Process-related Data Research Articles

The impact of machine translation on the development of info-mining and thematic competences in legal translation trainees: a focus on time and external resources

ABSTRACT This study combines product- and process-oriented research methods and tools to observe whether and how the presence of pre-translated text affects translation quality and influences the translator’s research patterns. It is part of the LeMaTTT project, a simulated longitudinal empirical study exploring the impact of MT on info-mining and thematic competences in legal translation. Data were elicited through a translation task completed by a cohort of 110 final-year MA trainees with training in legal translation and basic MT literacy, and a cohort of 54 first-year MA trainees with no to very limited experience in specialised translation and post-editing. This paper provides a first analysis of selected process-related data concerning the allocation of time and the use of external resources throughout the translation or post-editing processes of 40 participants, 20 from each cohort. Preliminary results highlight some correlations between the use of time and external resources and both (a) the development of thematic and info-mining competences and (b) the specific type of task, i.e. whether from-scratch translation or post-editing.

Convolutional Neural Networks for Raw Signal Classification in CNC Turning Process Monitoring.

This study addresses the need for advanced machine learning-based process monitoring in smart manufacturing. A methodology is developed for near-real-time part quality prediction based on process-related data obtained from a CNC turning center. Instead of the manual feature extraction methods typically employed in signal processing, a novel one-dimensional convolutional architecture allows the trained model to autonomously extract pertinent features directly from the raw signals. Several signal channels are utilized, including vibrations, motor speeds, and motor torques. Three quality indicators-average roughness, peak-to-valley roughness, and diameter deviation-are monitored using a single model, resulting in a compact and efficient classifier. Training data are obtained via a small number of experiments designed to induce variability in the quality metrics by varying feed, cutting speed, and depth of cut. A sliding window technique augments the dataset and allows the model to seamlessly operate over the entire process. This is further facilitated by the model's ability to distinguish between cutting and non-cutting phases. The base model is evaluated via k-fold cross validation and achieves average F1 scores above 0.97 for all outputs. Consistent performance is exhibited by additional instances trained under various combinations of design parameters, validating the robustness of the proposed methodology.

Individual learning paths mastering teachers’ professional vision

IntroductionPromoting a professional vision of teaching as a key factor of teachers’ expertise is a core challenge for teacher professionalization. While research on teaching has evolved and successfully evaluated various video-based intervention programs, a prevailing emphasis on outcome measures can yet be observed. However, the learning processes by which teachers acquire professional vision currently remain a black box. The current study sought to fill this research gap. As part of a course dedicated to promoting a professional vision of classroom management, students were imparted knowledge about classroom management that had to be applied to the analysis of authentic classroom videos. The study aimed to determine the variety of individual strategies that students applied during their video analyses, and to investigate the relationship between these and the quality of the students’ analyses, measured by their agreement with an experts’ rating of the video clips.MethodsThe sample comprised 45 undergraduate pre-service teachers enrolled in a course to acquire a professional vision of classroom management. By applying their imparted knowledge of classroom management, students engaged in the analysis of classroom videos to learn how to notice and interpret observable events that are relevant to effective classroom management. Implementing a learning analytical approach allowed for the gathering of process-related data to analyze the behavioral patterns of students within a digital learning environment. Video-based strategies were identified by conducting cluster analyses and related to the quality of the students’ analysis outcomes, measured by their concordance with the experts’ ratings.ResultsWe gained insight into the learning processes involved in video-based assignments designed to foster a professional vision of classroom management, such as the areas of interest that attracted students’ heightened attention. We could also distinguish different approaches taken by students in analyzing classroom videos. Relatedly, we found clusters indicating meticulous and less meticulous approaches to analyzing classroom videos and could identify significant correlations between process and outcome variables.DiscussionThe findings of this study have implications for the design and implementation of video-based assignments for promoting professional vision, and may serve as a starting point for implementing process-based diagnostics and providing adaptive learning support.

Sistem Pendukung Keputusan Penentuan Dakian Gunung Di Pulau Jawa Menggunakan Fuzzy Logic Mamdani

Mountain climbing is an activity with a relatively high level of danger that requires climbers to walk in the forest and spend quite a long time with increasingly low oxygen levels and very cold temperatures that can even reach 0 degrees Celsius. In this research, decisions play an important role in determining mountain climbing routes. especially on the island of Java which has a variety of mountains with different characteristics, therefore we help climbers in choosing mountain climbs that suit their individual preferences and personal conditions. The method used is Mamdani's Fuzzy Logic which can overcome uncertainty and complexity of information in decision making. To collect data regarding height, distance, track length and climbing costs, namely through interviews and related analysis. Fuzzy Logic Mamdani is implemented to manage and process related data and can produce output in the form of recommendations for climbing routes. This fuzzy model can integrate environmental variables, such as height, distance, cost and track length.

Taking full advantage of ‘low-quality’ datasets in watershed modeling and management: From a perspective of parameter calibration

The quality of calibration datasets is critical for establishing well-calibrated models for reliable decision-making support. However, the analysis of the influence of calibration dataset quality and the discussion on how to use flawed and/or incomplete datasets are still far from sufficient. An evaluation framework for the impact of model calibration data on parameter identifiability, sensitivity, and uncertainty (ISU) was established. Three quantitative and normalized indicators were designed to describe the magnitude of ISU. With the case study of the upper Daqing River watershed, China and the model SWAT (Soil and Water Assessment Tool), one ideal dataset without quality flaws and 79 datasets with different types of flaws including observation error, low monitoring frequency, short data duration and low data resolution were evaluated. The result showed that 4 of 13 parameters that control canopy, groundwater and channel processes have higher ISU values, indicating the high identifiability, high sensitivity, and low uncertainty. The largest gap of parameter ISU between dataset with quality flaw and ideal dataset was 0.61 due to short data duration, while the smallest gap was −0.28 due to low monitoring data frequency. Although some defective datasets caused unacceptable calibration results and model output, some defective datasets can still be valuable for model calibration which depends on the hydrological processes of interest when applying the model. Equivalent calibration results were yielded by the datasets with similar statistical properties. When using datasets with traditional defective issues for calibration, a new step checking the consistency among decision goal, representative system process, determinative parameters and calibration datasets is suggested. Practices including process-related data selection, dataset regrouping and risk self-reporting when using low-quality datasets are encouraged to increase the reliability of model-based watershed management.

A Survey on IoT-Edge-Cloud Continuum Systems: Status, Challenges, Use Cases, and Open Issues

The rapid growth in the number of interconnected devices on the Internet (referred to as the Internet of Things—IoT), along with the huge volume of data that are exchanged and processed, has created a new landscape in network design and operation. Due to the limited battery size and computational capabilities of IoT nodes, data processing usually takes place on external devices. Since latency minimization is a key concept in modern-era networks, edge servers that are in close proximity to IoT nodes gather and process related data, while in some cases data offloading in the cloud might have to take place. The interconnection of a vast number of heterogeneous IoT devices with the edge servers and the cloud, where the IoT, edge, and cloud converge to form a computing continuum, is also known as the IoT-edge-cloud (IEC) continuum. Several key challenges are associated with this new computing systems’ architectural approach, including (i) the design of connection and programming protocols aimed at properly manipulating a huge number of heterogeneous devices over diverse infrastructures; (ii) the design of efficient task offloading algorithms aimed at optimizing services execution; (iii) the support for security and privacy enhancements during data transfer to deal with the existent and even unforeseen attacks and threats landscape; (iv) scalability, flexibility, and reliability guarantees to face the expected mobility for IoT systems; and (v) the design of optimal resource allocation mechanisms to make the most out of the available resources. These challenges will become even more significant towards the new era of sixth-generation (6G) networks, which will be based on the integration of various cutting-edge heterogeneous technologies. Therefore, the goal of this survey paper is to present all recent developments in the field of IEC continuum systems, with respect to the aforementioned deployment challenges. In the same context, potential limitations and future challenges are highlighted as well. Finally, indicative use cases are also presented from an IEC continuum perspective.

Identification of Patterns in the Stock Market through Unsupervised Algorithms

Making predictions in the stock market is a challenging task. At the same time, several studies have focused on forecasting the future behavior of the market and classifying financial assets. A different approach is to classify correlated data to discover patterns and atypical behaviors in them. In this study, we propose applying unsupervised algorithms to process, model, and cluster related data from two different data sources, i.e., Google News and Yahoo Finance, to identify conditions in the stock market that might help to support the investment decision-making process. We applied principal component analysis (PCA) and a k-means clustering approach to group data according to their principal characteristics. We identified four conditions in the stock market, one comprising the least amount of data, characterized by high volatility. The main results show that, regularly, the stock market tends to have a steady performance. However, atypical conditions are conducive to higher volatility.

Integrated energy carbon emission monitoring and digital management system for smart cities

In recent years, although China’s economy has continued to grow, the environmental impact is greatly affected by the use of primary energy, such as global warming, which has become more and more serious. Under the background of energy conservation and emission reduction, China’s emission reduction pressure is very great. In this paper, an online monitoring system for carbon emissions is developed for real-time monitoring of carbon emissions, and the ant colony algorithm is used to perform multi-objective optimization based on “construction period-cost-carbon emissions.” Through the organic integration of wireless sensors, communication networks, cloud servers, and mobile devices, a real-time monitoring system for carbon emissions has been developed, which can monitor and visualize the carbon emissions generated by major machinery on site in real time. At the same time, the resource consumption of each process in different modes is sorted out, and the multi-objective optimization problem of “construction period-cost-carbon emission” is designed to seek the optimal solution by combining the multi-objective optimization theory. In this paper, the developed real-time monitoring system is applied in the actual field, the stability and practicability of the system are verified, and the process-related data is obtained by combining the monitoring system and field investigation. The experimental results show that the relative deviations of the two units are consistent, fluctuating between 0.54% and 6.14%, and the overall deviations are 3.61% and 3.63%, respectively. Therefore, the online carbon emission monitoring system has stable data and high accuracy. By comparing the data trends of the online monitoring method and the emission factor method, it is found that the two trends are consistent, which verifies the applicability of the online monitoring method in the field of carbon emission monitoring.

Process safety assessment of thermal technologies for biomass valorization by numerical descriptive approach

Process-based safety assessment of biomass thermal valorization technologies including hydrothermal gasification (HTG), pyrolysis, and conventional gasification has been evaluated based on the Numerical Descriptive Logistics Equation (NuD). Process-related data such as temperature, pressure, heat of reaction, and process inventory were collected from the literature for this purpose. The data has been analyzed by using the NuD equations to obtain the process safety total score (PSTS), and then hierarchy of control (HOC) has been applied to propose the strategies for risk mitigation. According to the NuD results, the HTG process has a lower PSTS (210.2) compared with pyrolysis (226.4) and gasification (228.5), and it reveals that HTG is the safest among these three technologies. HTG has the highest score for pressure (60.48) because it has the high pressure (20–25 MPa) process requirement, but the lowest scores for temperature and heat of reaction due to lower temperature operations (374–400 °C) compared with pyrolysis and gasification. The results determined by the NuD have been compared with subjective safety evaluation methods (Process Safety Index Analysis and Inherent Safety Index), and the results of inherent safety index (ISI) are also consistent with that determined by the NuD. Hierarchy of control has been proposed to control and reduce the process risk. As per results, engineering and administrative measures could be drafted for more effective process safety control.

“Get Haircut” Application based on Mobile Android Using React Native Framework

Salon or Barbershop is one of the businesses that engaged in services. At this time, hair care is one of the appearance factors that are often considered by both men and women. This is further strengthened by the increase in the salon or barbershop business in Cirebon. Every time the number of customers at the salon or barbershop is increasing and of course it creates problems, such as, regarding reservations (Booking) and also incoming transaction reports. From these problems, we need a system that can handle these problems. Get Haircut Application is an android-based application built using the React Native framework with the PHP programming language that can make it easier for salons or barbershops to receive orders from customers online. In addition, this application is able to process queue number data, service data and related data. This application is made using the Rational Unified Process (RUP) development method.

Recent advances to accelerate purification process development: A review with a focus on vaccines

The safety requirements for vaccines are extremely high since they are administered to healthy people. For that reason, vaccine development is time-consuming and very expensive. Reducing time-to-market is key for pharmaceutical companies, saving lives and money. Therefore the need is raised for systematic, general and efficient process development strategies to shorten development times and enhance process understanding. High throughput technologies tremendously increased the volume of process-related data available and, combined with statistical and mechanistic modeling, new high throughput process development (HTPD) approaches evolved. The introduction of model-based HTPD enabled faster and broader screening of conditions, and furthermore increased knowledge. Model-based HTPD has particularly been important for chromatography, which is a crucial separation technique to attain high purities. This review provides an overview of downstream process development strategies and tools used within the (bio)pharmaceutical industry, focusing attention on (protein subunit) vaccine purification processes. Subsequently high throughput process development and other combinatorial approaches are discussed and compared according to their experimental effort and understanding. Within a growing sea of information, novel modeling tools and artificial intelligence (AI) gain importance for finding patterns behind the data and thereby acquiring a deeper process understanding.

Design of Intelligent Garbage Classification System Based on Internet of Things Technology

With the increasingly prominent problem of environmental pollution, it is extremely urgent to carry out garbage classification. This paper designs an intelligent garbage classification system based on Internet of Things technology, The system is mainly composed of relay driving circuit, infrared induction, metal detection and humidity detection modules. Single chip microcomputer and multi-channel sensors are used to collect and process related data to realize metal garbage recovery, dry garbage and wet garbage classification and delivery, and the collected related data are displayed on the display screen through serial communication. The experimental results show that the system has the characteristics of simple structure, stable performance and convenient operation, which provides a feasible solution for the current garbage classification and treatment.

Root-cause analysis of process-data quality problems

ABSTRACT Process mining provides analytical tools and methods which can distil insights about process behaviour from big process-related data. Yet challenges relating to the impact of poor quality data on event logs, the input to process mining analyses, remain. Despite researchers raising concerns about event log data quality, event log preparation is, in practice, generally handled mechanistically, focusing on fixing symptoms rather than on uncovering the root causes of event log data quality issues. To address this, we introduce the Odigos (Greek for “guide”) framework. Based on semiotics and Peircean abductive reasoning, the Odigos framework facilitates an informed way of dealing with data quality issues in event logs. Odigos supports both prognostic (foreshadowing potential quality issues) and diagnostic (identifying root causes of discovered quality issues) approaches. We examine in depth how the framework supports a detailed root-cause analysis of a well-known collection of event log imperfection patterns.

Data-driven failure mode and effect analysis (FMEA) to enhance maintenance planning

Nowadays, the availability of data from the manufacturing environment, such as process and operation related data or past maintenance activities enable new possibilities for advanced data analytics like prediction of failure behavior. Possible predictions could consider faults of specific components or even the current product and component properties.The paper presents a data-driven Failure Mode and Effect Analysis (FMEA) methodology by using deep learning models on historical and operational data from the use stage of industrial investment goods. The developed methodology is supposed to support the maintenance planning for industrial investment goods by enhancing transparency and providing decision support.The developed framework is applied to and validated by a case study from the aviation sector. The results show that the accuracy of the fault prediction is around 95 %. By integrating these results into a data-driven FMEA framework, risk and failure occurrence estimations are no longer subjective. Especially the estimation of failure probabilities no longer solely depends on the experience and knowledge from employees.

Foamy slag practice to enhance the energy efficiency of electric arc furnace: An industrial scale validation

The aim of the present work is to demonstrate the importance of foamy slag practice during steelmaking in electric arc furnaces (EAF). Steel making practices with foamy slag in the EAF not only reduces the energy consumption but it also protects the refractory materials of furnace linings from the high energy intensity generated by electrodes. It also decreases the noise pollution in conjunction with reduced melting time during the process. Foamy slag practices in electric arc furnace operations persists since long in steel making industries but here authors tried to modify the aforesaid practice at the concern plant in most economical way without impairing the steel quality.In the present study a process-controlled feeding of fine metallurgical coke for optimized foaming has been developed and simultaneous increment in energy efficiency has been noticed by collecting and analyzing the process related data. To carry out the present study more than 140 heats have been taken into consideration, with and without foamy slag generation in electric arc furnace. Considerable reduction in the electrical energy consumption (i.e. from ∼510 kwh per tone to ∼450 to 460 kwh per tone) has been noticed along with the reduction in total melting time from ∼87 min to ∼76 min. Present work also confirms that optimized met coke injection in the range of 15–18 kg/tone certainly helps in increasing the foaminess of the slag. FeO content of the slag was also assessed and found in the range of 17–21 wt% having the basicity of 1.8–2.2, which is a good agreement in prospects of yield as well as quality of the steel.

A Novel Frame-Slotted ALOHA Algorithm for Radio Frequency Identification System in Supply Chain Management

Abstract Radio Frequency Identification (RFID) is an essential wireless technology with different supply chain management (SCM) potential applications. Managing a supply chain primarily requires an ‘information sharing’ architecture and visibility of business process-related data. This information architecture helps the operational event management team identify critical incidents and deploy substitute actions. The RFID-based information system provides a technological infrastructure to supply chain business process-related data. The occurrence of tag-collision incident essentially challenges the tag detection ability of RFID systems. Tag collision takes place when more than one tag responds simultaneously to the reader at the identification process. Different algorithmic solutions are available to the tag collision problem. In general, the algorithmic solutions are of two types: (i) tree-based solutions, and (ii) ALOHA-based solutions. This paper presents a novel dynamic frame slotted ALOHA algorithm. It uses probability theory for tags to access the frame and improve the system efficiency. This paper presents experimental details to evaluate the performance of the Electronic Product Code Generation-2 (EPC Gen-2) arbitration algorithm. Finally, the paper discusses the experimental results of the proposed algorithm, and concludes with the scope of future research.

Contribution to Optimization and Evaluation of IPv6 Signals Based Constrained Devices Networks

Small Objects Networks is a wireless network that we used to define any type of network. These networks use the IPv6 protocol for communication. Indeed, it allows, via standardized and unified electronic identification systems, to identify directly, unambiguously physical objects and thus ability to retrieve, store, transfer, and process related data. In such a network, the IPv6 Neighbor Discovery protocol is used by an object to communicate with its neighbors in order to establish the relationship between the IPv6 (Internet Protocol Version 6) address and that MAC. Since the NDP (Neighbor Discovery Protocol) messages are relatively long and large, several operating parameters, such as power consumption, are affected accordingly which reduces the lifetime of the connected objects and adversely affects the proper functioning of the network. The goal of our paper is to reduce the use of these messages in 6lowpan networks to minimize energy consumption and maximize network lifetime… The results of our simulation show a big reduction of the signals used in the network compared to the standard NDP-RFC4861. Our algorithm also shows better performance in terms of energy consumption.

Chatbots at Digital Workplaces – A Grounded-Theory Approach for Surveying Application Areas and Objectives

Abstract Background: Chatbots are currently on the rise as more and more researchers tackle this topic from different perspectives. Simultaneously, workplaces and ways of working are increasingly changing in the context of digitalization. However, despite the promised benefits, the changes still show problems that should be tackled more purposefully by chatbots. Application areas and underlying objectives of a chatbot application at digital workplaces especially have not been researched yet. Method: To solve the existing problems and close the research gap, we did a qualitative empirical study based on the grounded-theory process. Therefore, we interviewed 29 experts in a cross-section of different industry sectors and sizes. The experts work in the information systems domain or have profound knowledge of (future) workplace design, especially regarding chatbots. Results: We identified three fundamental usage scenarios of chatbots in seven possible application areas. As a result of this, we found both divisional and cross-divisional application areas at workplaces. Furthermore, we detected fifteen underlying objectives of a chatbot operation, which can be categorized from direct over mid-level to indirect ones. We show dependencies between them, as well. Conclusions: Our results prove the applicability of chatbots in workplace settings. The chatbot operation seems especially fruitful in the support or the self-service domain, where it provides information, carries out processes, or captures process-related data. Additionally, automation, workload reduction, and cost reduction are the fundamental objectives of chatbots in workplace scenarios. With this study, we contribute to the scientific knowledge base by providing knowledge from practice for future research approaches and closing the outlined research gap. Available at: https://aisel.aisnet.org/pajais/vol12/iss2/3/ Recommended Citation Meyer von Wolff, Raphael; Hobert, Sebastian; Masuch, Kristin; and Schumann, Matthias (2020) "Chatbots at Digital Workplaces – A Grounded-Theory Approach for Surveying Application Areas and Objectives," Pacific Asia Journal of the Association for Information Systems: Vol. 12: Iss. 2, Article 3. DOI: 10.17705/1pais.12203 Available at: https://aisel.aisnet.org/pajais/vol12/iss2/3

Chatbots at Digital Workplaces – A Grounded-Theory Approach for Surveying Application Areas and Objectives

Abstract Background: Chatbots are currently on the rise as more and more researchers tackle this topic from different perspectives. Simultaneously, workplaces and ways of working are increasingly changing in the context of digitalization. However, despite the promised benefits, the changes still show problems that should be tackled more purposefully by chatbots. Application areas and underlying objectives of a chatbot application at digital workplaces especially have not been researched yet. Method: To solve the existing problems and close the research gap, we did a qualitative empirical study based on the grounded-theory process. Therefore, we interviewed 29 experts in a cross-section of different industry sectors and sizes. The experts work in the information systems domain or have profound knowledge of (future) workplace design, especially regarding chatbots. Results: We identified three fundamental usage scenarios of chatbots in seven possible application areas. As a result of this, we found both divisional and cross-divisional application areas at workplaces. Furthermore, we detected fifteen underlying objectives of a chatbot operation, which can be categorized from direct over mid-level to indirect ones. We show dependencies between them, as well. Conclusions: Our results prove the applicability of chatbots in workplace settings. The chatbot operation seems especially fruitful in the support or the self-service domain, where it provides information, carries out processes, or captures process-related data. Additionally, automation, workload reduction, and cost reduction are the fundamental objectives of chatbots in workplace scenarios. With this study, we contribute to the scientific knowledge base by providing knowledge from practice for future research approaches and closing the outlined research gap.

Managing arrival variability in healthcare services: Case of an out-patient department

Similar to other services, healthcare is also experiencing an evolution of patients into sophisticated customers with high expectations from the service providers. This has led to the application of operations research tools such as discrete event simulation modelling in delivering improved healthcare services. The present study is conducted in an obstetrics and gynaecology out-patient department of a multi-specialty hospital. Process related data is collected from the department and is used to build a discrete event simulation model. Scenarios are developed considering strategies suggested in literature to manage arrival variability, the most crucial form of customer-induced variability. Based on the results obtained, the most optimal solution is highlighted. This study shows that with recent research advancements in simulation modelling, scepticism towards methods adopted from other sectors can be reduced which is beneficial for both, the modern-day healthcare service provider and the patients.