Geospatial technologies are rapidly emerging as pivotal tools for advancing sustainable urban and rural development through citizen empowerment in India and worldwide. This study systematically reviews peer-reviewed and grey literature to examine their integration with global frameworks, such as the SDGs, Paris Agreement, and Sendai Framework, while aligning with Indian initiatives like NAPCC, Smart Cities, Digital India, SVAMITVA, AMRUT, and the National Geospatial Policy 2022, with emphasis on the citizen as a crucial feedback factor. Employing thematic mapping and comparative analysis between the Global North and South, we evaluate applications in urban planning, mobility, energy, resilience, and health, highlighting platforms like PPGIS, VGI, Bhuvan, and 'Know Your DIGIPIN' for participatory data collection and decision-making.</p>
<p>Our analysis reveals regional disparities in India, with the southern zone leading in innovation (35% adoption) and the eastern region focussing on disaster management (15%), along with global successes in disaster relief, welfare targeting, and immunisation tracking. Quantitative impacts include India's geospatial market growth to ₹63,000 crores by 2025 and AMRUT 2.0's rapid water and sewerage coverage expansion in many major cities. However, persistent challenges include technical knowledge gaps in academia, insufficient institutional support for geospatial startups, and barriers like low digital literacy and language limitations that restrict broader participation.</p>
<p>We recommend enhanced geospatial education, open data policies, vernacular interfaces, and inclusive citizen science frameworks to bridge these gaps, foster equitable participation, and realise geospatial intelligence's full potential for resilient, data-driven sustainability.
To study the influence of edge effects on the resin filling flow pattern in the RTM process of composite materials, the finite element software PAM-RTM was used to simulate and analyze the impregnation process of the blade preforms in RTM.By observing separately the filling time of the resin and the flow state of the leading edge in the impregnation process under the conditions with and without edge effect, the influence of different fiber volume fractions in the edge area and different injection pressure differences on the filling time and flow process of the blades was discussed, and the feasibility of the simulation method was verified through experiments.The results show that the presence of edge effect can prolong the length of the resin filling front, thereby reducing the impregnation time of the blade by 15.8%, but the difference in edge permeability does not significantly affect the impregnation time of the blade. When the edge effect exists in the fiber preform, different injection pressures have significant influences on the filling time.The filling time decreases with the increase of pressure, and the decreasing trend gradually slows down.The simulated value of the resin filling time differs by 7.8% from the measured value, indicating that the simulation method is feasible to a certain extent.
Materials of engineering and construction. Mechanics of materials, Environmental engineering
Romeo Bandinelli, Gabriele Scozzafava, Bianca Bindi
et al.
The importance of traceability in food products in regard to consumer preferences, the difficulty of certifications in communicating credence attributes, and concerns about food safety, have led consumers to ask for more information about the credibility of information reported on the label with the product itself, due to the importance of traceability in food products. An objective of this study is to analyze the benefits of implementing blockchain technology in the supply chain for ancient wheat, as a technology capable of sharing reliable information about the products easily along the entire value chain. In particular, the aim is to evaluate how the consumer reacts when confronted with a package of ancient wheat pasta for which all the information on its origin and processing methods is available. In order to achieve these results, a survey based on the Technology Acceptance Model has been developed and conducted in the Italian country. Result indicates the importance of identifying an independent variable that represents the degree of security when faced with a threat that creates a circumstance, condition or event that can lead to economic hardship, for example, data destruction, disclosure, modification, fraud, waste, and abuse. The strength of blockchain lies precisely in its ability to guarantee the immutability of data throughout the supply chain, providing the end consumer with a high-quality product.
Systems engineering, Marketing. Distribution of products
Satyam Panchal, Krishna Gudlanarva, Manh-Kien Tran
et al.
In this paper, an analogous study of the velocity and temperature profiles inside microchannel cooling plates (with hydraulic diameter of 6 mm), placed on a large pouch-type LiFePO<sub>4</sub> battery, is presented using both the laboratory and simulation techniques. For this, we used reverse engineering (RE), computed tomography (CT) scanning, Detroit Engineering Products (DEP) MeshWorks 8.0 for surface meshing of the cold plate, and STAR CCM+ for steady-state simulation. The numerical study was conducted for 20 A (1C) and 40 A (2C) and different operating temperatures. For experimental work, three heat flux sensors were used and were intentionally pasted at distributed locations, out of which one was situated near the negative tab (anode) and the other was near the positive tab (cathode), because the heat production is high near electrodes and the one near the mid body. Moreover, the realizable <i>k</i>-ε turbulence model in STAR CCM+ is used for simulation of the stream in a microchannel cooling plate, and the computational fluid dynamics (CFD) simulations under constant current (CC) discharge load cases are studied. Later, the validation is conducted with the lab data to ensure sufficient cooling occurs for the required range of temperature. The outcome of this research work shows that as C-rates and ambient temperature increase, the temperature contours of the cooling plates also increase.
In a complex network, some nodes are relatively concentrated in topological structure, thus forming a relatively independent node group, which we call a community. Usually, there are multiple communities on a network, and these communities are interconnected and exchange information with each other. A node that plays an important role in the process of information exchange between communities is called an inter-community bridge node. Traditional methods of defining and detecting bridge nodes mostly quantify the bridging effect of nodes by collecting local structural information of nodes and defining index operations. However, on the one hand, it is often difficult to capture the deep topological information in complex networks based on a single indicator, resulting in inaccurate evaluation results; on the other hand, for networks without community structure, such methods may rely on community partitioning algorithms, which require significant computing power. In this paper, considering the multi-dimensional attributes and structural characteristics of nodes, a deep learning-based framework named BND is designed to quickly and accurately detect bridge nodes. Considering that the bridging function of nodes between communities is abstract and complex, and may be related to the multi-dimensional information of nodes, we construct an attribute graph on the basis of the original graph according to the features of the five dimensions of the node to meet our needs for extracting bridging-related attributes. In the deep learning model, we overlay graph neural network layers to process the input attribute graph and add fully connected layers to improve the final classification effect of the model. Graph neural network algorithms including GCN, GAT, and GraphSAGE are compatible with our proposed framework. To the best of our knowledge, our work is the first application of graph neural network techniques in the field of bridge node detection. Experiments show that our designed framework can effectively capture network topology information and accurately detect bridge nodes in the network. In the overall model effect evaluation results based on indicators such as Accuracy and F1 score, our proposed graph neural network model is generally better than baseline methods. In the best case, our model has an Accuracy of 0.9050 and an F1 score of 0.8728.
Abstract Studying the wagon accumulation process and the laws of accumulation cost is of great significance for determining the suitable conditions of wagon accumulation and shortening the accumulation time. Here, the process of relaxed fixed time accumulation is first taken as a stochastic service system, and derives the theoretical formula for the accumulation cost. Then based on actual data of wagon flows, a simulation model is built to analyse the influence of parameters in the theoretical formula such as the coordination of the traffic diagram and the accumulation process, the sizes and intervals of the arriving wagon groups and the minimum number of wagons. Finally, through comparing with the accumulation cost of fixed train length accumulation mode and considering the benefit of changing the minimum number of wagons in train sets, the optimal minimum number of wagons in the relaxed fixed time accumulation mode under different wagon flow intensities is determined.
Forum papers are thought-provoking opinion pieces or essays founded in fact, sometimes containing speculation, on a civil engineering topic of general interest and relevance to the readership of the journal. The views expressed in this Forum article do not necessarily reflect the views of ASCE or the Editorial Board of the journal.