Network Measures Research Articles

The Total Carbon Column Observing Network's GGG2020 data version

Abstract. The Total Carbon Column Observing Network (TCCON) measures column-average mole fractions of several greenhouse gases (GHGs), beginning in 2004, from over 30 current or past measurement sites around the world using solar absorption spectroscopy in the near-infrared (near-IR) region. TCCON GHG data have been used extensively for multiple purposes, including in studies of the carbon cycle and anthropogenic emissions, as well as to validate and improve observations from space-based sensors. Here, we describe an update to the retrieval algorithm used to process the TCCON near-IR solar spectra and to generate the associated data products. This version, called GGG2020, was initially released in April 2022. It includes updates and improvements to all steps of the retrieval, including but not limited to the conversion of the original interferograms into spectra, the spectroscopic information used in the column retrieval, post hoc air mass dependence correction, and scaling to align with the calibration scales of in situ GHG measurements. All TCCON data are available through https://tccondata.org/ (last access: 22 April 2024) and are hosted on CaltechDATA (https://data.caltech.edu/, last access: 22 April 2024). Each TCCON site has a unique DOI for its data record. An archive of all the sites' data is also available with the DOI https://doi.org/10.14291/TCCON.GGG2020 (Total Carbon Column Observing Network (TCCON) Team, 2022). The hosted files are updated approximately monthly, and TCCON sites are required to deliver data to the archive no later than 1 year after acquisition. Full details of data locations are provided in the “Code and data availability” section.

Utility of intracranial EEG networks depends on re-referencing and connectivity choice.

Studies of intracranial EEG networks have been used to reveal seizure generators in patients with drug-resistant epilepsy. Intracranial EEG is implanted to capture the epileptic network, the collection of brain tissue that forms a substrate for seizures to start and spread. Interictal intracranial EEG measures brain activity at baseline, and networks computed during this state can reveal aberrant brain tissue without requiring seizure recordings. Intracranial EEG network analyses require choosing a reference and applying statistical measures of functional connectivity. Approaches to these technical choices vary widely across studies, and the impact of these technical choices on downstream analyses is poorly understood. Our objective was to examine the effects of different re-referencing and connectivity approaches on connectivity results and on the ability to lateralize the seizure onset zone in patients with drug-resistant epilepsy. We applied 48 pre-processing pipelines to a cohort of 125 patients with drug-resistant epilepsy recorded with interictal intracranial EEG across two epilepsy centres to generate intracranial EEG functional connectivity networks. Twenty-four functional connectivity measures across time and frequency domains were applied in combination with common average re-referencing or bipolar re-referencing. We applied an unsupervised clustering algorithm to identify groups of pre-processing pipelines. We subjected each pre-processing approach to three quality tests: (i) the introduction of spurious correlations; (ii) robustness to incomplete spatial sampling; and (iii) the ability to lateralize the clinician-defined seizure onset zone. Three groups of similar pre-processing pipelines emerged: common average re-referencing pipelines, bipolar re-referencing pipelines and relative entropy-based connectivity pipelines. Relative entropy and common average re-referencing networks were more robust to incomplete electrode sampling than bipolar re-referencing and other connectivity methods (Friedman test, Dunn-Šidák test P < 0.0001). Bipolar re-referencing reduced spurious correlations at non-adjacent channels better than common average re-referencing (Δ mean from machine ref = -0.36 versus -0.22) and worse in adjacent channels (Δ mean from machine ref = -0.14 versus -0.40). Relative entropy-based network measures lateralized the seizure onset hemisphere better than other measures in patients with temporal lobe epilepsy (Benjamini-Hochberg-corrected P < 0.05, Cohen's d: 0.60-0.76). Finally, we present an interface where users can rapidly evaluate intracranial EEG pre-processing choices to select the optimal pre-processing methods tailored to specific research questions. The choice of pre-processing methods affects downstream network analyses. Choosing a single method among highly correlated approaches can reduce redundancy in processing. Relative entropy outperforms other connectivity methods in multiple quality tests. We present a method and interface for researchers to optimize their pre-processing methods for deriving intracranial EEG brain networks.

Unbiased Real-Time Traffic Sketching

In network measurement, sliding window measurement has the advantage of providing recent and timely measurement results. Recently, sketches have become the most popular method of conducting flow-level network measurements due to their favorable trade-off between small memory overhead and high measurement accuracy. However, it remains a challenge that no current sketches are able to support unbiased estimation toward flow size measurement, which can improve the performance of tasks including network diagnoses, delay measurement and heavy hitter detection. In this paper, we propose the first work that achieves unbiased flow size measurement in sliding windows, namely <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Unbiased Cleaning sketch (UC sketch)</b> . The key technique of the UC sketch is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Unbiased Cleaning</i> which can remove outdated keys from the sliding windows in a balanced way. Besides, we significantly reduce the variance of flow size by two optimization techniques, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Linear Scaling</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Column Randomizing</i> . To prove the result, we conduct rigorous mathematical analysis and reasonable experiments. All related source codes are open-sourced at Github anonymously.

Investigation of Indian stock markets using topological data analysis and geometry-inspired network measures

Financial markets are a quintessential example of complex systems wherein strategic connections arising through the trading (buying and selling) of different stocks gives rise to dynamic correlation patterns in stock prices. Network science based approaches have been employed for more than two decades to investigate the stock market as an inferential network, with nodes representing stocks and edges as pairwise cross-correlations between stock prices. However, a recent focus has been the characterization of the higher-order structure, beyond pairwise correlation, of such networks. To this end, geometry-inspired measures (such as discrete Ricci curvatures) and topological data analysis (TDA) based methods (such as persistent homology) have become attractive tools for characterizing the higher-order structure of networks representing the financial systems. In this study, our goal is to perform a comparative analysis of both these approaches, especially by assessing the fragility and systemic risk in the Indian stock markets, which is known for its high volatility and risk. To achieve this goal, we analyze the time series of daily log-returns of stocks comprising the National Stock Exchange (NSE) and the Bombay Stock Exchange (BSE). Specifically, our aim is to monitor the changes in standard network measures, edge-centric discrete Ricci curvatures, and persistent homology based topological measures computed from cross-correlation matrices of stocks. In this study, the edge-centric discrete Ricci curvatures have been employed for the first time in the analysis of the Indian stock markets. The Indian stock markets are known to be less diverse in comparison to the US market, and hence provides us an interesting example. Our results point that, among the persistent homology based topological measures, persistent entropy is simple and more robust than L1-norm and L2-norm of persistence landscape. In a broader comparison between network analysis and TDA, we highlight that the network analysis is sensitive to the way of constructing the networks (threshold or minimum spanning tree), as well as the threshold values used to construct the correlation-based threshold networks. On the other hand, the persistent homology is a more robust approach and is able to capture the higher-order interactions and eliminate noisy data in financial systems, since it does not take into account a single value of threshold but rather a range of values.

The thalamic covariance network is associated with cognitive deficits in patients with cerebral small vascular disease.

Abnormalities in the gray matter structure of cerebral small vessel disease (CSVD) have been observed throughout the brain. However, whether cortico-cortical connections exist between regions of gray matter atrophy in patients with CSVD has not been fully elucidated. This question was tested by comparing the gray matter covariance networks in CSVD patients with and without cognitive impairment (CI). We performed multivariate modeling of the gray matter volume measurements of 61 patients with CI (CSVD-CI), 85 patients without CI (CSVD-NC), and 108 healthy controls using source-based morphological analysis (SBM) to obtain gray matter structural covariance networks at the population level. Then, correlations between structural covariance networks and cognitive functions were analyzed in CSVD patients. Finally, a support vector machine (SVM) classifier was used with the gray matter covariance network as a classification feature to identify CI among the CSVD population. The results of the analysis of all the subjects showed that compared with healthy controls, the expression of the thalamic covariance network, cerebellum covariance network, and calcarine cortex covariance network was reduced in patients with CSVD. Moreover, CSVD-CI patients showed a significant reduction in the expression of the thalamic covariance network, encompassing the thalamus and the parahippocampal gyrus, relative to CSVD-NC patients, which persisted after excluding CSVD patients with thalamic lacunes. In patients with CSVD, cognitive functions were positively correlated with measures of the thalamic covariance network. More than 80% of CSVD patients with CI were correctly identified by the SVM classifier. Our findings provide new evidence to explain the distribution state of gray matter reduction in CSVD patients, and the thalamic covariance network is the core region for early gray matter reduction during the development of CSVD disease, which is related to cognitive deficits. Reduced expression of thalamic covariance networks may provide a neuroimaging biomarker for the early identification of cognitive impairment in CSVD patients.

An effective and accurate flow size measurement using funnel-shaped sketch

Accurate measurement of network traffic is an essential part of current network management tasks. Sketch is a kind of probabilistic data structure widely accepted in network measurement. However, it is challenging for classic sketches to reduce the storage cost with little loss of accuracy for highly skewed network traffic. Therefore, most sketch-based schemes store elephant flows and mouse flows separately to deal with the skewed network traffic. But they are not usually suitable for estimating the size of mouse flows and thus may lose some information of traffic. To this end, a novel sketch, called Funnel Sketch (FS), is proposed in this paper. FS utilizes a funnel-shaped architecture to separately store the elephant flows and mouse flows, while maintaining the mouse flows as possible. Thus, FS can not only adapt to skewed network traffic to improve memory efficiency, but also accurately estimate the size of mouse and elephant flows with efficient memory consumption. Moreover, FS is implemented on CPU and OVS platform to evaluate its performance. The experimental results show that FS greatly reduces the error by 90% for flow size estimation and elephant flow detection compared with the state-of-the-art sketch-based schemes.

Hybrid RNN-LSTM Networks for Enhanced Intrusion Detection in Vehicle CAN Systems

Electric vehicles (EVs) use electric motors for propulsion, relying on electric energy stored in batteries or other energy storage devices. The standard communication protocol used in EVs is the Control Area Network (CAN), a communication protocol widely used in the automotive industry for networking and communication between various components within a vehicle. CAN protocol, designed without any care about protection, as automotive systems become more connected, the vulnerability to cyber threats, including intrusion attacks. The most common intrusion attacks on EVs are Denial of Service (DoS), Fuzzy, and Impersonation Attacks. These become a significant challenge due to the imperative need for robust Intrusion Detection Systems (IDS) in CAN networks. This paper explores the application of advanced deep learning techniques, specifically Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks, to enhance the effectiveness of intrusion detection in the EV domain. We will use a hybrid Deep-learning model to improve the analysis. First, we will apply the RNN model, and the output will come as input for the second model, LSTM. Our proposed hybrid model achieved an accuracy of 93%. The outcomes of this research contribute to advancing cybersecurity measures in vehicular networks, ensuring the integrity and safety of connected vehicles. The applicability of RNN and LSTM techniques in the context of CAN networks demonstrates their potential to evolve as integral components of next-generation intrusion detection systems, fostering a secure and resilient automotive ecosystem.

Structural connectivity as a predictive factor for perampanel response in patients with epilepsy

ObjectivesThis study aimed to identify clinical characteristics that could predict the response to perampanel (PER) and determine whether structural connectivity is a predictive factor. MethodsWe enrolled patients with epilepsy who received PER and were followed-up for a minimum of 12 months. Good PER responders, who were seizure-free or presented with more than 50 % seizure reduction, were classified separately from poor PER responders who had seizure reduction of less than 50 % or non-responders. A graph theoretical analysis was conducted based on diffusion tensor imaging to calculate network measures of structural connectivity among patients with epilepsy. Results106 patients with epilepsy were enrolled, including 26 good PER responders and 80 poor PER responders. Good PER responders used fewer anti-seizure medications before PER administration compared to those by poor PER responders (3 vs. 4; p = 0.042). Early PER treatment was more common in good PER responders than poor PER responders (46.2 vs. 21.3 %, p = 0.014). Regarding cortical structural connectivity, the global efficiency was higher and characteristic path length was lower in good PER responders than in poor PER responders (0.647 vs. 0.635, p = 0.006; 1.726 vs. 1,759, p = 0.008, respectively). For subcortical structural connectivity, the mean clustering coefficient and small-worldness index were higher in good PER responders than in poor PER responders (0.821 vs. 0.791, p = 0.009; 0.597 vs. 0.560, p = 0.009, respectively). ConclusionThis study demonstrated that early PER administration can predict a good PER response in patients with epilepsy, and structural connectivity could potentially offer clinical utility in predicting PER response.

Spatial propagation of different drought types and their concurrent societal risks: A complex networks-based analysis

This study proposes a novel complex networks-based framework to examine the spatial propagation of one type of drought to another (concurrent droughts) in a region and the associated risks. The methodology involves two major steps: (1) Analysis of drought connection patterns using spatial networks of different drought types in a region, using event synchronization and network measures; and (2) Building concurrent drought risk networks to identify smaller-scale (district-level) homogeneous drought risk communities. Meteorological, agricultural, and hydrological droughts as well as hot events are considered for developing the methodology. The proposed method is implemented to study droughts in Peninsular India, a water-stressed region that often witnesses intra- and inter-basin conflicts during drought periods. The results indicate that drought sources of all types and severity are located primarily on the high-altitude divides of river basins. This finding has great significance, since any drought occurring at a source could hint at a forthcoming event in the connected locations. Analysis of the distances of drought connections reveals that meteorological droughts are more localized when compared to the other drought types and that the distances increase with severity levels for all drought types. Furthermore, based on the construction of a correlation-based concurrent drought risk network (using a copula-based multivariate drought index), seven distinct communities of districts are demarcated. The network thus formed is found to inherently capture the topographical and rainfall pattern along with the socio-economic aspects existing in the area. Therefore, the communities detected could facilitate devising appropriate drought mitigation strategies.

Intra-Examiner Reliability and Validity of Sagittal Cervical Spine Mensuration Methods Using Deep Convolutional Neural Networks.

Background: The biomechanical analysis of spine and postural misalignments is important for surgical and non-surgical treatment of spinal pain. We investigated the examiner reliability of sagittal cervical alignment variables compared to the reliability and concurrent validity of computer vision algorithms used in the PostureRay® software 2024. Methods: A retrospective database of 254 lateral cervical radiographs of patients between the ages of 11 and 86 is studied. The radiographs include clearly visualized C1-C7 vertebrae that were evaluated by a human using the software. To evaluate examiner reliability and the concurrent validity of the trained CNN performance, two blinded trials of radiographic digitization were performed by an extensively trained expert user (US) clinician with a two-week interval between trials. Then, the same clinician used the trained CNN twice to reproduce the same measures within a 2-week interval on the same 254 radiographs. Measured variables included segmental angles as relative rotation angles (RRA) C1-C7, Cobb angles C2-C7, relative segmental translations (RT) C1-C7, anterior translation C2-C7, and absolute rotation angle (ARA) C2-C7. Data were remotely extracted from the examiner's PostureRay® system for data collection and sorted based on gender and stratification of degenerative changes. Reliability was assessed via intra-class correlations (ICC), root mean squared error (RMSE), and R2 values. Results: In comparing repeated measures of the CNN network to itself, perfect reliability was found for the ICC (1.0), RMSE (0), and R2 (1). The reliability of the trained expert US was in the excellent range for all variables, where 12/18 variables had ICCs ≥ 0.9 and 6/18 variables were 0.84 ≤ ICCs ≤ 0.89. Similarly, for the expert US, all R2 values were in the excellent range (R2 ≥ 0.7), and all RMSEs were small, being 0.42 ≤ RMSEs ≤ 3.27. Construct validity between the expert US and the CNN network was found to be in the excellent range with 18/18 ICCs in the excellent range (ICCs ≥ 0.8), 16/18 R2 values in the strong to excellent range (R2 ≥ 0.7), and 2/18 in the good to moderate range (R2 RT C6/C7 = 0.57 and R2 Cobb C6/C7 = 0.64. The RMSEs for expert US vs. the CNN network were small, being 0.37 ≤ RMSEs ≤ 2.89. Conclusions: A comparison of repeated measures within the computer vision CNN network and expert human found exceptional reliability and excellent construct validity when comparing the computer vision to the human observer.

Performance analytics for the value chain process of international airports: a dynamic network measure with the shared and unsplittable links

Performance analytics for the value chain process of international airports: a dynamic network measure with the shared and unsplittable links

AGC Sketch: An effective and accurate per-flow measurement to adapt flow size distribution

Network measurements are indispensable for efficient network management. As a probabilistic data structure, sketch is widely used in network measurements. To adapt to the skewed and dynamic distribution of network traffic, most of the existing sketch-based schemes separate large and small flows either by asymmetric structures or multilayer structures. However, the former approach requires further accuracy of measurement due to increased errors of small flows. And the latter approach limits the throughput due to the increased hash calculations for large flows. To this end, a novel sketch, called AGC Sketch, is proposed in this paper. By storing the large flows and small flows separately while maintaining the small flows, it can not only adapt to the skewed network traffic to achieve efficient memory utilization, but also accurately estimate the size of flows with high throughput. Moreover, AGC Sketch is implemented on CPU and OVS platform. The experimental results show that AGC Sketch greatly reduces the error by 90% for flow size estimation compared with the state-of-the-art schemes.

Plant networks are more connected by invasive brome and native shrub facilitation in Central California drylands

Dominant vegetation in many ecosystems is an integral component of structure and habitat. In many drylands, native shrubs function as foundation species that benefit other plants and animals. However, invasive exotic plant species can comprise a significant proportion of the vegetation. In Central California drylands, the facilitative shrub Ephedra californica and the invasive Bromus rubens are widely dispersed and common. Using comprehensive survey data structured by shrub and open gaps for the region, we compared network structure with and without this native shrub canopy and with and without the invasive brome. The presence of the invasive brome profoundly shifted the network measure of centrality in the microsites structured by a shrub canopy (centrality scores increased from 4.3 under shrubs without brome to 6.3, i.e. a relative increase of 42%). This strongly suggests that plant species such as brome can undermine the positive and stabilizing effects of native foundation plant species provided by shrubs in drylands by changing the frequency that the remaining species connect to one another. The net proportion of positive and negative associations was consistent across all microsites (approximately 50% with a total of 14% non-random co-occurrences on average) suggesting that these plant-plant networks are rewired but not more negative. Maintaining resilience in biodiversity thus needs to capitalize on protecting native shrubs whilst also controlling invasive grass species particularly when associated with shrubs.

Abnormal longitudinal changes of structural covariance networks of cortical thickness in mild traumatic brain injury with posttraumatic headache

BackgroundIt is widely acknowledged that mild traumatic brain injury (MTBI) leads to either functionally or anatomically abnormal brain regions. Structural covariance networks (SCNs) that depict coordinated regional maturation patterns are commonly employed for investigating brain structural abnormalities. However, the dynamic nature of SCNs in individuals with MTBI who suffer from posttraumatic headache (PTH) and their potential as biomarkers have hitherto not been investigated. MethodsThis study included 36 MTBI patients with PTH and 34 well-matched healthy controls (HCs). All participants underwent magnetic resonance imaging scans and were assessed with clinical measures during the acute and subacute phases. Structural covariance matrices of cortical thickness were generated for each group, and global as well as nodal network measures of SCNs were computed. ResultsMTBI patients with PTH demonstrated reduced headache impact and improved cognitive function from the acute to subacute phase. In terms of global network metrics, MTBI patients exhibited an abnormal normalized clustering coefficient compared to HCs during the acute phase, although no significant difference in the normalized clustering coefficient was observed between the groups during the subacute phase. Regarding nodal network metrics, MTBI patients displayed alterations in various brain regions from the acute to subacute phase, primarily concentrated in the prefrontal cortex (PFC). ConclusionsThese findings indicate that the cortical thickness topography in the PFC determines the typical structural-covariance topology of the brain and may serve as an important biomarker for MTBI patients with PTH.

Effect of ground tyre rubber content on self-healing properties of natural rubber composites

Abstract In this investigation, we study the influence of the loading of ground tyre rubber (GTR), referred to as rubber dust 40 mesh (RD40), on the intrinsic self-healing natural rubber (NR) based on metal-thiolate ionic networks. The loading of RD40 particles was varied at 10, 20, and 30 phr to assess the optimum amount for interfacial bonding, mechanical and dynamic properties, and healing efficiency. The quantitative measurement of metal-thiolate ion networks and covalent crosslinks was assessed through swelling tests. The effects of RD40 loading on the dynamic mechanical properties and morphological characteristics were also investigated. Tensile properties of the rubbers before and after healing were measured using universal testing tester to obtain healing efficiency of the materials. The results showed that the developed materials have the ability to autonomously repair themselves at room temperature without the need for manual intervention. The results also revealed the tensile strength and elongation at break of self-healing NRs filled with 10 phr of RD40 were recovered 92 % and 93 %, respectively. The findings demonstrated the benefits of utilizing GTR as an effective sustainable filler and advanced the understanding of self-healing strategies and the interaction between ground tyre rubber and rubber matrices.

Grey matter networks in women and men with dementia with Lewy bodies

Sex differences permeate many aspects of dementia with Lewy bodies (DLB), yet sex differences in patterns of neurodegeneration in DLB remain largely unexplored. Here, we test whether grey matter networks differ between sexes in DLB and compare these findings to sex differences in healthy controls. In this cross-sectional study, we analysed clinical and neuroimaging data of patients with DLB and cognitively healthy controls matched for age and sex. Grey matter networks were constructed by pairwise correlations between 58 regional volumes after correction for age, intracranial volume, and centre. Network properties were compared between sexes and diagnostic groups. Additional analyses were conducted on w-scored data to identify DLB-specific sex differences. Data from 119 (68.7 ± 8.4 years) men and 45 women (69.9 ± 9.1 years) with DLB, and 164 healthy controls were included in this study. Networks of men had a lower nodal strength compared to women. In comparison to healthy women, the grey matter networks of healthy men showed a higher global efficiency, modularity, and fewer modules. None of the network measures showed significant sex differences in DLB. Comparing DLB patients with healthy controls revealed global differences in women and more local differences in men. Modular analyses showed a more distinct demarcation between cortical and subcortical regions in men compared with women. While topologies of grey matter networks differed between sexes in healthy controls, those sex differences were diluted in DLB patients. These findings suggest a disease-driven convergence of neurodegenerative patterns in women and men with DLB, which may inform precision medicine in DLB.

Lack of consistency in measurement methods and semantics used for network measures in adolescent health behaviour studies using social network analysis: a systematic review

BackgroundSocial network analysis (SNA) is often used to examine how social relationships influence adolescent health behaviours, but no study has documented the range of network measures used to do so....

Three-Dimensional Signal Source Localization with Angle-Only Measurements in Passive Sensor Networks

Some passive sensors can provide only relative angles of a signal source. To obtain the signal source location, multiple passive sensors can be constructed into a passive sensor network through communication links. This paper investigates the source localization problem with angle-only measurements in three-dimensional space. First, we present an intersection localization method, which estimates the target position by minimizing the sum of distances between lines formed by angle-only measurements. It has the same target position estimate as the widely used least-squares (LS) method, but with a lower computational cost. Furthermore, considering the differences in measurement accuracy of sensors, the weighted least-squares (WLS) algorithm can achieve better localization performance than the LS method. Unfortunately, since the coefficient matrix and the noise vector are correlated, the WLS method is biased. The bias-compensation WLS (BCWLS) method is also presented in this paper to reduce the bias by estimating the correlation between the coefficient matrix and the pseudolinear noise vector. To evaluate the performance of the presented algorithms, numerical simulations are conducted, indicating that the superiority of the intersection localization method in computational cost and the superiority of the BCWLS method in localization accuracy.

Sensitivity analysis of cumulus and microphysics schemes in the WRF model in simulating Extreme Rainfall Events over the hilly terrain of Nagaland

A comprehensive study is carried out to investigate the sensitivity of various combinations between cumulus (CU) and microphysics (MP) schemes using the Weather Research and Forecasting (WRF) model for simulating Extreme Rainfall Events (EREs) over the hilly terrain of Nagaland (25.1 oN – 27.2 oN; 93.2 oE– 95.3 °E). The study explores fifty-four distinct simulation combinations resulting from six CU and nine MP schemes. The simulated rainfall is compared with ground-based rain gauge network and Global Precipitation Measurement (GPM) satellite observations. The investigation focuses primarily on finding the optimal CU-MP scheme combination based on spatiotemporal precision achieved by the identified schemes. The C14 (New Simplified Arakawa Schubert) cumulus scheme is found to be the optimum individual CU scheme. Similarly, the M14 (WDM5) microphysics scheme performs as the optimum individual MP scheme. The study highlights the significance of evaluating the performance of combined schemes over individual ones. Among the fifty-four combinations, sensitivity analysis identified six top-performing combinations, out of which the C11-M8 (Multiscale Kain Fritsch - New Thompson) combination stands out to be optimum with a robust correlation coefficient (CC) of 0.75, lower standard deviation (STD) of 0.76 mm, and RMSD of 0.51 mm. A variation in lead/lag time is found to vary from (−) 3.2 to (+) 5.0 h, with a median value of (+) 1.3 h, suggesting that, on average, the model tends to predict the occurrence of peak rainfall events slightly earlier than observed one. The absolute difference in latitudes (longitudes) of peak rainfall variation ranges from 0.42o - 0.8o (0 o - 0.51o) with a median value of 0.66o (0.37o), indicating that the model could better represent the longitudinal peak rainfall positions with notable accuracy. The spatial correlation coefficient (SCC) ranges between 0.31 and 0.68 with a median value of 0.50. From the overall analysis, it is also found that double moment MP schemes (New Thompson, Morrison (2 M), WDM5, WDM6) consistently perform better than the single moment MP schemes (Kessler, Lin et al., WSM3, WSM5, WSM6), whether in combination with CU schemes or individually. Spatiotemporal variations observed across different cases underscore the pivotal role of accurate topography representation in complex terrains, emphasizing the significance of terrain data in shaping rainfall patterns.

Learning site-invariant features of connectomes to harmonize complex network measures.

Multi-site diffusion MRI data is often acquired on different scanners and with distinct protocols. Differences in hardware and acquisition result in data that contains site dependent information, which confounds connectome analyses aiming to combine such multi-site data. We propose a data-driven solution that isolates site-invariant information whilst maintaining relevant features of the connectome. We construct a latent space that is uncorrelated with the imaging site and highly correlated with patient age and a connectome summary measure. Here, we focus on network modularity. The proposed model is a conditional, variational autoencoder with three additional prediction tasks: one for patient age, and two for modularity trained exclusively on data from each site. This model enables us to 1) isolate site-invariant biological features, 2) learn site context, and 3) re-inject site context and project biological features to desired site domains. We tested these hypotheses by projecting 77 connectomes from two studies and protocols (Vanderbilt Memory and Aging Project (VMAP) and Biomarkers of Cognitive Decline Among Normal Individuals (BIOCARD) to a common site. We find that the resulting dataset of modularity has statistically similar means (p-value <0.05) across sites. In addition, we fit a linear model to the joint dataset and find that positive correlations between age and modularity were preserved.