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Many organizations have adopted agile values and principles in their products and services to provide increasing value to their customers. Although agile started with software development, it has been applicable and used in different industries and disciplines, including the space industry. This article presents the practical implications of teams transitioning to be agile teams using a general inductive analysis of 33 individuals from different NASA agile teams. The findings and recommendations are part of a larger effort performed as part of the development of a model for agile leadership. Some organizations may see agile as very risky and costly, and this article provides some guidance derived from the data to help support planning for agile transformation efforts. • Agile transformations of NASA Agile teams. • Being Agile with a continuous learning mindset. • Ability to create sustainable value to the customer in a technology-driven organization. • Recommendations to becoming Agile organizations. [ABSTRACT FROM AUTHOR]

Abstract Risk management is often overlooked in the software industry, especially in agile methodology-based projects, which can have negative impacts on the schedule, budget, and quality of a project, eventually leading to customer dissatisfaction. Hence, it is essential to identify the risks and prioritize them based on their level of importance. This study has utilized the Analytic Hierarchy Process (AHP) methodology for prioritizing the software risk factors identified during our previously executed risk identification phase experimental study in small and medium software enterprises that follow agile and traditional methodologies for software development. Among the 16 risk factors categorized under four different risk natures named 'difficulties', 'changes', 'mistakes', and 'challenges', the 'challenges' risk nature category is found to be the most significant one by the research findings, followed by 'difficulties', 'mistakes', and 'changes' risk natures. The results also highlight that 'proper utilization of funds', 'complicated tasks', and 'convincing the customer with alternative technologies' are the most significant risk factors. The study gives an insight into the possible risk factors in a software environment, along with their significance. [ABSTRACT FROM AUTHOR]

The forthcoming industrial requirements to ensure increasingly flexible and agile production processes will require an ever-increasing ability to recognise, predict and optimise the state of processes with respect to the planned state. Advanced tools such as Digital Twins (DT) and Cyber Physical Systems (CPS) can help manage and address these problems. This paper presents some results of the authors' research on the adoption of a basic methodology for the design and implementation of Digital Twins aimed at manufacturing applications and subsequent integration into a Cyber Physical Production Systems (CPPS). Reference publications were selected based on the level of detail of DT and CPS architectures. The aim is also to combine methodological development with industrial tools and requirements. The architecture is potentially applicable to different production scenarios. Finally, the first results of application on a scale production line are shown. [ABSTRACT FROM AUTHOR]

Lithium-ion cells (LIC) are emerging as the critical energy source in space applications for spacecraft. The analysis of the failure of small satellites reveals that 41.3% have either suffered partial or total failures in the space environment around Earth, resulting in the accumulation of debris. The defects in batteries caused more failures than thrusters. The present paper details a framework for ensuring the durability of LIC systems in space applications. It is discussed in three verticals viz. a) Durability through Design' that gives prominence to best practices in cell chemistry selection, processing and, design through parametric modelling; b) 'Durability through quality control and testing by the manufacturer and by identifying relevant reliability models; c)'On-orbit management' through the State of Health (SOH) assessment by Random Forest algorithm and a well-equipped intelligent Battery Management System (BMS). Lacuna in standards for cell development process is brought out. Importance of quantitative approach for design, reliability and asset management during space application is highlighted. Use of agile, contemporary techniques of artificial intelligence in this quantitative approach is also discussed. The framework tries to bring out a systematized protocol to be followed during the development and operational life cycle of LIC for ensuring highly reliable systems for space application. [ABSTRACT FROM AUTHOR]

Abstract —Cultural appropriation is a complex issue that can lead to the loss of a nation's cultural identity. With the widespread adoption of mobile phones in Indonesia, there is an opportunity to use technology, specifically augmented reality (AR). To address this issue, our user-friendly AR app allows users to interact with Indonesian culture in a virtual environment. Through AR technology, users can engage with Indonesian culture in an immersive and fun way. Our target market is the younger generation, and our app provides a user-friendly interface that allows users to scan AR markers to access different cultural settings. Users can choose from two cultural regions, Bali and Papua. Once a region is selected, a scene showcases the desired region's iconic places and objects accompanied by a traditional folk song. In developing the AR app, we used the agile software development method for approximately two months with four developers. At the end of the development phase, we conduct four testing aspects of the application—namely, angle testing, functionally testing, usability testing, and hardware testing. The testing revealed that the application could perform well under various testing conditions. Through our AR solution, we aim to raise awareness of the importance of Indonesian culture and provide an educational experience that can inspire users to take ownership of their cultural heritage. [ABSTRACT FROM AUTHOR]

In all methods of Agile Software Development methods, an estimation process which determines the size of all Product Backlog Items (PBI) is essentially important. One of those methods is Planning Poker. This method is familiar among developers in the globe and one of the famous analogy-based estimation techniques known. In Planning Poker, the goal is to have a consensus vote on how big a product backlog should be. This consensus is reached via open discussion. As discussion is involved, many aspects of human errors could cause problems such as: anchoring effect and overconfidence which often leads to underestimation. To comprehend this, we offer an AI method that can be applied alongside the discussion to minimize this human error factors. We will be using NLP and ANN to classify a Product Backlog Item based on previous projects. The aim is individual estimate given by the AI that is free from influence from human to give better estimation. The result is expected at above 90% accuracy to give the best performance. [ABSTRACT FROM AUTHOR]

The article explicates the Lean Startup methodology, a popular framework for constructing nascent and pioneering startup enterprises. Since its inception, this methodology has garnered recognition from academic scholars and business practitioners. Its precepts, grounded in Agile Project Management and the Customer Development philosophy, are ideally suited for implementing innovative projects to introduce product, technological, or organizational innovations into the market. Many of them are focused on creating solutions to support innovation in the green sectors of the economy. Many of them are focused on creating solutions to support innovation in the green sectors of the economy - in particular, those related to the ICT industry. In addition, the importance of technologies created by startups in terms of positive environmental impact has been reflected in scientific works worldwide. They have a powerful impact on the building and dynamic development of green economies manifested in various aspects of society. The significance of startups for advancing economies across the globe has been acknowledged for many years, as they exert a substantial influence on critical economic spheres, including the labor market, technological advancement, and the progress of every industry in which innovative solutions are employed. Despite the challenges, startup entrepreneurs can act as powerful change agents, reducing inefficiencies, creating jobs, and boosting economic development. Through these activities, startups stimulate the sustainable growth of economies. The Lean Startup methodology allows testing the created value before introducing it to the market. Thanks to the application of Customer Development guidelines where, through an iterative process of creating innovation, it is tested by a panel of potential customers. Successively created value is supplemented with additional elements that potential users constantly verify. In this way, the value delivered to the market is ideally suited to the requirements and expectations of its buyers. The article also reviews the literature on startups regarding their impact on the Sustainable Development (SD) of enterprises and entire economies. The most important concepts were presented, and an attempt was made to define the concept initially. The last part of the article systematically presents the limitations and directions of further research and startups and the possibilities of using Lean Startup. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic and mandatory lockdowns have forced businesses to look more enthusiastically at telework issues in order to maintain some activity during this difficult time. But, depending on the nature of the work, teleworking is not always a very suitable solution. We wonder here whether agile project management is part of the practices facilitated or, on the contrary, constrained by remote work. Our research led us to compare interviews with experts, responses to a survey, and existing literature on the subject. We propose a guide for project managers to check whether everything has been put in place to facilitate agile project management in telework. We validated our guide with another expert on an ongoing project in a French company. [ABSTRACT FROM AUTHOR]

Selecting the appropriate project management methodology is one of the challenges faced by project managers. Predicting the adoption of agile or traditional methodology during the early stage of the project could contribute to its success. The purpose of this paper is to predict project management methodology based on machine learning algorithms. Our study aims to help project managers make the right decisions and select the most suitable project management approach. The performance of Machine Learning (ML) models was evaluated using metrics such as accuracy, precision, recall, and F1-score. Gradient Boosting outperformed other ML models, achieving an accuracy of 94%. [ABSTRACT FROM AUTHOR]

• Development of the systematic Standard Operating Procedure using photogrammetry technology to reproduce the indoor scene in Virtual Reality accurately. • Early and accurate crime scene visualisation enables investigative teams to conduct a fast, agile, and informed decision-making process. • The accurate documentation of a crime scene is paramount as part of the recording process associated with the assurance of the continuity of evidence required by the courts. Early and accurate visualisation of a crime scene is highly desirable such that a rapid, agile, and informed decision-making process can be undertaken by an investigative team. We present a new standard operating procedure for imaging an indoor scene using DSLR cameras conventionally used by crime scene investigators and examiners. The standard operating procedure (SOP) enables the systematic photography of indoor spaces in such a way that the Structure from Motion (SfM) photogrammetry technique can be implemented, allowing the scene to be recreated in Virtual Reality (VR). To demonstrate the method's validity, we compare two VR-rendered representations of an example scene using (a) photographs taken by an experienced crime scene examiner using a conventional photographic method and (b) photographs taken by a novice photographer following the developed SOP. [ABSTRACT FROM AUTHOR]

The field of computer science, like its sub-field of cyber threat modelling, is rapidly evolving. The prerequisites for key changes can be summarized as follows: cyber threats are evolving; there are leaks of special services tools; agile development methodology is being introduced everywhere; the boundaries of the object of protection are blurred; the scope of application of artificial intelligence is expanding; potentially vulnerable API integrations are increasingly being used. These factors lead to the fact that the processes of analysis of cyber threats, analysis of protective measures, generalization of data, and development of protective tools should now be considered continuous, not discrete. At the same time, the cost of cybersecurity increases like an avalanche in an attempt to avoid reputational and information losses. The only way to avoid this tendency is to apply a rational, scientific, accurate method of cognition to these processes. Thus, the creation of mathematical models of processes in the field of cybersecurity is now more relevant than ever. The article is devoted to the investigation of the process of the influence of an active viral cyber threat on a high-availability cluster in the paradigm of the provisions of the theory of Markov processes, graph theory and the theory of mathematical analysis. The main contribution of the research is a formalized computationally efficient method of approximate estimation of the average number of affected elements of the target high-availability cluster under the influence of an active viral cyber threat. Also, a criterion that allows estimating the trend of the quantitative parameter of the metric of the model of the studied process at medium and long time intervals is proposed. To obtain the declared scientific result, the authors: - formulated a Markov model of the process of the influence of an active viral cyber threat on a high availability cluster; - substantiated a compact metric for accurate assessment of the average number of cluster elements affected by an active viral cyber threat at any time; - formulated a computationally efficient method of approximate estimation of the parameter of the mentioned metric for the model of the target studied process; - proposed a criterion that allows researchers to evaluate the trend of the parameter of the mentioned metric for the model of the target researched process at medium and long intervals of time. The adequacy of the formulated method has been proven empirically. [ABSTRACT FROM AUTHOR]

Adherence to the schedule for completion of a project is one of the basic requirements for the success of a management team. Failure to meet this schedule can have a negative impact on both the business side and the reputation of the company, this issue is a constant challenge in project management. This challenge becomes even greater with agile project management. Agile methodology has increasingly become the method of choice for project delivery. However, in this type of project, uncertainty increases in terms of execution time due to the uncertainty that arises from the agile characteristics that are so valued. Optimized assignment of agile teams based on their performance to project tasks can reduce this uncertainty and increase the probability of success. However, it appears that the tools available in the literature have very limited applicability to this problem. In this paper, we present a model for agile team assignment that uses the Program Evaluation Review Technique (PERT) along with the effective risk model and the Monte Carlo model. An illustrative case study was used to illustrate the application of the proposed model. The results show that the project execution time is highly dependent on the performance of the agile teams and their respective assignment to the project tasks. In practice, the proposed model enables the determination of the execution time of a given agile project considering the performance of the respective teams. [ABSTRACT FROM AUTHOR]

Ontology building can greatly influence the development cycle of an information system and enhance interoperability among its constituent elements. Throughout the projects we have been developing we have detected, by studying the current literature, a need to develop an agile method to conceive and mapping ontologies, which allows a quick and effective response to R&D projects. Designing a method for building an ontology, which is integrated and aligned with a systematic development approach, represents a crucial challenge in new approaches to system design and exploitation. Extant proposed methods for building an ontology, especially following agile approaches, have achieved interesting results but lack integration and alignment with a wider-view development framework. Thus, we have defined the first version of a semantic model allowing the alignment with the previously defined information model. Following the best practices for ontology building and based on our previous work on software system development, we now propose a method for designing an ontology, the 4SRS Method for Ontological Design based on the V-Model 4SRS, aligning it with a proven development method. We further demonstrate this approach by applying the proposed method in a real case, to develop an ontology for a choen restricted scope within the domain problem. [ABSTRACT FROM AUTHOR]

The purpose of this research is to build a healing environment that can be experienced through virtual reality (VR) to reduce stress levels of the user because of the lengthy period of COVID-19 pandemic that forced people to stay at home. The healing environment is created based on several theories on the principles of Healing Environments related to Architectural design. And as for the development of the simulation software, it is using the method of Extreme Programming that is based on Agile principle that combined with Architectural design flow. The VR is targeted to run optimally in low-end devices with cardboard and common Bluetooth controller so that it can be accessed inclusively. The evaluation is conducted using a user acceptance test with expert judgment and survey to 32 respondents. The findings are that they enjoyed the simulation because of the guidance is clear and the virtual environment looks more real compared to others that looks cartoonish. [ABSTRACT FROM AUTHOR]

In the data-centric era, interconnected devices must be able to communicate efficiently and securely with their hosts even over untrusted networks. This led to the adoption of several end-to-end security protocols that employ various efficient and elegant encryption algorithms, such as Advanced Encryption Standard (AES), often implemented with hardware modules since there is usually no good solution to install security software on the device itself. State-of-the-art AES hardware implementations typically focus on optimizing a single metric, e.g. throughput or area, and are tedious to adapt to a wider set of design constraints. Applying an agile approach to hardware development is increasingly important, and this is especially critical for hardware security primitives that need to be consumed in various systems. In this work, we develop Agile-AES, an open-source, flexible, parameterizable, hardware implementation of AES, combining many best-known practices. The agile and feature-rich implementation was based on the Chisel framework, through which flexibility is instrumented to support various key length, topology, mode of operation, local memory type, S-box fabric, side-channel attack defense techniques, and interfaces. Despite covering a larger design space, our proposed implementation has 50% fewer lines of code compared to existing Verilog versions — a crucial advantage in terms of maintainability and development productivity. To evaluate the QoR out of this implementation approach, we pick representative configurations and evaluate utilization, power and throughput on both entry level and server-grade FPGAs and compare against the Verilog and HLS counterpart implementations. It shows very comparable results to the Verilog implementation and better QoR compared to the HLS-based implementation. • Hardware security module design calls the urgency of agile design approaches. • Agile-AES is able to ease the design effort for adapting to various design constraints. • Open-source Agile-AES is developed with Chisel and combines the best-known AES practices. • Agile-AES requires less design effort with comparable QoR to Verilog counterpart. [ABSTRACT FROM AUTHOR]

Highly volatile, uncertain, complex and ambiguous environments (VUCA) complicate and condition project management. With the emergence of agile project management, it is proposed to co-construct it with the client's active participation. Two used agile methodologies are Scrum and Kanban. Scrum is based on executing fast, interactive cycles (Sprints) for the incremental construction of products. Kanban promotes the balance of the continuous workflow through synchronizing tasks and seeking perfection. The combined use of Scrum-Kanban facilitates the integration of the best of both approaches. The Theory of Constraints (TOC) proposes a method for managing constraints in a system (Constraint Management). The Drum-Buffer-Rope (DBR) method and Buffer Management are practical applications of this theory. This study seeks to maximize the continuous flow of value (Throughput) in agile project management by synergistically integrating the DBR method with Scrum-Kanban. The five-step process is implemented for the planning, executing, and controlling the Kanban board in a Scrum Sprint cycle. Four scenarios are evaluated: (1) Balanced Line; (2) Unbalanced Line; (3) Unbalanced Line Modification 1 - Stable, Robust and Fast; and (4) Unbalanced Line Modification 2 - Focusing and Elevation. Measurement of completed work (Kanban cards in the "Done" column) and final inventory for the Sprint cycle reveals that Simulation 4 is the optimal scenario, achieving the highest average "output" ("Done" cards) with reduced inventory ("Doing" cards). The integration of DBR with Scrum-Kanban maximizes the completed work (Throughput) and minimizes the final inventory of the Sprint cycle, which is corroborated by the principle of Little's Law. [ABSTRACT FROM AUTHOR]

The main problem associated with designing buildings and structures is the poor flexibility of such projects. Designing is a long-term process that takes place long before the actual construction begins. Depending on project complexity, it can last from a few to several months, during which it is frequently changed and updated. A design project usually follows a top-down pattern where subsequent steps succeed each other. Given all this, it seems justified to say that designing buildings and structures is a process marked by little flexibility, meaning that any response to changes is long, complicated, or sometimes unfeasible. And so the following question arises: how to improve the process of designing buildings and structures so that the very project more easily adapts to changes arising inside and outside the organization? The solution may be the implementation of selected parts of agile project management methodologies. The aim of the paper is to select elements from agile methodologies in order to streamline the work of a design project team. Section one deals with the analysis of different project management methodologies, focusing mainly on agile. This is followed by a description of project management in construction, with particular emphasis on the design process. Section three highlights the opportunities for implementing the elements of agile project management methodologies in construction by using an example of the mpiBIM student project that is held by Warsaw University of Technology. The research delivers ready-to-use forms that may be useful for a team that would like to implement agile work in building design projects. [ABSTRACT FROM AUTHOR]

The multi-objective dynamic agile earth observation satellite scheduling problem (MO-DAEOSSP) aims to schedule a set of real-time arrival requests and form a reasonable observation plan to satisfy various criteria. According to the requirements in practical applications, the total profit and the average image quality of scheduled requests are taken as optimization goals in this study. Compared to manually designed heuristics and iterative-based methods used in previous research, genetic programming based hyper heuristics (GPHH) can automatically evolve high-quality heuristic rules (HRs) for real-time scheduling without being highly dependent on expert knowledge. In this paper, a knowledge-transfer based multi-objective GPHH framework (KT-MOGP) is proposed, equipped with a heuristic-based simulation considering the idle monitoring, to evolve non-dominated HRs for solving MO-DAEOSSP. The heuristic-based simulation generates feasible schedules and returns fitness values for given HRs, which are the individuals evolved by KT-MOGP. KT-MOGP applies a knowledge transfer mechanism to accelerate convergence. Once a source problem is trained, its non-dominated solutions are extracted and their feature importance is transferred to guide the initialization of another target problem, by which the knowledge generated during the training process can be fully utilized. Experimental results on three sets of instances show that KT-MOGP outperforms the existing GPHH-based method and that the evolved HRs are competitive compared to several classical constructive heuristics and multi-objective evolutionary algorithms. The results also show the effectiveness of the proposed knowledge transfer-based initialization. To the best of our knowledge, this study is the first attempt to consider both multi-objective scenarios and real-time arrival requests. [ABSTRACT FROM AUTHOR]

Project management has long been a challenge for Information Technology (IT) organizations to learn how to manage projects effectively and efficiently while balancing the iron triangle: cost, time and scope without sacrificing quality. Agile project management (APM) and traditional project management (TPM) are two different approaches related to software development, the core tenet of TPM is that projects are relatively simple, predictable, and linear with well-defined boundaries, whereas APM has emerged as a new methodology for managing high-risk, time-sensitive and scope flexible projects. While it is undeniable that the use of agile project management is increasing, the traditional opposite side continues to exist. The agile approach has an impact on all phases of software development, and they must adapt to this new way of thinking. Software testing is also impacted as a fundamental link in the software development life cycle that ensures quality. Software testing is a vital component of software Quality Assurance (QA) since it serves as the final review of the specification, design, and code. Has software testing evolved along with the evolution of project management? Is there a difference between a traditional and an agile software testing? This paper answers these questions by comparing software testing in the two approaches. [ABSTRACT FROM AUTHOR]

For over a decade, artificial intelligence (AI) has been accelerating in its development and adoption. It allows people, and therefore companies, to be more productive and cost-effective, and ensures the sustainability and growth of the firm. The logistics industry is already using artificial intelligence, especially for multi-criteria route optimization. AI makes it easier to extract useful information from customer interaction, and also relevant patterns from the mass of information coming from the Internet of Things. In fact, artificial intelligence as an agile innovation has several benefits for the logistics sector. So, the rapid pace of technological evolution experienced by our society requires speed, responsiveness and an ability to understand the systemic dimension of challenges, whatever the sector or the type of organizations and actors involved, which is why companies started embracing agility through AI technologies. It is in this perspective that the present research is conducted, seeking to understand the extent to which AI can be qualified as an agile innovation that can optimize supply chain performance in Moroccan logistics companies. To this end, a qualitative methodological approach of an exploratory nature was adopted. To this end, fifteen semi-structured interviews will be conducted with logistics and supply chain managers in Moroccan companies, and data analysis will be conducted using the IRAMUTEQ analysis software. Our findings show that the majority of managers interviewed don't follow an AI strategy within their company due to many reasons, mainly their low budget and the lack of skills. [ABSTRACT FROM AUTHOR]

In the recent years, the advances in mobile technology have brought an exorbitant change in daily lifestyle of individuals. Smartphones/mobile devices are rampant in all aspects of human life. This has led to an extreme demand for developing software that runs on mobile devices. The developers have to keep up with this high demand and deliver high-quality app on time and within budget. For this, estimation of development and testing of apps play a pivotal role. In this paper, a Systematic Literature Review (SLR) is conducted to highlight development and testing estimation process for software/application. The goal of the present literature survey is to identify and compare existing test estimation techniques for traditional software (desktop/laptop) and for mobile software/application. The characteristics that make mobile software/application different from traditional software are identified in this literature survey. Further, the trend for developing the software is towards agile, thus this study also presents and compares estimation techniques used in agile software development for mobile applications. The analysis of literature review suggests filling a research gap to present formal models for estimating mobile application considering specific characteristics of mobile software. [ABSTRACT FROM AUTHOR]

Uncertainty has become a challenge in project management during the age of the Covid-19 pandemic. Project management required a more flexible and dynamic approach; therefore, it is believed that Agile project management could provide a solution to this problem. Agile has been widely used in multiple industry areas as a practical and flexible approach in project management practices. In the project, the scope is considered one aspect that can directly affect the budget and timing. Unfortunately, it seems to be one of the most neglected domains in Agile and conventional. Agile is recognized for its rapid improvement as well as its willingness to embrace change. However, uncontrolled change can potentially delay and make the project overspend. This paper aims to examine the challenges and discover the best practices in handling scope and change in Agile. A systematic literature review (SLR) was carried out through the literature sources, which entailed IEEE Explore, ScienceDirect, Emerald Insight, ProQuest, and Wiley Online. The finding was seven categories, and eighteen issues were found. Then twenty-four best practices were mapped to the challenges. We found the most significant challenge came from People & Organization, User Requirement Prioritization, Over-scope Requirement, and Communication & Coordination. Those areas are considered the most significant challenge because they can create delays and overspend in the project. [ABSTRACT FROM AUTHOR]

Supervised Artificial Neural Networks (ANN) is considered as a popular machine learning framework for year-ahead, month-ahead and day-ahead electric load forecasting but is yet to be optimized for very short term predictions like hour-ahead forecasting granularity. This study conducted a performance analysis of ANN models for hour-ahead electric load forecasting that power utility companies can use for agile reaction to its participation among spot markets. Data preparation procedures were conducted which transformed the historical electric load records of a certain geographic area served by a power utility into appropriate forms resulting to partitioned, represented and normalized datasets for ANN training and testing processes. Thirty-six ANN models all having nine input neurons and one output neuron were evaluated and found out that ANN models with Sigmoid activation function exhibited promising forecasting performance shown in terms of Mean Absolute Percentage Error (MAPE) with Back Propagation training algorithm with three hidden neurons having 2.85% MAPE; Quick Propagation training algorithm with six hidden neurons having 2.91% MAPE; and Resilient Propagation training algorithm with six hidden neurons having 3.49% MAPE. The performance analysis presented in this study shows how these ANN models were able to generate close to accurate forecasting results which power utility companies can effectively use for optimal resource management as they participate in spot markets that will require internationally accepted forecasting tolerance error in hour-ahead electric load nomination. [ABSTRACT FROM AUTHOR]

Currently, the organizations are constantly confronted with rapid and radical changes, making the company in turbulence that requires a transformation from a state to a target state. This turbulence needs agility on the part of companies, i.e. the ability to monitor their environment and be ready to react. Enterprise architecture needs transformation if its levels of abstraction are affected by its changes, so enterprise architecture needs to be agile to evolve over time. To measure the impact of change on the abstraction levels. This article proposes different algorithms to proceed to the change impact analysis in the different abstraction levels to be able to analyze the change impact of different projects on the elements of the enterprise architecture and to make it agile. [ABSTRACT FROM AUTHOR]

Fault distance estimation (FDE) in transmission lines with a thyristor controlled series compensator (TCSC) is a challenging task since the performance of FDE techniques gets affected by the variations in measured terminal voltages and line currents caused by the operation of TCSC during faults in the lines. Therefore, a robust FDE technique is utmost required for quicker power system restoration in the event of lines experiencing faults. Consequently, a matching pursuits algorithm (MPA) based traveling wave detection technique is presented in this work. The method is further utilized for FDE in lines with TCSC. The aerial-mode terminal bus voltage of the line is considered. The proposed technique first reconstructs the aerial-mode signals using MPA followed by determination of residuals from the difference of original and reconstructed signals. The next step determines time of arrival waves from respective peaks of the Teager energy of residual signals. Finally, the obtained ATWs are utilized for FDE. For simulation, a 3-machine 9-bus test system with one of its line compensated with TCSC is modeled in PSCAD/EMTDC platform. Different case studies are undertaken to validate the effective performance of the proposed technique. Further, comparative assessment is carried out to test the superiority of presented technique. • Fault distance estimator (FDE) for TCSC compensated transmission lines is proposed. • The proposed estimator utilizes the information of traveling waves terminating at two terminals. • Matching pursuits algorithm (MPA) is used detect traveling waves by decomposing modal voltage signals. • The peak of Teager energy of decomposed signal estimates arrival time of wave at the terminal of the line. • For simulation, WSCC 9 bus test system with TCSC compensation is used. [ABSTRACT FROM AUTHOR]

To overcome the singularity problem of control moment gyros (CMGs) and simultaneously provide a lightweight, agile, and simple dynamical system, a CMG system called a double-gimbal scissored-pair control moment gyro (DGSPCMG) was recently proposed. The DGSPCMG is a hybrid mechanism that combines scissored-pair CMGs and a double-gimbal mechanism; further, the outer singularities in this system (saturation singularities) can be escaped by steering the scissored-pair gimbals. This system generates less perturbation torque and has almost no inner singularities except at the origin and the line along the outer gimbal axis. This paper proposes model predictive control (MPC) based steering laws for the mentioned CMG system. The proposed MPC based steering laws can directly provide the optimal gimbal rate under the nonlinear constraints in real time without using the inverse of the CMG Jacobian matrix, and hence, the singularities do not have to be considered when determining the gimbal steering rate of this system. Three objective functions are considered in the steering law design: fast maneuver-weighted type, minimum control energy type, and a hybrid of these two types. The control performances of these three MPC-based steering laws are compared via numerical simulations. Results demonstrate that the hybrid type can achieve both a short rise time of the angular velocity at the initial stage of control, and smooth stabilization to the target attitude; moreover, its settling time and control effort is between those of the other two types. • Double-gimbal scissored-pair control-moment gyros (DGSPCMG) is considered. • Model-predictive control (MPC) based steering laws are designed for this system. • Fast maneuver, minimum control energy types, and hybrid of these two are considered. • Hybrid type achieved a short rise time of angular velocity and smooth stabilization. • As setting a long receding horizon length, the settling time was shortened. [ABSTRACT FROM AUTHOR]

Effective knowledge management is a powerful driver for product and organizational growth. Knowledge management allows for the efficient creation, sharing and use of information. But it is not entirely clear what basic knowledge is involved in agile practice and how teams handle it. The main aim of the article was the theoretical and empirical analysis of the agile knowledge management approach. The principles of knowledge management in connection with agile methodology were examined in the cognitive-theory section. The methodology is based on a case study applying an observational technique within an online organization. The empirical part presents a theoretical model that describes how knowledge management is used in the agile approach. These findings can aid in the understanding of agile knowledge management practices inside an online company, which includes iterative development, knowledge management and engineering practices. The results show that agile knowledge management improved in the organization's proactive process management. The significant influence is observed on staff efficiency, economic growth, and hence on customer loyalty, which boosts corporate morale while reducing resource consumption. [ABSTRACT FROM AUTHOR]

In the current competitive and dynamic environment, libraries must remain agile and flexible, as well as open to new ideas and ways of working. Based on a comparative case study of two academic libraries in Belgium, this research study investigates the opportunities of using Time-Driven Activity-Based Costing (TDABC) to benchmark library processes. To this end, two major research questions are addressed: 1) Can TDABC be used to enhance process benchmarking in libraries? 2) Do results at activity level provide additional insights compared to macro results in a process benchmarking? We first start by describing the TDABC implementation. Then, we discuss and compare the workflow of 10 library processes covering the four principal library functions: acquisition, cataloging, circulation and document delivery. Next, based on the benchmarking exercise, we report and discuss potential processes and performance improvements that can be realized from using library time and costs information, in particular concerning the two libraries analyzed. We conclude this article by discussing the advantages of using TDABC as a tool to enhance process benchmarking in libraries. [ABSTRACT FROM AUTHOR]

[Display omitted] • Integration of AR/VR with engineering simulations for engineering education. • A facile and agile development strategy was proposed for non-experts. • Importance of interdisciplinary skills and collaboration in technical development. • Enhancing operability of learning environments with AR and engineering simulations. • Discussions on sustainability of long-lasting digital tools. Engineering simulations have opened several gates for today's chemical engineers. They are powerful tools to provide technical content as physics-based numerical solvers. Augmented reality (AR) and virtual reality (VR), on the other hand, are already underway to digitize environments in many fields. The combination of AR/VR environments and simulations in engineering education has been attracting widespread interest. Literature has demonstrated a massive amount of digital educational environments in several contexts as being complementary to conventional educational methods. Nevertheless, hosting technical content produced by engineering simulations with educational AR/VR is still challenging and requires expertise from multiple disciplines throughout the technical development. Present work provides a facile and agile methodology for low-cost hardware but content-wise rich AR software development. Inspired by the Covid-19 pandemic, a case study is developed to teach chemical-engineering concepts using a liquid-soap synthesis process. Accordingly, we assess and conclude the digital development process to guide inexperienced developers for the digitalization of teaching content. The present contribution serves as an example of the power of integrating AR/VR with traditional engineering simulations for educational purposes. The digital tool developed in this work is shared in the online version. [ABSTRACT FROM AUTHOR]

Software systems evolve continuously and must be developed quickly to fit user requirements and new advances in technology. This has led the software engineering to propose several methods and approaches to overcome the development and maintenance of these software systems. In this regard, Agile Methodologies and Model-Driven Engineering (MDE) are two main approaches that have emerged in recent years and suggest a solution to some of the issues associated with Software systems developments. MDE focuses on software reuse through models and on generative approaches based on separation of concerns whereas Agile Methods promote the use of simpler models and best practices for programming to achieve quick feedback from clients within a development process. However, these two approaches have evolved separately and there are only a few works related to their combination. This paper presents a customized V development life cycle based on models which combines the two MDE variants: The MDA approach in the V left branch with the MBT approach to generate tests of the V right branch. In addition, we integrate this customized V life cycle in the agile Scrum methodology to facilitate the management of each Scrum sprint. [ABSTRACT FROM AUTHOR]

Software development projects are becoming increasingly important to achieve successful results in the global digitalization trend that has spread throughout the world at the time of the pandemic. The main software development methodologies (SDM) are waterfall and agile. This study investigates the achievement of success methodology through the evaluation of software development projects, personnel under the scope of SDM's. Software projects require strong human resources to be successful in terms of on-time completion and success. For software projects that include the human component, it is important to make project team SDM assignments more meaningful and accurate. The power of linguistic variables and the use of expert ratings in such assignments have been the focus of this paper. The reason for the strong representativeness is the conversion of linguistic variables to mathematical fuzzification, Interval Valued Neutrosophic Z-Numbers (NZN) are used to determine which SDM should be used first in projects. The results of the NZN are then incorporated into the NZN-Area model. This replaces the distance measurement equation used to rank alternatives with an area calculation. The NZN-Area Model is the new proposed form of the model. The NZN-Area model produced 22% better results in terms of defect numbers and 25% better results in terms of defect resolution time compared to the NZN method. Additionally, a sensitivity analysis was performed by comparing the NZN method and NZN-Area method with real data. The proposed NZN-Area method was found to be more consistent under variable conditions. [ABSTRACT FROM AUTHOR]

Pharmaceutical continuous manufacturing provides the appropriate tools (e.g. the understanding of process dynamics and appropriate and adaptable control strategy) in order to deal with Quality-by-Design expectations and even to the future smart manufacturing described by Quality-by-Control. Those tools form part of the given framework of the regulatory agencies led by an effective quality risk management. Soft sensors and control algorithms such as model predictive control are stepping stones for more agile processes and increased robustness by keeping the quality attributes of the final drug product in their acceptable ranges and by mitigating undesired events. The implementation of a model predictive control (MPC) system on a pharmaceutical continuous manufacturing plant for the wet granulation process is described. The control objectives and strategy are presented as well as the selected variables, the process dynamics identification, the MPC performance and its specific tuning where a commercial software has been used, setting the framework of this study. MPCs have been applied successfully on two pharmaceutical drug products (Diclofenac and Paracetamol): an accurate control of the API content and of the LOD was achieved in order to produce a constant quality of tablets on both drug products. In addition, some of the process parameters have been identified as mandatory to be step tested for each change of drug product, leading to a simplified MPC implementation. [Display omitted] • Advanced automation and process control applied on a continuous wet granulation line with model predictive controller (MPC). • Process Analytical Technology (e.g. API content by NIR spectroscopy) as key enabler of advanced process automation. • High level of process understanding and MPC calibration via process step tests. • Real time regulation of water content of the granules and API content of the blends, granules and tablets. • Implementation of the developed MPC on a real process plant used for GMP clinical supply. [ABSTRACT FROM AUTHOR]

In order to support fast development cycles and deploying software components in productive environments, there are three crucial trends in data science. These are agile process models, development of many technologies and increasing usage of cloud platforms. Therefore, effective architectures are needed to support this trend in data science context. This paper explores and evaluates first approaches for, why and how microservice architecture style can support fast development cycles for data science workflows. Microservices are becoming a popular architectural style for designing modern applications due to several advantages like scalability, reliability and maintainability. First, this paper points out the research gap on why microservices could be a suitable way to design data science workflows. Second, it defines relevant research questions for future research that addresses challenges of the microservice architectural style in the data science context. An essential prerequisite for this architecture style is to identify the "right context" of a microservice for data science workflows. [ABSTRACT FROM AUTHOR]

Information systems development (ISD) project control has been traditionally seen as a tool against agency concerns, i.e. to align the interests of project participants with the organization's interests. However, this perspective barely fits into the agile worldview. Agile methods, that became popular choices for ISD projects, shifted away from the commander role of project leaders, and propagate the importance of collaboration and facilitation. Therefore, it became ambiguous why project managers of agile ISD projects engage in control activities. This qualitative study employs the "Gioia method" to investigate managerial reasons for these exercises and draws attention to the range of problems and needs these procedures can address. The analysis found that control mechanisms are enacted not only due to agency concerns, but also to enhance cooperation and communication, to signal to senior management and to become isomorphic with other projects. [ABSTRACT FROM AUTHOR]

The matrix assisted laser desorption/ionization and time-of-flight mass spectrometry (MALDI-TOF MS) technology has revolutionized the field of microbiology by facilitating precise and rapid species identification. Recently, machine learning techniques have been leveraged to maximally exploit the information contained in MALDI-TOF MS, with the ultimate goal to refine species identification and streamline antimicrobial resistance determination. The aim was to systematically review and evaluate studies employing machine learning for the analysis of MALDI-TOF mass spectra. Using PubMed/Medline, Scopus and Web of Science, we searched the existing literature for machine learning-supported applications of MALDI-TOF mass spectra for microbial species and antimicrobial susceptibility identification. Original research studies using machine learning to exploit MALDI-TOF mass spectra for microbial specie and antimicrobial susceptibility identification were included. Studies focusing on single proteins and peptides, case studies and review articles were excluded. A systematic review according to the PRISMA guidelines was performed and a quality assessment of the machine learning models conducted. From the 36 studies that met our inclusion criteria, 27 employed machine learning for species identification and nine for antimicrobial susceptibility testing. Support Vector Machines, Genetic Algorithms, Artificial Neural Networks and Quick Classifiers were the most frequently used machine learning algorithms. The quality of the studies ranged between poor and very good. The majority of the studies reported how to interpret the predictors (88.89%) and suggested possible clinical applications of the developed algorithm (100%), but only four studies (11.11%) validated machine learning algorithms on external datasets. A growing number of studies utilize machine learning to optimize the analysis of MALDI-TOF mass spectra. This review, however, demonstrates that there are certain shortcomings of current machine learning-supported approaches that have to be addressed to make them widely available and incorporated them in the clinical routine. [ABSTRACT FROM AUTHOR]

The growing trend in agile approach to software development today shows a tendency towards combining practices from different approaches and methodologies. This trend can be explained by the difficulties in managing agile processes. A potential solution to these challenges could be a multi-agent model of software development process of supporting management in the cycle of continuous product quality improvement. The AGent-Object MOdel (AGOMO) was constructed to investigate this idea and an experiment was carried out in which a typical medium-sized project developed using hybrid methodology was reproduced. This article describes the first phase of research, in which replicative adequacy of this model was demonstrated, thus opening avenues for further research related to project management support. [ABSTRACT FROM AUTHOR]

With the launch of the Education Informatization 2.0 action plan by the Ministry of Education, a large number of college information systems have been born in China. Most of these systems are single page web applications (SPA) based on traditional MVC structures. Due to the complex logic and high coupling between educational businesses, developers need to write a lot of code. The education information system has many businesses and high coupling between businesses that the system often face problems such as bloated frontend businesses, iterative system updates, and difficult incremental function developments. Combined with the idea of service-oriented architecture, this paper proposes a micro frontends solution and applies it to the new generation of graduate information platform of East China Normal University, which has better agile development capabilities. From the aspects of service separation, efficient development, and incremental upgrade, this paper verifies that the architecture can well adapt to the needs of future educational management information system. The design of the micro frontends provides a new idea for the development of a new generation of education information system. [ABSTRACT FROM AUTHOR]

Requirements Engineering (RE) is a critical process in the development of any software. It requires intensive communication among many stakeholders in order to identify software requirements accurately. As RE activities require intensive communication, cultural influences might need to be considered to improve these activities and to avoid issues that might affect the software project outcomes. In our work, we explored the influence of national culture on RE activities. We conducted an empirical study in two countries that have different cultural profiles: Saudi Arabia and Australia. To explore this issue, we conducted 41 face-to-face interviews. To consolidate the collected data, we conducted also 30 follow-up interviews with the same practitioners. The aim of our study was to empirically identify the set of cultural influences affecting RE activities, and analyse their implication on RE activities. This paper reports the results of the analysis of participants' responses with respect to their industrial profiles, in terms of three factors: the type of software methodology applied by the participants (Traditional vs. Agile), the size of their company/organisation (small, medium, and large), and its type (private vs. government). The results showed a number of significant differences in the cultural influences that determine the type of software development methodology, i.e., Traditional or Agile, being applied within the corresponding company. In this paper, we discuss the identified differences and the factors that determine them. This in-depth analysis provides a broader examination of the influence of culture on RE activities, which might assist RE practitioners to select the most suitable RE practices/methodology to overcome any negative effects of these influences. [ABSTRACT FROM AUTHOR]

Field-Programmable Gate Arrays (FPGAs) in heterogeneous computing have been attracting more and more attention. Development of acceleration systems based on FPGAs involves the cooperation of both hardware and software developers. However, although most hardware acceleration systems are motivated by software developers, software developers are difficult to participant in the system building because of the steep learning curve of the hardware concept and design tools. Moreover, due to the complexity of hardware-software integration in traditional ways, end-to-end performance is hard to evaluate before trivial engineering effort. To address these concerns, we propose an open-source Domain-Specific Language (DSL) based framework called VeriScala 1 1 https://www.github.com/VeriScala/VeriScala. to support hardware defining in a high-level language, programmatical testing, and rapid acceleration system deploying. By adopting DSL embedded in Scala language, we introduce modern software development concepts into hardware design and provide a familiar environment to software developers. And by building a stack of middle layers from hardware to software, we provide reduced hardware abstraction and communication interface to facilitate both software and hardware development and system deployment. Through the evaluation of some basic components and real-world demos, we show that VeriScala provides a practical approach to rapid prototyping of hardware acceleration systems. [ABSTRACT FROM AUTHOR]

• Potential of a drone-borne lidar system to assess the outcomes of a mixed-species restoration plantation experiment. • Brazil's Atlantic Forest, with 20 native tree species, combining two levels of planting density and two management levels. • We elaborate an AGB equation to use the drone-borne lidar for monitoring other restoration plantations in the region. • Leaf area density profile (lidar-derived) also differed among silvicultural treatments. • Drone-borne lidar system can effectively monitor forest restoration plantations. We are in an unprecedented moment for promoting forest restoration globally, with international and regional pledges to restore at least 350 million hectares by 2030. To achieve these ambitious goals, it is necessary to go beyond traditional plot-scale assessments and develop cost-effective technologies that can monitor the structure and function of restored forests at much broader scales. Lidar remote sensing in unmanned aerial vehicle (UAV) platforms can be an agile and autonomous method for monitoring forest restoration projects, especially under conditions when information updates are frequently needed in relatively small areas or, when using an airplane-borne lidar system may be not financially viable. Here, we explored the potential of an UAV-borne lidar system to assess the outcomes of a mixed-species restoration plantation experiment, designed to maximize aboveground biomass (AGB) accumulation. The experiment was established in Brazil's Atlantic Forest, with 20 native tree species, by combining two levels of planting density and two management levels, totaling four treatment combinations and one control (plots left over for natural regeneration). We analyzed three structural variables from lidar data (canopy height, gap fraction and leaf area index) and one from field inventory data (AGB). Structural differences between the treatments and the control plots were reliably distinguished by the UAV-borne lidar system. AGB was strongly correlated with canopy height, allowing us to elaborate a predictive equation to use the UAV-borne lidar system for monitoring structural features in other restoration plantations in the region. UAV-borne lidar systems showed enormous potential for monitoring relatively broad-scale (thousands of hectares) forest restoration projects, providing an important tool to aid decision making and accountability in forest landscape restoration. [ABSTRACT FROM AUTHOR]

Abstract This paper presents a general and systematic approach to automating a variety of agile maneuvers with a small fixed-wing unmanned aerial vehicle. The methodology begins by numerically solving optimal control problems off-line to generate a small set of reference trajectories and feedforward control inputs for maneuvers. A dynamic time warping-based interpolation process parametrizes these solutions, adding robustness to the maneuver, whilst allowing the on-board library of state and control time histories to remain compact. To handle errors, inaccuracies, noise, and disturbances that are not accounted for by feedforward control, feedback control laws stabilize about the reference trajectories. The work focuses mainly on one agile maneuver: an aggressive turn-around, in which the aircraft undergoes a rapid 180 degree heading reversal, beginning and ending in straight and level flight. To establish the generality of the methodology, it is also applied to transition maneuvers between straight and level flight, and a nose-up hover. The proposed automation scheme is computationally light during flight, consisting of a simple feedforward/feedback controller coupled to a compact library of maneuvers that are optimized over the full flight envelope. The methodology is validated in simulations and flight tests. The automation scheme is implemented successfully on a small fixed-wing unmanned aerial vehicle. Highlights • A general approach to automating agile maneuvers with a fixed-wing UAV is proposed. • Dynamically feasible maneuvers are optimized over the aircraft's flight envelope. • Trajectories are parametrized using a novel application of dynamic time warping. • Parametrization is computationally light. • Agile maneuvers are demonstrated in flight using only on-board sensing and control. [ABSTRACT FROM AUTHOR]

During agile requirements engineering, developers need to assimilate and transform the original requirements information into system functions in the form of user stories. Obviously, this is a challenging cognition-based process, in which developers' cognition plays a key role. However, prior research has not explored developers' cognition during the process. The purpose of this study is to investigate and understand developers' cognitive representation styles and interaction patterns in agile requirements. A classification of developers' cognitive representation styles and interaction patterns was first proposed based on literature review. Then, an empirical research was conducted in a capstone software engineering course. Students were playing the role of developers and engaging in agile software development during this course, so their conversation about splitting and defining user stories was recorded and analyzed to examine developers' cognition via a content analysis method. The results show that, even when facing requirements analysis, developers tend to exhibit a technology-oriented cognitive representation style. Additionally, developers have more cognitive difficulty in determining activity and granularity than the role and business value of a user story. Developers also exhibit a preference for cognitive interaction pattern; assertion, comment, and two question-patterns are the major four patterns in our paper rather than information sharing, which was considered a major pattern in previous research. This paper contributes to our understanding of developers' cognition, further predicting and guiding developers' behaviors toward achieving good quality requirements analysis. • We propose developers' cognitive representation styles and interaction patterns in agile requirements. • We examine developers' cognition by their utterances of splitting user stories. • Developers prefer to technology-oriented cognitive representation style even in requirements stage. • Developers have more cognitive difficulty in user stories activity and granularity. • Developers have a preference for four major cognitive interaction patterns. [ABSTRACT FROM AUTHOR]

The Internet of Things or the Internet of Everything is a new and a potentially disruptive technology paradigm. It describes several technologies such as RFID, short range wireless communications, and research disciplines that can connect physical objects from the real world to the internet. To implement IoT solutions, they exist software development approaches like Scrum and Kanban, also, others are adapted viz; Large-Scale Scrum, Scaled Agile Framework and so on., and many methods have proposed such as Ignite | IoT Methodology and IoT Methodology. Most of them have taken agile thinking as a strategy. However, they are monolithic, which are not easy to adopt. Then, a new method is needed to handle the real nature of IoT, particularity, to handle distributed, mobile and human out-of-the-loop concerns, and that can grow as new product evolve and new problems emerge. This research studies and analyses some of the existing IOT methodologies. Particularly, it focuses on these methodologies Scrum, Kanban, Scaled Agile Framework, Ignite | IoT Methodology and IoT Methodology. Our study evaluates their capabilities and compares their main characteristics and behaviors in terms of various methodologies of IoT. The comparison presented in this paper would benefit in selecting an appropriate methodology for the IoT projects. In addition, it identifies their advantages and limits in order to suggest a new approved IoT methodology. [ABSTRACT FROM AUTHOR]

Flexible iterative development life cycle, adaptive nature and fast delivery has given Agile an upper edge as compared to all other software development frameworks. In the current industry scenario agile methods are gaining popularity, owing to its people centric approach, hence organizations are adopting agile development methodologies at a large scale. Agile projects work in self-organizing small collaborative teams. Team size varies according to the project requirement however, agile development focus on smaller team size. Supervised machine learning is applied in this study to provide optimum prediction model. All the available regression models in Matlab R2019b are used to predict number of team members required for an agile project. Iterations from five different open source projects are considered for this study. The results after training all the variants of each regression model, namely Linear Regression models, Support Vector Machine models, Tree models, Ensemble models and Gaussian Process Regression models are compared using Root Mean Square Error (RMSE) score and R-squared values. On the basis of evaluative and comprehensive analysis, the most significant model to predict manpower requirement for an agile project has been chosen. [ABSTRACT FROM AUTHOR]

Model-Driven Architecture (MDA) is a framework for software development processes that allows an automatic transformation from a business process model to the code model. In MDA there are two transformation kinds: Transformation from the Computation independent model (CIM) to platform-independent model (PIM), and transformation from PIM to platform-specific model (PSM). In this paper, we based on CIM to PIM transformation. This transformation is done by developing a platform that generates a class diagram, presented in XMI file, from specifications that are presented in user stories, which are written in natural language (English). We used a natural language processing (NLP) tool named "Stanford CoreNLP" for extracting of the object-oriented design elements. Applying our approach to several case studies has given good results. [ABSTRACT FROM AUTHOR]

Novel machine elements often constitute the basis for innovative capital goods. While they are typically qualified in model experiments under predefined conditions, the real operating conditions in capital goods may differ considerably. In order to enable an agile development of innovative capital goods, an upgraded setup of the model experiments is proposed: Uncertainties in the installation conditions of the machine elements are represented by a new type of phase change actuators (PCA). The approach is demonstrated by a novel type of backlash-free high-load bearing. [ABSTRACT FROM AUTHOR]

Data visualization and decision-making are critical aspects of infrastructure inspections. Numerous research efforts have been made to develop practical remote inspection systems that overcome the limitations of conventional inspection methods. Although the integration of Immersive Virtual Reality (IVR) into remote inspection is currently being addressed, most IVR frameworks have focused on data accuracy and there has been a lack of research on developing human-centered IVR frameworks through usability evaluation. This paper addresses this limitation by proposing a new IVR development framework utilizing agile User Experience (UX) design techniques, which can be implemented in remote infrastructure inspections for data visualization and decision-making. Usability tests were conducted through a case study with 22 participants, using an IVR prototype developed in accordance with this framework. Following its implementation, the framework demonstrated its ability to identify damage via remote bridge inspection and achieved acceptable usability. Future research can build on the framework for semantic enrichment tasks. [ABSTRACT FROM AUTHOR]