Assessing foundation behaviour under complex loading near tunnels

The stability of strip footings subjected to eccentrically inclined loads is critical for reliable foundation design.This study investigates the effect of a circular unlined tunnel in a rock mass on the ultimate bearing capacity(UBC)of a foundation with width B under inclined and eccentric loads.Adaptive finite element limit analysis was employed to evaluate the reduction in UBC of the footing resting above a tunnel.The examined critical parameters include normalized load eccentricity(e/B),load inclination(β),and horizontal and vertical distances of the tunnel from the foundation(P/B and Q/B,respectively),along with rock mass properties.The results reveal that for e/B≥0.25 and β≤60°,the reduction coefficient,R_(c)≥0.90,suggesting that the presence of a tunnel has a minimal impact on the load-bearing capacity of the footing,with failure primarily governed by load eccentricity and inclination.Additionally,potential failure mechanisms are explored,showing that at lower e/B,higher β,and lower Q/B,the tunnel significantly affects footing's failure envelope.Conversely,at higher e/B and lower β,failure is due to rotational effects of footing,regardless of the tunnel's position.To predict the Rc more accurately,due to the time-consuming nature of direct calculations,both MLR and ANN models were developed.The MLR model provided a baseline for comparison,while the ANN model,with a coefficient of determination(R2)of 0.98,demonstrated superior accuracy compared to the R2=0.96 of MLR.Using both approaches ensured robust and efficient predictions of Rc.Since Rc does not directly provide the reduced UBC of footing due to presence of tunnel,the study introduced bearing capacity factor(Nc)to enable direct calculation of the reduced UBC of footing.These findings offer theoretical guidelines for preliminary design and provide practitioners with an effective tool for evaluating UBC reduction in complex loading scenarios involving tunnels.

Raman spectroscopy as a promising noninvasive tool in brain cancer detection

Despite intensive therapy regi men,brain cancers present with a poor prognosis,with an esti-mated median survival time of less than 15 months in case of glioblastoma.Early detection and improved surgical resctions are suggested to enhance prognosis;several tools are being explored to achieve the purpose.Raman spectroscopy(RS),a nondestructive and noninv asive technique,has been extensively explored in brain cancers.This review summarizes RS based studies in brain cancers,categorized into studies on animal models,ex tivo human samples,and in tito human subjects.Findings suggest RS as a promising tool which can aid in improving the accuracy of brain tumor surgery.Further adv ancements in instrumentation,market assessment,and clinical trials can facilitate translation of the technology as a noninvasive intraopenative guidance tool.

LoSI: Large Scale Location Inference Through FM Signal Integration and Estimation

In this paper we present a large scale, passive positioning system that can be used for approximate localization in Global Positioning System(GPS) denied/spoofed environments. This system can be used for detecting GPS spoofing as well as for initial position estimation for input to other GPS free positioning and navigation systems like Terrain Contour Matching(TERCOM). Our Location inference through Frequency Modulation(FM)Signal Integration and estimation(LoSI) system is based on broadcast FM radio signals and uses Received Signal Strength Indicator(RSSI) obtained using a Software Defined Radio(SDR). The RSSI thus obtained is used for indexing into an estimated model of expected FM spectrum for the entire United States. We show that with the hardware for data acquisition, a single point resolution of around 3 miles and associated algorithms, we are capable of positioning with errors as low as a single pixel(more precisely around 0.12 mile). The algorithm uses a largescale model estimation phase that computes the expected FM spectrum in small rectangular cells(realized using geohashes) across the Contiguous United States(CONUS). We define and use Dominant Channel Descriptor(DCD) features, which can be used for positioning using time varying models. Finally we use an algorithm based on Euclidean nearest neighbors in the DCD feature space for position estimation. The system first runs a DCD feature detector on the observed spectrum and then solves a subset query formulation to find Inference Candidates(IC).Finally, it uses a simple Euclidean nearest neighbor search on the ICs to localize the observation. We report results on 1500 points across Florida using data and model estimates from 2015 and 2017. We also provide a Bayesian decision theoretic justification for the nearest neighbor search.