A joint analysis of accessibility and household trip frequencies by travel mode.

This paper examines the endogenous relationship between residential level of accessibility and household trip frequencies to tease out the direct and indirect effects of observed behavioural differences. We estimate a multivariate ordered probit model system, which allows dependence in both observed and unobserved factors, using data from the 2016 Transportation Tomorrow Survey (TTS), a household travel survey in the Greater Golden Horseshoe Area (GGH) in Toronto. The modelling framework is used to analyse the influence of exogenous variables on eight outcome variables of accessibility levels and trip frequencies by four modes (auto, transit, bicycle and walk), and to explore the nature of the relationships between them. The results confirm our hypothesis that not only does a strong correlation exist between the residential level of accessibility and household trip frequency, but there are also direct effects to be observed. The complementarity effect between auto accessibility and transit trips, and the substitution effect observed between transit accessibility and auto trips highlight the residential neighbourhood dissonance of transit riders. It shows that locations with better transit service are not necessarily locations where people who make more transit trips reside. Essentially, both jointness (due to error correlations) as well as directional effects observed between accessibility and trip frequencies of multiple modes offer strong support for the notion that accessibility and trip frequency by mode constitute a bundled choice and need to be considered as such.

Biomechanical Response of Head Surrogate With and Without the Helmet.

Measurements of brain deformations under injurious loading scenarios are actively sought. In this work, we report experimentally measured head kinematics and corresponding dynamic, two-dimensional brain simulant deformations in head surrogates under a blunt impact, with and without a helmet. Head surrogates used in this work consisted of skin, skull, dura, falx, tentorium, and brain stimulants. The head surrogate geometry was based on the global human body models consortium's head model. A base head surrogate consisting of skin-skull-brain was considered. In addition, the response of two other head surrogates, skin-skull-dura-brain, and skin-skull-dura-brain-falx-tentorium, was investigated. Head surrogate response was studied for sagittal and coronal plane rotations for impactor velocities of 1 and 3 m/s. Response of head surrogates was compared against strain measurements in PMHS. The strain pattern in the brain simulant was heterogenous, and peak strains were established within ∼30 ms. The choice of head surrogate affect the spatiotemporal evolution of strain. For no helmet case, peak MPS of ∼50-60% and peak MSS of ∼35-50% were seen in brain simulant corresponding to peak rotational accelerations of ∼5000-7000 rad/s2. Peak head kinematics and peak MPS have been reduced by up to 75% and 45%, respectively, with the conventional helmet and by up to 90% and 85%, respectively, with the helmet with antirotational pads. Overall, these results provide important, new data on brain simulant strains under a variety of loading scenarios-with and without the helmets.

Genetic Variants of Steroidogenesis and Gonadotropin Pathways and Polycystic Ovary Syndrome Susceptibility: A Systematic Review and Meta-analysis.

Genetic variants are predisposing factors to polycystic ovary syndrome (PCOS), a multifactorial condition that often gets triggered due to various environmental factors. The study investigates the association of the variants of genes that are involved in the steroidogenesis pathway or gonadotropin pathway with the risk of PCOS. Appropriate keywords for predetermined genes were used to search in PubMed, Google Scholar, Science Direct, and Central Cochrane Library up to January 11, 2023. PROSPERO (CRD42022275425). Inclusion criteria: (a) case-control study; (b) genotype or allelic data. Exclusion criteria were: (a) duplicate studies; (b) clinical trials, systematic reviews, meta-analysis or conference abstract, case reports; (c) other than the English language; (d) having insufficient data; e) genetic variants for which meta-analysis has been reported recently and does not have a scope of the update. Various genetic models were applied as per data availability. Overall 12 variants of 7 genes were selected for the analysis. Relevant data were extracted from 47 studies which include 10,584 PCOS subjects and 16,150 healthy controls. Meta-analysis indicates a significant association between TOX3 rs4784165 [ORs = 1.08, 95% CI (1.00-1.16)], HMGA2 rs2272046 [ORs = 2.73, 95% CI (1.97-3.78)], YAP1 rs1894116 [OR = 1.22, 95% CI (1.13-1.33)] and increased risk of PCOS. Whereas FSHR rs2268361 [ORs = 0.84, 95% CI (0.78-0.89)] is associated with decreased PCOS risk. When sensitivity analysis was carried out, the association became significant for CYP19 rs700519 and FSHR rs6165 under an additive model. In addition, C9Orf3 rs3802457 became significantly associated with decreased PCOS risk with the removal of one study. Insignificant association was observed for CYP19A (rs2470152), FSHR (rs2349415, rs6166), C9Orf3 (rs4385527), GnRH1 (rs6185) and risk of PCOS. Our findings suggest association of CYP19A (rs700519), TOX3 (rs4784165), HMGA2 (rs2272046), FSHR (rs6165, rs2268361), C9orf3 (rs3802457), and YAP1 (rs1894116) with risk for PCOS.

Deep learning and data fusion to estimate surface soil moisture from multi-sensor satellite images.

We propose a new architecture based on a fully connected feed-forward Artificial Neural Network (ANN) model to estimate surface soil moisture from satellite images on a large alluvial fan of the Kosi River in the Himalayan Foreland. We have extracted nine different features from Sentinel-1 (dual-polarised radar backscatter), Sentinel-2 (red and near-infrared bands), and Shuttle Radar Topographic Mission (digital elevation model) satellite products by leveraging the linear data fusion and graphical indicators. We performed a feature importance analysis by using the regression ensemble tree approach and also feature sensitivity to evaluate the impact of each feature on the response variable. For training and assessing the model performance, we conducted two field campaigns on the Kosi Fan in December 11-19, 2019 and March 01-06, 2022. We used a calibrated TDR probe to measure surface soil moisture at 224 different locations distributed throughout the fan surface. We used input features to train, validate, and test the performance of the feed-forward ANN model in a 60:10:30 ratio, respectively. We compared the performance of ANN model with ten different machine learning algorithms [i.e., Generalised Regression Neural Network (GRNN), Radial Basis Network (RBN), Exact RBN (ERBN), Gaussian Process Regression (GPR), Support Vector Regression (SVR), Random Forest (RF), Boosting Ensemble Learning (Boosting EL), Recurrent Neural Network (RNN), Binary Decision Tree (BDT), and Automated Machine Learning (AutoML)]. We observed that the ANN model accurately predicts the soil moisture and outperforms all the benchmark algorithms with correlation coefficient (R = 0.80), Root Mean Square Error (RMSE = 0.040 [Formula: see text]), and bias = 0.004 [Formula: see text]. Finally, for an unbiased and robust conclusion, we performed spatial distribution analysis by creating thirty different sets of training-validation-testing datasets. We observed that the performance remains consistent in all thirty scenarios. The outcomes of this study will foster new and existing applications of soil moisture.

Investigation of role of falx and tentorium on brain simulant strain under impact loading.

Mild Traumatic brain injury (mTBI) is a major health concern. The role of the falx and tentorium (i.e., membranes) in exacerbating mTBI has been conjectured due to the involvement of clinically confirmed midbrain regions. Recent brain biomechanics investigations, mainly using computational head models, also support such a hypothesis. However, data in this regard is limited. Towards this end, using a surrogate head model, we investigate the role of membranes on brain biomechanics. Two different materials-thermoplastic polyurethane with various elastic moduli values (20, 150, 205 MPa) and polylactic acid (elastic modulus 1500 MPa) were used to examine the effect of membrane stiffness on brain simulant strain. The head surrogate was mounted on the Hybrid-III neck and subjected to coronal and sagittal plane rotations using a linear impactor system. Corresponding 6-DOF head kinematics and 2D brain simulant strains in midcoronal and midsagittal planes were measured. Our results elucidate the paradigm of strain evolution in the brain simulant in the presence of membranes. The cortical strains are decreased, whereas strains in the subcortical regions are either equivalent or increased in the presence of membranes. The elastic modulus of the membranes governs the amount of strain reduction or increase. We found that the falx displacement and constraints on stress wave propagation are dominant mechanisms dictating the mechanics of the interaction of membranes with the brain simulant. Overall, these results provide novel experimental insights into the role of membranes on brain deformations, which will motivate futuristic investigations in numerous subdomains of brain injury biomechanics.

AutoML-ID: automated machine learning model for intrusion detection using wireless sensor network.

Momentous increase in the popularity of explainable machine learning models coupled with the dramatic increase in the use of synthetic data facilitates us to develop a cost-efficient machine learning model for fast intrusion detection and prevention at frontier areas using Wireless Sensor Networks (WSNs). The performance of any explainable machine learning model is driven by its hyperparameters. Several approaches have been developed and implemented successfully for optimising or tuning these hyperparameters for skillful predictions. However, the major drawback of these techniques, including the manual selection of the optimal hyperparameters, is that they depend highly on the problem and demand application-specific expertise. In this paper, we introduced Automated Machine Learning (AutoML) model to automatically select the machine learning model (among support vector regression, Gaussian process regression, binary decision tree, bagging ensemble learning, boosting ensemble learning, kernel regression, and linear regression model) and to automate the hyperparameters optimisation for accurate prediction of numbers of k-barriers for fast intrusion detection and prevention using Bayesian optimisation. To do so, we extracted four synthetic predictors, namely, area of the region, sensing range of the sensor, transmission range of the sensor, and the number of sensors using Monte Carlo simulation. We used 80% of the datasets to train the models and the remaining 20% for testing the performance of the trained model. We found that the Gaussian process regression performs prodigiously and outperforms all the other considered explainable machine learning models with correlation coefficient (R = 1), root mean square error (RMSE = 0.007), and bias = - 0.006. Further, we also tested the AutoML performance on a publicly available intrusion dataset, and we observed a similar performance. This study will help the researchers accurately predict the required number of k-barriers for fast intrusion detection and prevention.

LT-FS-ID: Log-Transformed Feature Learning and Feature-Scaling-Based Machine Learning Algorithms to Predict the k-Barriers for Intrusion Detection Using Wireless Sensor Network.

The dramatic increase in the computational facilities integrated with the explainable machine learning algorithms allows us to do fast intrusion detection and prevention at border areas using Wireless Sensor Networks (WSNs). This study proposed a novel approach to accurately predict the number of barriers required for fast intrusion detection and prevention. To do so, we extracted four features through Monte Carlo simulation: area of the Region of Interest (RoI), sensing range of the sensors, transmission range of the sensor, and the number of sensors. We evaluated feature importance and feature sensitivity to measure the relevancy and riskiness of the selected features. We applied log transformation and feature scaling on the feature set and trained the tuned Support Vector Regression (SVR) model (i.e., LT-FS-SVR model). We found that the model accurately predicts the number of barriers with a correlation coefficient (R) = 0.98, Root Mean Square Error (RMSE) = 6.47, and bias = 12.35. For a fair evaluation, we compared the performance of the proposed approach with the benchmark algorithms, namely, Gaussian Process Regression (GPR), Generalised Regression Neural Network (GRNN), Artificial Neural Network (ANN), and Random Forest (RF). We found that the proposed model outperforms all the benchmark algorithms.

ECS-NL: An Enhanced Cuckoo Search Algorithm for Node Localisation in Wireless Sensor Networks.

Node localisation plays a critical role in setting up Wireless Sensor Networks (WSNs). A sensor in WSNs senses, processes and transmits the sensed information simultaneously. Along with the sensed information, it is crucial to have the positional information associated with the information source. A promising method to localise these randomly deployed sensors is to use bio-inspired meta-heuristic algorithms. In this way, a node localisation problem is converted to an optimisation problem. Afterwards, the optimisation problem is solved for an optimal solution by minimising the errors. Various bio-inspired algorithms, including the conventional Cuckoo Search (CS) and modified CS algorithm, have already been explored. However, these algorithms demand a predetermined number of iterations to reach the optimal solution, even when not required. In this way, they unnecessarily exploit the limited resources of the sensors resulting in a slow search process. This paper proposes an Enhanced Cuckoo Search (ECS) algorithm to minimise the Average Localisation Error (ALE) and the time taken to localise an unknown node. In this algorithm, we have implemented an Early Stopping (ES) mechanism, which improves the search process significantly by exiting the search loop whenever the optimal solution is reached. Further, we have evaluated the ECS algorithm and compared it with the modified CS algorithm. While doing so, note that the proposed algorithm localised all the localisable nodes in the network with an ALE of 0.5-0.8 m. In addition, the proposed algorithm also shows an 80% decrease in the average time taken to localise all the localisable nodes. Consequently, the performance of the proposed ECS algorithm makes it desirable to implement in practical scenarios for node localisation.

A rare case of femoral herniation of female internal genitalia.

We report a rare case with herniation of the uterus, fallopian tube, and ovary in a femoral hernia. A female patient was admitted with complain of the painful lump in the left groin. Clinical examination indicated strangulated femoral hernia, which necessitated an emergency surgery. During surgical procedure, the uterine tube, left fallopian tube and left ovary, were observed as the contents of the hernia. The contents were reduced back into the pelvic cavity, and the hernia was repaired. The patient made good recovery postsurgery.