Development Process Research Articles

Window of OCMO Opportunity: the Communication Competency Index for Media Teams in Nuclear Cities

The issue of evaluating the effectiveness of communications and PR has long been a staple topic of debate among industry specialists. In late 2023, driven by the initiative of the Rosatom State Corporation and within the framework of a media project accelerator, an effort was made to synthesize various approaches for assessing the performance of media teams. This effort resulted in the creation of the Communication Competency Index for media teams in nuclear cities. The creators of the index discuss its development process, the criteria it encompasses, and its potential applications.

Review of the Cooperative Development Framework in Language Teacher Education

This review article introduces the Cooperative Development (CD) model of professional development following its development process from Cooperative Development (Edge, 1992) to Continuing Cooperative Development (Edge, 2002) and to Computer-Mediated Cooperative Development (2006a). Some other professional development models are also briefly introduced in this review, including the Counseling-Learning approach, Collaborative Conversations, Collaborative Supervision, Professional Learning Communities, Communities of Practice, and Peer Coaching.

Dataset for reporting of the invasive carcinoma of the breast: recommendations from the International Collaboration on Cancer Reporting (ICCR).

Current national or regional guidelines for the pathology reporting on invasive breast cancer differ in certain aspects, resulting in divergent reporting practice and a lack of comparability of data. Here we report on a new international dataset for the pathology reporting of resection specimens with invasive cancer of the breast. The dataset was produced under the auspices of the International Collaboration on Cancer Reporting (ICCR), a global alliance of major (inter-)national pathology and cancer organizations. The established ICCR process for dataset development was followed. An international expert panel consisting of breast pathologists, a surgeon, and an oncologist prepared a draft set of core and noncore data items based on a critical review and discussion of current evidence. Commentary was provided for each data item to explain the rationale for selecting it as a core or noncore element, its clinical relevance, and to highlight potential areas of disagreement or lack of evidence, in which case a consensus position was formulated. Following international public consultation, the document was finalized and ratified, and the dataset, which includes a synoptic reporting guide, was published on the ICCR website. This first international dataset for invasive cancer of the breast is intended to promote high-quality, standardized pathology reporting. Its widespread adoption will improve consistency of reporting, facilitate multidisciplinary communication, and enhance comparability of data, all of which will help to improve the management of invasive breast cancer patients.

ОПТИМИЗАЦИЯ СИСТЕМЫ ПРОТИВОЭРОЗИОННЫХ МЕРОПРИЯ-ТИЙ В ООО «АГРОФИРМА «ЧЕРНОВСКОЕ» НИЖЕГОРОДСКОЙ ОБ-ЛАСТИ

In modern conditions, it becomes necessary to improve a complex system of anti-erosion measures in connection with the development of erosion processes that have arisen as a result of the intensification and commercialization of agricultural production. According to various estimates, in the Nizhniy Novgorod region, the area of eroded lands ranges from 141.02 thousand hectares to 667.6 thousand hectares or from 5.2% to 24.7% of the area of agricultural land [1, 2, 3]. The main negative as-pect of the manifestation of water erosion on arable soils is a decrease in soil fertility and crop yields. It should be noted that erosion processes in the region are developing unevenly. The development of water erosion of the Right Bank is facilitated by the geological structure of the area, the hilly–rocky type of relief and the high ploughing of the territory. The article presents the results of solving the economic and mathematical problem of planning anti-erosion measures in an agricultural enterprise located in the Right-bank part of the Nizhniy Novgorod region. It is shown that due to the implementation of the project of anti-erosion organization of the territory, soil fertility will significantly increase, and the net income from increasing the economic efficiency of production in the agricultural firm will amount to 212.6 million rubles, which is 41% higher than income without carrying out a complex of anti-erosion measures.

Micro-Scale Particle Tracking: From Conventional to Data-Driven Methods

Micro-scale positioning techniques have become essential in numerous engineering systems. In the field of fluid mechanics, particle tracking velocimetry (PTV) stands out as a key method for tracking individual particles and reconstructing flow fields. Here, we present an overview of the micro-scale particle tracking methodologies that are predominantly employed for particle detection and flow field reconstruction. It covers various methods, including conventional and data-driven techniques. The advanced techniques, which combine developments in microscopy, photography, image processing, computer vision, and artificial intelligence, are making significant strides and will greatly benefit a wide range of scientific and engineering fields.

A Cognitive Approach for Modeling Customer Demand Dynamics for Optimal Product Release Strategies

Abstract As agile processes are increasingly adopted for product design and as consumer preferences are rapidly evolving with increasing information available from digital media, there is a need for a demand model that can accommodate the dynamics of product development. However, existing models of demand estimation, such as the discrete-choice models, do not capture the dynamics of product development and decision-making processes and thus are unable to effectively capture the effect of product updates and the release of information. To address this gap, we present a dynamic demand model and demonstrate how it can be used to determine the optimal time to release product updates. The demand model is based on decision field theory (DFT), which enables the modeling of the dynamic behavior of human decision makers. The contributions of this paper are as follows. First, we formulate a computational model for demand modeling built on DFT and demonstrate the viability of using the model to determine product release strategies. Second, we provide analytical approximations of the demand model and compare the accuracy of the approximated demand against the demand predicted by the dynamics model. Third, we show an example of a game played by competitors trying to optimize demand for their products by choosing the optimal update time relative to each other. Finally, we demonstrate the feasibility of parameter estimation using only the demand data.

Transcriptomics reveals age-related changes in ion transport–related factors in yak lungs

Yaks inhabit high-altitude, low-oxygen regions, where ion transport functions play a crucial role in maintaining intracellular and extracellular ionic balance and regulating pulmonary vascular tension. These functions affect pulmonary ventilation and blood flow rate, aiding tissue development and enhancing oxygen transfer efficiency, thus facilitating better adaptation to hypoxic environments. To investigate the regulatory mechanisms of ion transport-related factors on the growth and development of yak lungs, we employed RNA sequencing (RNA-seq)for sequencing the transcriptome in the lung tissues of neonatal (1-day-old), juvenile (1-year-old), and adult (4-year-old) yaks. We also performed differential gene expression and functional analyses. The results yielded 26 genes associated with ion transport, mainly enriched in the salivary and pancreatic secretion pathways. Finally, we used several methods including quantitative polymerase chain reaction (qRT-PCR), and Western blotting (WB), immunohistochemical (IHC) and immunofluorescence (IF) staining to determine the distribution of the expression of the ion transport genes FOXI1, KCNMA1, and SLC12A2 in yak lung tissues. qRT-PCR and WB results indicated that mRNA and protein relative expression levels of FOXI1 and SLC12A2 were significantly higher in neonatal yaks than in juvenile and adult yaks (all p < 0.05), whereas those of KCNMA1 were significantly higher in adult yaks than in neonatal and juvenile yaks (all p < 0.05). IHC and IF results demonstrated that FOXI1, KCNMA1, and SLC12A2 were distributed among the epithelial mucosal layers (including ciliated, goblet, and Clara cells) of the yaks’ bronchi and their branches in the lungs across different age groups of yak. Therefore, our results suggested that FOXI1, KCNMA1, and SLC12A2 may be strongly associated with the development and aging processes in yak lungs. These results provide insights into the molecular mechanisms underlying the yak’s adaptation to high-altitude environments and valuable references for further research.

Innovative integrated workflow for data-driven production forecasting and well completion optimization: A Montney Formation case study

With the use of advanced data analytics and machine learning (ML) techniques, the oil and gas industry has transformed significantly in recent years. These innovations have opened new opportunities in reservoir engineering, allowing engineers to leverage data for better decision-making and well completion optimization. A key challenge in hydrocarbon exploration and production is forecasting gas production rates from unconventional sources. This is a crucial factor that affects how resources are allocated and decisions are made, requiring the application of novel techniques to enhance hydrocarbon production. Here, a novel and comprehensive automated workflow is introduced covering data collection, data preparation, feature selection, ML model development, and operational parameter optimization. The workflow uses various ML and optimization methods, including a modified Differential Evolution (MDE) algorithm, to improve gas production forecasting and optimize well completion parameters. We applied the proposed workflow to 3144 horizontal gas wells in the Montney Formation, British Columbia (BC), Canada, to assess its performance. The results showed the advantages of incorporating the initial 24 months of cumulative gas production data alongside well and completion data. Also, following the fine-tuning of hyperparameters for the ML algorithms, we identified the most effective model for forecasting cumulative gas production within the Montney Formation as a two-layer Artificial Neural Network (ANN) model. Moreover, using the MDE algorithm, the optimization results revealed the possibility of increasing gas production by more than 10% for about 47% of the wells. This research makes a significant contribution to the field of unconventional gas exploration by introducing an automated workflow that simplifies data processing, model development, and decision-making processes. It also offers valuable insights for optimizing unconventional gas production in the Montney Formation and similar reservoirs, as the energy industry moves towards more sustainable and efficient practices.

The developmental activities of women’s professional pathway cricketers

ABSTRACT The growth of investment in women’s cricket has led to the development of new professional pathways. However, there is lack of specific evidence to support the development of these athletes. This study aimed to take a first step in understanding the participation histories of female cricketers selected into English women’s professional academies. 84 players completed the Participation History Questionnaire (PHQ) to capture details of activities engaged in across development, including milestones, cricket activity, and engagement in other sport activities. There were relative age effects where players' birthdate distributions differed from national norms (X 2 = 8.451, p = .003, V = 0.227), and more players than expected attended independent schools (X 2 = 7.980, p = .005, V = 0.232). Participants engaged in cricket informally before coach led training, have siblings (particularly brothers) interested in cricket and often played boy’s cricket before engaging in the girl’s or women’s game. Developmental activities were characterised by early engagement, high levels of diversification, and very low levels of play compared to high levels of formal practice and match play. These findings have implications for the advancement of talent identification and development policies and processes in women’s cricket.

SPEED-MODE cell line development (CLD): Reducing Chinese hamster ovary (CHO) CLD timelines via earlier suspension adaptation and maximizing time spent in the exponential growth phase.

Chinese hamster ovary (CHO) cells are the preferred system for expression of therapeutic proteins and the majority of all biotherapeutics are being expressed by these cell lines. CHO expression systems are readily scalable, resistant to human adventitious agents, and have desirable post-translational modifications, such as glycosylation. Regardless, drug development as a whole is a very costly, complicated, and time-consuming process. Therefore, any improvements that result in reducing timelines are valuable and can provide patients with life-saving drugs earlier. Here we report an effective method (termed SPEED-MODE, herein) to speed up the Cell line Development (CLD) process in a targeted integration (TI) CHO CLD system. Our findings show that (1) earlier single cell cloning (SCC) of transfection pools, (2) speeding up initial titer screening turnaround time, (3) starting suspension adaptation of cultures sooner, and (4) maximizing the time CHO cultures spend in the exponential growth phase can reduce CLD timelines from ~4 to ~3 months. Interestingly, SPEED-MODE timelines closely match the theoretical minimum timeline for CHO CLD assuming that CHO cell division is the rate limiting factor. Clones obtained from SPEED-MODE CLD yielded comparable titer and product quality to those obtained via a standard CLD process. Hence, SPEED-MODE CLD is advantageous for manufacturing biotherapeutics in an industrial setting as it can significantly reduce CLD timelines without compromising titer or product quality.

Development of a conceptual framework to understand the stakeholder’s perspectives on needs and readiness of rural tele-practice for childhood communication disorders

Background Tele-practice promotes universal and equitable access to quality health services and emerged as an alternative to overcome physical barriers to intervention access in the 90s. There has been a steady increase in adoption since then, and during the COVID-19 pandemic, there was a surge in online modes of healthcare service delivery. Yet, tele-practice adoption and utilization in rural and remote areas are not spontaneous. Therefore, as a first step, prior to the implementation of a comprehensive tele-practice model, a baseline situational analysis was undertaken to assess the needs and readiness of parents of children with disabilities and different cadres of health care providers towards accepting tele-practice services in their settings. This paper describes the process of development of the conceptual framework that guided the baseline needs and readiness assessment (situational analysis). Methods The Bowen's feasibility framework served as the primary framework to evaluate the feasibility outcomes of the implementation. Therefore, this framework also guided the baseline situational analysis. For specificity of the framework to tele-practice, several telemedicine planning frameworks relevant for low- and middle-income countries were reviewed to identify and map suitable constructs and attributes to the Bowen’s constructs. A description of the framework selection process and a review of each of the selected telemedicine frameworks are provided. Results The constructs and attributes from this conceptual framework were used to develop the guides for focus group discussions (FGDs) and semi-structured interviews (SSIs). The guides were prepared separately for each stakeholder group. Conclusions The developed framework facilitated the assessment of needs and readiness suited to the context and among various stakeholders involved in the proposed implementation of the comprehensive model of tele-practice for childhood communication disorders in rural communities.

User-generated content effectiveness in co-creation of online higher educational services

ABSTRACT Improvement of student services is increasingly important for institutions providing higher education in today's competitive market. Consequently, leveraging the online content generated by students is an effective means of actively involving customers in the educational process. As a result, the co-creation of innovative educational services through user-generated content (UGC) has become imperative. Engaging students in the service development process requires the adoption of an appropriate New Service Development (NSD) model, enabling the implementation of a co-creation strategy. The present study investigates this phenomenon by examining user-generated content on a website dedicated to higher education services. Employing the thematic analysis approach, 46 themes were identified that can be utilized in the development of new higher educational services. The findings demonstrate the potential of user-generated content in implementing a co-creation strategy at every stage of the new service development process.

Recommendations for the Model-Based Systems Engineering Modeling Process Based on the SysML Model and Domain Knowledge

Model-based systems engineering (MBSE) is a modeling approach used in industry to support the formalization, analysis, design, checking and verification of systems. In MBSE modeling, domain knowledge is the basis of the modeling. However, modeling does not happen overnight; it requires systematic training and a significant investment of resources. Unfortunately, many domain experts lack the expertise required for modeling, even though they know the domain well. The question arises about how to provide system modelers with domain knowledge at the right time to support the efficient completion of modeling. Since MBSE research that integrates AI is just beginning to take off, no public dataset is available. In this paper, aerospace SysML models are constructed based on spacecraft-related domain knowledge to form SysML model data. The validation rules are studied to validate the SysML model data, and combined with the concept of the recommended system, a recommendation method for the MBSE modeling process based on the knowledge and SysML model is proposed. A GLOVE language model is pre-trained by using domain knowledge and general knowledge; the model data are also used to fine-tune the GLOVE language model combined with the pre-training to recommend some domain development processes. The recommendation list is manually quality-verified and fed into the pre-training phase, while new requirement texts are continuously added in the fine-tuning phase, resulting in a more relevant and accurate recommendation list. Experiments show that the incremental recommender system can not only effectively recommend SysML models, but also improve the quality and efficiency of MBSE development.

Enhancing DevOps Engineering Education Through System-Based Learning Approach

Abstract System-based learning (SBL) in engineering domains integrates systems thinking and engineering principles to develop a system. In software engineering, to develop software using the DevOps process, using SBL environment, students gain a comprehensive understanding of the DevOps software development process and apply theoretical concepts to real-world problems by implementing a complete system pipeline, encompassing the design, development, testing, and deployment of software systems. This article introduces an SBL approach to teaching DevOps engineering, addressing the limitations of traditional methods in equipping students with the necessary skills and knowledge. To evaluate the effectiveness of the SBL approach, a case study was conducted to teach a DevOps course within an undergraduate software engineering program. Students completed a project involving the implementation of a system pipeline from requirement gathering to deployment. Results from the case study demonstrate that the SBL approach has improved students’ understanding of DevOps engineering and the software development big picture. The approach enhanced students’ systems thinking and problem-solving capabilities and prepared students for the challenges of a rapidly evolving technological landscape.

Unleashing capacity in the water sector: A framework for public entities

ABSTRACT Capacity development in the water sector is crucial to achieving universal, adequate, sustainable, and resilient services. It has been broadly recognized as a priority, connected with governance and management of water resources and water and sanitation services. Through a scoping review including 153 scientific articles and 103 other documents, complemented with a consultation of experts, this article presents how the concept of ‘capacity development’ has evolved, what levels it consists of, and which are the key elements of success for effective capacity development processes. The article finds that most existing frameworks do not conduct any in-depth analysis of the determinants that define capacity at different levels and that there is no specific framework for public water-related entities. To address this gap, a dedicated capacity development framework is proposed for the public water sector, defining the key determinants to be pondered in the analysis of the situation at different levels and types of interventions to achieve outcomes at each level. The framework will enable practitioners and policymakers to acknowledge the complexity and develop more systematic work on capacity development in water through various types of interventions that can support them.

Haram Lost and Found

With its great religious significance, the Holy Mosque in Makkah welcomes millions of tourists every year. The problem of misplaced objects at this holy site poses a problem for both guests and guards. In order to tackle this issue and improve worshippers' and visitors' overall experience, this study project explores the ideation, creation, and testing of a state-of-the-art mobile application called the "Lost and Found Assistance" app. This software makes use of modern technologies to expedite the process of finding misplaced items inside the Holy Mosque. The 'Lost and Found Assistance' app's main objective is to offer a workable answer to anyone who have misplaced their belongings inside the enormous Holy Mosque. It presents an easy-to-use and effective platform that enables users to report misplaced things, facilitating their prompt recovery. Additionally, it enables users to look for misplaced items, providing comfort to individuals who could have momentarily misplaced priceless items while visiting. In order to provide insight on the Holy Mosque's hallowed legacy and importance in the Islamic world, this study initiative examines the historical significance of the building. It explores the issues surrounding misplaced objects at this hallowed location, highlighting the demand for an up-to-date, technologically advanced solution. In addition, the research highlights the revolutionary role that mobile applications play in a variety of contexts as it examines the current state of lost and found services. Through an analysis of current applications and technological breakthroughs, it pinpoints chances to modify these advances to meet the particular difficulties presented by the Holy Mosque. The entire development process of the 'Lost and Found Assistance' app is described, from the first conceptualization to the design and coding stages. The project emphasizes the value of intuitive design concepts and user-friendly interfaces in making sure that the software is useful and accessible to a variety of users. An analysis of the app's usefulness in helping users find misplaced goods within the Holy Mosque completes the research endeavor. It talks about user pleasure and feedback, highlighting how the software improves users' experiences. It also looks at how widely the app might be used and offers ideas for improvements down the road. The 'Lost and Found Assistance' app helps to maintain a calm and serene atmosphere inside the Holy Mosque by utilizing contemporary technologies. The ability of technology to solve long-standing issues and improve the pilgrimage and visitor experience for millions of worshippers and tourists who visit this holy site is demonstrated by this study effort.

LES and RANS Spray Combustion Analysis of OME3-5 and n-Dodecane

Clean-burning oxygenated and synthetic fuels derived from renewable power, so-called e-fuels, are a promising pathway to decarbonize compression–ignition engines. Polyoxymethylene dimethyl ethers (PODEs or OMEs) are one candidate of such fuels with good prospects. Their lack of carbon-to-carbon bonds and high concentration of chemically bound oxygen effectively negate the emergence of polycyclic aromatic hydrocarbons (PAHs) and even their precursors like acetylene (C2H2), enabling soot-free combustion without the soot-NOx trade-off common for diesel engines. The differences in the spray combustion process for OMEs and diesel-like reference fuels like n-dodecane and their potential implications on engine applications include discrepancies in the observed ignition delay, the stabilized flame lift-off location, and significant deviations in high-temperature flame morphology. For CFD simulations, the accurate modeling and prediction of these differences between OMEs and n-dodecane proved challenging. This study investigates the spray combustion process of an OME3 − 5 mixture and n-dodecane with advanced optical diagnostics, Reynolds-Averaged Navier–Stokes (RANS), and Large-Eddy Simulations (LESs) within a constant-volume vessel. Cool-flame and high-temperature combustion were measured simultaneously via high-speed (50 kHz) imaging with formaldehyde (CH2O) planar laser-induced fluorescence (PLIF) representing the former and line-of-sight OH* chemiluminescence the latter. Both RANS and LES simulations accurately describe the cool-flame development process with the formation of CH2O. However, CH2O consumption and the onset of high-temperature reactions, signaled by the rise of OH* levels, show significant deviations between RANS, LES, and experiments as well as between n-dodecane and OME. A focus is set on the quality of the simulated results compared to the experimentally observed spatial distribution of OH*, especially in OME fuel-rich regions. The influence of the turbulence modeling is investigated for the two distinct ambient temperatures of 900 K and 1200 K within the Engine Combustion Network Spray A setup. The capabilities and limitations of the RANS simulations are demonstrated with the initial cool-flame propagation and periodic oscillations of CH2O formation/consumption during the quasi-steady combustion period captured by the LES.

Extrapolating time development curves of clear-water scour around piles: From empirical fitting to physics-based approach

The currently available prediction formulas for the scour development around a pile predominantly rely on empirical fittings, lacking clear physical mechanisms. This study integrates the principle of sediment mass conservation, the flat-bed sediment transport model, and the phenomenological theory of turbulence to propose a semi-analytical model for predicting clear-water scour development around circular piles. This model overcomes scale-related issues and is governed by three fundamental parameters: the equilibrium scour depth, the characteristic time, and the ratio of the scour depth coefficient to the primary horseshoe vortex coefficient. The results demonstrate that the proposed model is consistent with experimental data. The proposed model offers significant improvements by addressing two critical issues observed in existing models: (i) empirical formulas expressed purely in mathematical terms exhibit low prediction accuracy for long-term scour, resulting in underestimation of the extrapolated equilibrium scour depth and premature attainment of equilibrium conditions; (ii) other models exhibit a divergence phenomenon, where the extrapolated equilibrium scour depth greatly exceeds the pile diameter, contradicting prevailing understanding. Applying the proposed model allows for a substantial reduction in test duration while accurately capturing the subsequent scour development process.

Special Issue “Recent Process Design and Development Strategies for Dental Materials”

The field of dental materials is rapidly evolving, and this Special Issue of the International Journal of Molecular Sciences offers a comprehensive examination of the latest advancements in process design and development strategies [...]

Overview: Machine Learning for Segmentation and Classification of Complex Steel Microstructures

The foundation of materials science and engineering is the establishment of process–microstructure–property links, which in turn form the basis for materials and process development and optimization. At the heart of this is the characterization and quantification of the material’s microstructure. To date, microstructure quantification has traditionally involved a human deciding what to measure and included labor-intensive manual evaluation. Recent advancements in artificial intelligence (AI) and machine learning (ML) offer exciting new approaches to microstructural quantification, especially classification and semantic segmentation. This promises many benefits, most notably objective, reproducible, and automated analysis, but also quantification of complex microstructures that has not been possible with prior approaches. This review provides an overview of ML applications for microstructure analysis, using complex steel microstructures as examples. Special emphasis is placed on the quantity, quality, and variance of training data, as well as where the ground truth needed for ML comes from, which is usually not sufficiently discussed in the literature. In this context, correlative microscopy plays a key role, as it enables a comprehensive and scale-bridging characterization of complex microstructures, which is necessary to provide an objective and well-founded ground truth and ultimately to implement ML-based approaches.