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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]

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]

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]

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]

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]

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]

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]

• The hierarchical expression of conflict is applied to accurately identify conflict. • Agility attribute is involved in the calculation of the conflict solution tendency. • Ant colony optimization is applied to identify the multi-conflicts solution path. • The scientific and effectiveness of the method are verified by a case. An agile solution strategy is proposed to solve the multi-conflicts problem and low efficiency in the innovation process of product development. A hierarchical representation of the conflicts is formed using a layered conflict resolution diagram based on the current reality tree (CRT) for the problem identification. The conflict solution tendency is searched using a conflict solution tendency network (CSTN). An ant colony optimization algorithm is introduced to analyze the CSTN model for the global optimal solution. The multi-conflict agile solution path is obtained based on the mutually exclusive relationship between conflicts. The TRIZ tool is used to search the agile solution of the multi-conflicts problem. A case study of the innovative design of the multi-functional nursing bed verifies the proposed method. [ABSTRACT FROM AUTHOR]

A Product Configuration System (PCS) is a software system that facilitates the sales and production processes of defined customizable products. PCS are specific software developments in the sense that they are knowledge-intensive so that they require models to formalize the complex knowledge inherent to product configurations also leading to dependencies between software functionalities. Scrum is a widely used agile method, but its training has been the subject of little research. Model-driven development implicitly impacts the way development is conducted especially when adopting an agile method as Scrum. This paper, as exploratory research, evaluates Scrum training for PCS projects through a qualitative case study. The goal is to identify the elements that should be focused on within Scrum background training. This research first studies and assesses the training experiences at the case company. Then, it reports on respondents' opinions about the strengths and challenges of applying Scrum in the mentioned context. The latter is based on multiple data sources: documentation, interviews, participant observations, and workshops. Issues in applicability lead to enhanced training support for learning how to (i) combine Scrum with the model-driven approach inherently required within PCS development, (ii) manage time and effort estimation on the basis of accurate artifacts and (iii) access key employees possessing domain or specific technical knowledge indispensable for pursuing the development. [ABSTRACT FROM AUTHOR]

• Proposing the defuzzification relations of equality constraints with Nec measure. • Offering a new fuzzy network slack based model on the basis of necessity measure. • The proposed relations combine the Nec equality constraints and network SBM. • Using agile supply chain theory to validate the proposed approach. With respect to the necessity (Nec) measure, suggesting by Dubois and Prade, there is a dearth of any procedure or formula to deal with equality chance constraints. Therefore, this study attempted to introduce (Nec) and possibility (Pos) equality constraints in a real-case network non-oriented slack based model problem via agile supply chain (ASC) theory. The efficiency of different levels of agility of a dairy supply chain (SC), using a new fuzzy network Date Envelopment Analysis slack based model (FNDEA-SBM) on the basis of (Pos)/(Nec) measures, has been measured in this paper. The measurement took place in a multi-stage process. Since the agility features were rather qualitative in nature, the measures were likely to be delineated subjectively using linguistic expressions and fuzzy data. The application of (Pos)/(Nec) measures and different confidence levels made decision makers capable of getting information of all efficiency and sub efficiency scores through optimistic-pessimistic lenses and taking appropriate decisions on the basis of different optimistic-pessimistic points of view and confidence levels. The validity of the suggested approach was demonstrated through a real-life case study related to Iranian top twenty dairy firms. The results indicated the success of the suggested approach and computations were straightforward. [ABSTRACT FROM AUTHOR]

• An ultrasensitive double-layers CF-based piezoresistive sensor has been proposed. • The CF@A-CNTs/APP exhibits outstanding flame retardancy. • The CF@A-CNTs/APP triggers the fire-alarming system in just 2 s. • The CF@A-CNTs/APP could monitor the behavioral actions of firefighters. With the high frequency of fires, multifunctional cotton fabrics with high flame retardancy and agile fire-warning ability are urgently needed. Herein, a newly cotton fabric-based piezoresistive sensor (denoted as CF@A-CNTs/APP) with intelligent fire protection and high sensitivity is proposed, which is composed of amino-functionalized carbon nanotubes (A-CNTs) and ammonium polyphosphate (APP) wrapped on cotton fabric (CF) using layer-by-layer self-assembly. The CF@A-CNTs/APP demonstrated excellent flame retardancy due to the synergistic formation of a dense flame retardant layer on the CF surface by A-CNTs and APP. The char residue of CF@A-CNTs/APP reached 44.0 wt% at 700 °C (nitrogen atmosphere), and the limiting oxygen index of CF@A-CNTs/APP was as high as 37.6%. More importantly, the CF@A-CNTs/APP triggered the fire-alarming system within 2 s. Benefiting from the pattern microstructures of double-layers CF and the high conductivity of A-CNTs, the CF@A-CNTs/APP has exhibited a high sensitivity of 3.8 kPa−1 (0 kPa-16.62 kPa). In view of outstanding flame retardancy, high sensitivity and robustness, the CF@A-CNTs/APPs could be integrated into firefighting suits or electronic skins to monitor the behavioral actions and equipment operating conditions. This work provides a novel strategy for a new generation of CF-based sensors with fire resistance and high sensitivity, and offers new insights into the preparation and application of smart fire-resistant materials in the field of the internet of things. [ABSTRACT FROM AUTHOR]

Researchers have proposed that generating conceptual models automatically from user stories might be useful for agile software development. It is, however, unclear from the state-of-the-art what a consistent and complementary set of models to generate is, how these models can be generated such that relationships and dependencies in a set of related user stories are unveiled, and why these models are useful in agile software development projects. In this paper, we address these questions through a Design Science research study. First, we define four stylized versions of Unified Modeling Language (UML) diagrams (i.e., use case diagram, class diagram, activity diagram, state machine diagram) that will be the target of the model generation. Although these stylized UML diagrams have a reduced abstract syntax, they offer different perspectives on the software system in focus with potential usefulness for requirements and software engineering. Second, we develop an automated model generation approach based on different design artifacts including a Natural Language Processing (NLP) tool that implements our approach. Key to our solution is the use of the Behavior-Driven Development (BDD) scenario template to document user stories. Using an example set of BDD scenarios as source of the model generation, we demonstrate the feasibility of our approach via the NLP tool that implements our approach. Third, we conduct an empirical study with experts in agile software development involving the researcher-guided interactive use of our tool to explore the use of the generated models. This study shows the perceived usefulness of the models that our tool can generate and identifies different uses and benefits of the models for requirements analysis, system design, software implementation, and testing in projects that employ agile methods. [ABSTRACT FROM AUTHOR]

Flexible approaches, such as agile, hybrid, and extreme project management methods in the software project environment, are increasingly being used in nonsoftware environments. Nevertheless, only a few methods and no topological, time-related or resource-related indicators or project databases can address projects of a flexible nature. This study proposes a unified matrix-based model (UMP) to consider single-mode, multimode, individual and multiple projects. Based on the UMP, a CMPD is specified to collect 11 existing project databases. In addition, a flexible structure generator (FSG) is proposed to generate flexible tasks and dependencies for analyzing the effects of flexibility on topology and project demand-related indicators. By correlation graphs, the relations between structure-related and demand-related indicators are analyzed. The comparison of the simulated and real-life databases shows that the interpretation range of the indicators is different; however, considering the flexibility, this problem can be resolved. • Matrix-based models are proposed to unify existing project datasets. • Seven existing single and 4 multiple project databases are combined. • A flexible structure generator is proposed to specify agile and hybrid projects. • New, flexibility-dependent indicators are specified. • The effects of structural flexibility for project topology and demands are examined. [ABSTRACT FROM AUTHOR]

With the rise of agile development notion, software test automation is widely adopted by software organizations to reduce the testing time and costs during the software development process. However, it is essential to carefully implement the test automation to ensure the project's success. In the literature, several studies have indicated that decision-making is crucial for the adoption of test automation. For deciding about test automation adoption, several factors, including the skill level of testers, test tool, and development process, need to be considered since automated testing performance heavily depends on the mentioned factors and their interactions. Thus, it is of vital importance to ascertain the factors that influence test automation in the context of Agile-based Software Development (ASD). To accomplish this, we perform a systematic literature review and a questionnaire-based survey. In total, 21 factors are identified that significantly impact test automation in the ASD context. Additionally, the identified factors are categorized into five main classes. Following the identified factors' assessment, this work proposes a conceptual model that supports agile practitioners in deciding about the adoption of test automation in the context of ASD. Finally, this work validates the proposed model to assess its practical applicability. The validation of the proposed model is accomplished by using an expert-based validation approach. The attained results show that the proposed model supports agile practitioners in estimating the possibility of success when implementing test automation in the ASD context. [ABSTRACT FROM AUTHOR]

• 'Agile cost estimation in scrum development. • Cost estimation methods in scrum and limitations. • Suggested a cost estimation model. Agile has been invented to improve and overcome the deficiencies of efficient software development. At present, the agile model is used in software development vastly due to its support to both developers and clients resourcefully. Agile methodology increases the interaction between the developer and client to make the software product defect-free. The agile model is getting to be a well-known life cycle model because of its particular features and most owing is to allow changes at any level of the project from the product owner. However, on other hand, this novel feature is a disadvantage of the agile model due to frequent change requests from the client has increased the cost and time. To overcome cost and time estimation issues different cost estimation techniques are being used in agile development but no one is pertinent for accurate estimation. Therefore, this study has proposed a cost estimation technique. The proposed estimation technique is predictions-based and has different categorizations of projects based on user stories complexities and the developer's expertise. We have applied the suggested technique to ongoing projects to find the results and effectiveness. We have used two projects with different sizes and user stories. Both projects have different modules and developers with different expertise. We have used the proposed estimation technique on projects and done a survey session with the teams. This survey session's main objective is to reveal the statistical findings of the proposed solution. We have designed the 12 hypotheses for statistical analysis. [ABSTRACT FROM AUTHOR]

• In this article, all 36 dimensional success factors are considered to estimate the required effort and cost to develop any Agile-Scrum based software project. • The work presented in the article computes the required time, effort and cost of projects mentioned in a data set on the basis of different inputs to the system. • For computation purposes, we divided the projects into three categories as Low, Medium and High level project for the unbiased estimation. • The datasets considered for computation purposes, also validated by experienced professionals from software industries. • It is found that the results obtained from the extended algorithm are cost-effective and less effort-oriented to a great extent as a compared to the existing algorithm. Effort and cost estimation is essential processes during software development. For agile methodologies as well, several approaches have been proposed so far to estimate the required effort and cost to develop any software system. Genuine assessment of software products is not an easy task as it requires the constant attention of the product owner. For effective estimation, we need to consider various success factors associated with all possible dimensions. In this article, we look at Scrum-based Agile projects that are developed over multiple sprints. We proposed an extension to the existing algorithm based on a total of 36 success factors; Which estimates the development effort and cost required to complete the project. For assessment and calculation, we have taken a data-set of a total of 30 projects categorized into three groups as low, medium, and high-level projects. This dataset is further validated by experienced professionals. We also compared our results with the existing approach and found that our results are cost-effective and require less effort even after considering more success factors. [ABSTRACT FROM AUTHOR]

• Software scrum development. • Software cost estimation is still a complex task. • Effort estimation techniques in scrum still has some flaws that need to be resolve. Scrum methodology is the most useful and adopted part of the agile methodology. Many organizations are adopting Scrum for the last decade to meet their software requirements. It is facilitating the software industries to develop software applications according to the user's requirements. Scrum helps to fulfill the customer's requirements by interacting with the client and developer. Instead of this, scrum still has some challenges that influence the cost of a scrum. These challenging factors include coordination, team size, change request, complexity, and issues in daily meeting sessions. Due to these factors mostly the cost and time exceed scrum-based projects. To overcome these issues from the accurate and precise estimation in the scrum project we have developed a web-based software system to estimate the effort of a user story as well as the budget. There are some sophisticated cost estimation techniques for scrum-based projects. But all estimation techniques have some limitations that make them less useful for estimation and overcoming the effects of these factors in the scrum. We have surveyed the multiple software industries to get the static findings of the survey. We have designed the 30 hypotheses that main aim to reveal how much the proposed estimation technique is valuable for the accuracy of estimation. For the inclusion of participants, we have set the age criteria to get the opinions of the most experienced programmers. The main aim of this paper is to provide knowledge about key challenging factors in Scrum that cause exceed in cost and time, and also briefly explain why existing scrum cost estimation techniques are not pertinent for projects. In the last, we propose a framework as a solution to manage these factors and control cost and time from exceeding. We also adopted this estimation technique in different case studies in software industries for findings and results. The technique calculates the efforts autonomously. We design a software-based estimation technique that collects inputs from team/s and estimates the cost and time of a project. In outcomes, we found that our cost estimation approach reduced the issues from scrum software project development and make it more proficient for estimation of projects. Our proposed estimation technique introduced the new concept of estimation that facilitate the client, software industry, and developers as well to meet the requirements of the client, fulfill the changes during the sprints/s development and deliver the project within the time and cost. [ABSTRACT FROM AUTHOR]

This paper presents an all-aspect attack guidance law for agile missiles based on deep reinforcement learning (DRL), which can effectively cope with the aerodynamic uncertainty and strong nonlinearity in the high angle-of-attack (AOA) flight phase. First, to make the training environment more authentic, the full flight envelope of the missile is modeled and highly accurate aerodynamic data is obtained through Computational Fluid Dynamics (CFD) technique. Subsequently, the DRL algorithm is applied to generate an AOA guidance law for the agile turn phase. A hierarchical scheme that consists of a meta-controller for real-time decision making according to combat scenario and a sub-controller for generating guidance command is designed, which enables the guidance law to cover the whole process of the engagement and ensures the convergence of the training in the agile turn phase. Considering the current limitations of missile maneuverability, two agile turn guidance laws are developed to accommodate both limited and unlimited AOA scenarios. Moreover, the proposed guidance law has excellent generalization capability and ensures the implementation of static training and dynamic execution, which means that the missile can adapt to the realistic combat scenarios that have not been encountered during the training. Simulation results indicate that the DRL-based guidance law is nearly optimal and robust to disturbances. In addition, the proposed guidance law enables the missile to track time-varying desired turn angles to lock the maneuvering target in the rear hemisphere during the agile turn phase, providing advantageous initial conditions for the terminal guidance. Furthermore, the computational efficiency is high enough to satisfy the requirement on onboard application. [ABSTRACT FROM AUTHOR]

• The results of this paper help to create a roadmap for agile organizations transitioning from CMMI-DEV V1.3 to CMMI-DEV V2.0 or adopting CMMI-DEV V2.0 from scratch. • Content analysis was applied to identify 31 agile artifacts to support the alignment with CMMI-DEV V2.0, passing from 0% to 41% coverage at practice level at maturity levels 2 and 3. • Special effort is required for contextualizing the governance (GOV), implementation infrastructure (II), and organizational training (OT) practices areas to Agile, as it has not been possible to enrich them with agile artifacts using the content analysis performed in this paper. Agile-CMMI alignment is becoming increasingly popular. The literature abounds with research and experience reports associating these two approaches. A new model, CMMI V2.0, and in particular CMMI-DEV V2.0, was published recently. This latest model only provides partial alignment guidelines with Agile, and the experiences reported in previous literature (referred to CMMI-DEV V1.3) are not directly applicable to CMMI-DEV V2.0. Therefore, agile organizations that want to align with CMMI-DEV 2.0, either from scratch or as a transition from CMMI-DEV 1.3, face a major challenge. In this paper, we apply the content analysis method to several CMMI Institute sources to bring to the light the Agile artifacts related to CMMI-DEV V2.0 Practices. As a result, we identify 31 Agile artifacts to address alignment with CMMI-DEV V2.0, passing from 0% to 41% coverage for Practices at Maturity Levels 2 and 3. These Agile artifacts can help to materialize the business value of CMMI-DEV V2.0 Practices and provide a starting point for agile organizations to define their roadmap. Consequently, the contribution of this paper is to present a detailed practice-level mapping based on which to design an Agile-CMMI V2.0 roadmap. [ABSTRACT FROM AUTHOR]

• Demand response is one of the core applications of the smart grid. • A process assessment model provides for implementation, evaluation and improvement of customer's demand response processes. • Demand response process assessment model (DRPAM) was developed using ISO/IEC 33,004 standard, the second maturity level of "Customer" domain in SGMM and expert opinions gathered by Delphi study technique. • DRPAM was evaluated with an exploratory case study. • DRPAM is the first detailed model intended to reduce time and energy loss for demand response implementations. • DRPAM provides roadmap for the identification of capabilities of demand response processes and for their improvement. Demand response is a new study area and many countries intend to create roadmaps to activate their implementations. Smart Grid Maturity Model (SGMM) defines the characteristics and targets related to customer, but it does not adequately elaborate and provide a guiding perspective for demand response. In addressing the gap mentioned, this article presents a Demand Response Process Assessment Model (DRPAM) as a fundamental proposal towards evaluating, implementing, and improving capabilities of customers' demand response processes. The standard of ISO/IEC 33004 and the second maturity level of "Customer" domain in SGMM were referenced to develop the DRPAM. Literature studies and technical reports were examined to determine the key processes of the model, while Waterfall and Agile software development models and Action Workflow Loop were used to identify process practices. The set of key processes was defined, updated and verified using expert opinions gathered by Delphi study technique applied in five rounds. It was then used for an exploratory case study assessment in an organization that conducts research and development activities in this field. Eight processes were identified with purpose, outcomes, base practices, work products (as inputs and outputs), and responsible roles of implementation. DRPAM is the first detailed model intended to reduce time and energy loss for demand response pilot studies of customers. The results of the case study assessment indicated that DRPAM was found satisfactory in identifying capabilities of demand response processes and of providing a roadmap for their improvement. As future work, the model will be validated by conducting multiple case studies. [ABSTRACT FROM AUTHOR]

• In this paper, the Fuzzy TOPSIS method has been applied to identify the most suitable SDLC model for software development in the present scenario. • The work presented in the article computes the ranks and closeness coefficients of all the alternative SDLC models based on different criteria. • Six widely accepted SDLC models were compared with their core features by applying fuzzy logic and decision support systems in the form of the Multi-Criteria Decision Making method. • Five experienced decision-makers from software industries, assigned weights in linguistic terms to each alternative model against criteria. • By applying the Fuzzy TOPSIS method, it is validated that Agile Methodology is an Ideal selection for software development processes. Agile methodologies have been an emerging choice of software professionals for the past decade and a half. However, apart from this, some other SDLC models are also available for selection in front of software developers to develop any software. Usually, project managers select any of these models to develop software through their past experiences. There is no logical basis for this selection to be completely correct, as a result of which there is always a risk of software failure or over budget if an inappropriate model has opted. Keeping this problem of software industries in mind, an ideal SDLC model has been identified mathematically in this article. In this article, we applied the Fuzzy TOPSIS method that validates Agile software development as an ideal choice. We have taken a total of six software development processes that are being applied globally. Feedback from five experienced decision-makers has been taken in the form of linguistic terms and further converted into fuzzy values to perform the computation of the closeness coefficient rank of each experimented alternative software development process. [ABSTRACT FROM AUTHOR]

As many software development teams have started to adopt agile methods, a vast amount of valuable experiences have been reported on in both academic and industrial knowledge bases. This information has been used through various approaches to guide and help practitioners finding suitable practices for their software development projects. Nevertheless, not many of these approaches could gather the available experiences to make them systematically reusable and help practitioners understanding agile practices in depth. To the best of our knowledge, only one ontology has been created to solve this problem; some limitations related to its quality and usability make it nevertheless unqualified to serve the intended purpose. The aim of this paper is to build an expert system (i.e. an evidence-based tool) to ease agile practices adoption by efficiently and effectively providing information on them. Firstly, we improve the concepts and relationships in the aforementioned ontology and theoretically validate it using a large data-set of agile practices adoption experiences collected through a Systematic Literature Review (SLR). Secondly, we develop a supporting tool having a friendly Graphical User Interface (GUI) allowing to use the ontology as a concrete agile practice knowledge provider. Finally, we empirically validate the enhanced ontology and evaluate the supporting tool using a survey with agile experts. Our supporting tool can help practitioners to decide what practice to adopt, how to adopt it, how to solve practical issues, etc. The ontology and the tool materialize our contribution to the field of systematic agile practices adoption. • Successful agile practice adoption requires an in depth understanding of its content. • Existing reports on agile practice adoption experiences are essentially unexploitable. • An ontology is used as an efficient solution to recycle practices adoption experience. • A corpus-based approach and a survey are used for validating the ontology. • An expert system supports practitioners querying knowledge on practice adoption. [ABSTRACT FROM AUTHOR]

This paper discusses a proposed antenna that can simultaneously reconfigure three different antenna properties. Those properties are radiation pattern, Half-Power Beamwidth (HPBW), and multibeam reconfiguration in the azimuth plane. To make all this feasible, a new unconventional Single Pole Six Throw (SP6T) switching network has been designed and analyzed. To introduce the reconfigurability, five switchable open stub lines are inserted into the switching network. It makes it feasible to drive the input power to the desired number of output ports by providing the impedance matching at power splitting points. At last, six curved printed dipole (CPD) antennas have been designed with the switching network. This antenna can be switched in 13 different cases. Among all, it produces six narrow beams, three wide beams, three dual beams, and one triple beam in the azimuth plane with an equal interval of angles. The measured result agrees with the simulation outcomes. The average gain in single wide and single narrow beams is 3.8 dBi and 4.8 dBi, respectively. [ABSTRACT FROM AUTHOR]

• Our approach is agnostic to programming languages, location, or development practices. • Moderate correlation was found between software and process cyclomatic complexity. • Refactoring practices were predicted with an accuracy up to 92.95%. • Using process metrics, software complexity was predicted with an accuracy of 94.36%. • Our method can be used to find non efficient software teams in generic software tasks. Context : Software comprehension and maintenance activities, such as refactoring, are said to be negatively impacted by software complexity. The methods used to measure software product and processes complexity have been thoroughly debated in the literature. However, the discernment about the possible links between these two dimensions, particularly on the benefits of using the process perspective, has a long journey ahead. Objective : To improve the understanding of the liaison of developers' activities and software complexity within a refactoring task, namely by evaluating if process metrics gathered from the IDE, using process mining methods and tools, are suitable to accurately classify different refactoring practices and the resulting software complexity. Method : We mined source code metrics from a software product after a quality improvement task was given in parallel to (117) software developers, organized in (71) teams. Simultaneously, we collected events from their IDE work sessions (320) and used process mining to model their processes and extract the correspondent metrics. Results : Most teams using a plugin for refactoring (JDeodorant) reduced software complexity more effectively and with simpler processes than the ones that performed refactoring using only Eclipse native features. We were able to find moderate correlations (≈ 43%) between software cyclomatic complexity and process cyclomatic complexity. Using only process driven metrics, we computed ≈ 30,000 models aiming to predict the type of refactoring method (automatic or manual) teams had used and the expected level of software cyclomatic complexity reduction after their work sessions. The best models found for the refactoring method and cyclomatic complexity level predictions, had an accuracy of 92.95% and 94.36% , respectively. Conclusions : We have demonstrated the feasibility of an approach that allows building cross-cutting analytical models in software projects, such as the one we used for detecting manual or automatic refactoring practices. Events from the development tools and support activities can be collected, transformed, aggregated, and analyzed with fewer privacy concerns or technical constraints than source code-driven metrics. This makes our approach agnostic to programming languages, geographic location, or development practices, making it suitable for challenging contexts, such as, in modern global software development where many projects adopt agile methodologies, and low/no code platforms. Initial findings are encouraging, and lead us to suggest practitioners may use our method in other development tasks, such as, defect analysis and unit or integration tests. [ABSTRACT FROM AUTHOR]

As an important branch of wearable electronics, flexible pressure sensors have aroused extensive concern owing to their wide range of applications, such as human health monitoring, human–machine interfaces, and artificial intelligence. Therefore, higher requirements are put forward for the dominating performances of sensitivity, pressure measurement range and response time about flexible pressure sensors. Here, we present a flexible piezoresistive pressure sensor (FPPS) based on carbon fibers (CFs)/carbon nanotubes (CNTs)-polydimethylsiloxane (PDMS) composite and interdigital electrode, which displays a wide measurement range of stress and an agile response at loading and unloading forces. Due to the cooperating advantages of the multiscale carbon fillers, the microstructures on the surface of the piezoresistive film and the interdigital electrode, the FPPS reveals a wide measurement range (from 0 kPa to 185.9 kPa) and a high sensitivity (2.02 kPa‐1 at pressures between 36 Pa and 50.2 kPa). Meanwhile, owing to the carefully designed trilateral double-sided adhesive structure, the FPPS exhibits an agile response (response time of 43 ms) and a stable output (test time of 9000 s). Moreover, the FPPS is capable of identifying clapping and trampling motions, demonstrating its effectiveness as a flexible sensing unit. The novel pressure sensor was integrated with additional software and hardware to realize a real-time pressure acquisition and display system, providing a potential use of the device in an intelligent industry application. [Display omitted] • Developed a FPPS based on multiscale CFs/CNTs-PDMS composite and interdigital electrode. • The FPPS reveals a wide pressure measurement range from 0 to 185.9 kPa. • The FPPS reveals an agile response time of 43 ms. • The FPPS is integrated with software and hardware to realize a pressure display system. [ABSTRACT FROM AUTHOR]

• Dextr is a semi-automated data extraction tool that can capture complex data. • Dextr connects extracted entities to support hierarchical data extraction. • Dextr supports development of annotated datasets within a standard review workflow. • Dextr's user verification option assures user-driven semi-automated data extraction. There has been limited development and uptake of machine-learning methods to automate data extraction for literature-based assessments. Although advanced extraction approaches have been applied to some clinical research reviews, existing methods are not well suited for addressing toxicology or environmental health questions due to unique data needs to support reviews in these fields. To develop and evaluate a flexible, web-based tool for semi-automated data extraction that: 1) makes data extraction predictions with user verification, 2) integrates token-level annotations, and 3) connects extracted entities to support hierarchical data extraction. Dextr was developed with Agile software methodology using a two-team approach. The development team outlined proposed features and coded the software. The advisory team guided developers and evaluated Dextr's performance on precision, recall, and extraction time by comparing a manual extraction workflow to a semi-automated extraction workflow using a dataset of 51 environmental health animal studies. The semi-automated workflow did not appear to affect precision rate (96.0% vs. 95.4% manual, p = 0.38), resulted in a small reduction in recall rate (91.8% vs. 97.0% manual, p < 0.01), and substantially reduced the median extraction time (436 s vs. 933 s per study manual, p < 0.01) compared to a manual workflow. Dextr provides similar performance to manual extraction in terms of recall and precision and greatly reduces data extraction time. Unlike other tools, Dextr provides the ability to extract complex concepts (e.g., multiple experiments with various exposures and doses within a single study), properly connect the extracted elements within a study, and effectively limit the work required by researchers to generate machine-readable, annotated exports. The Dextr tool addresses data-extraction challenges associated with environmental health sciences literature with a simple user interface, incorporates the key capabilities of user verification and entity connecting, provides a platform for further automation developments, and has the potential to improve data extraction for literature reviews in this and other fields. [ABSTRACT FROM AUTHOR]

• Systematic Literature Review about Metamodel-Based Simulation Optimization (MBSO). • The integration of new machine learning tools could improve metamodels. • The main applications, concepts and state of the art in MBSO were discussed. • The research found and discussed gaps that can be addressed by future studies. Over the past few decades, modeling, simulation, and optimization tools have received attention for their ability to represent and improve complex systems. The use of metamodeling techniques in optimization via simulation problems has grown considerably in recent years to promote more robust and agile decision-making, determining the best scenario in the solution space. The objective of this article is to conduct a systematic literature review of metamodeling-based simulation optimization (MBSO). The main contributions of this paper are related to systematically gather, analyze, and discuss the knowledge disseminated in this area, support future research and expand the literature related to MBSO techniques. Research questions were planned to assist MBSO researchers and practitioners, by presenting the most frequent contexts, applications, methods, tools, and metrics found in metamodeling studies in optimization via simulation problems. We considered papers published in scientific journals and listed in the Web of Science, Scopus, ACM Digital Library, IEEE Xplore, and Science Direct databases. The conclusion related the gaps, opportunities, and future perspectives found during the development of this research, suggesting that this research area is growing in the past 15 years. [ABSTRACT FROM AUTHOR]

• Influence Diagram accounts for uncertainties affecting lead time estimation. • Interactive tool to visually communicate probabilistic estimates to stakeholders. • Sensitivity analysis identified team velocity to most drastically impact lead time. • Cumulative distribution function can be an aid to prioritize project backlog queue. This research introduces an influence diagram based approach to estimating task lead times for Agile Kanban project management. Derived from the principles of lean manufacturing, Agile methodologies including Scrum, Scrumban, and Kanban are common in the software industry and are spreading to other fields. Many teams estimate task delivery to better manage stakeholder expectations and improve decision making. However, the current technique involves calculating a team's story point completion velocity, requiring hours of weekly effort while overlooking the addition, removal, and reprioritization of backlog tasks. While these factors may be critical in Scrum, teams practicing Kanban experience these conditions frequently due to the emphasis on continuous integration and reprioritization. Our alternative approach applies an influence diagram, or Bayesian belief network, to model the uncertainties affecting the lead time. To partially automate the estimation process, an influence diagram based expert system is developed and populated with data from a practicing Kanban team and used to generate a cumulative distribution function to facilitate the communication of probabilistic estimates. A sensitivity analysis is conducted to better understand how each factor influences lead times. This system can assist Kanban teams' stakeholder communication and reduce estimation workload through a more holistic model of lead times. [ABSTRACT FROM AUTHOR]

• Digital transformation strategies need to include new generation process engineering solutions and agile methodologies need to be adopted throughout the enterprise. • A software platform approach to process simulation, with AVEVA process Simulation, to replace the single-purpose software to be used over the entire project lifecycle. • Combining process simulation with an engineering and design database, with AVEVA unified Engineering, allows collaboration of process engineers with other engineering disciplines, building up a digital twin. • An agile workflow to process engineering with a combined physical and behavioral digital twin enables simultaneous engineering with continuous validation. The process engineering discipline has been largely excluded from Digital Transformation trends. The objective of this paper is to provide an overview of the obstacles to Digital Transformation for the process discipline and explain how these can be overcome. A special emphasis will be given on the role of the process simulation tool (white box modeling) as a catalyst for transformational change. The paper will examine several challenges specific to process engineering: 1) Process simulators are divided into single-purpose point solutions. Separate models may be created for process design, control strategy design, operator training simulation, performance monitoring and online optimization. 2) Process simulators are typically poorly integrated into engineering workflows beyond the process world, and if so, with a single directional information flow. 3) Legacy process simulators are overloaded with niche features and functions only usable by experts. Industry stakeholders see the Digital Twin as the most important building block for Digital Transformation of the process industries. While legacy simulators are well-suited to accurately simulate processes, their decades-old architectures mean they are not ideal to serve the entire plant lifecycle and support the Digital Transformation. We will use the AVEVA Process Simulation as an example of how the identified obstacles can be overcome with a next generation process simulator. [ABSTRACT FROM AUTHOR]

Umple is an open-source software modeling tool and compiler. It incorporates textual language constructs for UML modeling, including associations and state machines. It includes traits, aspects, and mixins for separation of concerns. It supports embedding methods written in many object-oriented languages, enabling it to generate complete multilingual systems. It provides comprehensive analysis of models and generates many kinds of diagrams, some of which can be edited to update the Umple code. Umple runs on the command line, in a web browser or in integrated development environments. It is designed to help developers reduce code volume, while they develop in an agile, model-driven manner. Umple is also targeted at educational users where students are motivated by its ability to generate real systems from their software models. • Easy-to-use tool to generate code from software models such as state machines and UML associations. • Allows editing and compilation of models and code from the web, the command line, Eclipse and other IDEs. • Blends model representation seamlessly with traditional code in languages like Java and C++. • Incorporates traits, mixins and aspects for separation of concerns, that operate on code and models. [ABSTRACT FROM AUTHOR]