Nova Management (United States)

Purpose This study investigates the long-term evolution of the Project Management Office (PMO) within the foundation of Organizational Project Management (OPM). It examines how the PMO'smandate, authority, and responsibilities developed in a large European organization, tracing its transition from departmental support to strategic governance and, more recently, to a central role in innovation and artificial intelligence (AI) initiatives. Design/methodology/approach A longitudinal single-case study design was employed, combining semi-structured interviews, internal documentation, and a follow-up questionnaire with Enterprise PMO (EPMO) professionals to identify PMO functions across stages and analyze mandate and authority expansion. Findings Three main findings emerged. First, the PMO evolved through four stages, expanding from IT support to enterprise-wide governance. This followed a layered mandate logic, where foundational functions were retained while governance and strategic roles were progressively added. Second, a decisive transformation occurred when the PMO was relocated from IT to board-level reporting, expanding its autonomy, process performance, and strategic influence. Third, the PMO was explicitly mandated to lead innovation and AI initiatives, positioning it as both a governance body and a driver of digital transformation. Originality/value This study provides a rare longitudinal examination of PMO evolution in the context of OPM, addressing limited empirical evidence on sustained PMO development beyond early support and governance roles. It advances the concept of the layered mandate and offers guidance for structuring PMOs to sustain stability, expand authority, and assume responsibility for innovation and AI governance.

Purpose Scholarly attention towards the Gulf Cooperation Council (GCC) region (Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates) has been limited due to its various unique features, making it an ideal testing ground for ESG practices. This paper is the first scholarly endeavor to comprehensively analyze the bidirectional causal relations between ESG and financial performance in the Gulf region.Materials and methods This study analyzes a sample of 54 publicly traded firms, using firm-level monthly observations of ESG scores and stock prices from January 2009 to May 2023. Stock returns—measured contemporaneously and with lags of 12, 18, and 24 months—serve as the indicator of financial performance. The analysis includes market returns, company size, and industry as control variables, consistent with established literature. Market returns represent the overall performance of the stock market during the relevant periods. All financial data were obtained from the Bloomberg Terminal, and ESG data were collected from ESG Book.Results The statistical analysis of the impact of both aggregated (ESG) and individual (E, S, and G) scores on stock returns indicate a weak relationship between sustainability and financial performance in the Gulf region. The assessment of public companies in the region demonstrates that stock markets display minimal responsiveness to changes in ESG metrics, indicating that improvements in ESG scores do not translate into higher stock returns. Similarly, the analysis of reverse causality—assessing whether financial performance influences future ESG performance—shows a weak association. While there is a slight indication that some firms with better stock returns may allocate some profits toward governance and environmental initiatives, this relationship is marginal and lacks strong statistical support.Conclusions Both the direct and reverse relationships between ESG scores and financial performance are weak, underscoring that ESG factors have yet to become significant drivers or consequences of stock returns in the Gulf region. These findings highlight the need to explore whether ESG progress is instead shaped by state leadership, regulatory mandates, and alignment with national strategic priorities, rather than by market-driven forces.

Summary Multipolar migration is a conserved neuronal migration mode in the developing brain, enabling emerging neurons to navigate crowded environments and reach precise laminar positions. Yet how these cells interpret external cues to guide their migration remains unclear. We investigate this question in the developing vertebrate retina using horizontal cells as a model. Combining transcriptomics, targeted CRISPR screening, and live imaging, we reveal the spatiotemporal guidance system underlying horizontal cell lamination: repulsive Slit1b/2–Robo2 signaling in the amacrine cell layer is essential to initiate apical horizontal cell migration, while attractive Neurturin–Gfrα1 signaling from photoreceptors fine-tunes final positioning beneath the photoreceptor layer. Disruption of these pathways causes basal retention of horizontal cells, highlighting the importance of spatially coordinated signaling for proper lamination and functional retinal circuitry. Our results uncover how positional signals and tissue architecture cooperate to achieve neuronal precision, an organizing principle likely relevant across the developing central nervous system.

Las actividades y operaciones empresariales podrían estar asociadas con los conflictos armados y con las partes en conflicto de diferentes formas y, en consecuencia, las empresas deberían evitar contribuir a impactos negativos en esas áreas. Este artículo tiene como objetivo examinar los marcos de empresas y derechos humanos aplicables en situaciones de conflicto armados con especial atención a los Principios Rectores sobre Empresas y Derechos Humanos y las recientes directrices proporcionadas por el Grupo de Trabajo de la ONU sobre Empresas y Derechos Humanos, el PNUD y la ACNUDH. El artículo aborda la importancia de la noción de diligencia debida reforzada en materia de derechos humanos en esos escenarios, dónde y cuándo debe implementarse, así como la decisión de retirarse y sus consecuencias.

Abstract In the current context of big data, the operational forecasting problems are more and more frequently involving the prediction of collections of multivariate, high-dimensional, related time series. The challenge of forecasting groups of time series can be tackled by fitting a single function to all series (global approach) or assuming each series to be a separate prediction problem and fitting one function to each problem (local approach). Although some global models show promising results against local benchmarks, there is still unclarity on how to best control generalization and structural assumptions by calibrating the exploitation of global patterns with local components. This is heavily reliant on the level of complexity from the dependency relationships exhibited between the series within a collection (e.g. indirect latent, local covariate relationships or noise effects) and other dimensions, such as the heterogeneity of the series and its length. For these reasons, there is an acknowledged need to invest further in developing scalable and data-driven hybrid models. The literature proposes Deep Learning (DL) based models built from different data generating processes (such as Auto-Regressive) which combine global models with classical local models (global-local frameworks). These methods frequently capture globality through factorization models or latent deep components and locality via classical local models or networks. These methods present non-linear modelling capabilities but are still either limited by solid theoretical assumptions (e.g. linearity of the underlying data) or predominantly one-dimensional, i.e., the future predictions for a single dimension mainly depend on past values from that same dimension. Simultaneously, dynamic Reinforcement Learning (RL) based models have been widely explored to optimize the balance of global and local signals in general regression problems, frequently through Q-learning algorithms. These derive from the equilibrium of cooperative strategies or the societal value in multi-agent learning systems. Similar models have been successfully applied to time series forecasting problems (such as stock market predictions), addressing the risk of method generalization under varying conditions. This paper conducted a concise literature review focused on these two research streams (global-local frameworks and RL based models) to optimize the balance between globality and locality in forecasting collections of time series. It focuses on their evolution across time and hints at opportunities to close some of the research gaps by intersecting both propositions. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and achieved a selection of 118 publications since 2000. The main findings revealed that global models have achieved strong expressiveness in capturing the most complex structural patterns while still enabling probabilistic outcomes to be delivered through uncertainty estimates. On the other hand, RL based methods depict great benefits in mitigating the risks of generalization by imprinting contextual diversity when predicting each step ahead for each series. Within those, the adoption of other computational learning or evolutionary-based methods (e.g. Genetic Programming) to improve the parametrization of the learning policies is also highlighted as an area of future work yet to be uncovered.

The related problems of improving existing buildings' energy performance, reducing energy consumption, and improving indoor comfort and their many consequences are well known. Considering increasing urbanization and climate change, governments define strategies to enhance and measure buildings' energy performance and energy efficiency. This work aims to contribute to the improvement of buildings' characteristics by conducting a thorough systematic literature review and adopting a data science approach to these problems, presenting initial results with an open-access energy performance certificate dataset from the Lombardy Region, in Italy. We provide a pre-processing method to the data, applicable for future research, aiming to address challenges such as automatic classification of existing buildings' energy performance certification, and predicting energy-efficient retrofit measures, using machine learning techniques. The analysis of this dataset is challenging because of the high variability and dimensionality of this dataset. For this purpose, a robust iterative process was developed. First, the data dimensionality was reduced with Pearson Correlation to find the best set of variables against the non-renewable global energy performance index (EPgl, nren). Then, the outliers were handled by utilizing Box Plot and Isolation Forest algorithms. The main contribution is to inform private and public building sectors on dealing with high dimensional data to achieve enhanced energy performance and predict energy-efficient retrofit measures to improve their energy performance.