Visions in Indian cities lack commitment from actors and possess low normative power. To address these concerns, we take up the task of examining vision-making with a focus on institutional settings across 31 million-plus populated cities. Ostrom's Institutional Analysis and Development (IAD) framework and ADICO grammar tool are used for analysis. We ask how rules-in-use within the vision-making settings are organized and geared towards collective action, and how they affect outcomes, i.e., visions. The analysis underscores three commonalities that hinder collective action, i.e., bureaucratic hegemony and allegiance to sectoral mandates, fragmented actor constellation and weak regulatory power of visions. Within visioning settings, facilitators serve as key agents for ensuring a level playing field. We found that when facilitators are external (i.e., consultants), stakeholder coverage is wider and documentation is exhaustive. In large metropolises and megacities, the visioning process acknowledges stakeholder consultation responsibly. Perhaps fair information exchange and informal monitoring of visions by civil society actors contribute to the politicisation of vision-making. In institutional terms, visions are closer to being a strategy and less to norm-setting. The paper argues that participatory discussions in diverse and heterogeneous settings with the scope of repeat interactions will build homophily and address concerns of structural fragmentation.
To enable efficient planning and operation of optical networks, it is essential to evaluate the Quality of Transmission (QoT) of lightpaths before they are established. The nonlinear effects in optical fibers cause deployed lightpaths to impact each other’s QoT. Therefore, reliable QoT estimation is essential, as it enables the assessment of lightpath feasibility prior to deployment and supports efficient network planning and optimization. Existing deep learning-based QoT estimation methods typically predict the Q-factor directly, which can make learning challenging under slowly varying network conditions. In this paper, we propose a Residual Transformer for Q-factor estimation that incorporates a novel temporal-mean residual prediction formulation. Instead of directly regressing the absolute Q-factor, the proposed model predicts the deviation from the temporal-mean Q-factor within the input window, and reconstructs the final prediction by adding the estimated residual to the baseline. By exploiting the strong temporal persistence of QoT measurements, the proposed formulation simplifies the regression task, enabling the model to focus on learning residual temporal dynamics rather than the complete Q-factor trajectory. The proposed framework combines this residual prediction formulation with a single-head Transformer encoder and is evaluated on a real-world dataset collected from Microsoft’s North American optical backbone network. Experimental results demonstrate that the proposed method achieves an MSE of 0.00296 and an MAE of 0.0318, outperforming nine standard baseline models, including a multilayer Long Short-Term Memory (LSTM) network, a multilayer perceptron (MLP), Support Vector Regression (SVR), a simple neural network (NN), a Gated Recurrent Unit (GRU), Temporal Convolutional Network (TCN), PatchTST, iTransformer, and TimesNet. Simulation results reveal that the proposed model consistently achieves outstanding performance across multiple channels in the dataset, with an MSE of 0.00296 and MAE of 0.0318, highlighting its suitability for advanced flexible optical networks. Additional experiments show that incorporating the proposed temporal-mean residual prediction formulation into GRU and LSTM models also substantially improves their prediction accuracy, indicating that the formulation is not limited to the Transformer backbone.
Multi-omics is the coordinated acquisition, integration, and interpretation of multiple datasets generated from diverse molecular layers of a biological system, intending to capture a comprehensive understanding of interactions between molecular hierarchies and reveal the complex regulatory architecture underlying cellular states, physiological processes, and disease phenotypes. Multi-omics integration signifies a transformative approach in cancer research, facilitating a systems-level comprehension of tumor biology that goes beyond the analysis of individual data layers. Through the combined analysis of data from genomics, transcriptomics, epigenomics, proteomics, and metabolomics, this method reveals the intricate molecular networks that influence tumorigenesis and its variability. High-throughput technologies play a crucial role in this context, enabling the identification of new biomarkers, the detection of actionable therapeutic targets, and the classification of unique cancer subtypes. Integrative omics is essentially transforming precision oncology by enhancing patient risk assessment and forecasting treatment responses, thus guiding personalized diagnosis and therapy approaches. The effectiveness of this method increases when molecular data are integrated with clinical and imaging information, resulting in stronger predictive models for personalized patient treatment. Nonetheless, considerable obstacles remain, such as the integration of diverse data, the adjustment of batch effects, and the clinical understandability of intricate computational models. Tackling these challenges requires sophisticated machine learning methods, uniform data processing workflows, and ongoing cross-disciplinary teamwork. With the advancement of these methodologies, multi-omics integration will act as the essential link between large-scale data and precision medicine, providing unmatched chances to unravel the intricacies of cancer and produce effective, tailored treatments. This review highlights the latest advancements, ongoing challenges, and future pathways that are influencing the next wave of cancer research and clinical applications.
High-entropy alloys (HEAs) based on CoCrFeMnNi continue to attract attention to their balanced mechanical properties and corrosion resistance; however, their tribological response remains strongly path-dependent. This review combines processing routes such as casting, powder metallurgy/spark plasma sintering (SPS), additive manufacturing (SLM/LPBF), and coating methods such as PVD/thermal spray with the resultant phase constitution (FCC, BCC, σ, and Laves) and defect structures to describe their trends in hardness, friction, and wear at room temperature up to approximately 800 o C. To balance different literature reports, we standardize the units of wear and cluster complete test data (counterface, load, kinematics, atmosphere, and temperature) so that quantitative comparison between studies is possible. Determining (i) systematic decreases when the FCC changes to BCC or intermetallic-reinforced state and (ii) a crossover in temperature at which many coating types can minimize wear at approximately 400 o C before increasing in temperature, a causal map was constructed linking route-controlled phase selection and secondary reinforcers (e.g., carbides/solid lubricants) to effect sizes in selective wear and friction. A route-selection guide, important information gaps in tribocorrosion (sliding-electrochemistry coupling), and template reporting tools that improve design and reproducibility are included at the conclusion of the review. All these factors lead to a consistent foundation on which to build the engineering of Cantor based HEAs and coating to meet desired wear windows in commercial environments.
Landslides pose significant threats to life, property and sustainable development in mountainous regions worldwide, with their occurrence increasingly influenced by climate change. This study addresses the critical need for accurate landslide susceptibility models in the Western Province of Rwanda, where traditional methods have shown limitations. It employed and compared three deep learning models: convolutional neural network (CNN), deep neural network (DNN), and multi-layer perceptron (MLP), to assess the landslide risks, incorporating climate change considerations. The study utilised 16 conditioning factors, carefully selected to avoid multicollinearity, with the digital surface model (DSM) showing the highest variance inflation factor (VIF) of 3.9730. The CNN model demonstrated superior performance, achieving the highest overall accuracy (93.7
