no interference is occurring, and the chosen development approach allows the seamless integration of IEC 61131-3 and IEC 61499 in one combined development framework.

CoronaBio: Using Crowdsourcing for Biomedical Research on COVID-19 to Manage a Pandemic

CoronaBio: Using Crowdsourcing for Biomedical Research on COVID-19 to Manage a Pandemic

London Heathrow Airport Uses Real-Time Analytics for Improving Operations

Improving airport collaborative decision making is at the heart of airport operations centers (APOCs) recently established in several major European airports. In this paper, we describe a project commissioned by Eurocontrol, the organization in charge of the safety and seamless flow of European air traffic. The project’s goal was to examine the opportunities offered by the colocation and real-time data sharing in the APOC at London’s Heathrow airport, arguably the most advanced of its type in Europe. We developed and implemented a pilot study of a real-time data-sharing and collaborative decision-making process, selected to improve the efficiency of Heathrow’s operations. In this paper, we describe the process of how we chose the subject of the pilot, namely the improvement of transfer-passenger flows through the airport, and how we helped Heathrow move from its existing legacy system for managing passenger flows to an advanced machine learning–based approach using real-time inputs. The system, which is now in operation at Heathrow, can predict which passengers are likely to miss their connecting flights, reducing the likelihood that departures will incur delays while waiting for delayed passengers. This can be done by off-loading passengers in advance, by expediting passengers through the airport, or by modifying the departure times of aircraft in advance. By aggregating estimated passenger arrival time at various points throughout the airport, the system also improves passenger experiences at the immigration and security desks by enabling modifications to staffing levels in advance of expected surges in arrivals. The nine-stage framework we present here can support the development and implementation of other real-time, data-driven systems. To the best of our knowledge, the proposed system is the first to use machine learning to model passenger flows in an airport.

IEEE 1815.1-Based Power System Security With Bidirectional RNN-Based Network Anomalous Attack Detection for Cyber-Physical System

The introduction of the cyber-physical system (CPS) into power systems has created a variety of communication requirements and functions that existing legacy systems do not support. To this end, the IEEE 1815.1 standard defines the mapping between existing distributed network protocol networks and IEC 61850 networks that reflect new requirements. However, advanced CPS cyberattacks have been reported, and in order to address cyberattacks, security research on new power systems that use network devices and heterogeneous communication is necessary. In this study, we propose an intrusion detection system for an IEEE 1815.1-based power system using CPS. We 1) analyze an IEEE 1815.1-based power system network and propose a suitable application method for an intrusion detection system, 2) suggest a bidirectional recurrent neural network-based anomaly detection system for an IEEE 1815.1-based network, and 3) demonstrate the verification of the proposed technique using various power system-specific attack data, including real power system using CPS network traffic, CPS malware behavior (CMB), false data injection (FDI), and disabling reassembly (DR) attacks. Proposed technique successfully detected five types of CMB attacks, three types of FDI and DR attacks.

London Heathrow Airport Uses Real-Time Analytics for Improving Operations

Improving airport collaborative decision making is at the heart of airport operations centers (APOCs), recently established in several major European airports. In this paper, we describe a project commissioned by Eurocontrol, the organization in charge of the safety and seamless flow of European air traffic. The project’s goal was to examine the opportunities offered by the co-location and real-time data sharing in the APOC at London’s Heathrow airport, arguably the most advanced of its type in Europe. We developed and implemented a pilot study of a real-time data sharing and collaborative decision-making process, selected to improve the effciency of Heathrow’s operations. In Guo et al. (2019), we describe the system that we developed for Heathrow to better estimate passenger connection times, which takes advantage of the integration and data sharing that Heathrow’s APOC offers. In this paper, we describe the process of how we chose the subject of the pilot, namely the improvement of transfer-passenger flows through the airport, and how we helped Heathrow move from its existing legacy system for managing passenger flows to an advanced machine learning-based approach using real-time inputs. The system, which is now in operation at Heathrow, can predict which passengers are likely to miss their connecting flights, reducing the likelihood that departures will incur delays while waiting for delayed passengers. This can be done by offloading passengers in advance, by expediting passengers through the airport, or by modifying the departure times of aircraft in advance. By aggregating estimated passenger arrival time at various points throughout the airport, the system also improves passenger experiences at immigration and security desks, by enabling modifications to staffing levels in advance of expected surges in arrivals. The nine-stage framework we present here can support the development and implementation of other real-time data-driven systems. To the best of our knowledge, the proposed system is the first to use machine learning to model passenger flows in an airport.

A Space-Time Coded Mills Cross MIMO Architecture to Improve DOA Estimation and Its Performance Evaluation by Field Experiments

Conventional Mills Cross architecture suffers from poor direction-of-arrival angle estimation accuracy in the dimension that the transmitter is aligned. To improve the estimation accuracy, a space-time coded, multiple-input multiple-output (MIMO) direction finding method, with complementary codes is presented. The performance of the suggested MIMO Mills Cross architecture has been evaluated by underwater field experiments. Field experiments confirm the feasibility of the proposed approach and illustrate the performance gains. Since the proposed approach does not require any changes in the conventional Mills Cross hardware, the performance improvement can also be realized by a software reconfiguration for existing legacy systems.

Exploring The Use of Cognitive Models for Nuclear Power Plant Human-System Interface Evaluation

Many of the United States’ commercial fleet of nuclear power plants (NPPs) are approaching the end of their operating licenses. To extend the life of these plants, advanced human-system interface (HSI) technologies are being researched to address aging and reliability concerns with existing legacy systems. Human factors engineering (HFE) a critical role in ensuring these technologies are designed in a way that does not introduce new failure modes and promotes optimal human-system performance. An important focus of HFE in NPP modernization is early involvement to inform design of the HSI. This includes traditional formative evaluations, which are done to collect design feedback. While these evaluations are useful, they are limited in providing convincing quantitative data for efficiency of use. This paper discusses the use of cognitive models to provide quantitative data early in the HSI design process. A comparison is made of three open-source and readily accessible cognitive modeling tools.

How Robot/human Orchestration Can Help in an HR Department: A Case Study From a Pilot Implementation

Abstract Background and Purpose: Motivation of this research is to explore the current trend in automating the business processes through software robots (Robotic Process Automation – RPA) and its managing within enterprise environment where most of the processes are executed by human workforce. As the RPA technology expands the demand for its coordinating grows as well. The possible solution to this challenge is shown in case study research in form of implementing orchestration platform to a concrete business process of onboarding in HR department of a multinational company. The aim of this paper is to explore the phases and activities of the pilot project implementation of Robotic Service Orchestration (RSO) in combination with RPA technology and to assess the potential benefits. Design/Methodology/Approach: Case study research approach was selected to explore the research phenomena, which is the implementation of RSO platform in combination with RPA technology and assessing incoming benefits. The case is formed with 2 companies – (1) multinational company with ongoing effort of automating onboarding process, (2) technology and consulting company delivering the automation solution. Data were collected through semi-structured interviews with respondents from two involved companies and by analysing internal documents. Results: The analysis of case provided in this paper revealed some key insights: (1) strategical position of RSO and tactical position of RPA towards the existing legacy systems, (2) need for increased focus on initial process modelling phase, (3) Application Programming Interface (API) integration is more viable solution for RPA, (4) the biggest benefit of RPA - its agility, (5) future potential of the RSO replacing the BPMS. Conclusions: First of all, there is a need of higher number of software robots adopted in a company before orchestration could pay off. On the other side, current Business Process Management Systems (BPMS) solutions don’t offer functionalities for managing human and software robots workforce altogether. RPA is expected to expand and without proper orchestration the effectivity will not grow constantly.

Future Prospects for Particle Therapy Accelerators

Particle therapy is the expanding radiotherapy treatment option of choice for cancer. Its cost, however, is currently hindering its worldwide expansion. Also, the ideal application of particle therapy is restricted by a series of unsolved technical challenges. Both the cost and technical limitations are directly traceable to dependence on legacy accelerators and their associated treatment possibilities. This chapter is written to address these needs. Firstly, a technical overview is presented of photon and particle therapy for cancer tumours. Secondly, the underlying limitations of the existing legacy systems are identified, especially those related to accelerators, and suggestions are made for current and future developments to address these shortcomings. The legacy systems referred to here are of the slow scanning variety using large, circular accelerators.This paper also attempts to make a scientific comparison of the various types of accelerators currently used or being developed for particle therapy.The following procedure is pursued to perform a comparison between various types of accelerators:(1)The parameters which are pertinent to particle therapy accelerators (‘specified parameters’) are identified from clinical efficiency and overall cost considerations.(2)The range and values of ‘specified parameters’ associated with each type of particle therapy accelerator are identified.(3)A comparison is made on the best match between the various types of accelerators for each of the ‘specified parameters,’ i.e., the best in class accelerator, when compared to each criterion.(4)Based on this match, an overall conclusion is made on the type of accelerator which best fits the needs for particle therapy.