A geospatial approach for site suitability and accessibility for healthcare services in Bankura district, West Bengal, India

Healthcare site selection assumes an imperative part in healthcare development and management. From part of the public authority, proper medical site selection will help the distribution of clinical assets, coordinating with the arrangement of medical care with the social and economic demands, organizing the metropolitan and rural healthcare administration advancement, and facilitating social logical inconsistencies. Site suitability analysis is a variety of analysis utilized in GIS to work out the simplest place or site for one thing. The main objective of the current study was to select a site for new healthcare services with geospatial technologies to intermix spatial and non-spatial data to create a weighted result. The current study had been done into three phases, where many processes are intermixed into a single phase. In the first phase of analysis, distance, density, and proximity were mapped to seek out poor and lower accessible areas of healthcare from existing healthcare. To selecting new healthcare sites, four-factor criteria (Buffer around road and rail, land use land cover and buffer around settlement,) and some constrain criteria considered in the second phase of analysis. Finally, the shortest network path analysis has been done in the third phase to determine the shortest and best route from selected healthcare sites towards district medical college. The current study presents some suitable sites in the poor and inaccessible areas of the district. This study will be very helpful for the decision support system of healthcare management in the future.

Glass Half Full: Preserved Anatomical Bypasses Predict Variance in Language Functions After Stroke

The severity of post-stroke aphasia is related to damage to white matter connections. However, neural signaling can route not only through direct connections, but also along multi-step network paths. When brain networks are damaged by stroke, paths can bypass around the damage to restore communication. The shortest network paths between regions could be the most efficient routes for mediating bypasses. We examined how shortest-path bypasses after left hemisphere strokes were related to language performance. Regions within and outside of the canonical language network could be important in aphasia recovery. Therefore, we innovated methods to measure the influence of bypasses in the whole brain. Distinguishing bypasses from all residual shortest paths is difficult without pre-stroke imaging. We identified bypasses by finding shortest paths in subjects with stroke that were longer than those observed in the average network of the most reliably observed connections in age-matched controls. We tested whether features of those bypasses predicted scores in four orthogonal dimensions of language performance derived from a factor analysis of a battery of language tasks. The features were the length of each bypass in steps, and how many bypasses overlapped on each individual direct connection. We related these bypass features to language factors using grid-search cross-validated Support Vector Regression, a technique that extracts robust relationships in high-dimensional data analysis. We discovered that the length of bypasses reliably predicted variance in lexical production (R2 = .576) and auditory comprehension scores (R2 = .164). Bypass overlaps reliably predicted variance in Lexical Production scores (R2 = .247). The predictive elongation features revealed that bypass efficiency along the dorsal stream and ventral stream were most related to Lexical Production and Auditory Comprehension, respectively. Among the predictive bypass overlaps, increased bypass routing through the right hemisphere putamen was negatively related to lexical production ability.

GIS-modelled built-environment exposures reflecting daily mobility for applications in child health research

Background Inaccurately modelled environmental exposures may have important implications for evidence-based policy targeting health promoting or hazardous facilities. Travel routes modelled using GIS generally use shortest network distances or Euclidean buffers to represent journeys with corresponding built-environment exposures calculated along these routes. These methods, however, are an unreliable proxy for calculating child built-environment exposures as child route choice is more complex than shortest network routes.Methods We hypothesised that a GIS model informed by characteristics of the build environment known to influence child route choice could be developed to more accurately model exposures. Using 884 GPS-derived walking commutes to and from school we used logistic regression models to highlight built-environment features important in child route choice (e.g. road type, traffic light count). We then recalculated walking commute routes using a weighted network to incorporate built-environment features. Multilevel regression analyses were used to validate exposure predictions to the retail food environment along the different routing methods.Results Children chose routes with more traffic lights and residential roads compared to the modelled shortest network routes. Compared to standard shortest network routes, the GPS-informed weighted network enabled GIS-based walking commutes to be derived with more than three times greater accuracy (38%) for the route to school and more than 12 times greater accuracy (92%) for the route home.Conclusions This research advocates using weighted GIS networks to accurately reflect child walking journeys to school. The improved accuracy in route modelling has in turn improved estimates of children’s exposures to potentially hazardous features in the environment. Further research is needed to explore if the built-environment features are important internationally. Route and corresponding exposure estimates can be scaled to the population level which will contribute to a better understanding of built-environment exposures on child health and contribute to mobility-based child health policy.

Channel design for 3D models with applications in powder-bed additive manufacturing

Purpose For any 3D model with chambers to be fabricated in powder-bed additive manufacturing processes such as SLM and SLS, powders are trapped in the chambers of the finished model. This paper aims to design a shortest network with the least number of outlets for efficiently leaking the trapped powders. Design/methodology/approach This paper proposes a nonlinear objective with linear constraints for solving the channel design problem and a particle swarm optimization algorithm to solve the nonlinear system. Findings Structural optimization for the channel network leads to fairly short channels in the interior of the 3D models and very few outlets on the model surface, which achieves the cleaning of the powders while causing almost the least changes to the model. Originality/value This paper reveals the NP-harness of computing the shortest channel network with the least number of outlets. The proposed approach helps the design of lightweight models using the powder-bed additive manufacturing techniques.

A Reduced Uncertainty-Based Hybrid Evolutionary Algorithm for Solving Dynamic Shortest-Path Routing Problem

The need of effective packet transmission to deliver advanced performance in wireless networks creates the need to find shortest network paths efficiently and quickly. This paper addresses a reduced uncertainty-based hybrid evolutionary algorithm (RUBHEA) to solve dynamic shortest path routing problem (DSPRP) effectively and rapidly. Genetic algorithm (GA) and particle swarm optimization (PSO) are integrated as a hybrid algorithm to find the best solution within the search space of dynamically changing networks. Both GA and PSO share context of individuals to reduce uncertainty in RUBHEA. Various regions of search space are explored and learned by RUBHEA. By employing a modified priority encoding method, each individual in both GA and PSO are represented as a potential solution for DSPRP. A complete statistical analysis has been performed to compare the performance of RUBHEA with various state-of-the-art algorithms. It shows that RUBHEA is considerably superior (reducing the failure rate by up to 50%) to similar approaches with increasing number of nodes encountered in the networks.