Developer Experience (Dev-X) has emerged as a critical driver of software quality, developer productivity, and project success. However, the empirical literature remains fragmented, with limited understanding of how specific Dev-X interventions affect downstream software and business outcomes. This study aims to synthesize and map the current empirical evidence on Dev-X interventions, identifying how they influence software outcomes (e.g., code readability, collaboration) and propagate to organizational key performance indicators (KPIs) such as product quality and process efficiency. We conducted a systematic mapping study of 160 empirical articles published between 2006 and 2024. We identified 146 unique interventions that impacts software outcomes such as role match may impact on developers’ workload, likewise managing burnout in developer feedback may impact organizational culture. Using grounded coding, we also classified such mappings to 8 KPIs and 5 Dev-X dimensions based on the strength of empirical relationships (correlational, causal, classification-based) reported in the literature. Our analysis reveal that quality-related KPIs are more frequently impacted than productivity metrics, challenging traditional emphasis. Emotional state and values-oriented interventions dominate current practices, while cognitive load and motivation-focused strategies remain underexplored. Only a minority of studies (39 out of 160) explicitly trace full chains of intervention–outcome–KPI, highlighting persistent fragmentation; in other cases, we reconstructed these chains using cross-sectional synthesis of study data. Notably, recent years show an emergence of AI-assisted and value-centered intervention designs. This study consolidates the fragmented Dev-X landscape by offering an evidence-based, open-access Ready-Reckoner tool for navigating interventions and their effects. While not prescriptive, it enables structured, context-aware exploration of empirical findings and lays the foundation for future causal, longitudinal, and multi-channel Dev-X research. • We systematically map 160 empirical studies to trace how Developer Experience (Dev-X) interventions influence software outcomes and business-critical KPIs. • The study identifies 146 unique interventions and categorizes them by their targeted Dev-X facets, intermediate outcomes, and relationship strength (causal, correlational, qualitative). • Our results highlight a gap in empirical traceability between Dev-X practices and long-term organizational success, emphasizing the need for integrated evaluation frameworks. • The study identifies underexplored connections across Dev-X dimensions and advocates for research into their combined influence on developer performance and project outcomes. • We deliver an open-access, practitioner-oriented Dev-X Ready-Reckoner Tool that supports evidence-based selection of interventions aligned with organizational goals.

Unveiling varietal specificity in non-destructive grape quality monitoring: Explainable AI and feature selection for sugar and organic acid prediction using NIR spectroscopy.

Architectural excellence has traditionally been associated with creativity, innovation, aesthetic quality, and conceptual strength. While these characteristics remain essential, contemporary project environments increasingly demonstrate that design quality alone is insufficient to guarantee successful outcomes. Many projects begin with ambitious architectural visions yet encounter challenges during technical development, construction execution, operational occupancy, or long-term asset management. As a result, a growing distinction has emerged between design excellence and execution excellence. The most successful architectural projects are not merely those that generate compelling concepts but those that effectively translate conceptual intent into built environments that perform successfully throughout their lifecycle. This article proposes a comprehensive framework for understanding architectural execution excellence as the integration of conceptual design, technical delivery, construction implementation, and operational performance. Rather than treating these stages as independent activities, the paper argues that they form a continuous system in which decisions made during one phase influence outcomes across all others. Architectural success therefore depends on the quality of coordination, communication, and decision-making throughout the entire project lifecycle. The study examines the mechanisms through which design intent is translated into executable architectural systems, explores the role of technical coordination in preserving project quality, and analyzes how construction processes influence the realization of architectural objectives. Particular attention is given to interdisciplinary collaboration, stakeholder management, digital technologies, lifecycle thinking, and operational performance evaluation. The paper further investigates how architects can strengthen project outcomes by expanding their focus beyond design production and engaging more actively with implementation and performance management. The findings suggest that execution excellence represents a strategic capability that bridges the traditional divide between design and delivery. Projects that successfully align architectural vision with technical feasibility, construction realities, and operational objectives are more likely to achieve long-term value, user satisfaction, and organizational success. The article concludes by presenting a holistic model that positions architectural execution excellence as a critical competency for contemporary architectural practice.

ABSTRACT Efficient resource allocation and leveling are critical to successful construction project scheduling, yet conventional methods often fail to balance project duration with stable resource utilization. This study introduces a modified mountain gazelle optimizer (mMGO), which extends the original single-objective MGO into a multi-objective optimization approach to address the complex trade-offs inherent in construction scheduling. In addition, the algorithm integrates opposition-based learning and dynamically controlled chaotic mapping to enhance global search capability and avoid premature convergence. The mMGO is embedded within a two-phase scheduling framework, in which the first phase generates resource-feasible baseline schedules, while the second phase redistributes activities to minimize fluctuations in multi-resource demand. To validate its performance, mMGO was verified through two case studies. Case study 1 consisted of five projects, each containing six activities, whereas case study 2 included three projects, each involving 60 activities. The results show that mMGO consistently achieved the highest hypervolume values, indicating closer convergence to the true Pareto front and greater diversity of non-dominated solutions. Moreover, mMGO produced schedules that simultaneously reduced project makespan, resource intensity, and resource utilization instability metrics. These findings indicate that mMGO has potential as a computational decision-support approach for multi-objective resource scheduling in benchmark multi-project environments.

ABSTRACT In the current digital era, implementing an effective governance system is crucial for project-based organizations (PBOs). The scientific issue addressed in this study is the lack of understanding of how project governance interacts with digital maturity and organizational parameters in driving innovative project success in PBOs. This study aims to investigate the relationship between project governance and innovative project success, mediated by digital maturity, and moderated by transformational leadership and flexible culture. Drawing on the resource-based view (RBV) and dynamic capabilities theory (DCT), we develop a theoretical model and employ a field survey with 215 Pakistani PBOs, analyzed using PLS-SEM. RBV examines how project governance within an organization operates, while DCT demonstrates the adaptive capabilities of digital maturity through culture and leadership practices that are associated with success. The empirical findings demonstrate that project governance is positively associated with innovative project success both directly and indirectly through the mediation of digital maturity. Furthermore, the relationship between digital maturity and innovative project success is also positively associated with transformational leadership and flexible culture. The study findings show that effective governance in digitally mature organizations, in conjunction with leadership and culture, is essential to foster innovation-driven project success in PBOs. Future research may investigate other external conditions, including agile organizational structure and governance issues throughout the project life cycles, leadership and culture, and the competency of individual team members in connection with organizational digital maturity.

The article is devoted to the analysis of public-private-philanthropic partnership and the improvement of the regulatory and legal regulation of its activities, to create a regulatory basis for attracting charitable funds to socially important investment projects, in particular, regarding rehabilitation, reintegration, preservation and restoration of human capital. The study focuses on the analysis of the current state of the private medical services market in Ukraine and existing cases of involving charitable partners in investment projects in the field of rehabilitation. The preference for the development of public-private-philanthropic partnerships is justified, as opposed to classic public-private partnerships, which are associated with the social significance of rehabilitation problems and insufficient funding. It was found that the activities of rehabilitation centers with the support of charitable partners are aimed not only at the quantity, but also at the quality of rehabilitation, orientation to the best international practices and advanced training of medical personnel. Therefore, such an investment approach is relevant especially in times of unstable economy and social challenges. The purpose of the article is to summarize the practical experience of creating and operating rehabilitation centers on the principles of public-private-philanthropic partnership in Ukraine, and to develop recommendations for improving the regulatory and legal framework to create a favorable legislative framework for the implementation of this model. It was found that today the regulatory and legal framework does not regulate the activities of such partnerships, as they are new to Ukrainian practice and are a response to the challenges of Ukrainian realities. Therefore, the proposals provided for improving the regulatory and legal framework are designed to provide a legislative basis for the development of this investment model. This will allow scaling up successful projects and will contribute to increasing the role of the charitable sector in areas related to the humanitarian dimension, such as: rehabilitation, reintegration, preservation and restoration of human capital. The novelty of the study lies in the attempt to adapt existing regulatory documents to the activities of these partnerships, because despite the lack of special regulatory and legal regulation, partnerships of this type already exist in Ukraine and are innovative. The article uses general scientific and special research methods: case study method, comparative analysis and logical generalization. Their application allowed us to widely explore the issues of the development of public-private-philanthropic partnerships, and to identify their advantages over classical public-private partnerships. Since Ukraine already has successful experience in the functioning of public-private-philanthropic partnerships in the field of rehabilitation, further scaling up of this practice requires the identification of criteria by which institutional support for sanatorium-resort and rehabilitation institutions should be carried out within the framework of this investment model. Therefore, the proposed criteria are designed to intensify the attraction of funds from the charitable partner in those areas that require optimization of the costs of the private partner and the state.

The pervasive issue of cost overruns remains a significant barrier to ensuring project success within budget and schedule constraints. While the causes of overruns are known, a major gap exists in providing validated, high-accuracy predictive tools for proactive expenditure control. This research addresses this gap by aiming to identify the most significant cost overrun factors in building construction projects and develop an accurate predictive model using advanced machine learning. The methodology involved collecting data via questionnaire surveys and interviews, assessing 49 pre-identified factors from industry experts. The Relative Importance Weight (RIW) method was used to prioritize these causes. Key findings revealed that 70% of the most significant factors were classified as internal project control deficiencies, including changes in scope and design, inadequate documentation, and poor site management. To achieve proactive forecasting, the study developed and systematically compared seven diverse Machine Learning (ML) architectures. The General Regression Neural Network (GRNN) was identified as the optimal model, achieving superior predictive performance with an average error (Mean Absolute Percentage Error, MAPE) of less than 3% and a correlation (R) of 0.95. The novel contribution of this work is the validated GRNN model, which transforms problem recognition into a reliable, actionable decision-support system for expenditure optimization and project value preservation in the construction sector.

Purpose This study aims to examine how cross-cultural dynamics shape the effectiveness of structured change-management frameworks in complex international organisations. Focusing on the United Nations system, it investigates how cultural value alignment and processual frameworks jointly influence the success of change initiatives under conditions of change saturation. Design/methodology/approach This study combines gap-spotting and problematisation with an empirical analysis of 52 United Nations change initiatives complemented by 12 peer-reviewed Kotter case studies. It operationalises framework type and cultural alignment through a CVScale index that captures leadership engagement, two-way communication, cultural alignment and feedback practices across culturally diverse entities. Non-parametric techniques (Spearman’s rank-order correlation and Kruskal–Wallis tests) are applied to examine relationships between these cross-cultural mechanisms, framework use and project outcomes. Findings The results show that using a structured change framework is moderately associated with project success, but higher CVScale scores are more strongly linked to positive outcomes. This research suggests that in cross-cultural, change-intensive settings, processual change models work best when they are enacted through culturally aligned leadership and communication practices. Research limitations/implications The findings from this research may have limited generalisability due to the secondary case data. Therefore, this calls for further research with larger data sets and longitudinal designs. Practical implications For leaders in global public organisations, the findings suggest that CVScale-style diagnostics should be integrated into change programme design as a primary readiness assessment, given that cultural alignment is a stronger predictor of success than framework selection alone. Originality/value This study provides empirical evidence that cultural alignment is a stronger predictor of change success than the use of structured frameworks alone. It also offers a context-sensitive approach to managing change in multicultural organisations.

ABSTRACT Precise wind speed projection is crucial for planning and utilizing renewable energy effectively. One of the most successful time-series projection techniques is support vector regression (SVR), a subset of artificial intelligence methods. Even with its benefits, including predictive precision and stability, this tactic lacks a well-defined rule for parameter fitting. The methods used today to adjust these parameters are different enhancement schemes. In this exploration, the optimal data input combination was selected by comparing five combinations developed using autocorrelation (ACF) and partial autocorrelation functions (PACF). Then, using this combination, six meta-heuristic enhancement schemes were explored, and the strongest scheme was introduced for optimal parameter fitting for SVR. The outcomes indicate that the M-3 input data series provides the best run time, and MSE, RMSE, NMSE, and WI statistical indexes of about 0.008, 0.091, 0.0017, and 0.997, respectively. Moreover, the Battle Royale Optimization with acceptable accuracy presents the best run time. In this regard, the M-3 input data series with the Battle Royale Enhancement scheme provides the best solution for the wind speed projection.

E-business projects may face several risks that are potential to affect the success of such projects. As an important issue for a project manager in this field, the potential risks should be prioritized according to their potential impact on the success of the project. In this study, the potential risks of the e-business sector are to be evaluated and ranked based on the success criteria of such projects. For this aim a complete set of risks compared to the literature is considered and their impact on the project success criteria are determined linguistically by the experts of the field as a case study. To respect the uncertain nature of the evaluations, the linguistic evaluations are converted to belief-degree-based uncertain values. Then for the first time, a data envelopment analysis (DEA) model is used to evaluate and rank such risks. For this aim, the classical Tchebycheff norm DEA model is extended to a belief-degree-based uncertain environment for the first time. An extensive computational study including sensitivity analysis and comparative study is performed by the uncertain Tchebycheff norm model to evaluate and rank the risks of the case study. Based on the obtained results “introducing new technology” and “cultural risk” are the most and least important risks respectively.

Knowledge governance is a critical factor to play and manage the knowledge related activities in projects and consequently the overall success of a project. This research paper aims to investigate the role of Knowledge Governance (KG) on Project Success (PS) in Pakistani major construction projects. Further, the paper investigates the mediating role of Efficient Team Leadership (ETL) and Knowledge Sharing (KS), and the moderating role of Organizational Culture (OC) between KG and PS. A quantitative study design was carried out using survey data collected through construction professionals in Pakistan. A total of 374 valid replies were examined using Structural Equation Modeling (SEM) via SmartPLS 4 to evaluate the potential relationship among the studied variables. The results demonstrate that KG substantially enhances PS. The exchange of knowledge, encompassing explicitly stated and implicit aspects, partially mediates this relationship with differing degrees of impact. ETL partially mediates by enhancing coordination, decision-making, and project execution. Furthermore, OC favorably influences the relationship between KG and PS, hence augmenting the efficacy of approaches to governance. This research expands contingency theory and knowledge-based theory by introducing a comprehensive framework that elucidates the mediating and regulating mechanisms connecting knowledge governance to project success within Pakistan’s construction industry

The Australian Magnetic Resonance Imaging (MRI) Linear Accelerator program (MRI linac) was a major research project that aimed to build and test a unique MRI linac prototype for cancer treatment. It aimed to improve radiotherapy anatomical targeting and explore physiological targeting. The purpose of this report is to summarise the development and achievements of the program so as to provide an example of a successful large-scale research project in Australian radiation oncology. The project involved six Australian universities and international collaborators. We developed and built a unique MRI linac configuration comprising a 1 T magnetic field and a 6 MV accelerator, with the beam delivered in line with B0 and the patient placed across B0 in the split between the two halves of the magnet. The broad research domains were personalised disease targeting, medical device innovation, and biodiscovery. Specific projects included Artificial Intelligence image enhancement, radiation dosimetry in high magnetic fields, MRI characterisation of cancer heterogeneity in human tumours, and animal and human studies. Over $27 million was obtained to support the program from competitive sources. The program published over 120 papers and supported 25 PhD completions. The learnings from the Australian MRI linac program are that Australia has world-class radiotherapy research in physics and engineering, that major projects need a lot of time and a lot of collaboration, and that large, novel radiotherapy projects can attract significant funding and produce significant results.

Purpose The relationship between ebullient supervision and project success remains a nascent area of inquiry in project management literature. Leveraging the Conservation of Resources theory, this study aims to investigate the potential mediating role of employee creativity in the link between ebullient supervision and project success within the IT industry. Design/methodology/approach At the dyad level of analysis, data from 48 project teams (48 project supervisors and 232 project team members – 280 employees in total) from the IT industry was collected using a structured questionnaire. PLS-SEM was considered by employing SmartPLS4 for data analysis. Findings The findings demonstrates that ebullient supervision contributes to project success through employee creativity. Contrary to expectations, thriving at work had a negative moderating effect, indicating that the benefits of ebullient supervision on employee creativity may be diminished when employees are thriving at work. Practical implications This study provides practical insights for IT project managers to drive project success by fostering ebullient supervision and prioritizing employee creativity, ultimately informing resource allocation decisions. Originality/value This paper provides a new outlook on the role of ebullient supervision in the success of a project, by starting to investigate the role of employee creativity as a mediating variable in this process.

Detection and quantification of mold spores in wheat using hyperspectral imaging and data fusion techniques.

The “Sustainable Livelihood Development of Rural Communities in Low Land Along the Mara River Basin” project, implemented by Mogabiri Farm Extension Centre (MFEC) in Tarime District, Tanzania, seeks to address persistent socio-economic challenges in rural communities by promoting climate adaptation, income diversification, and gender equality. Despite its ambitious objectives, the project operates within a complex socio-economic and cultural environment that can undermine its intended outcomes. Evaluating such projects requires a critical examination of both their successes and the barriers that persist. Using a mixed-methods approach, this evaluation combined quantitative data from 265 smallholder farmers (SHFs) and qualitative insights from focus group discussions (FGDs), key informant interviews (KIIs), and document reviews. In terms of “What Works?”, findings indicate that 75% of SHFs adopted climate-resilient practices (p < 0.05), and 90% of communities implemented gender-responsive action plans (p < 0.01), with 68% of households engaging in income diversification activities such as poultry farming and beekeeping. However, the question of “What Doesn’t Work?” revealed notable shortcomings, including limited market access (p = 0.03) and cultural barriers restricting full gender participation (p = 0.04). The persistence of these challenges, despite project successes, underscores the need for a deeper investigation into structural and contextual factors that constrain the effectiveness of such initiatives. Understanding “Why?” these barriers remain highlights the interplay of systemic market integration challenges and cultural resistance to gender equity. While the project has made commendable progress, addressing these underlying issues is crucial for achieving long-term success. Strengthening market linkages, expanding gender-sensitive interventions, and fostering sustainability through community ownership and local partnerships are recommended to enhance scalability and impact.

Hyperspectral remote sensing technology is one of the key technical methods for detecting rice blast in the field, but existing hyperspectral dimensionality reduction methods still suffer from information redundancy and insufficient feature interpretability. This study aimed to develop a feature wavelength selection method integrating deep learning and model attribution analysis to extract key spectral features across different disease severity levels. A residual network model Dilated Convolution and Deformable Convolution-Residual Network (DCR-ResNet) combining dilated convolution and deformable convolution was constructed to deeply mine spectral features across varying disease severities. Meanwhile, the Integrated Gradient (IG) and Gradient-weighted Class Activation Mapping (Grad-CAM) methods were combined to enable the selection of spectral wavelengths. The effectiveness of the proposed method was validated using statistical analysis (transformed divergence, within-class scatter) and modeling analysis. Findings reveal that the spectral feature wavelengths identified by DCR-ResNet in conjunction with the IG-GradCAM approach exhibit excellent inter-class separability and intra-class compactness. Furthermore, when benchmarked against conventional dimensionality reduction techniques such as Successive Projections Algorithm, Random Frog, and Competitive Adaptive Reweighted Sampling, the Support Vector Machine, Extreme Learning Machine, and Random Forest models developed using IG-GradCAM-selected feature wavelengths demonstrate superior classification performance. The overall accuracy reaches 85.9%, 85.5% and 86.2%, with kappa values of 81.3%, 80.6% and 81.6%, respectively. The feature wavelength selection method combining DCR-ResNet with IG-GradCAM not only improves the accuracy of hyperspectral feature extraction but also provides an efficient and feasible approach for the precise identification of rice blast. © 2026 Society of Chemical Industry.

Seeds storage-year have a significant impact on high-oleic peanut seed vigor and quality. Therefore, it is essential to identify different storage-year seeds for planting, direct consumption, industrial processing, and marketing. In this study, hyperspectral images with 616 spectral bands (from visible light to near-infrared) were employed to classify different storage-year peanut seeds. To extract characteristic information for classification, we proposed a hybrid band selection (HBS) method based on the successive projection algorithm (SPA) by fusing the color-sensitive bands and moisture-sensitive bands. Then three classifiers, support vector machine (SVM), extreme learning machine (ELM), and K-nearest neighbors (KNN), were selected for storage-year classification. The experimental results demonstrated that the features extracted with the HBS method can obtain higher classification accuracy than other methods'. Specifically, the HBS-ELM model achieved the highest classification performance, with accuracy of 90.22%.

Adoption of building information modeling (BIM) by the construction industry signifies a paradigm shift in its project management and design approaches. This study uses a serial mediation model to explain the relationship between BIM benefits and project success (PS), focusing on the crucial aspects of BIM implementation (BIMI), internal stakeholder satisfaction (ISS), and top management support (TMS). A survey was conducted, and a structural equation modelling approach (SEM) was used to validate the constructs. A total of 314 valid responses were collected from Pakistan Engineering Council (PEC) registered firms to test the proposed hypotheses. The results indicate that BIMI mediates the relation between BIMB and PS. Also, ISS mediates the relation between BIMB and PS. Furthermore, BIMI and ISS sequentially mediate the relationship between BIMB and PS. However, the moderation of TMS was not established between BIMB and BIMI. Findings show that the influence of BIM benefits on project success follows a sequential mediation pathway organized by effective BIM implementation and internal stakeholder satisfaction with BIM outputs. This study indicates future research direction by providing both theoretical insights and practical consequences for construction project management, as well as concrete tactics to maximize project success in the BIM environment in the construction industry.

Heavy metals in soils typically exhibit weak or indirect spectral responses in hyperspectral data, limiting their direct detectability. This limitation arises because their spectral behavior is strongly mediated by spectrally active soil constituents, particularly iron oxides, which dominate absorption features and control spectral response pathways. To address this limitation, a coupled spectral feature framework was developed for the physically interpretable hyperspectral prediction of soil chromium (Cr), integrating Cr-feature wavelengths with those associated with dominant soil constituents to enhance indirect spectral coupling and mechanistic interpretability. A total of 85 soil samples were collected from Wuhan, China, characterized by low soil organic matter and slightly acidic to near-neutral pH conditions. Key spectral variables were extracted using a hybrid feature selection strategy combining the successive projections algorithm (SPA) and recursive feature elimination (RFE), and subsequently used to construct a partial least squares regression (PLS) model. The proposed SPA-RFE-PLS framework effectively reduced hyperspectral dimensionality and identified stable and informative spectral features associated with iron-bearing soil constituents across repeated model runs, indicating that Cr prediction is primarily driven by indirect coupling with spectrally active soil constituents rather than direct spectral absorption features of Cr. The model achieved strong predictive performance for Cr estimation ( ; RPIQ ). The proposed framework outperforms conventional approaches by explicitly leveraging this coupling mechanism, thereby improving both predictive accuracy and model interpretability. Overall, this study provides a mechanism-driven and physically interpretable framework for hyperspectral estimation of soil heavy metals, offering enhanced reliability.

When Science Outpaces Infrastructure: People, Process, and Technology Challenges for Precision Dosing in Children.