Process Automation

The financial stability of a business depends on its legacy ERP systems, but these systems can hinder innovation and adaptation to changing market conditions. Businesses now more than ever depend on real-time data insights to stay ahead of the competition and comply with regulations that are always changing. This study investigates how integrating cloud-based analytics, AI, and ML with current ERP systems may lead to a rethinking of business finance. More openness, data innovation, and both real-time and predictive decision-making are made possible by the suggested ERPdriven financial transformation paradigm. Several case studies in a variety of industries, including manufacturing, retail, and finance, have demonstrated improvements in forecast accuracy, operational efficiency, and compliance assurance. The transition from transactional ERP systems to smart, flexible platforms that free up new value for businesses is evident through comparison. Data innovation is the driving force behind contemporary enterprise resource planning (ERP), which has implications for the financial future of businesses.

This research paper presents a study to implement the concept of incorporating Camunda-based workflow orchestration and AI-powered intent classification in a voice customer care environment, leveraging the fact that customer care operations and call handling mechanisms are highly event-driven in real-time, particularly in terms of call routing and handling. This research, based on secondary data analysis, reviews the effectiveness of implementing these technologies, along with the challenges and good practices. As suggested by the results, the combination of workflow automation and AI can increase efficiency in operations, decrease the wait time of a customer, and improve the quality of services. The paper also establishes some difficulties regarding system compatibility and scalability, which further presents research opportunities to be conducted with the multi-channel customer service scenario.

This paper explores the advanced modules within SAP ERP systems and their role in addressing industry-specific challenges and seizing emerging opportunities in business. SAP ERP, with its vast array of integrated business management tools, has evolved to offer tailored solutions for various industries, including manufacturing, healthcare, retail, and finance. The research aims to analyze the key modules of SAP ERP that cater to these industries, evaluate their functionalities, and highlight the benefits they provide in enhancing operational efficiency, decision-making, and business agility. By integrating technologies like Artificial Intelligence (AI), Machine Learning (ML), and Cloud Computing, SAP ERP systems are becoming more intelligent, flexible, and scalable, helping organizations stay competitive in today's dynamic business environment. This study also explores best practices for implementing these advanced modules, considering integration challenges, and offers insights into the future enhancements that could further elevate SAP ERP's impact on business transformation.

The integration of Artificial Intelligence (AI) and Machine Learning (ML) into SAP platforms is revolutionizing enterprise resource planning by driving automation, predictive insights, and intelligent decision-making. This paper explores the transformative potential of AI/ML in SAP systems such as S/4HANA, SAP BTP, and SAP Leonardo. It outlines how organizations can leverage AI-driven analytics, intelligent automation, and real-time data processing to enhance operational efficiency and deliver measurable business value. By presenting a comprehensive literature review, technical working principles, and practical use cases, this study highlights key enablers and challenges in AI/ML adoption within SAP environments. The paper concludes with forwardlooking enhancements and strategies for future-proofing enterprise systems through intelligent technologies.

This report examines the transformation of customer experience through AI in CRM platforms. The introduction highlights the relevance of the topic, defining the research objectives and methodology. The theoretical framework explores the fundamental concepts related to AI and CRM, as well as the evolution of these technologies. Key AI technologies, including machine learning, Natural Language Processing, predictive analytics, and computer vision, are discussed in terms of their contribution to optimizing CRM systems. The report distinguishes between traditional CRM systems that do not use AI and modern AI-powered CRM platforms. It analyzes how AI reshapes the structure, functionalities, and operations of CRM by automating processes, enhancing personalization, and providing predictive analytics. The next section focuses on the impact of AI on customer experience. Key aspects of this transformation include service personalization, process automation, predictive support, and the use of intelligent assistants to improve customer communication. In conclusion, the main findings are summarized, and future AI trends in CRM are outlined.

Although genetic services have potential value for health care and disease control, there is no systematic solution for knowledge capture in Medical Genetic Testing (MGT) Laboratories. This paper addresses such knowledge management (KM) technology weakness in genetics domain, then proposes an Information System (IS) architecture to establish process automation and content management of the distributed workflow of knowledge generation and knowledge management (KG&KM) during MGT result interpretation. The presented IS will validate the interpretation decision by using Information Systems / Information Technologies (IS/IT), esp. KM tools, such as Workflow Management System (WfMS), search engine and groupware. Once developed and implemented, our integrated system will significantly improve MGT lab researchers' KG&KM performance through increasing knowledge capture, improving documentation quality and maintaining (if not improving) users' information satisfaction.

Maintaining financial stability and strategic planning in companies depends on cash flow projections. Often relying on static spreadsheets and historical data, traditional approaches might not precisely reflect real-time financial situations. This study presents an AI-driven cash flow forecasting method included into Enterprise Resource Planning (ERP) systems. Combining real-time transaction data with outside economic indicators-such as inflation rates, interest rates, commodity prices, and geopolitical sentiment-helps companies to create more accurate and responsive cash flow models. This paper offers a unique AI-driven forecasting system that combines real-time ERP transaction data with external economic indicators-including inflation rates, interest trends, commodity prices, prices and geopolitical sentiment-to generate responsive, context-aware cash flow projections. Using a hybrid modeling strategy that includes LSTM-based deep learning, ARIMA time-series models, and ensemble machine learning algorithms (e.g., XGBoost), the suggested system adjusts constantly to internal and external financial dynamics. Our hybrid forecasting system combines time-series models, ensemble learning, and economic feature engineering inside cloud-native ERP platforms (e.g., Oracle Fusion Cloud). We show how artificial intelligence can dynamically predict cash inflows and outflows, early risk identification, and treasury operations support in maximizing liquidity situations. Simulations and case studies reveal notable decreases in working capital inefficiencies and up to 30% increase in forecast accuracy.

Through improving diagnosis, therapy personalizing, and operational efficiency, artificial intelligence (AI) and machine learning (ML) are transforming healthcare. Faster and more accurate diagnosis, predictive analytics, and enhanced automation spanning clinical and administrative operations are made possible by these technologies. Although its advantages are clear-cut, integration of artificial intelligence/machine learning with ERP (Enterprise Resource Planning) systems-especially on platforms like Oracle Cloud-remains limited and understudied in healthcare environments. This work highlights present use cases, explores the transforming possibilities and major hurdles of AI-ERP integration in healthcare. We provide a paradigm for seamless AI-ERP integration to improve decision-making, resource allocation, and patient outcomes by means of a mix of literature study, case analysis, and model design.

The development of Robotic Process Automation (RPA) has altered financial management by automating repetitive activities, reducing errors, and providing real-time reports. Objective: To assess the effectiveness and scalability of RPA integration in cost accounting and financial systems. Methods: A structured implementation approach occurred, involving process identification, workflow design, RPA creation, and performance evaluation. Results: RPA lowered processing time by 95%, increased cost allocation accuracy to 99.5%, and reduced errors by 95%. Conclusion: RPA integration improves operational efficiency and financial correctness, demonstrating its ability to provide scalable and error-free financial optimisation.

Industrial Process Automation (IPA) is being progressively integrated into numerous processes to enhance productivity, reliability, quality and cost optimization in all aspects. Currently, IPA is focusing on real time process control only even though system is upgraded with highspeed controllers, improved resolution time stamping, historian & other next Gen technology. With technological advancement, IPA can be developed to next generation so that prediction of process abnormality, control failure time to Alarm & Trip, Root Cause, Suggestion for Defensive Action etc. can be explored. Researchers, Designers Technocrats will get new insights Predictive Operation & Defensive Action (PrODA) for IPA.

Pega's embedded AI capabilities have become a transformative tool for workflow orchestration in organizations across various sectors. By integrating AI directly into workflow management systems, Pega enhances operational efficiency through intelligent task prioritization, resource allocation, and outcome prediction. Unlike traditional systems that rely on static rulebased workflows, Pega's embedded AI leverages real-time data, learning algorithms, and predictive analytics to optimize processes dynamically.This article explores how Pega's embedded AI capabilities are reshaping workflow orchestration, with specific emphasis on its application in industries such as financial services, healthcare, and customer service. Through real-world case studies, the article demonstrates how AI can identify bottlenecks, predict task outcomes, and automatically adjust workflows to ensure optimal resource utilization and faster decision-making. The integration of AI also allows for real-time adjustments, ensuring that workflows remain responsive to changing conditions.In this paper, we discuss how organizations can achieve faster time-to-value, improved operational efficiency, and enhanced customer satisfaction by embedding AI into their workflow orchestration strategies. We also delve into the methodologies behind Pega's AI-driven orchestration, its potential limitations, and the challenges organizations face when integrating this technology into existing business systems.

Pega has emerged as a key enabler of hyperautomation, integrating robotic process automation (RPA), artificial intelligence (AI), and advanced workflow management to drive digital transformation across enterprises. This article explores Pega's strategic approach to automating complex business processes, emphasizing its ability to seamlessly orchestrate collaboration between humans and machines. By leveraging data-driven insights and realtime analytics, Pega empowers organizations to eliminate silos, enhance operational agility, and significantly boost productivity. The article highlights key case studies and industry examples, demonstrating how Pega's platform not only streamlines routine tasks but also fosters intelligent decision-making, enabling sustainable digital transformation. Through a detailed analysis of Pega's technical architecture, implementation strategies, and performance outcomes, this research provides actionable insights for organizations seeking to harness the power of hyperautomation. The findings underscore Pega's leadership in driving innovation and its pivotal role in shaping the future of enterprise automation.

Event-driven architectures have fundamentally transformed Business Process Management (BPM) by facilitating real-time responsiveness to a myriad of external and internal triggers. This paradigm shift is particularly evident in the logistics and supply chain sectors, where dynamic and unpredictable events frequently impact operations. This article explores the implementation of Pega’s event-driven BPM systems and their efficacy in reducing delays and optimizing operations within these industries. Through a comprehensive analysis of case studies and empirical data, the study demonstrates how real-time event processing enhances decision-making processes, improves operational efficiency, and fosters greater adaptability to changing market conditions. The integration of Pega’s BPM solutions enables organizations to automate responses to events such as shipment delays, inventory shortages, and demand fluctuations, thereby minimizing downtime and maximizing resource utilization. Furthermore, the article discusses the methodological approaches employed to assess the performance improvements achieved through event-driven BPM, highlighting key metrics such as response time reduction, process throughput, and overall system reliability. The findings underscore the pivotal role of event-driven architectures in modern BPM, offering a robust framework for organizations aiming to achieve heightened responsiveness and operational excellence in an increasingly volatile business environment.

The integration of Business Process Management (BPM) and Robotic Process Automation (RPA) within the Pega platform provides a transformative approach to process automation. BPM enables the orchestration of complex workflows and case management, while RPA automates repetitive tasks, driving efficiency across various business functions. Pega's unified platform integrates these capabilities with AI-driven decision-making, intelligent case management, and adaptable automation, allowing seamless collaboration between human workers and robotic agents. This article explores the technical architecture, integration strategies, and best practices for combining BPM and RPA within the Pega ecosystem. Supported by real-world case studies, it demonstrates how this synergy enhances operational excellence, reduces costs, ensures compliance, and accelerates digital transformation in diverse industries. Through a detailed exploration of implementation steps and key challenges, businesses can uncover how this powerful integration transforms workflows to deliver sustainable results.

Sustainability has been highly promoted in the construction industry. However, the adoption of sustainability practice has not gained the momentum yet. Sustainability is not only a technical pursuit but also a dialogue of values. It requires collective vision, multiple efforts, dynamic participation and governance of multi-disciplinary stakeholders. Different stakeholders exert various degrees of influences in determining the extent of sustainability implementation in built environment. The purpose of this paper is to determine which stakeholders have greater influences in driving sustainability in built environment. This paper reviews relevant literature and examines the stakeholders' roles in driving effective implementation of sustainable practice in construction. Interviews were carried out with various stakeholders including developers, contractors, surveyors, architects, landscape architect, urban designer, engineers and facilities manager. The result shows that each stakeholder has his/her roles in bringing impacts on sustainability practice, with varying degrees. Although there are disparities in the opinions of stakeholders, the result suggests that the government and project clients play an extremely important role in the pursuit of sustainability practice. The government can not only lead the industry to pioneer the innovative technologies in relation to sustainability but also introduce sustainability into legislation by enforcing the construction-related laws. Meanwhile, clients create the demand for sustainability since the construction industry is principally client-driven. Stakeholders at the lower echelon of the organisational structure such as contractors and end users are also significant to ensure sustainability is well embedded in the built environment. Being the implementers or users, they could pose challenges which can decelerate progress in the implementation of sustainable construction. The realization of sustainability requires collective vision, multiple efforts, dynamic participation and governance of all stakeholders. Encouraging sustainability behaviour in all the stakeholders is thus the key to transform current practice in the built environment into more sustainable solutions.

This paper explores the transformative potential of Artificial Intelligence (AI)powered process automation in driving cost reduction and operational efficiency within healthcare systems. Drawing from pre-2021 literature, the study examines how automation technologies-such as Robotic Process Automation (RPA), Natural Language Processing (NLP), and machine learning-have been implemented to streamline administrative workflows, enhance clinical decision-making, and optimize resource allocation. Through structured analysis and visual models, this paper outlines key benefits, implementation strategies, challenges, and outcomes of AI integration in healthcare. Emphasis is placed on the architectural, procedural, and socio-technical aspects that underpin sustainable automation initiatives. The findings advocate for a system-wide shift towards intelligent automation as a catalyst for scalable and resilient healthcare delivery.

The article addresses the challenges faced by organizations in selecting the most suitable Robotic Process Automation (RPA) product from a plethora of available options. To overcome the subjective biases that often mar the selection process, the article proposes a scientific method for indexing RPA products. This methodology is based on establishing clear evaluation criteria, such as functionality, performance, scalability, and user-friendliness. A rating scale is created for objective comparison and rigorous data collection from various sources. The data is then analyzed quantitatively, employing weighted analysis to account for the varying importance of different criteria. Organizations can make data-driven decisions by calculating an overall index score for each product. Moreover, the utility of a Proof of Concept (POC) in the final selection phase is highlighted, allowing businesses to test the applicability of shortlisted RPA tools in real-world scenarios. In conclusion, the article advocates for a systematic and scientific approach to RPA product selection, ensuring that businesses can align their automation initiatives with products that best fit their unique requirements and objectives.

BPM integration with aviation IT systems receives evaluation through research that examines automation processes built by Camunda BPM in large-scale aerospace operations. The research adopted mixed method approaches to explore BPM process automation improvements in flight scheduling as well as effects on aircraft maintenance and workforce efficiency and compliance management performance. Implementation of BMI software brought substantial performance enhancements that cut process runtimes between 38-42% while saving up to 53.3% from documentations costs. The combination of digital twins and AI during implementation led to a 36% better performance in predictive maintenance alongside blockchain integration which cut down regulatory audit failures by more than 70%. The study demonstrated that employee productivity reached 60.6% higher levels while manual workload suffered a decrease of 136.4%. BPM automation directly relates to work productivity measurements according to gathered data. Aerospace organizations can leverage Camunda BPM as a flexible platform that enhances operational efficiency while saving costs and adhering to regulations with its digital transformation speed benefits secured through cybersecurity measures.

In the Affective Health project we explore possibilities of how to, through biofeedback support users in making sense of the relationship between their stress and their behavior in everyday life. Affective Health is a tool for visualizing patterns and trends of bodily and contextual information. It is particularly important that the design reflects changes over time as this is how people start recognizing patterns in their own behavior and connect it to their bodily reactions. We spent substantial effort sketching and testing ways of portraying time that would move us away from more mathematically inspired representations such as for example graphs and calendars. Instead, we want users to see the signals our bodies emit as part of themselves, of their own ways of being in the world, alive, acting and reacting to their environment. We have explored many possible, alternative ways of visualizing temporal representations through biofeedback. For example as the relation between different places and with time as different layers of history in a concept inspired from ecology. The latest and most developed concept is a cyclic repetition of biodata mapped on a spiral shape.

The development of integrated automation systems for continuous production plants is a very complicated process. A variety of factors must be taken into account, such as their different components ͑e.g., production units control systems, planning systems, financial systems, etc.͒, the interaction among them, and their different behavior ͑continuous or discrete͒. Moreover, the difficulty of this process is increased by the fact that each component can be viewed in a different way depending on the kind of decisions to be made, and its specific behavior. Modeling continuous production complexes as a composition of components, where, in turn, each component may also be a composite, appears to be the simplest and safest way to develop integrated automation systems. In order to provide the most versatile way to develop this kind of system, this work proposes a new approach for designing and building them, where process behavior, operation conditions and equipment conditions are integrated into a hierarchical automation architecture.

When automation is driving the competitiveness of your business, organizations need ML to optimize their
process. Process automation is vital within enterprise systems such as SAP to decrease manual interference,
raise accuracy, and enhance operational flexibility. This paper investigates how to integrate ML into the SAP
frameworks to automate core business functions, such as predictive analytics, anomaly detection, and decision
processes. This study offers a framework of process automation using contemporary ML algorithms applied to
process data, which exploits SAP's inherent capabilities for optimization. The research is to review existing
literature on SAP’s capacity to automate and implement ML and the challenges in implementation. The paper
further details a complete methodology to embed ML models into SAP’s enterprise systems and discusses case
studies for implementations. Performance metrics and optimization outcomes are referenced via graphs and
tables as visuals. Finally, this study concludes with future research directions and the possibility of future
advancements in ML-enabled SAP process automation.

This paper presents the design and validation of a fuzzy logic controller implemented with an industrial programmable logic controller (PLC). The chosen device belongs to the S7-1200 series of Siemens, whereas the code has been developed in Ladder Diagram language using the software TIA Portal. The fuzzy controller is of Mamdani type and is applied to control the speed of a servomotor. A comparison with a Simulink/Matlab fuzzy controller is done to validate the developed software module and to show the feasibility of the PLC to manage this kind of control algorithm.

The notion of Internet of Things (IoT), as well as related topics such as Cyber-Physical Systems, Industrie 4.0 and Smart Manufacturing are currently attracting a lot of attention within the process and manufacturing industries. Clearly, IoT offers many potential applications for automation, ranging from engineering installation of new plants to production management and more intelligent maintenance schemes including novel sensor technologies. The focus of this paper is, however, on the control and operations. Most likely IoT leads to new system architectures where open standards play a significant role. Through better connectivity, information will be much more easily available, which could result in that previously isolated functions will become more closely integrated. Here modeling at the right level of fidelity will be absolutely key. It can be expected that the importance of optimization will increase and this paper discusses some aspects related to the opportunities, challenges and changes triggered by IoT.

The multi-modal transportation comprising diverse infrastructures, means & operations, energy resources, rules & regulations and a broad community of stakeholders constitute a complex yet real candidate for formalisation, analysis and optimization. It is shown that the complexity of the system is best described by viewing the challenges of its complexity as a System of Systems (SoS). The objective of this paper is to make an attempt to define and formalise the loose concept of "System of Systems" within the standard framework of Systems Theory and exploit this new theoretical foundation as a basis for understanding, characterisation, evaluation, assessment and management of complexity, relationships and optimal performance in a multi-modal transportation context. The longer term objective is to develop a robust systems framework for scientific treatment of requirements, constraints, risks, resilience, capacity, performance and trade-offs in multi-modal transportation setting.

En el desarrollo de problemas que involucren sistemas secuenciales, se emplean diversas metodologias tales como Cascada, Paso a Paso y Grafcet, entre otros. En este articulo, se expone el metodo Cascada para ilustrar la solucion de una secuencia de operaciones, usando elementos neumaticos y electro neumaticos

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The main subject o f this work is the application and development o f the advanced predictive control strategy, DMC 'Dynamic Matrix Control', from the monovariable approach (DMC-SISO) to the multivariable (DMC-MIMO) as well as the multivariable with described in this work as D-AMPC. The multivariable controller developed in this work was tested by computer simulation in a complex industrial process application, to know, the industrial fermentation process to produce Ethanol under hard operational conditions. The multivariable predictive/adaptive controller was tested in the Phenol hydrogenation reactor to produce Cyclohexanol (RHODIA-Paulínia-SP-Brazil). This test could be made in a short time interval and the process was controlled in Real time under typical operational conditions. The fermentation process considered in this work is a process with low dynamic response, high non-linearities with typical characteristic of distributed systems. This behaviour and the large time delay in the measurement of the controlled variable (total sugar reductor) contribute to make the design ofthe controller very difficult. The Phenol hydrogenation reactor to obtain Cyclohexanol, a RHODIA unit in Paulinia SP (Brazil), is a three phase reactor with solid catalyst in the reaction mixture. This process has complex heat transfer mechanisms and fast dynamic behaviour when compared with the industrial fermentation process considered in this work. The hydrogenation reactor is a process with distributed characteristics and has some effects, as the catalyst deactivation, that are difficulty to prevent and to monitor. The motivation to use the DMC controllers in the adaptive form was given by the high potential results presented by DMC when applied to various processes, and the lack of tests of the adaptive DMC in the literature when compared to conventional algorithms. The algorithm developed in this work join both high potentialities of advanced control found in industrial process applications, since it couples prediction and adaptation in a very convenient fashion. The results obtained have shown the high efficiency o f the developed controllers, both in the simulation studies (DMC-SISO, DMCGS-SISO and DMC-MIMO applied to the fermentation process) and in an industrial process under normal operation (D-AMPC in the hydrogenation reactor from RHODIA-Brazil).

Papel das Tecnologias da Informação e Comunicação (TIC) no futuro da transparência ativa e nos desafios que se colocam para o futuro de um Estado mais aberto e em tempo real, Será que já atingimos em Portugal um nível adequado de abertura e transparência do Estado? Qual o caminho para lá chegar? Quais os desafios a enfrentar? Como contribuir para o combate ao crime e à corrupção? Quais as oportunidades e os riscos na utilização das tecnologias para a transparência ativa e em tempo real? Como enfrentar um futuro hiperdigital caótico na administração pública?

The management of oil-producing reservoirs is a set of activities that aim to ensure and enhance their productivity. The formation damage management is one of these activities, whose objective is to identify and mitigate the reduction of reservoir permeability next to the well. Formation damage management techniques include analyzing well pressure and oil rate data collected continuously, mostly in periods when the well is closed to production, called build-ups. The identification of build-ups in the pressure data requires efficient and precise methods that reduce analysis efforts. Considering this context, this work discusses the semiautomatic identification of build-ups through two methods: using the first derivative and using a convolution filter, both of them with the support of DBSCAN clustering algorithm. Both methods were tested for synthetic pressure and oil rate data, having the convolution filter method showed better results. The obtained results suggest the method proved to be a reliable tool to aid in the identication of well build-ups. Resumo: O gerenciamento de reservatórios produtores de petróleoé um conjunto de atividades que visa garantir e melhorar sua produtividade. A gestão de dano de formaçãoé uma destas atividades, cujo objetivoé identificar e mitigar a redução de permeabilidade do reservatório próximo ao poço de petróleo. Técnicas de gestão de dano incluem a análise de dados de pressão e vazão colhidos continuamente do poço, principalmente em períodos em que o poço esteja fechado, denominados estáticas. A identificação destes períodos nos dados de pressão requer métodos rápidos, precisos e que reduzam esforços de análise. Considerando este contexto, este trabalho discute a identificação semi-automática de estáticas por meio de dois métodos: utilizando a derivada primeira e utilizando um filtro de convolução, ambos com o apoio do algoritmo de clusterização DBSCAN. Os dois métodos foram testados para dados sintéticos de pressão e vazão, tendo o método do filtro de convolução apresentado os melhores resultados. Os resultados obtidos sugerem que o método seja uma ferramenta confiável para auxiliar na identificação de estáticas.

Jelen tanulmány az értékesítés digitális átalakulását befolyásoló tényezők feltárására vállalkozik. A kutatás során a szerzők felvázolnak egy elméleti modellt, amely egyaránt támaszkodik a Technológiaelfogadás Modelljére (TAM), a Technológiai Felkészültség Indexére (TRI), valamint az értékesítési tevékenységek és eszközök szakirodalmára. Az elméleti modell megalkotásához az empíria egy 2018-ban végzett, 122 különböző értékesítővel folytatott interjúsorozatból származik. A kutatás során sikeresen feltártak számos olyan tényezőt, amely végeredményben befolyásolja az értékesítés digitális transzformációját, valamint a köztük lévő kölcsönhatások azonosítására is lehetőség nyílt. Legfontosabb következtetéseik, hogy a technológiai felkészültség, a technológia jellemzői, a tevékenységportfólió, valamint az ügyfélkör mind hatást gyakorolnak az elfogadást végsősoron befolyásoló attitu...

Resumo-Decompor sinais para obter uma representação com poucas componentesé um dos princípios da compactação. Neste artigo,é proposto um método para identificação e seleção automática de spikes relevantes para a representação do sinal. Utilizando o algoritmo k-means, os spikes são agrupados segundo a intensidade. A redução na representação permite redução de ruído e realce de características dos sinais. Em geral, o desempenho obtido foi satisfatório com redução de dimensão média de 32,18% associada a um ganho médio de 19,24dB na redução de ruído. Para o realce de características foi obtido um erro médio de 7,09% e redução de 98,6%.

This paper analyzes the rapid onset of Robotic process automation (RPA) technologies in the Czech financial sector between 2015-2020. The development and experience from the "hype-and-fear" phase contributed to business process integration and technological spillovers are expected in the future. If ICT capabilities are the source of performance differences, then most likely candidates are inventory and finances business process integration and implemented Enterprise Resource Planning and Customer Relationship Management systems. The RPA should not be seen only as simple automation but as a complex instrument offering a lot of advantages with a focus on benefits for internal and external stakeholders. PURPOSE. The goal is to qualitatively analyze the experience with RPA implementation and quantitatively assess ICT capabilities via analysis of differences between various organizational ICT activities and types of companies in the Czech financial sector. DESIGN/METHODOLOGY/APPROACH. The qualitative case study was performed in the bank ČSOB, a.s. in 2019, respondents, owners of processes in the final part of automation, were chosen randomly from different departments of the bank. Data for the quantitative part comes from the ICT survey (Czech Statistical Office), business performance dataset (EMIS) and a case study about ICT capability implementation. Differences are assessed and indirectly interpreted using goodness-of-fit approach. FINDINGS AND IMPLICATIONS. The results from the RPA case study revealed that the Czech financial sector is past the "hype and fear" phase and many companies focused on their return on investment and are beginning to focus more on other stakeholders. According to this development, the requirements and outputs are suggested in the phases of RPA implementation. The possible source of profitability performance differences are integrated business processes. LIMITATIONS. Financial sector data are anonymized in ICT surveys and the measurement of the competitive advantage of ICT capabilities is only indirect. Qualitative approach is suggested with focus on technological efficiency measurement using data envelopment approach. ORIGINALITY. This paper provides an understanding of the strong experience in RPA in Czech financial sector. Certain initial setbacks in RPA are expected and this paper suggest to focus on knowledge management (lessons learned) and other requirements influencing the successful RPA prototyping and implementation process.

This paper analyzes the rapid onset of Robotic process automation (RPA) technologies in the Czech financial sector between 2015-2020. The development and experience from the “hype-and-fear” phase contributed to business process integration and technological spillovers are expected in the future. If ICT capabilities are the source of performance differences, then most likely candidates are inventory and finances business process integration and implemented Enterprise Resource Planning and Customer Relationship Management systems. The RPA should not be seen only as simple automation but as a complex instrument offering a lot of advantages with a focus on benefits for internal and external stakeholders. PURPOSE. The goal is to qualitatively analyze the experience with RPA implementation and quantitatively assess ICT capabilities via analysis of differences between various organizational ICT activities and types of companies in the Czech financial sector. DESIGN/METHODOLOGY/APPROACH. Th...

The liquefied petroleum gas (LPG) production industry, commercially known as residential gas, presents challenges such as continuous production, the need to control the production process in real time and the variability of product quality due to the behavior of parameters thermodynamics of production. In this scenario, this work presents two distinct algorithms and proposes methods to monitor the quality of LPG in real time. At the end, an analysis tool and the control measures that must be implemented will be presented to guarantee the reduction of variability according to the principles of lean production. Resumo: A indústria de produção de gás liquefeito de petróleo (GLP), comercialmente denominado como gás de cozinha, apresenta desafios como a produção contínua, a necessidade do controle de processo de produção em tempo real e a variabilidade da qualidade do produto devido ao comportamento dos parâmetros termodinâmicos da produção. Neste cenário, este trabalho apresenta dois algoritmos distintos e propõe métodos para monitorar a qualidade do GLP em tempo real. Ao final será apresentado uma ferramenta de análise e as medidas de controle que devem ser implementadas para garantir a redução da variabilidade de acordo com os princípios de produção enxuta.

Multipliers are the fundamental components in many digital signal processing systems. Many important signal processing systems are designed on VLSI platform as the integration growing rapidly. The signal processing systems & applications requires large computational capability, hence takes considerable amount of energy. In the VLSI system design performance, area and power consumption are three important parameters, of which power consumption is the gets prime importance. In today's world, power consumption is very important factor. The largest contribution to the power consumption in multiplier is due to generation and reduction of partial products. So it is very much important to know the efficiency of different multipliers. This paper represents a detailed comparison between array multiplier, Wallace multiplier, Dadda multiplier, modified array multiplier on the basis of speed, area, power consumption using verilog simulation.

Lean practices, because of their importance and effects on production processes, have contributed significantly to changes in the processes where they are successfully implemented, promoting the initiation of a process of cultural change within the organizations that have resulted in best practices in search of excellence operational. The combination of lean practices and the process of gamification enabled us to explore aspects related to the teaching-learning process through interactive and collaborative methods to boost knowledge of the teams and, so to speak, the organization. The case study of this work intends to share practical results of the adoption of the gamification processes, which allowed, through the integration and involvement of the work teams, the development of automated document control systems and self-assessment processes in the scope of the productive processes, as well as interactive training, as a component associated to the lean practices adopted by the company object of this study, and its contributions to the results of the organization in the scope of quality management, production, and management teams.

Copy number variation (CNV) is a form of structural variation in the human genome that provides medical insight into complex human diseases; while whole-genome sequencing is becoming more affordable, whole-exome sequencing (WES) remains an important tool in clinical diagnostics. Because of its discontinuous nature and unique characteristics of sparse target-enrichment-based WES data, the analysis and detection of CNV peaks remain difficult tasks. The Savitzky–Golay (SG) smoothing is well known as a fast and efficient smoothing method. However, no study has documented the use of this technique for CNV peak detection. It is well known that the effectiveness of the classical SG filter depends on the proper selection of the window length and polynomial degree, which should correspond with the scale of the peak because, in the case of peaks with a high rate of change, the effectiveness of the filter could be restricted. Based on the Savitzky–Golay algorithm, this paper introduces a novel...

This paper proposes a new method for identification of fractional order linear time invariant systems, represented through stable transfer functions. This method was inspired in the procedure presented by (Kosaka et al., 2005) and (Silva et al., 2007) for integer order transfer functions. The difference is in the order of integrators that can be equal to α, with 0 < α ≤ 1. To validate the method, two identifications were made, the first for the transfer function of fractional order G(s) = 1/(s + s 0.5 + 1), disregarding their prior knowledge, and the second for transfer function considered in studies of control and evaluation of movements of lower limbs of paraplegic and hemiplegic using electrical stimulation, performed by the

Vedic mathematics is the ancient techniques of mathematics, based on 16 simple sutras (formulae). Decimal number system multiplication technique based on such ancient mathematics is reported in this paper. Improvement in speed was achieved through stage reduction by "Nikhilam Navatascaramam Dasatah (NND)" (all from 9 and last from 10) which was adopted from Vedas, during multiplication. Binary coded decimal (BCD) methodology was incorporated with Vedic mathematics, to implement such multiplier for practical VLSI applications. The functionality of these circuits was checked and performance parameters such as propagation delay, dynamic switching power consumptions were calculated by spice spectre using 90nm CMOS technology. BCD implementation of Vedic multiplier ensures the stage reduction for decimal number, hence substantial reduction in propagation delay compared with earlier reported one, has been investigated. Implementation result offered propagation delay of the resulting (5×5) digit decimal multiplier was only ~5.798ns while the power consumption of the same was ~23.487μW. Almost ~26% improvement in speed from earlier reported decimal multiplier, e.g. parallel implementation methodology, the best architecture reported so far, has been achieved.

A digitális átalakulás minden iparágban és üzleti szektorban kihívást jelent. Jelen cikkben a digitális átalakulás kiterjedt, sokrétű és szigetszerű szakirodalmának célzott multidiszciplináris és horizontális áttekintését nyújtják át a szerzők, tisztázva a jelenségnek a GreenTech és a fenntarthatóság szempontjából történő vizsgálati peremfeltételeit. Ugyanis egyértelmű elmozdulás tapasztalható a gazdasági döntéshozatalban a fenntarthatóság irányába, amely köré a GreenTech megoldások épülnek. Közelmúltbeli tanulmányok rávilágítottak a zöld digitalizációs megoldások fontosságára, hangsúlyozva a (digitális) technológia jelentőségét a fenntarthatósági kihívások kezelésében, valamint az üzleti és lakossági felhasználásban. Következésképpen azt találták, hogy a digitális átalakulás terjedésének felgyorsulása és a GreenTech megoldások ebben betöltött szerepe a közeljövőben jelentősen növekedni fog. Nem létezik energiaátmenet digitalizáció nélkül.

This paper represents an experimental study on Field Operators and their Application's User Interface. In Process Automation Industries, Field Operator's jobs are very crucial due to working in various ambient environments. The application’s User Interface Design is not designed based on any HMI User Interface Design guidelines. To measure the environment, and frustrations while using the User Interfaces on Field Operations, we studied their User Experiences through User Interfaces designed for them. The experiment was conducted with the text-input experiment, and a questionnaire to gather data. The study concludes with User Interface Design complexity in terms of visibility in various
environments, text-inputs with small Industrial Grade Field Operation Hand-held devices have a significant effect on the time taken to locate the target actions, interface design should be very clear and as simple as possible. The research outcome is a User Interface Design Framework which tested for Field Operators and deployed in Process Automation Industries (Oil Refineries/Chemical Process Plants).

Keywords: User Interface Design . Field Operators . Ambient Environment. Text-input Methods . Process Automation Industries . Design Guidelines